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machine vision based bacteria-colony counter

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1. MACHINE VISION BASED BACTERIA COLONY COUNTER Thesis submitted in partial fulfillment of the requirement for the award of degree of Master of Engineering In Electronic Instrumentation and Control By Monita Goyal 80651012 Under the supervision of Mandeep Singh Assistant Professor ELECTRICAL AND INSTRUMENTATION DEPARTMENT THAPAR UNIVERSITY PATIALA 147004 JUNE 2008 Declaration The proposed method relates to a colony counter for bacterial specimens on a petri dish I hereby declare that the report entitled Machine Vision based Bacteria Colony Counter is an authentic record of my own work carried out as requirement for the award of degree of M E Electronic Instrumentation amp Control at Thapar University guidance of Mr Mandeep Patiala under the Singh Assistant Professor during January to June 2008 Sage e GOYAL Date AeEy 4 2208 Roll No 80651012 It is certified that the above statement made by the student is correct to the best of our knowledge and belief bere Mr Mandeep Singh Assistant Professor EYED Thapar University Patiala Dr Smarajit Ghosh Professor amp Head EIED Thapar University Patiala Dr R K Sharma Dean of Academic Affairs Thapar University Patiala Acknowledgment _ Te real spirit of achieving a goal is through the way of excellence and austerous Ssecpline I would have never succeeded in completing my task wi
2. The present work relates to a colony counter for bacterial specimens on a petri dish using Machine Vision which overcomes all of the disadvantages of the previously known devices A colony counter is an instrument used to count colonies of bacteria or other microorganisms growing on an agar plate Bacterial colony is a group of bacteria growing on a plate that is derived from one original starting cell An agar plate is a sterile Petri dish that contains a growth medium typically agar plus nutrients used to culture microorganisms Bacterial colony counting process is usually performed by well trained technicians manually However there might exist hundreds of colonies in a traditional 100mm Petri dish as shown in figure 1 1 Figure 1 1 Bacteria colonies in a Petri dish Therefore this manual enumeration process has a very low throughput and is time consuming and labor intensive in practice In addition the manual counting is an error prone process since the counting results of the same plate obtained from different technicians might vary especially when a vast number of colonies appear on the plate Another possible cause of variation is the judgment of the indistinguishable colony overlaps Thus it is important to have consistent criteria for measuring overlapped colonies To produce consistent and accurate results and improve the throughput the existing colony counter device has been developed Machine vision based bacteria colony
3. 5 5 7 Isolating Circular Particles Particle Filter removes or keeps particles in an image as specified by the filter criteria Heywood Circularity Factor is the Perimeter which is divided by the circumference of a circle with the same area The closer the shape of a particle is to a disk the closer the Heywood circularity factor to 1 A particle filter that isolates and keeps the circular particles and removes the non circular particles from the image is showing by the following steps 7i 1 Select Particle Filter from the Binary tab of the Processing Functions palette or select Binary Particle Filter 2 Select Heywood Circularity Factor from the list of particle filters This function calculates the ratio of the perimeter of the particle to the perimeter of the circle with the same area The more circular the particle the closer the ratio to 1 3 To find more circular and less oblong particles enter a Minimum Value of 0 and a Maximum Value of 0 for the parameter range 4 Select the Remove option to remove particles that do not fit in this range 5 Click OK to add this step to the script The image now contains only circular particles 6 If we want the filter to be applied outside of the specified parameter range check the Exclude Interval option The image of isolating circular particles is shown in figure 5 13 y NI Vision Assistant File View Help PP RQ S M Image How To Controls my bmp RGB
4. C Code creation It creates a C file corresponding to the algorithm we prototype in Vision Assistant Based on the options we select the C Code Creation Wizard creates a C function that implements the image processing steps of the current script Builder file ASCII text file that lists the C and Microsoft Visual Basic functions and parameters for the algorithm we prototyped in Vision Assistant 4 3 Processing functions of NI Vision Assistant 4 3 1 Image functions The image functions that analyze the content of an image are included in this group such as Histogram counts the total number of pixels in each grayscale value and graphs it Line Profile displays the grayscale distribution along a line of pixels in an image Measure calculates measurement statistics associated with a region of interest in the image 3D View displays the light intensity of an image in a three dimensional coordinate system Image Mask builds a mask from an entire image or an ROI Geometry modifies the geometrical representation of an image Image Buffer stores and retrieves images from buffers 47 Get New Image opens a new image from the script Calibrate Image calibrates an image to perform measurements in real world units Calibrate from Image applies the calibration information in an image file to the current image Image Correction transforms a distorted image acquired in a calibrated setup by correcting perspective errors and lens distortion
5. 4 3 2 Color functions The image functions that analyze color images are included in this group such as Color Operators performs arithmetic and logical operations on color images Extract Color Planes extracts the RGB HSV and HSL planes from an image Color Threshold applies a threshold to the three planes of a color image and places the result in an 8 bit image Color Location locates colors in a color image Color Pattern Matching checks the presence of a template file in the entire color image or in an ROL Color Matching learns the color content of an ROI in an image and compares it to the color content of another ROI 4 3 3 Grayscale Functions The image functions that analyze grayscale images are included in this group such as Lookup Table improves contrast and brightness in an image by applying a lookup table Filters prepare an image for processing so we can extract only the information you need from the image 48 Gray Morphology modifies the shape of objects in an image FFT Filter applies a frequency filter to the image Operators perform arithmetic and logical operations on an image Conversion converts the current image to the specific image type Threshold selects ranges of pixels in grayscale images Quantify quantifies the content of an image or ROIs within the image Centroid calculates the energy center of the image 4 3 4 Binary Functions The image functions that analyze binary images are included i
6. ES in an image as specified by the filter criteria Invert Binary Image the dynamic of an in two different gray i Particle Analy srement OB results for se urements performed on the image Remove border objec cle Fiter 1 Particle Analysis 1 Shape Matching Finds objects in an image that are shaped like the object ified by the template ai tat Figure 5 16 Processing Script Continued 75 The user simply has to select the file name of image and click once for obtaining the final count 5 5 10 Estimating Processing Time Vision Assistant can estimate the time in milliseconds that IMAQ Vision will take to process the active image with the open script The Performance Meter gives both an estimate of the total time IMAQ Vision will take to process the image and an estimate of the time each function within the script will require as shown in figure 5 17 We estimate how many milliseconds IMAQ Vision will use to process mv jpg with mv scr by the following steps 1 Select Tools Performance Meter The Performance Meter estimates the total time IMAQ Vision will take to run the script 2 Click Details to view an itemized list of the time IMAQ Vision will take to perform each function in the script 3 Click OK to close the Performance Meter my bmp RGB 752x480 gt Performance Meter An estimation of the time required by NI Vision Assistant to perform 4 gt i t 50 the inspection
7. 752x480 m al gt f mlels Particle Filter Parameter Range 752x480 1 1 147 187 lt Minimum Value 0 Pixels Maximum Value E 0 i Real World C Exclude Interval Action gt Remove Kee Maximum Value 3 2336 e Particle Filter 1 Mean Value 0 931441 Reset E Connectivity 4 8 OK Cancel Figure 5 13 Isolating Circular Particles Current Parameter Minimum Value 0 56419 72 5 5 8 Analyzing Circular Particles Particle Analysis displays measurement results for selected particle measurements performed on the image Results display the results for each particle in the image Number of Objects displays the total number of particles found in the image Connectivity 4 8 defines which of the surrounding pixels for any given pixel constitute its neighborhood Connectivity 8 shows that all adjacent pixels are considered neighbors and Connectivity 4 shows that only pixels adjacent in the horizontal and vertical directions are considered neighbors Select Measurements displays a list of object measurements that can be calculated and displayed Show Labels Vision Assistant assigns numeric labels to objects that it analyzes Now that we have isolated circular particles find the Number of Bacteria by the following steps 1 Select Particle Analysis from the Binary tab of the Processing Functions palette or select Binary Particle Analysis A results table displays all of the measurement results
8. Device 3 1 1 6 Connecting the CVS 1450 to the Development Computer 3 1 2 Setting up the Development Computer 3 1 3 Acquiring an Image 3 2 IMAQ for IEEE 1394 Fire wire compatible cameras 3 2 1 High level functions 3 2 2 Low level functions 3 3 Acquisition flow 3 3 1 Initialization 3 3 2 Configuration 3 3 3 Acquisition 24 26 27 28 30 31 32 32 32 33 34 35 36 37 37 38 38 39 39 41 42 vii Chapter 4 Vision Assistant 4 1 Introduction to Vision Assistant 7 1 4 2 System requirements and installation 4 2 1 Installing Vision Assistant 4 2 2 Features 4 3 Processing functions of NI Vision Assistant 4 3 1 Image functions 4 3 2 Color functions 4 3 3 Grayscale Functions 4 3 4 Binary Functions 4 3 5 Machine Vision Functions 4 4 Particle Analysis 4 4 1 Applications 4 4 2 Concepts 4 5 Thresholding 4 5 1 Applications 4 5 2 Intensity Threshold 4 5 3 Thresholding Example 4 5 4 Automatic Threshold Chapter 5 Problem Statement and Solution 5 1 Problem Statement 45 45 46 46 47 47 48 48 49 49 51 51 52 54 54 54 55 56 58 viii 5 2 Justification of Problem Statement 5 3 Problem solution 5 4 A warm up exercise 5 5 Problem Algorithm 5 5 1 Acquiring image of Petri dish 5 5 2 Opening the stored image 5 5 3 Extracting color planes from an image 5 5 4 Filtering the Image 5 5 5 Separating Particles from the Background with Thresholdin
9. Vision Assistant assigns numerical labels to each particle The first row of the results table lists the numerical label associated with each particle 2 Select Show Labels to view the labels 3 When we click a particle the measurement results for that particle are highlighted in blue When we click the results for a particle the particle is highlighted in green in the processing view 4 Click OK to record the particle analysis and add the step to the script The image of analyzing circular particles is shown in figure 5 14 73 Ni NI Vision Assistant View LER e M Image How To Controls my bmp RGB 752x480 wm st gt mlels Particle Analysis Number of Objects 347 fis Connectivity 4 8 752x480 1 1 704 370 V Show Labels Select Measurements First Pixel X First Pixel Y Bounding Rect Left 4 Figure 5 14 Counting Circular Particles 5 5 9 The Complete Processing Script The image acquired by camera is stored under appropriate file name mv bmp This image is processed by nine distinct steps namely acquiring image opening an image extracting color planes from an image filtering the image separating particles from background with thresholding proper close fill holes remove border objects isolating circular particles and counting circular particles It is very important to note that these nine steps do not require nine manual interventions and the entire process from opening
10. a tedious and time consuming process prone to human errors This can be automated using Machine Vision concepts In my thesis I propose a technique of Machine Vision based Bacteria Colony Counter which is based on particle analysis approach that enables to count bacterial colony on a Petri dish Machine vision based bacteria colony counter allows each plate to be counted with equal efficiency In the present work the image is captured by IEEE 1394 Digital Camera Prosilica 2 0 1 and processed by vision workstation Compact Vision System 1450 Configuring the two and optimizing their performance for the above application is one of the major components of this work Organization of Thesis The first chapter briefly introduces bacteria and colony counter The second chapter provides an overview of vision basic concepts which provide the anatomy of Machine Vision The third chapter describes the requirements to set up the development computer for image acquisition using IEEE 1394 digital camera The fourth chapter gives an introduction to Vision Assistant 7 1 on which our research work is focused The fifth chapter discusses the use of Particle analysis and its extension for counting the number of microorganisms finally concluding thesis in sixth chapter with future scope Declaration Acknowledgement Abstract Organization of Thesis Table of Contents List of Figures List of Tables List of Abbreviations Chapter 1 Introduc
11. available Table 2 3 Comparison between Machine Vision and Human Vision 2 8 Comparison between Machine Vision and Computer Vision The comparison between Machine Vision and Computer Vision as shown in table 2 4 Theoretical Unlikely Practical issues are likely to Yes Many academic papers dominate contain a lot of deep mathematics Cost Critical Likely to be of secondary et importance Dedicated electronic Possibly needed to achieve high speed No by definition hardware processing Use non algorithmic Yes There is a strong emphasis on solutions proven algorithmic methods In situ programming Possible to accommodate new products Unlikely Data source Human artifact such as a piece of metal Computer file CV specialists are plastic glass wood etc rarely able to control the image acquisition environment or redefine the application to make it tractable 28 Models human Most unlikely Very likely vision Most important a Easy to use Performance judged in a specific criteria way e g accuracy of b Cost effective measurement probability of c Consistent amp reliable recognising critical features etc d Fast Criterion for good Satisfactory performance Optimal performance solution Nature of subject Systems Engineering pragmatic Mathematics Computer Science often academic theoretical Human interaction a For interactive prototyping system Often relies on user having
12. bacteria in water used for pharmaceutical manufacturing Kawai M et al 1999 Paul C Olsztyn et al had given Bacteria colony counter and classifier in 1999 This invention relates to a colony counter and classifier for bacterial specimens on a Petri dish The device includes a housing having a tray which accepts the Petri dish containing the bacteria colonies A line scan camera is mounted within the housing above the tray Upon actuation a linear motor transports the tray across the scanning line of vision for the line scan camera Simultaneously a fiber optic illumination system illuminates the Petri dish from its side opposite the camera As the tray is transported by the linear motor the line scan camera successively obtains an optical image of the Petri dish containing the bacteria colonies Each line scan has a width equal to one pixel and the resulting optical images from the line scan camera are combined together to form an overall optical image of high 14 resolution That image is stored utilizing a digital computer The computer then analyzes the stored optical image to both identify and count the bacteria colonies Paul C Olsztyn et al 1999 John M Kramer et al had searched Evaluation of the spiral plate and laser colony counting techniques for the enumeration of bacteria in foods in 2004 In this method samples of food and dairy products bacterial cultures and spore suspensions were examined by two operators using both
13. can be essential for growth of a particular organism or group of organisms syntrophy http Wiki b Phases of Growth A typical bacterial growth curve depicts the changes in the number of viable bacteria that occur when a resting bacterium is exposed to fresh growth medium http Growth 2 This growth curve can be divided into four operationally distinct phases Lag phase Logarithmic log phase Stationary phase Sy SS Decline or death phase The Phases of growth is shown in figure 1 3 Stationary phase _ _ _ _ Decline or death Lag phase phase Logarithm of number of cells Time Figure 1 3 Bacterial growth curve 1 1 2 1 Lag Phase When a population of bacteria first enters a high nutrient environment that allows growth the cells need to adapt to their new environment The first phase of growth is the lag phase a period of slow growth when the cells are adapting to fast growth The lag phase has high biosynthesis rates as enzymes and nutrient transporters are produced Organisms do not increase significantly in number they are acclimating to the new medium or environment They are metabolically active The cells grow in size synthesize enzymes and incorporate molecules from the medium As the individual organisms grow in size also produce large quantities of energy 1 1 2 2 Log Phase The second phase of growth is the logarithmic phase log phase also known as the exponential
14. computer is connected to the network and is powered on 2 Using a standard CAT 5 Ethernet cable connect from the network port to the Ethernet port on the CVS 1450 device 3 Using a standard CAT 5 Ethernet cable connect from the network port to the Ethernet port on the development computer 1 Standard Ethernet Cable Connecting from the CVS 1450 Device to an Ethernet Hub 2 Standard Ethernet Cable Connecting from an Ethernet Hub to the Development Computer 3 Ethernet Hub or Other Network Port Figure 3 4 Ethernet Connection 35 The development computer communicates with the CVS 1450 device over an Ethernet connection Use a standard Ethernet cable to connect from the network port to the CVS 1450 device If CVS 1450 is not connecting through a network we use an Ethernet crossover cable to connect the CVS 1450 device directly to the development computer 3 1 2 Setting up the Development Computer We must install LabVIEW LabVIEW Real Time and the Vision Development Module software before installing the NI IMAQ FOR IEEE 1394 Cameras driver software We can complete the following steps to install LabVIEW LabVIEW Real Time the Vision Development Module and NI IMAQ for IEEE 1394 Cameras into the development computer 1 Insert the LabVIEW CD into the CD ROM drive 2 When the installation splash screen appears click Install LabVIEW and follow the setup instructions 3 Insert the LabVIEW Real Time CD into th
15. connected for the first time the CVS 1450 device runs a program that acquires images This program verifies that all hardware components are properly connected and functioning The following items are necessary for hardware setup CVS 1450 device 24 VDC 10 50 W power supply DCAM compliant IEEE 1394 camera IEEE 1394 cable Ethernet cable Oye ar er as te Monitor CVS 1450 hardware setup is shown in figure 3 1 1 Power LED 5 IEEE 19940 Ports 9 VGA 2 Sttus LED 6 TILVOandboabd VO 10 RS 292 Senal 3 Isolated Digtal input 7 Reset Button 11 R45 Ethemet Port 4 TTL Digtal Outputs 8 DIP Sattches Figure 3 1 CVS 1450 Series Front Panel 31 3 1 1 1 Subnet Considerations To configure the CVS 1450 device it must reside on the same subnet as the development computer Once the CVS 1450 device is configured other subnets can access and use it To use the CVS 1450 device on a subnet other than the one on which the development computer resides first connect and configure the CVS 1450 device on the same subnet as the development computer Next physically move the CVS 1450 device to the other subnet and reassign an IP address Contact network administrator for assistance in setting up the development computer and CVS 1450 device on the same subnet 3 1 1 2 CVS 1450 Hardware The following items are necessary for setting up the CVS 1450 device CVS 1450 device Ethernet equipped development computer running Windows 200
16. counter would remove a tedious chore and allow each plate to be counted with equal efficiency This method is used not only in medical examinations but also in the food and drug safety evaluations environmental monitoring and public health One of the functions of this Machine is to count discrete areas within a selected brightness range 1 1 Bacteria Bacteria are unicellular microorganisms They are typically a few micrometres long and have many shapes including curved rods spheres rods and spirals The study of bacteria is bacteriology a branch of microbiology Bacteria are ubiquitous in every habitat on earth growing in soil acidic hot springs radioactive waste seawater and deep in the earth s crust There are typically 40 million bacterial cells in a gram of soil and a million bacterial cells in a millilitre of fresh water in all there are approximately five nonillion 5 lt 10 bacteria in the world Bacteria are vital in recycling nutrients and many important steps in nutrient cycles depend on bacteria such as the fixation of nitrogen from the atmosphere However most of these bacteria have not been characterised and only about half of the phyla of bacteria have species that can be cultured in the laboratory There are approximately 10 times as many bacterial cells as human cells in the human body with large numbers of bacteria on the skin and in the digestive tract Although the vast majority of these bacteria are rendere
17. experienced vision engineer specialist skills e g medicine satellite imagery forensic science b For target system in factory low skill ste level during set up Autonomous in inspection mode Operator skill level a For interactive prototyping system May rely on user having specialist medium high required skills e g medical b For target system in factory must be able to cope with low skill level Output data Simple signal to control external Complex signal for human being equipment e g simple accept reject device or multi axis robot Prime factor a For interactive prototyping Human interaction Speed is often determining of secondary importance system human interaction processing speed b For target system in factory speed of production Table 2 4 Comparison between Machine Vision and Computer Vision 29 Chapter 3 Setup and Configuration For acquiring images in real time we have to configure system according to our requirements There are total four CDs to be installed in system Drivers for Data Acquisition Instrument Control Motion and Vision are in two CDs NI vision Development module are in one CD and TEEE 1394 PROSILICA camera 2 0 1 are in one CD We used NI IEEE 1394 DCAM for capturing the image and NI Vision Assistant 7 1 for processing and analyzing it NI CVS 1450 Series devices are easy to use distributed real time imaging systems that acquire process and display i
18. in Vision Assistant We can select an image to process by double clicking it in the Image Browser Processing window It updates the image as we change parameters Because this view immediately reflects the changes we have made in the Parameter window we can continue modifying parameters until we get the result we want Functions window Parameter window It displays a list of image processing functions we can use to develop an algorithm or displays parameters that we can set for an image processing function Each function available through the Functions window has a Parameter window in which we set the parameters for that function Reference window Embedded Help window The Image tab of the Reference window displays the image source as we manipulate it in the Processing window The other tabs in the Embedded Help window contain context help for the function we are using Solution wizard It displays a list of industries and corresponding quality assurance tasks that those industries perform The wizard loads an IMAQ Vision based solution for the task we 46 select Performance meter It estimates how long a script will take to complete on a given image LabVIEW VI creation It creates a LabVIEW VI corresponding to the algorithm we prototype in Vision Assistant Based on the options we select the LabVIEW VI Creation Wizard creates a new VI that implements the image processing steps of the current script or of a saved script file
19. of Bacterial Culture Images Journal of Microbiological Methods Volume 19 Issue 4 Page No 279 295 Gilchrist J E Campbell J E Donnelly C B Peeler J T and Delaney J M 1973 Spiral Plate Method for Bacterial Determination Volume 25 Page No 244 252 http Colony Colony is available at http www ruf rice edu bioslabs bios3 18 colony htm http Functions Functions and Applications of Machine Vision is available at http www autovis com courses Fundamentals_syllabus html http Growth 1 Growth and Reproduction of Bacteria is available at http www textb ookofbacteriology net growth html http Growth 2 Phases of Growth is available at http biology clc uc edu fankhause t Labs Microbiology GrowthCurve GrowthCurve htm http Machine a Anatomy of Machine Vision is available at http www melles griot com products machinevision machinetutorial asp http Machine b Components of Machine Vision is available at http en wikipedia org wiki Machinevision http Wiki a Agar Plate is available at http en wikipedia org wiki Agar_ plate 83 13 14 15 16 17 18 19 20 21 22 23 24 http Wiki b Bacteria is available at http en wikipedia org wiki Bacteria http Wiki c Colony Counter is available at http en wikipedia org wiki Colony_ counter John M Kramer Margaret Kendall and Richard J Gilbert 1978 Evaluation of th
20. on the current image is 47 ms or 21 42 parts s a 70 Average Inspection Time 46 70 ms 3 a s04 Longest Inspection Time 81 ms 0 Standard Deviation 14 02 ms a em amp Basic Morphology Modifies the shape of ie binary objects in an image Step Name Extract RGB Red Plane 1 Convolution Highlight Details 1 Threshold 1 Proper Close 1 Fill holes 1 Remove border objects 1 Particle Filter 1 Particle Analysis 1 Adv Morphology Performs high level D3 operations on blobs in binary images rei9ms 0 906me 1362m Particle Fiter Removes or keeps partich WE in an image as specified by the filter criteria Invert Binary Image Reverses the dynamic of an image containing two different grayscale populations Particle Analysis Displays measurement E results for selected particle measurement performed on the image Particle Analysis 1 Shape Matching Finds objects in an image that are shaped like the object specified by the template hes i Figure 5 17 Estimating Processing Time of an image 76 The system shows the number of microorganisms present in processed image From our analysis we have come to the conclusion that colonies in a Petri dish can be easily counted by Particle Analysis We perform a particle analysis to detect connected regions or groupings of pixels in an image and then make sel
21. or select Grayscale Threshold The Threshold Parameter window displays a histogram A histogram counts the total number of pixels in each grayscale value and graphs it From the graph we can tell if the image contains distinct regions of a certain grayscale value We also can select pixel regions of the image 2 Select Manual Threshold from the Threshold list 3 Select a range of 118 to 255 66 4 We noticed that the particles of interest circular and non circular are highlighted in red When we apply the threshold everything highlighted is set to 1 and all other pixels are set to 0 We can select the range using the pointers on the histogram rather than entering numbers in the Min and Max fields Adjust the pointers until all of the objects we want to select are red The black pointer marks the minimum value and the white pointer marks the maximum value 5 Click OK to apply the threshold and add this step to the script The separating particles image is shown in figure 5 9 D NI Vision Assistant Image How To Controls slon View Tools Help zea aE AAA em gt lo elolelole my bmp RGB 752x480 l ajeje Template Threshold Type Image Source Auto Threshold Clustering Auto Threshold Entropy Auto Threshold Metric y 752480 1 1 49 183 lt Threshold Values Min frie Max Sess seve titled Script 3 pa r y ya il 1 JD g e eie I 1 1 1 I I I 1 I I iW LET
22. phase The log phase is marked by rapid exponential growth The rate at which cells grow during this phase is known as the growth rate and the time it takes the cells to double is known as the generation time During log phase nutrients are metabolised at maximum speed until one of the nutrients is depleted and starts limiting growth Once the organisms have adapted to a growth medium Growth occurs at an exponential log rate This is the healthiest time for a culture It is the best to use for experiments or research The population doubles in each generation time which in shown table 1 1 For example Organisms per ml Time minutes 1 000 0 minutes 2000 20 minutes 4000 40 minutes 8000 60 minutes or 1I hr 64000 2 hr 512000 3 hr Table 1 1 Generation time Such growth is said to be exponential or logarithmic The generation time for most bacteria is between 20 minutes and 20 hours with most typically less than 1 hour 1 1 2 3 Stationary Phase The third phase of growth is the stationary phase and is caused by depleted nutrients The cells reduce their metabolic activity and consume non essential cellular proteins The stationary phase is a transition from rapid growth to a stress response state and there is increased expression of genes involved in DNA repair antioxidant metabolism and nutrient transport Cell division decreases to a point that new cells are produced at same rate as old cell die The
23. processing functions The accuracy of decision made by processor is simply dependent upon the resolution of camera The more the resolution the finer will be the image http Machine a 26 2 7 Comparison between Machine Vision and Human Vision The table 2 3 is showing the comparison between Machine Vision and Human Vision Feature Machine Vision Human Vision Spectral range Gamma rays to microwaves Visible light 4 107 10 10 m Spatial Resolution Currently 2002 4 10 pixels Effectively approximately 4000x4000 area scan growing rapidly pixels 8192 line scan Quantitative Yes Capable of precise No Ability to cope with Poor unseen events a Performance on Good Poor due to fatigue and boredom repetitive tasks rd Light level variability Fixed closely controlled Highly variable Light level min Equivalent to cloudy Quarter moon light greater if dark moonless night adaptation is extended Strobe lighting and Possible good screening is _ Unsafe lasers needed for safety Ld Ability to program in Limited Special interfaces Speech is effective situ make task easier Able to cope with Versatile Limited multiple views in space and or time 27 Able to work in toxic biohazard areas Non standard scanning Line scan circular scan Not possible methods random scan spiral scan radial scan Image storage Poor without photography or digital storage Optical aids Numerous
24. the spiral plate and surface drop techniques for counting bacteria An electronic laser counter used in this study was found to give comparable results r 0 966 to the grid method of colony counting in a substantially shorter time Analysis of operation times and material requirements for each method showed that significant savings in cost time space and support labour could be achieved with the spiral plate method over conventional techniques John M Kramer et al 2004 Putman M et al had given simplified method to automatically count bacterial colony forming unit in 2005 This method shows that an automated colony counter can process images obtained with a digital camera or document scanner and that any laboratory can efficiently have bacterial colonies enumerated by sending the images to a laboratory with a colony counter via internet Bacterial colony counting is a significant technical hurdle for vaccine studies as well as various microbiological studies Putman M et al 2005 Bang Wei et al had given an Effective and Robust Method for Automatic Bacterial Colony Enumeration in 2007 In this study they propose a fully automatic colony counter and compare its performance with Clono Counter These experimental results show that the proposed method outperforms Clono Counter in terms of Precision Recall and F measures Bang Wei et al 2007 15 Chapter 2 Machine Vision We see the world around through our eyes Our eyes are
25. 0 XP Me 98 DCAM compliant IEEE 1394 camera Any standard IEEE 1394 cable Facilitates plug and play connection from the CVS D POP OT 1450 device to up to three 1394 cameras To maintain signal integrity the IEEE 1394 cable length must be no longer than 4 5 m 5 NI desktop power supply part number 778794 01 or any 24 VDC 10 50 W power supply 6 10 m 10 100Base T Ethernet cable Standard Category 5 CAT 5 10 100Base T Ethernet cable that connects the CVS 1450 device to a network port To connect the CVS 1450 device directly to a local development computer use an Ethernet crossover cable To maintain signal integrity the Ethernet cable length must be no longer than 100 m 3 1 1 3 Connecting the CVS 1450 Device to a Network We use a standard CAT 5 or CAT 6 Ethernet cable to connect the CVS 1450 device to an Ethernet network If the development computer is already configured on a network we must configure the CVS 1450 device on the same network 32 If the development computer is not connected to a network we can connect the two directly using a CAT 5 or CAT 6 Ethernet crossover cable 3 1 1 4 Connecting a Camera and Monitor to the CVS 1450 Device Before connecting a camera and monitor to the CVS 1450 device we have sure that all CVS 1450 device DIP switches are in the OFF position To connect an IEEE 1394 camera and a monitor to the CVS 1450 device refer to figure 3 2 while completing the following steps
26. 1 Connect the VGA cable from the monitor to the VGA port on the CVS 1450 device 2 Plug the IEEE 1394 cable into one of the IEEE 1394a ports on the CVS 1450 device Plug the other end of the cable into the IEEE 1394 port on the camera If our camera requires an external power supply connect it to the camera and verify that the camera is powered on 3 Plug in and power on the monitor 1 VGA Cable 2 IEEE 1994 Cable Figure 3 2 Basic Hardware 33 3 1 1 5 Wiring Power to the CVS 1450 Device Connect the power to the CVS 1450 device as described in figure 3 3 while completing the following steps 1 Plug the 4 position connector from the power supply into the power receptacle on the CVS 1450 device 2 Plug the power cord into the power supply 3 Plug the power cord into an outlet To Outlet _ gt _ 1 4 Position Power Connector 2 NI Desktop Power Supply 3 Power Supply Cord to Outlet Figure 3 3 Wiring Power to the CVS 1450 Device Do not connect the CVS 1450 device main power to a source other than 24 VDC 10 Do not connect the CVS 1450 device isolated power to a source less than 5 VDC or greater than 30 VDC Doing so could damage the CVS 1450 device 34 3 1 1 6 Connecting the CVS 1450 Device to the Development Computer To connect the CVS 1450 device to the development computer refer to figure 3 4 while completing the following steps 1 Verify that the development
27. ETT m_e 0 25 50 75 100 125 150 175 200 225 255 Original Image Extract RGB Red Pla Convolution Threshold 1 ae eat Figure 5 9 Separating Particles from the Background with Thresholding 67 The Manual Threshold operation enables you to select ranges of grayscale pixel values Automatic threshold operations select threshold ranges for us Use automatic thresholds for images in which light intensity varies The pixels that we selected for processing appear red Unselected pixels appear black The image is now a binary image which is an image composed of pixels with values of 0 and 1 This image is displayed using a binary palette which displays the pixel intensities of an image with unique colors All pixels with a value of 0 appear black and pixels set to 1 appear red The red pixels are now referred to as particles 5 5 6 Modifying Particles with Morphological Functions Morphological functions affect the shape of particles on an individual basis Morphological operations prepare particles in the image for quantitative analysis such as finding the area perimeter or orientation Basic Morphology It affects the shape of particles in binary images Each particle or region is affected on an individual basis We can use these functions for tasks such as expanding or reducing objects filling holes closing particles or smoothing boundaries which are tasks we perform to delineate objects and prepare images f
28. For example this function would be suitable for fault detection Interclass Variance Interclass variance is based on discriminant analysis An optimal threshold is determined by maximizing the between class variation with respect to the threshold Metric this technique is used in situations similar to interclass variance For each threshold a value is calculated that is determined by the surfaces representing the initial gray scale The optimal threshold corresponds to the smallest value Moments This technique is suited for images that have poor contrast The moments method is based on the hypothesis that the observed image is a blurred version of the theoretically binary original The blurring that is produced from the acquisition process caused by electronic noise or slight defocalization is treated as if the statistical moments of average and variance were the same for both the blurred image and the original image This function recalculates a theoretical binary image 57 Chapter 5 Problem Statement and Solution 5 1 Problem Statement Bacterial colony is a group of bacteria growing on a plate that is derived from one original starting cell Bacterial colony enumeration has applications in many different assays such as antibiotic screening toxicology testing and genotoxicity measuring The number of microorganisms present in food has very important consideration Bacterial colony counting process is usually perf
29. age to the user buffer Grab functions perform an acquisition that loops continually on one or more internal buffers We can copy the last acquired buffer to a separate user buffer for processing or analysis Sequence functions acquire a specified number of internal buffers and then stops Trigger functions control the trigger mode of the IEEE 1394 camera 3 2 2 Low level functions Use when we require more direct control of the image acquisition Acquisition functions configure start stop and unconfigure an image acquisition or examine a user buffer during an acquisition Attribute functions examine and change the acquisition or camera attributes Utility functions display an image in a window save an image to a file or to get detailed error information Both high level and low level functions support snaps grab sequence and triggered acquisitions Using high level functions we can write programs quickly without having to learn 38 the details of the low level API and driver The low level functions give us finer granularity and control over the image acquisition process but we must understand the API and driver in greater detail to use these functions 3 3 Acquisition flow The basic steps of performing an acquisition with the NI IMAQ for IEEE 1394 cameras software are initialization configuration and acquisition NI a 3 3 1 Initialization To acquire images using the high level or low level function
30. aken in one second Frames are to be taken at fast rate such that we do not loose information about the object These frames are to be taken in real time that s why frames are blurred and this needs image processing Cameras used are mainly CCD Charge coupled Devices type or digital cameras Resolution of camera must be strictly decided according to the object s area and size In our work we have used digital camera PROSILICA 2 0 1 IEEE 1394 compatible 18 2 2 2 Image processor is used to get the best out of grabbed or captured images we make use of image processor Image processor is usually a computer laptop or a digital signal processor An image is defined as the representation of the real scene either in black and white or in color and either in print form or in digital form Printed images are reproduced either by multiple colors and gray scales or by a single ink source Most print application only one color of ink is available such as black ink on white paper in a newspaper or copier In such cases changing the ratio of black versus white areas produces all gray levels The effect of image processor is shown in figure 2 2 Image Better Image Processor Images Figure 2 2 Effect of image processor Image is divided into small sections on screen called picture cells or pixels where the size of all pixels is the same while intensity of light in each pixel is varied to create the images Image processor processes the
31. ameters and Camera Attributes tabs to modify the camera video parameters 6 Click Snap after adjusting the parameters to view 7 Click Save to save the current configuration 3 2 IMAQ for IEEE 1394 Fire wire compatible cameras The NI IMAQ for IEEE 1394 compatible cameras software lists the supported application development environments describes the fundamentals of creating applications using NI IMAQ for IEEE 1394 Cameras describes the files used to build these applications NI IMAQ Software for IEEE 1394 Cameras gives us the ability to use IEEE 1394 industrial digital video cameras to acquire images The cameras may operate at various resolutions and frame rates depending on camera capabilities 37 We use National Instruments Measurement amp Automation Explorer MAX to configure our ITEEE 1394 camera and Refer to the NI IMAQ for IEEE 1394 Cameras help for information about configuring our IEEE 1394 camera The camera configuration is saved in a camera file which the NI IMAQ for IEEE 1394 Cameras VIs and functions use to configure a camera and supported attributes The NI IMAQ for IEEE 1394 cameras application programming interface API is divided two main function groups high level and low level NI a 3 2 1 High level functions Use to capture images quickly and easily If we need more advanced functionality we can mix high level functions with low level functions Snap functions capture all or a portion of a single im
32. an image to counting circular particles in an image is done in one script as shown in figure 5 15 and figure 5 16 74 File Edit Image Color Grayscale Binary Machine Vision View Tools Help Shs ph SOP Le Image When To my bmp RGB 752x480 gt e gt Baas x Basic Morphology Modifies the shape of binary objects in an image 7 Adv Morpho Performs high level DI operatic n blobs in binary images 52 lt Particle Filter Removes or keeps particles 752x480 1 1 BEJ in an image as specified by the filter criteria ae Invert Binary Image Reverses the D Sa el i l gt lit dynam an image containing two different grayscale populations Particle Analysis Displays measurement BH results for selected particle measurements L performed on the image Original Image Extract RGB Red Pla Convolution Threshold 1 Proper 1 Shape Matching Finds objects in an image that are shaped like the object specified by the template l i La A Figure 5 15 Processing Script bof Image When To File Edit Image Color Grayscale Binary Machine Vision View Tools Help Sb P PNQ e M gy Modifies the shape of 5 binary objects in an image L Adv Morpl Performs high level DI operations on blobs in binary imag 52 lt Particle Filter Removes or keeps particles 752x490 171
33. ary image using morphological transformations and make measurements on the particles in the image 53 4 5 Thresholding Threshold consists of segmenting an image into two regions a particle region and a background region This process works by setting to 1 all pixels that belong to a gray level interval called the threshold interval and setting all other pixels in the image to 0 Use thresholding to isolate objects of interest in an image Thresholding converts the image from a grayscale image with pixel values ranging from 0 to 255 to a binary image with pixel values of 0 or 1 Thresholding enables to select ranges of pixel values in grayscale and color images that separate the objects under consideration from the background NI c 4 5 1 Applications Use thresholding to extract areas that correspond to significant structures in an image and to focus the analysis on these areas as shown in figure 4 2 Image Histogram Threshold Interval 0 0 166 Figure 4 2 Image Histogram Pixels outside the threshold interval are set to 0 and are considered as part of the background area Pixels inside the threshold interval are set to 1 and are considered as part of a particle area 4 5 2 Intensity Threshold 54 Particles are characterized by an intensity range They are composed of pixels with gray level values belonging to a given threshold interval overall luminosity or gray shade All other pixels are consider
34. as output images of type YUV 4 2 2 However IMAQ Vision does not natively support the YUV mode To process and display the image the driver automatically decodes the YUV image into a 32 bit RGB image Table 3 2 lists common video modes and their corresponding image types after being decoded by NI IMAQ for IEEE 1394 Cameras Raw Camera Output Decoded Destination Image 8 bit monochrome 8 bit monochrome 16 bit monochrome 16 bit monochrome YUV 4 1 1 32 bit color YUV 4 2 2 32 bit color YUV 4 4 4 32 bit color 24 bit RGB 32 bit color 48 bit RGB 64 bit color 8 bit Bayer 32 bit color 16 bit Bayer 32 bit color Table 3 2 Decoder inputs and corresponding outputs 44 Chapter 4 Vision Assistant 4 1 Introduction to Vision Assistant 7 1 Vision Assistant is a tool for prototyping and testing image processing applications To prototype an image processing application build custom algorithms with the Vision Assistant scripting feature The scripting feature records every step of the processing algorithm After completing the algorithm we can test it on other images to make sure it works The algorithm is recorded in a Builder file which is an ASCII text file that lists the processing functions and relevant parameters for an algorithm that we prototype in Vision Assistant We must have LabVIEW 6 1 or later and IMAQ Vision 7 1 for LabVIEW or later installed to use the LabVIEW VI Creation Wizar
35. ay open the door to this application category The table 2 2 shows how the functional categories and the application categories of machine vision overlap For any application there is more than one function that can serve it And any function can be applied in more than one way http functions 25 uonisod Huipul4 Test and calibration Data collection Machine monitoring Material handling Y N AA U0l 29 9p Me J SESS BSS 8 R RONSONA N SSS DD j a m MA aA Table 2 2 Functions and Applications of Machine Vision Lid 2 6 Advantages and Limitations of Machine Vision Machine vision can be implemented to various industrial locations where cameras can replace two or three workmen It can also be employed in hazardous conditions where it is very difficult for operator to watch the process disturbances continuously The decisions are taken by processor in real time therefore it is very useful in real time process control The grabbing rate of some specialized cameras can be too high as compared to human perception therefore it can detect very fast and minute changes in case moving object System performance is affected due to vibration and noise because camera produces blurred image in these conditions Natural light affect the camera performance due to changing of brightness of natural light from day to night Sometimes the images that are heavily distorted may loose useful information during various
36. ay scientists can identify bacteria In fact there is a book called Bergey s Manual of Determinative Bacteriology commonly termed Bergey s Manual that describes the majority of bacterial species identified by scientists so far This manual provides descriptions for the colony morphologies of each bacterial species 1 3 1 Features of Bacterial Colony Form is the basic shape of the colony For example circular filamentous etc Elevation is the cross sectional shape of the colony which is Turn the Petri dish on end Margin is the magnified shape of the edge of the colony Surface this shows the appearance of the surface of the colony For example smooth glistening rough dull opposite of glistening rugose wrinkled etc Opacity For example transparent clear opaque translucent almost clear but distorted vision like looking through frosted glass iridescent changing colors in reflected light etc Chromo genesis pigmentation For example white buff red purple etc In bacterial colony assays the patterns are formed within culture media that has been inoculated with bacterial cells This allows the cells to reproduce and form bacterial colonies within and or on the surface of the medium When the colonies are sufficiently large they are usually visible to the naked eye which allows researchers to determine the number of colonies formed In addition various visual characteristics of the colonies su
37. blurred image pixel by pixel or sometimes reference points are processed which represent the whole image and make the picture more clearly to the operator for further analysis The more the pixels present the better the resolution of camera We have used Machine Vision Assistant 7 1 simulator software to perform image processing tasks with CVS 1450 http Machine b 2 2 3 Display unit is mainly a screen having pixels to display the grabbed images A personal computer or laptop is used for this purpose Sometimes storage of grabbed images is also done with computer for offline analysis We have used simple PC monitor to get images from camera Camera is connected to monitor through CVS 1450 using LAN which directly displays the acquired image on monitor 2 3 Basics of Digital image Digital Image Processing is done to process any image to get useful information from it For Image processing we must know about the image basics These basics are given as 19 2 3 1 Digital image An image is a 2D array of values representing light intensity For the purposes of image processing the term image refers to a digital image An image is a function of the light intensity f x y where f is the brightness of the point x y and x and y represents the spatial coordinates of a picture element or pixel By convention the spatial reference of the pixel with the coordinates 0 0 is located at the top left corner of the image We can noticed in figu
38. cepts A typical particle analysis process scans through an entire image detects all the particles in the image and builds a detailed report on each particle For example we could use the area of the template particle as a criterion for removing all particles that do not match it within some tolerance We then can perform a more refined search on the remaining particles using another list of parameter tolerances The figure 4 1 shows a sample list of parameters that we can obtain in a particle analysis application The binary image in this example was obtained by thresholding the source image and removing particles that touch the border of the image We can use these parameters to identify and classify particles Figure 4 1 Sample list of parameters 52 We can use multiple parameters such as perimeter angle area and center of mass to identify and classify these particles Using multiple parameters can be faster and more effective than pattern matching in many applications Also by using different sets of parameters we can uniquely identify a feature in an image The following table shows the values obtained for the particle enclosed in a rectangle shown in table 4 1 Bounding Rect Left Top Right Bottom Center of Mass X y Orientation Dimensions Width Height Table 4 1 Values of the Particle To use particle analysis first create a binary image using a thresholding process We then can improve the bin
39. ch as shape size pigmentation and opacity can be used to help determine the type of bacterium present In bacterial colony counting the colonies formed are enumerated either manually or using automated image analysis techniques A general problem for microbiologists is determining the number of phenotypically similar colonies growing on an agar plate that must be analyzed in order to be confident of identifying all of the different strains present in the sample If a specified number of colonies are picked from a plate on which the number of unique strains of bacteria is unknown assigning a 9 probability of correctly identifying all of the strains present on the plate is not a simple task Refer to the figure1 5 for illustrated examples of form elevation and margin Form Circular Irregular Filamentous Rhizoid Elevation Raised Convex Flat Umbonate Crateriform Margin Entire Undulate Filiform Curled Lobate Figure1 5 Form Elevation and Margin With Escherichia coli of avian cellulites origin as a case study a statistical model was designed that would delineate sample sizes for efficient and consistent identification of all the strains of phenotypically similar bacteria in a clinical sample This model enables the microbiologist to calculate the probability that all of the strains contained within the sample are correctly identified and to generate probability based sample sizes for colony identification The probab
40. d 21 2 3 3 1 Grayscale images Grayscale images are composed of a single plane of pixels Each pixel is encoded using one of the following single numbers e An 8 bit unsigned integer representing grayscale values between 0 and 255 e A 16 bit signed integer representing grayscale values between 32 768 and 32 767 e A single precision floating point number encoded using four bytes represents grayscale values ranging from o to o 2 3 3 2 Color images Color images are encoded in memory as a red green and blue RGB image Color image pixels are a composite of four values RGB images store color information using 8 bits each for the red green and blue planes RGB 64 bit images store color information using 16 bits each for the red green and blue planes In the RGB and HSL color models an additional 8 bit value goes unused This representation is known as 4 x 8 bit or 32 bit encoding In the RGB 64 bit color model an additional 16 bit value goes unused This representation is known as 4 x 16 bit or 64 bit encoding 2 3 3 3 Complex images Complex image contains the frequency information of a grayscale image We can create a complex image by applying a Fast Fourier transform FFT to a grayscale image After we transform a grayscale image into a complex image we can perform frequency domain operations on the image Each pixel in a complex image is encoded as two single precision floating point values which represent th
41. d 4 2 System requirements and installation To run Vision Assistant system must meet the following minimum requirements 1 Personal computer using a 233 MHz Pentium class processor Using a Pentium III or Celeron 600 MHz or equivalent is recommended 2 Microsoft Windows 2000 NT XP If we are using Windows NT 4 0 we must have Service Pack 6 or later installed to run Vision Assistant 1024 768 resolution or higher video adapter 65 536 colors 16 bit or higher Minimum of 128 MB RAM 256 MB recommended Minimum of 200 MB of free hard disk space ON Gep If we are acquiring images the system must have National Instruments image acquisition IMAQ hardware and NI IMAQ 3 0 or later or NI IMAQ for IEEE 1394 Cameras 1 5 or later installed 45 4 2 1 Installing Vision Assistant e To install Vision Assistant on a Windows 2000 NT XP system we must be logged in with Administrator privileges e Insert the Vision Assistant CD into the CD ROM drive e If we do not have auto run enabled double click auto run exe If we have auto run enabled auto run exe runs automatically 4 2 2 Features Vision Assistant offers the following features Script window It Records a series of image processing steps and the settings we use for each of those steps We can run scripts on single images or in a batch to analyze a collection of images We also can modify and save scripts Image browser It contains all of the images currently loaded
42. d harmless or beneficial by the protective effects of the immune system a few pathogenic bacteria cause infectious diseases including cholera syphilis anthrax leprosy and bubonic plague The most common fatal bacterial diseases are respiratory infections with tuberculosis alone killing about 2 million people a 2 year mostly in sub Saharan Africa In developed countries antibiotics are used to treat bacterial infections and in various agricultural processes so antibiotic resistance is becoming common Bacteria like Esherichia coli cells is showm in figure 2 Figure 1 2 Bacteria In industry bacteria are important in processes such as wastewater treatment the production of cheese and yoghurt and the manufacture of antibiotics and other chemicals Bacteria are prokaryotes Unlike cells of animals and other eukaryotes bacterial cells do not contain a nucleus and rarely harbour membrane bound organelles Although the term bacteria traditionally included all prokaryotes the scientific classification changed after the discovery in the 1990s that prokaryotic life consists of two very different groups of organisms that evolved independently from an ancient common ancestor These evolutionary domains are called Bacteria and Archaea http Wiki b 1 1 1 Characteristics of bacteria Bacteria are relatively small Bacterial cells do not contain a nucleus or other membrane bound organelles These bacteria are Single celled amp
43. e 38 177 180 3 heey 39 233 210 23 m 40 105 103 2 Toa 41 90 92 2 ee 42 165 162 3 ees 43 77 75 2 pa 44 48 50 2 fae 45 296 300 4 rene 46 368 366 2 poe 78 47 407 410 3 aoe 48 374 376 2 0 53 49 348 345 3 eee 50 263 267 4 ree 51 136 134 2 1 47 52 439 441 2 0 46 53 295 300 5 ae x 54 75 88 13 art 55 187 190 3 1 60 56 170 168 2 Pee 57 387 385 2 ae 58 261 265 4 1 53 59 438 440 2 ey 60 98 101 2 eo 61 179 180 1 ee 62 184 179 5 PeR 63 138 142 4 ee 64 483 487 4 0 83 x a 65 356 386 30 sae 66 496 493 3 pe 67 186 185 1 E 68 48 50 2 ee 69 57 55 2 3 51 x 70 260 246 14 e 71 435 436 1 0 23 72 484 482 2 or Table 5 1 Results and discussion Barring six reading marked the remaining 66 readings were very much with in the tolerance limit of error of 5 79 The percentage Root Mean Squares of the remaining 66 readings were Sum of Percentage error Percentage error Number of readings 0 019212 66 II 1 71 80 Chapter 6 Conclusions amp Future scope 6 1 Conclusions The proposed Machine Vision based Method is a robust yet effective method for bacterial counter It has the ability to detect the dish plate regions isolate colonies on the dish plate and further separate the clustered colonies for accurate counting of colonies Machine
44. e Spiral Plate and Laser Colony Counting techniques for the Enumeration of Bacteria in foods Journal of Applied Microbiology and Biotechnology Volume 6 Page No 289 299 Kawai M Yamaguchin and Nasu M 1999 Rapid Enumeration of Physiologically active Bacteria in purified water used in the Pharmaceutical Manufacturing Process Journal of Applied Microbiology Volume 86 Page No 496 504 Khatipov Emir 2001 is available at http www bio net bionet mm methods 2001 October 090763 html Mukherjee Dipti Pal Amita Sharma S Eswara and Majumder D Dutta 1995 Water Quality Analysis A Pattern Recognition Approach Volume 28 Issue 2 Pages No 269 281 NI a IMAQ NI CVS 1450 series user manual is available at http www ni com pdf manuals 373610d pdf NI b NI VA tutorial manual NI c NI concepts manual Paul C Olsztyn James H Beyer and David Sullivan 1998 Bacteria Colony Counter amp Classifier Volume 83 Page No 382 400 Putman M Burton R and Nahm M H 2005 Simplified Method to Automatically Count Bacterial Colony Forming Unit Journal of Immunological Methods Volume 302 Page No 99 102 Whelan F 2001 Machine Vision Algorithms Volume 34 Page No 114 119 84
45. e CD ROM drive 4 When the installation splash screen appears click Install LabVIEW real time and follow the setup instructions 5 Insert the Vision Development Module CD into the CD ROM drive 6 When the installation splash screen appears click Install Vision Development Module and follow the setup instructions 7 Insert the NI IMAQ for IEEE 1394 Cameras CD into the CD ROM drive 8 When the installation splash screen appears click Install NI IMAQ for IEEE 1394 Cameras and follow the setup instructions 9 When prompted click yes to reboot the development computer 36 3 1 3 Acquiring an Image We use NI IMAQ to configure IEEE 1394 camera which gives us the ability to use IEEE 1394 industrial digital video cameras to acquire images The camera configuration is saved in a camera file The camera may operate at various resolutions and frame rates depending on camera capabilities Configuration When the camera is successfully connected we can configure the camera Complete the following steps to configure the IEEE 1394 camera for a grab acquisition 1 Connect the camera to the IEEE 1394 PORT on the computer 2 Expand NI IMAQ IEEE1394 Devices to obtain a list of available cameras 3 Click the camera name to select the appropriate camera 4 Click Snap to acquire a single image or Grab to acquire images continuously while focusing the camera click Grab again to stop the acquisition 5 Acquisition Par
46. e real and imaginary components of the complex pixel We can extract the following four components from a complex image the real part imaginary part magnitude and phase Table 2 1 shows how many bytes per pixel grayscale color and complex images use NI c 22 8 bit Unsigned Integer Grayscale 1 byte or 8 bit 16 bit Signed Integer Grayscale 2 bytes or 16 bit Image Type Number of Bytes per Pixel Data bit for the grayscale intensity 16 bit for the grayscale intensity 32 bit Floating Point Grayscale 4 bytes or 32 bit 32 bit for the grayscale intensity RGB Color 4 bytes or 32 bit 8 bit for the alpha value not used 8 bit for the green intensity 8 bit for the red intensity 8 bit for the blue intensity Table 2 1 Bytes per pixel 2 4 Functions of Machine Vision Machine vision functions are the capabilities of machine vision systems the type of information they extract from the image without regard to how that information is used The functional categories are Finding location and orientation This is an especially significant machine vision function It is used to locate parts for operations for a variety of purposes 23 Measurements One capability inherent in machine vision that is not a capability of human vision is making measurements Machine vision systems make measurements with a precision of one part in ten th
47. ected measurements of those regions Average Processing time of script for 72 cases is 47ms which is very less as compared to manual counting of bacteria 5 6 Results and discussion To validate our script we executed our program on 72 numbers of Petri dishes of culture bacteria like E coli Pseudomonas and Lactobacillus The colonies were counted to cross check the count given by one script Two or more overlapping colonies were counted as one in both manual and automatic mode The result of this count is given in Table 5 2 S No Manual Automatic Error Percentage counting counting error 1 104 106 2 i 2 272 268 4 Ha 3 162 156 6 eon 4 183 187 4 Sago 5 277 280 3 TOIR 6 120 116 4 abon 7 192 190 2 Per 8 192 189 3 Pre 9 193 189 4 Bez 10 218 235 17 a 11 459 461 2 TaN 12 347 350 3 Bge 13 350 352 2 sare 14 220 218 2 asa 15 240 243 3 PEN 16 447 449 2 ee 77 17 305 303 2 E 18 333 330 3 pem 19 247 250 3 ay 20 266 269 3 ae 21 278 280 2 ee 22 314 310 4 aoe 23 323 325 2 ee 24 115 117 2 a 25 236 233 3 pen 26 178 174 4 n 27 166 169 3 ae 28 489 491 2 ae 29 179 181 2 Sa 30 144 147 3 er 31 456 454 2 en 32 378 376 2 poe 33 431 435 4 E 34 401 405 4 ae 35 279 275 4 ae 36 258 260 2 eee 37 175 200 25 e
48. ed to be part of the background The threshold interval is defined by the two parameters Lower Threshold and Upper Threshold All pixels that have gray level values equal to or greater than the Lower Threshold and equal to or smaller than the Upper Threshold are selected as pixels belonging to particles in the image 4 5 3 Thresholding Example This example uses the following source image Figure 4 3 Example of Thresholding Highlighting the pixels that belong to the threshold interval 166 255 the brightest areas produces the following image Figure 4 4 Brightest area of Thresholding 55 A critical and frequent problem in segmenting an image into particle and background regions occurs when the boundaries are not sharply demarcated In such a case the choice of a correct threshold becomes subjective Therefore we may want to enhance our images before thresholding to outline where the correct borders lie We can use lookup tables filters FFTs or equalize functions to enhance images Observing the intensity profile of a line crossing a boundary area is also helpful in selecting a correct threshold value Finally morphological transformations can help us to retouch the shape of binary particles and therefore correct unsatisfactory selections that occurred during the thresholding 4 5 4 Automatic Threshold Various automatic thresholding techniques are available Clustering Entropy Interclass Variance Metr
49. encoding is sufficient if we need to obtain the shape information of objects in an image However if we need to precisely measure the light intensity of an image or region in an image we must use 16 bit or floating point encoding Software does not directly support other types of image encoding particularly images encoded as 1 bit 2bit or 4 bit images In these cases Software automatically transforms the image into an 8 bit image the minimum bit depth for Software when opening the image file 2 3 2 3 Number of planes Number of planes corresponds to the number of arrays of pixels that compose the image A grayscale or pseudo color image is composed of one plane while a true color image is composed of three planes one each for the red component blue component and green component In true color images the color component intensities of a pixel are coded into three different values A color image is the combination of three arrays of pixels corresponding to the red green and blue components in a Red Green Blue RGB image Hue Saturation Luminescence HSL images are defined by their hue saturation and luminance values 2 3 3 Image types There are three types of images grayscale color and complex images For an identical spatial resolution a color image occupies four times the memory space of an 8bit grayscale image and a complex image occupies eight times the memory of an 8 bit grayscale image These three types are describe
50. g 5 5 6 Modifying Particles with Morphological Functions 5 5 7 Isolating Circular Particles 5 5 8 Analyzing Circular Particles 5 5 9 The Complete Processing Script 5 5 10 Estimating Processing Time 5 6 Results and discussion Chapter 6 Conclusions amp Future scope 6 1 Conclusions 6 2 Future scope References 58 59 59 61 61 62 64 65 66 68 71 73 74 76 77 81 81 81 83 List of Figures Fig 1 1 Bacteria colonies on a 100mm Petri dish 1 Fig 1 2 Bacteria 3 Fig 1 3 Bacterial growth curve 5 Fig 1 4 Agar plate 8 Fig 1 5 Form Elevation and Margin 10 Fig 1 6 Colony Plate Types 12 Fig 2 1 Anatomy of a machine vision system 18 Fig 2 2 Effect of image processor 19 Fig 2 3 Spatial reference of the 0 0 pixel 20 Fig 3 1 CVS 1450 Series Front Panel 31 Fig 3 2 Basic Hardware 33 Fig 3 3 Wiring Power to the CVS 1450 Device 34 Fig 3 4 Ethernet Connection 35 Fig 3 5 Relationships between cameras interface files and camera files 40 Fig 4 1 Sample list of parameters 52 Fig 4 2 Image Histogram 54 Fig 4 3 Example of Thresholding 55 Fig 4 4 Brightest area of Thresholding 55 Fig 5 1 Synthetic Image of particle to be counted 60 Fig 5 2 Result of particle Counting 60 Fig 5 3 Setup of acquiring image 61 Fig 5 4 Hardware Setup 62 Fig 5 5 Acquiring images in Vision Assistant Fig 5 6 Opening an image Fig 5 7 Extracting RGB Red Plane Fig 5 8 Filtering the Image Fig 5 9 Separating Particle
51. hts those features important to the measurement while minimizing distracting artifacts The lens 17 must be capable of faithfully imaging the critical features of the object The camera electronically records the images Frame grabber A frame grabber is a processor board that accepts the video input from the camera digitizes it and stores it for analysis Some frame grabbers include special processing electronics that speed the image processing and feature extraction tasks Processor A processor is required to control the vision application Pentium class processors are capable of running vision applications with no special processing hardware Software Computer software to control and execute the vision tasks is needed There are many flexible machine vision software packages that provide extensive vision tools for programmers to use when writing applications Developing vision software requires skill and experience even when using good tools 2 2 Components of Machine Vision Machine Vision is related to artificial intelligence We take the images in real time and process them for extracting useful information from them The system making use of machine vision has basically three components Camera Prosilica 2 0 1 Image Processor CVS 1450 Display Unit PC monitor 2 2 1 Camera is used for taking pictures of desired field spot point or any moving object Camera must have high grabbing or sampling rate Frames to be t
52. ic Moments In contrast to manual thresholding these methods do not require that we set the minimum and maximum light intensities These techniques are well suited for conditions in which the light intensity varies Depending on our source image it is sometimes useful to invert the original grayscale image before applying an automatic threshold function such as moments and entropy This is especially true for cases in which the background is brighter than the foreground Clustering is the only multi class thresholding method available Clustering operates on multiple classes so we can create tertiary or higher level images The other four methods entropy metric moments and interclass variance are reserved for strictly binary thresholding techniques The choice of which algorithm to apply depends on the type of image to threshold 56 Clustering This technique sorts the histogram of the image within a discrete number of classes corresponding to the number of phases perceived in an image The gray values are determined and a barycenter is determined for each class This process repeats until it obtains a value that represent the center of mass for each phase or class Clustering also known as multi class threshold which is the most frequently used automatic thresholding method Entropy Based on a classical image analysis technique entropy is best for detecting particles that are present in minuscule proportions on the image
53. ility of cellulites lesions containing a single strain of E coli was 95 4 If one E coli strain is observed out of three colonies randomly selected from a future agar plate the probability is 98 8 that only one strain is on the plate http Colony 10 1 3 2 Bacteria in a Colony The colony was one cell at the beginning If you grew the culture for 10 h on LB without antibiotic gt assume that cells divide every 20 min gt your cells had 30 division cycles gt there is 230 1048576 cells in a colony Here we assume that there is no growth substrate depletion taking place as long as a colony grows In reality cells that are on top of the colony are much depleted in nutrients and grow very slowly if at all and the fastest growing cells are those growing in contact with the medium and on the outer rim of the colony Thus what we can say without titration is that the real number of cells in a colony is lower than theoretical How much lower depends of richness of medium and solid support morphology of colonies etc Cells growing in presence of antibiotics will grow slower they will divide every 40 60 min Khatipov 2001 1 4 Colony Counter A colony counter is an instrument used to count colonies of bacteria or other microorganisms growing on an agar plate Early counters were merely lighted surfaces on which the plate was placed with the colonies marked off with a felt tipped pen on the outer surface of the plate while the opera
54. ion perform the acquisition and stop the acquisition using a single function The number of images acquired is equal to the number of images in the images collection With a one shot acquisition we specify a certain number of internal buffers The camera transfers each image up to and including the specified number of buffers The driver acquires every image during a one shot acquisition National Instruments recommends one shot acquisition for applications that do not require real time acquisition or processing Continues acquisition We use a continuous acquisition to start an acquisition continuously acquire images into the internal buffers and explicitly stop the acquisition With continuous acquisition the driver acquires video data continuously from the camera and enables us to examine the most current buffer National Instruments recommends continuous acquisition for real time acquisition and processing Number of buffers Another aspect of configuration is specifying the number of internal buffers into which we want to acquire image data During configuration buffers are allocated from system memory and page locked Once the acquisition starts the camera transfers video data over the IEEE 1394 bus to the IEEE 1394 interface card FIFO Then video data is directly transferred to the internal 41 buffer This transfer requires negligible CPU resources Each internal buffer we allocate is the exact size of the raw data being tra
55. ition we must allocate a user buffer in addition to configuring internal buffers The driver copies or decodes image data from the internal buffer into the user buffer during acquisition Then process and analyze the image in the user buffer When acquiring data 42 into an IMAQ Vision image the driver resizes and casts the image as needed However if we acquire data into a user buffer we must allocate enough space for one decoded image A buffer number is a zero based index that represents the cumulated transferred image count For example during a continuous acquisition with three internal buffers the buffer number is updated as follows 0 1 2 3 4 5 and so on Buffer numbers 0 and 3 refer to the same internal buffer in the buffer ring For a one shot acquisition we can request only one of the available buffer numbers For a continuous acquisition we can request any present or future buffer number We can also request the next logical buffer or the buffer containing the most recently acquired data With high level grab acquisitions the buffer number defaults to the next transferred buffer When we complete the buffer acquisition step the driver returns the actual buffer number with the image Overwrite mode A continuous acquisition acquires and processes every image that is transferred from the camera However because of processing time fluctuations some images from the camera may not be processed before the camera transfer
56. mages from IEEE 1394 camera NI CVS 1450 acts as an interface between the camera and the development computer which is used for image processing and analysis An Ethernet connection between the CVS 1450 device and a development computer allows you to display measurement results and status information and to configure the CVS 1450 device settings When configured the CVS 1450 device can run applications without a connection to the development computer NI CVS 1450 series also provides multiple digital input output I O options for communicating with external devices to configure and start an inspection and to indicate results NI a 3 1 Setup Overview The sequence for setting up and getting started with the CVS 1450 device is Setting up the Hardware this section explains how to connect a camera monitor and power supply to a CVS 1450 device Setting up the Development Computer this section explains how to use either Vision Builder AI or LabVIEW Real Time with the Vision Development Module to perform the following tasks Connect the CVS 1450 device to the development computer and Install application and driver software 30 Acquiring an Image this section explains how to use either Vision Builder AI or LabVIEW Real Time with the Vision Development Module to acquire an image 3 1 1 Setting up the Hardware This section describes how to connect the basic hardware components of the CVS 1450 device When these basic components are
57. n this group such as Basic Morphology modifies the shape of binary objects in an image Adv Morphology performs high level operations on particles in binary images Particle Filter removes or keeps particles in an image as specified by the filter criteria Invert Binary Image reverses the dynamic of an image that contains two different grayscale populations Shape Matching searches an image for objects that are shaped similarly to a template object Particle Analysis displays measurement results for selected particle measurements that are performed on the image Circle Detection finds the center and radius of circular particles in an image 4 3 5 Machine Vision Functions Vision Assistant provides the following machine vision functions 49 Detecting the presence or absence of parts in an image Measuring the dimension of parts to determine if they meet specifications Locating objects in an image Reading 1D and 2D barcodes Reading text in an image DN shee a ge AR ee Classifying samples in an image The image functions that perform the following common machine vision inspection tasks are included in this group such as Edge Detection finds edges along a line that we draw with the Line Tool from the toolbar Find Straight Edge finds points within the edge of an object and then finds a line describing the edge Find Circular Edge locates the intersection points between a set of search lines within a circular area an
58. nnected regions or groupings of pixels in an image and then make selected measurements of those regions Using particle analysis we can detect and analyze any two dimensional shape in an image Therefore we dedicate our work to this area 5 3 Problem solution To find the number of bacteria we perform the following steps BO GO ee SN Acquire image of all Petri dishes to be counted in a batch by appropriate file name Then each image so acquired is analyzed by the following steps Extract one of the three color planes RGB HSV or HSL from an image Filter the image to sharpen edges and ease the separation of the particles from the background Threshold the image to isolate the appropriate particles Proper Close the image to fill tiny holes and smoothen inner contours of objects based on the structuring element Fill holes that appear in the particles after thresholding Remove all objects touching the border so that we remove partial particles Use a particle filter to find all circular particles and remove non circular elliptical particles Perform a particle analysis to find the number of objects 5 4 A warm up exercise To familaise with the working of real image we first performed a warm up exercise involving a few steps required for bacteria colony counting We created one synthetic image consisting of 7 pairs of overlapping circles ellipses as shown in figure 5 1 59 Figure 5 1 Synthetic Image of particle t
59. nsmitted by the camera For continuous acquisitions allocate three or more buffers Allocating a single buffer for a continuous acquisition may result in a high number of lost images For one shot acquisitions specify the number of buffers that the application requires For example if the application runs for two seconds and the camera acquires at 30 frames per second allocate 60 buffers to capture each image Region of interest The region of interest ROD specifies a rectangular portion of the image to be captured In Partial Image Size Format Format 7 video modes the ROI defines the portion of the image to transfer from the camera to system memory In non Format 7 video modes the entire image is transferred from the camera to system memory In all video modes the ROI specifies the amount of data decoded by the driver while acquiring into a user buffer By default the driver transfers the entire image We specify a smaller ROI for the following reasons e To acquire only the necessary subset of data e To increase the acquisition speed by reducing the amount of data transferred and or decoded e To allow for multiple simultaneous acquisitions by reducing bandwidth usage 3 3 3 Acquisition After configuring and starting our acquisition the camera sends data to the internal buffers To process the acquired image data we must copy the data from the internal buffer into our user buffer User buffer Before starting the acquis
60. nulus and then finds the best fit circle Clamp finds edges within a rectangular ROI drawn in the image and measures the distance between the first and last edge Match Pattern locates regions of a grayscale image that match a predetermined template Pattern Matching can find template matches regardless of poor lighting blur noise shifting of the template and rotation of the template Caliper computes measurements such as distances areas and angles based on results returned from other machine vision and image processing functions Read 1D Barcode reads values encoded in 1D barcodes Read Data Matrix Code reads values encoded in a Data Matrix code Read PDF417 Code reads values encoded in a PDF417 code OCR reads characters in a region of an image 50 Classification classifies samples in an image We used mainly two techniques in our work i e Particle Analysis and Threshold 4 4 Particle Analysis Particle analysis consists of a series of processing operations and analysis functions that produce some information about the particles in an image A particle is a contiguous region of nonzero pixels We can extract particles from a grayscale image by thresholding the image into background and foreground states Zero valued pixels are in the background state and all nonzero valued pixels are in the foreground In a binary image the background pixels are zero and every non zero pixel is part of a binary object We perf
61. number of live cells stays constant No net increase or decrease in cell numbers 1 1 2 4 Decline Death Phase If incubation continues after the population reaches stationary phase a death phase follows in which the number of cell population declines During the death phase the number of live cells decreases geometrically exponentially essentially the reverse of growth during the log phase Conditions in the medium become less and less supportive of cell division Cell loses their ability to divide and thus die 1 2 Agar plate An agar plate is a sterile Petri dish that contains a growth medium typically agar plus nutrients used to culture microorganisms Selective growth compounds may also be added to the media such as antibiotics Individual microorganisms placed on the plate will grow into individual colonies each a clone genetically identical to the individual ancestor organism except for the low unavoidable rate of mutation Thus the plate can be used either to estimate the concentration of organisms in a liquid culture or a suitable dilution of that culture using a colony counter or to generate genetically pure cultures from a mixed culture of genetically different organisms using a technique known as streaking In this technique a drop of the culture on the end of a thin sterile loop of wire is streaked across the surface of the agar leaving organisms behind a higher number at the beginning of the streak and a lowe
62. o be counted The objective was to count these groups without any manual intervention Step 2 9 given in section 5 3 were followed to give appropriate number of cells as shown in figure 5 2 Sy NI Vision Assistant File Image How To Controls f a n bmp RGB 371x398 mw lo lmle le Particle Analysis Number of Objects 7 Connectivity 4 8 371x398 1 1 98 180 Show Labels Select Measurements Beaks ss 46 Center of Mass X 30 136 19608 254 9 119 97937 Center of Mass Y 195 19739 9 766 281 45289 First Pixel X First Pixel Y poo pooo Boundina Rect Left OK Cancel lt 270 00000 60 00000 97 00000 214 00000 Figure 5 2 Result of particle Counting The details of these steps as employed on the real image are given in the subsequent section 60 5 5 Problem Algorithm 5 5 1 Acquiring image of Petri dish The Petri dish is placed in well illuminated area duly surrounded by light screen of avoid multiple shadows formed by ambient light as shown in figure 5 3 Figure 5 3 Setup of acquiring image The image is acquired using IEEE 1394 digital camera Prosilica 2 0 1 and stored in mv bmp file for further processing In our work we used 40 watt tube hanging on a stand of height 60cm approximately Camera is attached with the stand 61 Hardware setup The hardware setup which we used in our work is shown in figure 5 4 Comp
63. of 5 However the process needs to be modified optimized to include 100 of population Moreover the present work was restricted to strains of bacteria like E coli 81 Pseudomonas and Lactobacillus This may also be extended to cover the entire range of bacteria so available for laboratory analysis The present work restricted itself to counting only neat colonies that is those which could be counted by manual visual inspection However advanced image process techniques may enable to count the colonies so far as possible by manual visual inspection The scope for further work in this area is thus limited only by the imagination of research 82 10 11 12 References Bang Wei Zhang and Chengcui Chen 2007 An Effective and Robust Method for Automatic Bacterial Colony Enumeration Proceedings of the International Workshop on Semantic Computing and Multimedia Systems in conjunction with the International Conference on Semantic Computing Volume 17 Page No 581 588 Coyne M B Forage A J and Paice F 1974 Bacterial Colony Counting Using the M R C Image Analyser Journal of Radio pathology Volume 19 Page No 708 715 Caldwell R Daniel and Marvin P Bryant 1966 Medium Without Rumen Fluid for Nonselective Enumeration and Isolation of Rumen Bacteria Journal of Bacteriology Volume 14 Page No 794 801 Dubuisson Marie Pierre Jain Anil K and Jain Mahendra K 1994 Segmentation and Classification
64. on a rotating agar plate in an ever decreasing amount in the form of an Archimedes spiral After the sample is incubated different colony densities are apparent on the surface of the plate A modified counting grid is described which relates area of the plate of volume of sample By counting an appropriate area of the plate the number of bacteria in the sample is estimated This method was compared to the pour plate procedure with the use of pure and mixed cultures in water and milk Gilchrist E et al 1973 Coyne M et al had given Bacterial Colony Counting Using the M R C Image Analyzer in 1974 In this method the optical considerations in illuminating bacterial colonies grown on agar plates are described An arrangement which gives a high contrast image with almost total background suppression and good separation of touching and overlapping colonies is shown Coyne M et al 1974 Dubuisson Marie Pierrea et al had given Segmentation and classification of bacterial culture images in 1994 A procedure using simple image processing and pattern recognition techniques to automatically extract and count organisms using images of bacterial cultures is described in this paper The methanogens were grown in anaerobic serum vials for 1 3 week period Samples of the growing cultures were withdrawn from the vials and 13 photographed The procedure was applied to images containing one kind of organism as well as images containing both types of o
65. ons palette or select Binary Adv Morphology 2 Select Fill holes from the Morphology Advanced function list 69 3 Click OK to add this step to the script The fill holes in the particle of the image is shown in figure 5 11 D NI Vision Assistant Image How To Controls File view Help my bmp RGB 752x480 Ke 4 gt me S Advanced Morphology Remove large objects Remove border objects Convex Hull Skeleton v Fill holes 1 Connectivity 4 8 C ed h Figure 5 11 Modifying Particles with fill holes gt Remove border objects eliminate particles that touch the borders of an image by the following steps 1 Select Adv Morphology in the Binary tab of the Processing Functions palette or select Binary Adv Morphology 2 Select Remove border objects to remove any objects that touch the border of the image 70 3 Click OK to add this step to the script and close the Parameters window The remove border objects of an image is shown in figure 5 12 Sy NI Vision Assistant File View Help Image How To Controls my bmp RGB 752x480 Ka 4 gt Pe Advanced Morphology Remove small objects Remove large objects Remove border objects Fill holes Convex Hull v 752x480 171 147 187 lt pr z Hoz Remove border objects Connectivity 4 8 Figure 5 12 Modifying Particles with Remove Border Objects
66. or quantitative analysis Proper Close fills tiny holes and smoothes inner contours of objects based on the structuring element It is a finite and dual combination of closings and openings The inner contours of objects are smoothened by the following steps 1 Select Basic Morphology in the Binary tab of the Processing Functions palette or select Binary Basic Morphology 2 Select Proper Close from the Morphology Basic function list 3 Click OK to add this step to the script The proper close image is shown in figure 5 10 68 Si NI Vision Assistant Image How To Controls File view Help 2 5 ml eleg e my bmp RGB 752x480 Ke 4 gt Tee Basic Morphology Close objects Proper Open Proper Close Gradient In Gradient Out w 752x480 17 71 49 183 lt Size Structuring Element 4 ve a 3x3 HH Da CLL Square Hexagon ract RGE 4 o Thresho Proper Close 1 E Figure 5 10 Modifying Particles with Proper Close Advanced Morphology It performs high level operations on particles in binary images We are using these functions for tasks such as removing small particles from an image labeling particles in an image or filling holes in particles gt Fill holes found in a particle Holes are filled with a pixel value of 1 This function fills holes in the particles of the image by the following steps 1 Select Adv Morphology in the Binary tab of the Processing Functi
67. orm a particle analysis to detect connected regions or groupings of pixels in an image and then make selected measurements of those regions Using particle analysis we can detect and analyze any two dimensional shape in an image NI c 4 4 1 Applications Particle analysis can be used in the following applications 1 When we are interested in finding particles whose spatial characteristics satisfy certain criteria 2 In many applications where computation is time consuming we can use particle filtering to eliminate particles that are of no interest based on their spatial characteristics and keep only the relevant particles for further analysis 3 We can use particle analysis to find statistical information such as the presence of particles their number and size and location 4 We also can locate objects in motion control applications 5 In applications where there is a significant variance in the shape or orientation of an object particle analysis is a powerful and flexible way to search for the object 51 6 We can use a combination of the measurements obtained through particle analysis to define a feature set that uniquely defines the shape of the object 7 Machine vision inspection tasks such as detecting flaws on silicon wafers detecting soldering defects on electronic boards 8 Web inspection applications such as finding structural defects on wood planks or detecting cracks on plastics sheets 4 4 2 Con
68. ormed by well trained technicians manually However there might exist hundreds of colonies in a traditional 100mm Petri dish Therefore this manual enumeration process has a very low throughput and is time consuming and labor intensive in practice In addition the manual counting is an error prone process since the counting results of the same plate obtained from different technicians might vary especially when a vast number of colonies appear on the plate Another possible cause of variation is the judgment of the indistinguishable colony overlaps Thus it is important to have consistent criteria for measuring overlapped colonies To produce consistent and accurate results and improve the throughput we have worked on the Bacteria colony counter using Machine Vision 5 2 Proposed area of research To overcome the problems related to manual counting of bacteria colony we propose to automate this counting process by machine vision Image of Petri dish is acquired using IEEE 1394 digital camera Prosilica 2 0 1 and processed using workstation Compact vision system 1450 CVS 1450 Machine Vision is used because the images so acquired may be blurred and noisy and need to be processed to obtain better images Threshold technique can easily select the ranges of pixel values in grayscale image Particle Analysis technique works very efficiently for counting the 58 number of Bacteria in a growth medium We perform a particle analysis to detect co
69. ousand Human vision can only make rough comparative judgments on size Flaw detection A flaw is a defect of unknown size shape and location The most common use in industry of human visual inspection is to find flaws Verification Vision systems verify the presence or absence of a part or feature A vision system can tell if a cap has been installed on a bottle or it can tell if all the surgical components are in a packaged kit Identification Vision systems identify objects by performing optical character recognition OCR or by reading two dimensional codes Recognition Vision systems recognize parts based on their shape and other characteristics Tracking Vision systems track the movement of parts through their field of view and if desired provide information so that robots can be guided to the moving part http functions 2 5 Applications of Machine Vision Machine vision applications are categories describing how people use vision systems The categories of applications are Robot or machine guidance This ranges from using machine vision to locate a part and then repositioning the part or the robot to using machine vision to track the part s motion and maintain the part or the robot in position Quality assurance This involves inspecting parts for conformance to requirements The requirements may be to verify correctness of an assembly to measure critical dimensions or to insure the ab
70. que serial number described by the interface file camn iid opens where n is the reference to the camera If the camera is not present and a camera of the same make and model is present as described in the interface file the driver opens the available camera The interface file updates to use the new camera The camera file described by the interface file opens and all the user attributes are set in the driver If no camera of the same make and model is present the Initialize function returns an error Camera control mode The camera control mode parameter has two options controller and listener The default option controller controls the camera and receives video data The listener only receives video data We use the listener option in broadcasting applications 40 3 3 2 Configuration After initializing the interface configure the interface for acquisition by specifying the following parameters whether the acquisition is one shot or continuous the number of internal buffers to use and the region of interest for the acquisition During configuration the driver validates all the user configurable attributes If any attributes are invalid or out of range the driver returns an error and does not configure the acquisition If we want to reconfigure the acquisition call the Clear Acquisition function before calling the configure function again One shot Continuous acquisition We use a one shot acquisition to start an acquisit
71. r 7 number at the end At some point during a successful streak the number of organisms deposited will be such that distinct individual colonies will grow in that area which may be removed for further culturing using another sterile loop http Wiki a Agar plate is shown in figure 1 4 Figure 1 4 Agar plate 1 3 Bacterial Colony Bacterial colony is a group of bacteria supposed to be derived from a single bacterium Usually Bacteria is grown on a culture medium and a single bacterium divides by binary fission some archea also show budding to produce identical copies of itself therefore also called clones of each other So bacterial colony is a group of bacteria growing on a plate that is derived from one original starting cell All of the bacterial cells in one colony are clones of that original cell since the bacteria reproduce through binary fission Bacteria grow on solid media as colonies A colony is defined as a visible mass of microorganisms all originating from a single mother cell therefore a colony constitutes a clone of bacteria all genetically alike Bacteria grow tremendously fast when supplied with an abundance of nutrients Different types of bacteria will produce different looking colonies some colonies may be colored some colonies are circular in shape and others are irregular The characteristics of a 8 colony shape size pigmentation etc are termed the colony morphology Colony morphology is a w
72. re2 3 that the value of x increases moving from left to right and the value of y increases from top to bottom Figure 2 3 Spatial reference of the 0 0 pixel In digital image processing an imaging sensor converts an image into a discrete number of pixels The imaging sensor assigns to each pixel a numeric location and a gray level or color value that specifies the brightness or color of the pixel 2 3 2 Properties of a digitized image A digitized image has three basic properties resolution definition and number of planes These three properties are described 2 3 2 1 Image resolution Image Resolution is spatial resolution of an image which is determined by its number of rows and columns of pixels 2 3 2 2 Image definition Image definition indicates the number of shades that we can see in the image The bit depth of an image is the number of bits used to encode the value of a pixel For a given bit depth of n the 20 image has an image definition of 2n meaning a pixel can have 2n different values For example if n equals 8 bits a pixel can have 256 different values ranging from 0 to 255 If n equals 16 bits a pixel can have 65 536 different values ranging from 0 to 65 535 or from 32 768 to 32 767 NI c The manners in which we encode our image depends on the nature of the image acquisition device the type of image processing need to use and the type of analysis we need to perform For example 8 bit
73. rganisms The image analysis and pattern recognition techniques described in this paper are simple fast and able to identify organisms based on their shapes Dubuisson Marie Pierrea et al 1994 Mukherjee Dipti et al had given Water quality analysis which is based on a pattern recognition approach in 1995 In this method the development of a microcomputer based system for the automatic counting of colonies of indicator organisms that is bacteria whose presence is indicative of the presence of pathogens trapped on membrane filters from water samples is described Three different approaches based on mathematical morphology distance transform and fuzzy c means clustering respectively are implemented Mukherjee Dipti et al 1995 Kawai M et al had given Rapid enumeration of physiologically active bacteria in purified water used in the pharmaceutical manufacturing process in 1999 According to this method Bacteria with growth potential were enumerated using the micro colony technique and direct viable counting DVC Respiring and esterase active bacteria were detected by fluorescent staining A large number of bacteria in purified water retained physiological activity while most could not form colonies on conventional media The techniques applied in this study enabled bacteria to be counted within 24h so results could be available within one working day These rapid and convenient techniques should be useful for the systematic monitoring of
74. s we first must initialize a camera session A camera session is a process safe handle to an IEEE 1394 camera The driver uses a camera session to identify the camera to which further NI IMAQ for IEEE 1394 Cameras functions apply We can simultaneously open as many camera sessions as there are cameras connected to our system When initializing the camera session we need to specify two parameters Camera name and Camera control mode Camera name NI IMAQ for IEEE 1394 Cameras references all camera sessions by a name The driver creates default names for each camera in our system in the order that the cameras are connected The names observe the convention shown in Table 3 1 Camera Name IEEE 1394 Camera Installed Device 2 Device n Table 3 1 Camera naming conventions 39 Interface files store information about which physical camera is associated with a camera name Only a single camera can use each interface file Camera files store all the configurable attributes Camera files can be shared between identical cameras Use MAX to configure the default state of a particular camera Figure 3 5 shows the relationship between cameras interface files and camera files Cami Defaulted Figure 3 5 Relationships between cameras interface files and camera files Use the Enumerate function to query the number and names of available cameras When we open a camera session with the Initialize function the camera with the uni
75. s at aping this task of capturing images at fast rates and simultaneously processing and analysis of these images so that machine can see the way we do Machine Vision is concerned with the engineering of integrated mechanical optical electronic software systems for examining natural objects and materials human artifacts and manufacturing processes in order to detect defects and improve quality operating efficiency and the safety of both products and processes Whelan F 2001 16 2 1 Anatomy of Machine Vision There are a variety of components that comprise a machine vision system Decisions about components depend on the environment application and budget There are however several common ingredients to all vision systems http Machine a Anatomy of machine vision system is shown in figure 2 1 Front end optics for image acquisition Lighting lens es and camera s Frame grabber for image conversion Processor component that digitizes optics data and stores it Processor for image processing and feature extraction Software that uses algorithms to process stored image data and extract designated features Vision software for decisions and control Software that makes decisions on image data received and controls the vision tasks and overall system operation Figure 2 1 Anatomy of a machine vision system Front end optics The front end includes the lighting the lens and the camera The lighting highlig
76. s from the Background with Thresholding Fig 5 10 Modifying Particles with Proper Close Fig 5 11 Modifying Particles with fill holes Fig 5 12 Modifying Particles with Remove Border Objects Fig 5 13 Isolating Circular Particles Fig 5 14 Counting Circular Particles Fig 5 15 Processing Script Fig 5 16 Processing Script Continued Fig 5 17 Estimating Processing Time of an image 62 63 64 66 67 69 70 71 72 74 75 75 76 xi List of Tables Table 1 1 Generation time 6 Table 2 1 Bytes per pixel 23 Table 2 2 Functions and Applications of Machine Vision 26 Table 2 3 Comparison between Machine Vision and Human Vision 27 Table 2 4 Comparison between Machine Vision and Computer Vision 28 Table 3 1 Camera naming conventions 39 Table 3 2 Decoder inputs and corresponding outputs 44 Table 4 1 Values of the Particle 53 Table 5 1 Results and discussion 79 xii List of Abbreviations CAT 5 CCD CVS DCAM HSL VO IEEE LabVIEW LAN NI RGB ROI TV VI Application Programming Interface Category 5 Charged Coupled Device Compact Vision System Digital Camera Hue Saturation Luminescence Input Output Institute for Electrical and Electronic Engineers Laboratory Virtual Instrument Engineering Workbench Local Area Network National Instruments Red Green Blue Region of Interest Rumen Fluid Medium Television Virtual Instrument xiii Chapter 1 Introduction
77. s the next image Using multiple internal buffers in a continuous acquisition allows for a small amount of jitter However if a delay becomes too long the camera overwrites the requested buffer with new image data NI IMAQ for IEEE 1394 Cameras is able to detect overwritten internal buffers We can configure the driver to manage an overwritten buffer in one of the following ways e Get newest valid buffer e Get oldest valid buffer e Fail and return an error In all cases the camera continues to transfer data when a buffer is overwritten The default overwrite mode for all types of acquisition is to get the newest valid buffer This option which National Instruments recommends for most applications enables us to process the most recent image If we need to get the image closest in time to a requested buffer configure the driver to 43 get the oldest valid buffer If our application requires that every image be processed configure the driver to fail when a buffer is overwritten so that we are alerted Timeouts A timeout is the length of time in milliseconds that the driver waits for an image from the camera before returning an error A timeout error usually occurs if the camera has been removed from the system or when the camera did not receive an external trigger signal Decoding Except for 8 bit monochrome images all video modes require decoding before we can interpret the image data For example many color IEEE 1394 camer
78. sence of objectionable flaws Test and calibration Machine vision performs test and calibration by observing the positions of controls on an assembly or reading a display to verify the correct output 24 Real time process control This is performed when machine vision is used to feed back or feed forward information it gets from imaging a part so that the process is held in control Data collection This involves storing data gathered by the machine vision system as it observes parts coming through its field of view Most often data collection is used for statistical process control Machine monitoring This involves using machine vision to insure a machine s correct operating condition An example would be to insure that a drill bit is not broken on an automatic drilling machine Material handling This is facilitated by machine vision when it identifies recognizes or routes products through a factory or warehouse Sorting Machine vision sorts a variety of products ranging from nuts berries beans and potatoes to screws and other fasteners Counting Objects such as those being mass conveyed are easily counted by machine vision where other counting means do not work Safety Machine vision has the potential to improve safety in the factory To date this has not been pursued commercially However advances being made in automotive design using embedded vision systems for highway safety m
79. simple morphologies These bacteria are like primarily synthesizers or absorbers Most bacteria do not cause human diseases 3 but most infectious diseases are caused by bacteria and viruses More typically bacteria are beneficial whether to ecosystems or directly to individual organisms 1 1 2 Growth and reproduction of Bacteria Growth is an orderly increase in the quantity of cellular constituents It depends upon the ability of the cell to form new protoplasm from nutrients available in the environment In most bacteria growth involves increase in cell mass and number of ribosome duplication of the bacterial chromosome synthesis of new cell wall and plasma membrane partitioning of the two chromosomes septum formation and cell division http Growth 1 Unlike multicellular organisms increases in the size of bacteria cell growth and their reproduction by cell division are tightly linked in unicellular organisms Bacteria grow to a fixed size and then reproduce through binary fission a form of asexual reproduction Under optimal conditions bacteria can grow and divide extremely rapidly and bacterial populations can double as quickly as every 9 8 minutes In cell division two identical clone daughter cells are produced Some bacteria while still reproducing asexually form more complex reproductive structures that facilitate the dispersal of the newly formed daughter cells Examples include fruiting body formation by Myxobac
80. te reading Giles Scientific will work with laboratory to optimize our digital imaging for assays posing unique difficulties and challenges Figure 1 6 shows the types of colony plates In our work we have used different type of colony plates like nutrient agar media blood media amp water sample Filter Membrane Blood Media Spiral Inoculated Pour Plate Water Sample Figure 1 6 Colony Plate Types 12 1 5 Literature Survey Caldwell R et al had given Medium without Rumen Fluid for Nonselective Enumeration and Isolation of Rumen Bacteria in 1966 In this Method Colony counts which approximated those in a habitat simulating rumen fluid agar medium RFM were obtained in medium 10 Colony counts were also reduced when medium 10 was modified to contain higher concentrations of Trypticase or volatile fatty acids Significant differences were found between colony counts obtained from diluted rumen contents of animals fed a cracked corn urea diet and the colony counts obtained from animals fed either a cracked corn soyean oil meal or an alfalfa hay grain diet The results show that medium 10 is suitable for enumeration and isolation of many predominant rumen bacteria Caldwell R et al 1966 Gilchrist E et al had given Spiral Plate Method for Bacterial Determination in 1973 A method is described for determining the number of bacteria in a solution by the use of a machine which deposits a known volume of sample
81. teria and arial hyphae formation by Streptomyces or budding Budding involves a cell forming a protrusion that breaks away and produces a daughter cell In the laboratory bacteria are usually grown using solid or liquid media Solid growth media such as agar plates are used to isolate pure cultures of a bacterial strain However liquid growth media are used when measurement of growth or large volumes of cells are required Growth in stirred liquid media occurs as an even cell suspension making the cultures easy to divide and transfer although isolating single bacteria from liquid media is difficult Most laboratory techniques for growing bacteria use high levels of nutrients to produce large amounts of cells cheaply and quickly However in natural environments nutrients are limited meaning that bacteria cannot continue to reproduce indefinitely This nutrient limitation has led the evolution of different growth strategies Some organisms can grow extremely rapidly when nutrients become available such as the formation of algal and cyanobacterial blooms that often occur in lakes during the summer Other organisms have adaptations to harsh environments such as the production of multiple antibiotics by 4 Streptomyces that inhibit the growth of competing microorganisms In nature many organisms live in communities e g biofilms which may allow for increased supply of nutrients and protection from environmental stresses These relationships
82. the sensory organs that capture images and transmit to our brain at very fast rate The image is representation of real scene either in black amp white or in color The brain performs various processing functions and vision is perceived In human beings we make use of vision for accomplishing majority of our tasks Blindfolding ourselves and observing how our daily routine is seriously hampered without our vision can easily verify this fact Although the first machine that captured image was a pinhole camera that was invented way back in 1850s which was followed by many advances in image capturing techniques http Machine b Black amp white camera gave way to colored camera resolution of picture captured enhanced moving pictures were captured using monochrome T V Camera followed by colored T V camera and now a days we have digital cameras as small as a size of button embedded in our mobile phones at a price a student can afford from his pocket money However when we talk of machine vision these cameras alone do not see the way we do With human vision the process of seeing i e capturing images takes place at a very fast rate almost continuously for several hours The most important aspect is equally fast processing of these images so as to accomplish necessary decisions and actions Image processing is collection of programs and techniques that improve simplify enhance or otherwise alter an image Machine vision also aim
83. thout the cooperation meouragement and help provided to me by various personalities deep sense of gratitude I express my sincere thanks to my esteemed and worthy Sepervisor Mr Mandeep Singh Assistant Professor Department of Electrical amp ssTumentation Engineering Thapar University Patiala for his valuable guidance in svying out this work under his effective supervision encouragement enlightenment and Ssoperation T7 shall be failing in my duties if I do not express my deep sense of gratitude towards Dr Smerajit Ghosh Professor and Head of the Department of Electrical amp Instrumentation egineering Thapar University Patiala who has been a constant source of inspiration Sr me throughout this work m also thankful to all the staff members of the Department for their full co operation aad help M5 greatest thanks to all who wished me success especially my parents Above all I ar my gratitude to the ALMIGHTY who bestowed self confidence ability and Srength in me to complete this work oe sce Thapar University Patiala MEN Cova Darte SvEy u Lovy Abstract Machine Vision is an emerging area related to real time capturing processing and analyzing the images for various kinds of scientific and industrial applications Bacteria counting are required in number of applications in the fields such as Biotechnology Pathology etc Manual counting of large number of Bacteria in any of these applications can be
84. tion 1 1 Bacteria 1 1 1 Characteristics of bacteria 1 1 2Growth and reproduction of Bacteria 1 1 2 1 Lag Phase 1 1 2 2 Log Phase 1 1 2 3 Stationary Phase 1 1 2 4 Decline Phase 1 2 Agar Plate 1 3 Bacterial Colony Table of Contents ii iii iv xii xiii 1 3 1 Features of Bacterial Colony 1 3 2 Bacteria in a Colony 1 4Colony Counter 1 4 1 Colony Plate Types 1 5 Literature Survey Chapter 2 Machine Vision 2 1 Anatomy of Machine Vision 2 2 Components of Machine Vision 2 2 1 Camera 2 2 2 Image processor 2 2 3 Display unit 2 3 Basics of Digital image 2 3 1 Digital image 2 3 2 Properties of a digitized image 2 3 2 1 Image resolution 2 3 2 2 Image definition 2 3 2 3 Number of planes 2 3 3 Image types 2 3 3 1 Grayscale images 2 3 3 2 Color images 2 3 3 3 Complex images 2 4 Functions of Machine Vision 11 11 12 13 16 17 18 18 19 19 19 20 20 20 20 21 21 22 22 22 23 vi 2 5 Applications of Machine Vision 2 6 Advantages and Limitations of Machine Vision 2 7 Comparison between Machine Vision and Human Vision 2 8 Comparison between Machine Vision and Computer Vision Chapter 3 Setup and Configuration 3 1 Setup Overview 3 1 1 Setting Up the Hardware 3 1 1 1 Subnet Considerations 3 1 1 2 CVS 1450 Hardware 3 1 1 3 Connecting the CVS 1450 Device to a Network 3 1 1 4 Connecting a Camera and Monitor to the CVS 1450 3 1 1 5 Wiring Power to the CVS 1450
85. tness Alters the brightness Bha contrast and gamma of an image Image Mask Builds a mask from an entire image or a selected region of interest File Edit Image Color Grayscale Binary Machine Vision View Tools Help G S mE 9 9 el 7524480171 lt Original Image Figure 5 6 Opening an image 63 5 5 3 Extracting color planes from an image We can extract one of the three color planes Red Green Blue Hue Saturation Luminance and Value from an image by the following steps Click Color Extract Color Planes or select Extract Color Planes in the Color tab of the Processing Functions palette 2 Select the Red color plane 3 Click OK to add this step to the script The extract red plane of the image is shown in figure 5 7 YN Vision Assistant Image How To Controls l P View Help 24 o 29 9 1 mv bmp RGB 752x480 mw gt wife Extract Color Planes Image Source RGB Green Plane RGB Blue Plane v 752x480 1 1 _ 46 184 JA po Ly Bi 2 tt g E Figure5 7 Extracting RGB Red Plane 64 The color plane we extract is an 8 bit grayscale image because each color plane is made up of 8 bits 5 5 4 Filtering the Image Filters can smooth sharpen transform and remove noise from an image so that we can extract the information we need Most of these filters apply a kernel across the image A kernel represents a pi
86. tor kept the count manually More recent counters attempt to count the colonies electronically by identifying individual areas of dark and light according to automatic or user set thresholds and counting the resulting contrasting spots Such counters are used to estimate the density of microorganisms within a liquid culture An appropriate dilution or several dilutions within the estimated appropriate range is spread using sterile technique on the agar plate which is then incubated under the appropriate conditions for growth until individual colonies appear Each colony marks the spot where a single organism was originally placed thus the number of colonies on the plate equals the number of organisms within the volume of liquid spread on the plate That concentration is then extrapolated by the known dilution from the original culture to estimate the concentration of organisms within that original culture The maximum number of colonies which may be effectively counted on a single plate is somewhere between 100 and 1 000 depending on the size of the colony and the type of organism http Wiki c 11 1 4 1 Colony Plate Types Digital Counter excels at counting difficult to read plates High resolution color digital imaging better discriminates colonies True color enables counting mixed cultures and discriminates colonies from debris Lighting above and below dark field or bright field the samples provides better contrast and more accura
87. uter Compact vision system Figure 5 4 Hardware Setup 5 5 2 Opening the stored image Image Browser contains all of the images currently loaded in Vision Assistant as shown in figure 5 5 1 Make image active 2 Store acquired image in browser button 3 Acquisition functions Figure 5 5 Acquiring images in Vision Assistant 62 We can select an image to process by double clicking it in the Image Browser and processing window updates the image as we change parameters Because this view immediately reflects the changes we have made in the Parameter window we can continue modifying parameters until we get the result we want The image is opened from the previously stored images by the following steps 1 Select Start Programs National Instruments Vision Assistant 7 1 2 Click Open Image on the Welcome Screen 3 Select the required image and Click OK to load that image into Vision Assistant The image so opened is shown in figure 5 6 NI Vision Assistant Image when To my bmp RGB 752x480 CIRCE HoOoOmg Ss E Illabe Histogram Counts the total number of pixels in each grayscale value and graphs it Line Profile Displays the grayscale distribution along a line of pixels in an image Measure Calculates measurement HSE statistics associated with a region of interest in the image 3D View Displays the light intensity in a amp three dimensional coordinate system Brigh
88. vision is an emerging area of technology in automation and control wherein the images captured by camera or the images previously taken are processed and analyzed in real time There are different software tools available for image processing and analysis Vision Assistant 7 1 is one of these tools and is useful for doing real time operations In our work we have made use of IEEE 1394 digital camera for image acquisition and National Instruments Vision Assistant 7 1 software for image processing and analysis The performance of the proposed method is promising especially when processing the dish plate with color medium The manual counting of colonies by a lab worker results in inaccurate counts of the bacteria colonies Machine vision based bacteria Colony Counter would allow each plate to be counted with equal efficiency From our analysis we have come to the conclusion that bacteria colonies in a Petri dish can be easily counted by Particle Analysis 6 2 Future Scope The present work is a first attempt to count bacteria colonies by machine vision We find that after optimizing our script for various values of parameters like acquiring image opening an image extracting color planes from an image filtering the image separating particles from background with thresholding proper close fill holes remove border objects isolating circular particles and counting circular particles 66 out of 72 Petri dishes so presented were within counting error
89. xel and its relationship to neighboring pixels The weight of the relationship is specified by the coefficients of each neighbor To sharpen edges including the edges of any holes inside a particle and create contrast between the particles and the background by the following steps 1 Select Filters in the Grayscale tab of the Inspection steps or select Grayscale Filters 2 Select Convolution Highlight Details from the Filters list This function detects sharp transitions and highlights edge pixels according to a kernel to make gaps more prominent A kernel is a structure that represents a pixel and its relationship to its neighbors 3 Adjust the Kernel Size and coefficients if necessary 4 Click OK to filter the image and add this step to the script The filtered image is shown in figure 5 8 65 NI Vision Assistant Image How To Controls mv bmp RGB 752x480 m mfels Filters Edge Detection Prewitt Edge Detection Sobel Edge Detection Roberts Convolution Custom Figure 5 8 Filtering the Image 5 5 5 Separating Particles from the Background with Thresholding Threshold isolates pixels that interest you and sets the remaining pixels as background pixels Threshold also converts the image from grayscale to binary It Selects ranges of pixel values in grayscale images by the following steps 1 Select Threshold in the Grayscale tab of the Processing Functions palette

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