User Manual for AutoSpots - Chang Lab
Contents
1. Pre or Post respectively The two allowable formats recognized by AutoSpots are the single number or number number format Examples are Single number Pre 1Sample jpg Post Samplel jpg Number number Pre 1 1Treatment jpg Post Treatmentl 1 jpg If Auto is chosen AutoSpots will attempt to automatically identify what part of the file name is numbered and will sort accordingly Single number images will be sorted numerically on that number For number number formats images will be sorted based on the first number and then the second number If numbers are found at the beginning and end of the file name AutoSpots defaults to the numbers at the beginning The developers have not tested the Auto function against all possible combinations of numbers and letters Push the limits of the Auto functions at your own risk AutoSpots by Jason Cumbie and Rebecca Pankow C Execute and plot results of statistical analyses The user must define a minimum of 2 treatment groups with at least 2 samples and at least 1 JPEG image per sample for statistical analyses Specify a control to compare all treatments against in the Select control drop down menu item Specify how the standard error bars will be displayed using the Error bars drop down menu item The default is 2 standard errors Click Plot Run Stats to generate the statistical summaries and plot the statistical comparisons Statistical analyses ca2. aniline blue stained leaf The user is recommended to have gt 10 samples per treatment group Treatment group refers to a set of similarly treated samples e g 10 aniline blue stained leaves that were challenged with an incompatible pathogen The user is recommended to have at least three treatment groups one positive and one negative control and at least one test treatment
3. b Semi automated This option will automatically assign treatment group names The user manually assigns their corresponding samples and their corresponding JPEGS images Semi automated requires each directory to contain the JPEG images of one treatment group Each directory however can have different numbers of JPEG images from different numbers of samples AutoSpots by Jason Cumbie and Rebecca Pankow c Fully automated This option will automatically assign treatment groups and samples to JPEG images Fully automated requires each directory to contain all JPEG images from the same treatment group and for treatment groups to have the exact same number of samples and for samples to have the exact same number of corresponding JPEG images The user must also adhere to the proper naming nomenclature Treatment Groups Hooo Manual Sample s Imar Semi automated Fully automated a Manual assignment Select Manual A pop up menu item will appear Input a name to define a treatment group and click Add The group name will appear in the drop down menu This process can be iterated for each treatment group that the user needs to define Treatment Groupes b Semi automated assignment Select Semi automated A pop up menu item will appear that shows names previously assigned by the user as directories to catalog sets of JPEG images Select the directory names and click Add The g
4. dataset AutoSpots may not identify all callose deposits in a JPEG image 3 The user should capture multiple images per sample and examine multiple samples per treatment for valid statistical analyses 4 AutoSpots has been validated using JPEG images from leaves of Arabidopsis thaliana stained with aniline blue excited with UV light and detected by fluorescence microscopy il 2 25 10 AutoSpots by Jason Cumbie and Rebecca Pankow How to read this manual without having to read this manual AutoSpots was designed specifically for automated batch enumeration and analysis of JPEG images of aniline blue stained callose deposits In this manual the user will find detailed instructions on how to operate AutoSpots To facilitate your data analysis and reduce the time wasted reading this manual the developers would like to direct your attention to the most important points in this manual It is important to name JPEG files and organize them properly to fully use the automated features of AutoSpots JPEG images must be named in a format recognizable by AutoSpots The two recognized formats are as single numbers e g samplel jpg sample2 jpg or as number number e g treatmentl 1 treatmentl 2 The letters are irrelevant but try to resist the urge to include multiple series of numbers See File naming for fully automated assignment Additionally there must be the exact same number of JPEG images per sample per treatment group JPEG images
5. from for your directory of JPEG images i AutoSpots Upload Filter Settings Find Spots Compare Sets Upload pictures from Picture List Ee A Documents H A cumbiej H A De H A LT aa Uploaded Sets Set Images A Previewing images To preview the images double click the directory The list of images stored in that directory will appear in the text box under the heading Picture List Click the file name and the image will be displayed in the preview canvas to the right as shown in this image AutoSpots Upload Filter Settings Find Spots Compare Sets Upload pictures from Picture List Treatment1 01 01 jpg Treatment1 02 01 jpg Treatment 02 02 jpg Treatment 02 03 jpq Treatment 02 04 jpg Treatment 02 05 jpq Treatmen Uploaded Sets Set Images an AutoSpots by Jason Cumbie and Rebecca Pankow B Loading images Left click on a directory It will become highlighted Right click on the selected directory of JPEG images and left click the Load All Images pop up menu item You will then be prompted to specify a name for this set of JPEG images default is the directory name After you click Add button the set will be listed in the text box under the heading Uploaded Sets You can click on any of the uploaded sets to see a list of images for that set Upload pictures from Picture List Upload pictures from Picture List Treatment 01 01 Treatme
6. jpq 11 4 F c Fully automated assignment Select Fully automated A pop up menu item identical to the one described in semi automated assignment will appear After clicking Add another pop up menu item will appear The user must input the number of samples that were examined per treatment of Samples and input the number of JPEG images that were taken per sample of Images Sample The user must also select the numbering system next section Each treatment must have the exact same number of samples Each sample must have the exact same number of JPEG images i e each directory must have the exact same number of JPEG images AutoSpots by Jason Cumbie and Rebecca Pankow Although the assignment of samples and JPEG images can be fully automated the user can manually change assignments at any time To add follow the instructions for manual assignment To delete simply right click a group sample or image and click Delete Groups Sample Images Add Samples Images of Samples of Images Sample Numbering Auto Pr Post C Cancel d File naming for fully automated assignment JPEG image names must follow one of two formats for the fully automated assignment option In either format it is best to have only 1 set of numbers in the name of the JPEG image The number can be appended to the beginning or the end of the file name and the user should select the used naming method
7. veins Please read this section carefully It is very important to set the filters AND preview the results AND re set filters if necessary to ensure AutoSpots is accurately identifying callose deposits before initiating the automated batch enumeration Multiple filters can be developed and stored The user can test different combinations of filters to determine the best results AutoSpots by Jason Cumbie and Rebecca Pankow The user must create a size filter and either the color filter or RGB Ratio filter both can be created for AutoSpots to proceed The filters must be highlighted to be used by AutoSpots The filter tab has three functions that allow the user to train AutoSpots for the accurate detection and enumeration of callose deposits A Defining filters B Selecting filter conditions C Previewing filters A Defining filters When defining filters you must create a size filter and either the color filter or RGB ratio filter both can be used AutoSpots will not proceed without the user setting the filters Click one of the buttons corresponding to filter types of Color RGB Ratio or Size under the heading Filter based on The user must then define the settings for the corresponding filter Settings for each of the three filters are briefly described below The user can store the filter settings by clicking the Add New Filter button Name Add Mew Filter Pixel rgb walues are compared with
8. 01 02 jpg 17 reatment1 01 031pg 19 Ere ban 2 Std En ent 58 reatment1 02 01 jpg 33 reatment1 02 02 jpg 26 reatment1 02 03 ipg 26 Plot Run Stats i ESG 7 T T Trea Treatment1 01 05 jpg 5 T T T 4 B Assignment of images to treatment groups In an experiment there should be multiple JPEG images per sample and multiple samples per treatment group AutoSpots determines the average number of callose deposits in a treatment group by averaging the number of callose deposits 1t enumerated in each corresponding sample which was determined by averaging the number of callose deposits per corresponding JPEG image This section details the process by which the user assigns treatment groups to their corresponding samples and then to their corresponding JPEG images The user must define the group then the sample then their corresponding images There are three ways to accomplish this task manual semi automated and fully automated assignment The organization of the directories of JPEG images dictates which option the user can use Right click the drop down menu item immediately below Groups A pop up menu item will appear with three different options a Manual In this option the user must assign treatment names and assign their corresponding samples to their corresponding JPEG images Manual is used when the directories are not organized in a manner necessary for the other assignment methods
9. AutoSpots by Jason Cumbie and Rebecca Pankow User Manual for AutoSpots Developed by Jason Cumbie and Rebecca Pankow Oregon State University i 2 25 10 AutoSpots by Jason Cumbie and Rebecca Pankow Not so legal statements from biologists not lawyers AutoSpots requires the user to 1 Name and store a set of JPEG images in multiple folders hereafter referred to as directories in a manner recognizable by AutoSpots for automated batch analysis 2 Select a list of directories of JPEG images for automated batch analysis 3 Define and store filters to set the sensitivity and accuracy for distinguishing aniline blue stained callose deposits If the user prepares the files correctly AutoSpots will automatically 1 Batch enumerate callose deposits from all JPEG images in the user defined directories 2 Execute and present a series of statistical analyses of the data 3 Format the results for easy export into third party software programs The user must accept that 1 The quality and uniformity of the JPEG images and the proper use of user selected filters determine the sensitivity and accuracy of AutoSpots It is recommended that all images to be batch processed should be photographed using identical settings on the microscope camera as those corresponding to the control treatment s 2 The user must carefully set filters to balance sensitivity with accuracy and the user should use the same filter setting for all images within a
10. a 0 Bele Muil hypith tis bat cehetied dalplie 0 055 Pittierence of the Hamna 4 8333 Ocandard Error 28 5572 Eccieere of thea Digtarancea ai che Keane if 4 I0 fod goa Le Comperidgonm Treeteentl f Treelesnt F statigeie 4 0344 cutoff F stacistie 2 9786 vith alpha 0 058 J T L Teic a Equal Variance arpusptionm pot sccept ed P era i e cunatt F eraeti Takel DF LF 5L T gt tiftic T DOR Peal 0 0ML a ee ee ee ee Bia Fun Sate Sap rite LS Degree f Fraadce DF 24 F Standard Dbiriition a 92 553 Standard Error 9 8480 The ertimate of tha maan is 166 9 51 1009 117 64fI I1F SEQE 54 Grip Tr tika t i pinera V litat Tima Saaple ize 15 Degreed of Freedom Fi Ld Haan LEA ISET Variance 1165 1094 fronted Dbeviscion Af 8848 Eran ard Error AVY Thi fti t f ERa meer J 104 Poe tf LOS fad SOUP Lo 9725 Croup Treatment Ficentrol Values Summary V Contact information If you encounter any problems with AutoSpots please post your questions on our webpage http changlab cgrb oregonstate edu Questions can be addressed to Jason Cumbie Rebecca Pankow or Jeff Chang 13 AutoSpots by Jason Cumbie and Rebecca Pankow VI Appendix Aniline blue a stain that is absorbed by callose and fluoresces under UV light Callose a 1 3 B linked glucose that along with cellulose pectin lignin and hydroxyproline rich proteins are deposited as an ag
11. e probability that the means were equal 4 A statement of whether or not the null hypothesis of equal means was accepted or rejected using a significance cutoff of 95 alpha 05 accepted no significant difference rejected significant difference 5 An estimate of the difference of the means 6 A 95 confidence interval for the difference of the means AutoSpots by Jason Cumbie and Rebecca Pankow b Group summaries These statistics provide summaries for each treatment group Shown are 1 Sample size and Degrees of Freedom 2 Sample mean and variance 3 Standard Deviation and Standard Error 4 95 confidence interval for the estimate of the mean c Group assignment summaries This section summarizes how JPEG images were assigned to treatment groups as well as the average number of enumerated callose deposits from each sample within treatment groups It is recommended that the user examine this section for any errors in assignments Shown are 1 The name of the treatment group name The control group will be designated with control 2 For each sample within the group a line formatted as follows Sample lt its corresponding name gt AVE lt average gt Images lt list of JPEG images assigned to the samples gt 2 9708 wich alpha 0 05 Equal Variants bffuepticn BRE accepted Fe FESELPELE ASIE Fe ft ets Tal DF 19 Geel T itaLiF ig T GLa Prv el
12. glomeration believed to function as an apposition to infecting bacteria located in the apoplastic space and other penetrating type pathogens Callose is also abundant in elongating pollen tubes but 1s not normally found at high amounts in healthy tissues Cumbie Jason a PhD student in the Molecular and Cellular Biology program at Oregon State University He has a degree in Computer Science He enjoys long walks on the beach and reading xkcd online His PhD adviser is Dr Jeff Chang Directory folder of images derived from the same treatment group Distance The distance of the RGB values of pixel P from the ideal color I is calculated as Distance sqrt Pr Ir 2 Pg Ig 2 Pb Ib 2 where Pr Pg Pb are the red green and blue channel values for pixel P respectively and Ir Ig Ib are the color channel values of the ideal color This treats pixel colors as a distance in 3 dimensions to approximate a distance JPEG image refers to a saved picture taken from one microscopy field of one sample e g one picture from an aniline blue stained leaf The user is recommended to take gt 10 JPEG images per sample Pankow Rebecca an undergraduate student majoring in Biochemistry Biophysics and Computer Science at Oregon State University Elle parle francais Her mentor 1s Dr Jeff Chang RGB Red Green and Blue intensity values 0 to 255 Sample refers to the object that is being characterized e g an
13. gs for color filter acsecannvenvesantdnceanaveecaeees b Settings for ratio Foc caccivcanisanaccnsncerelodacenes T E VAA E OE d Color selection assistance ceeeceeseceeeeee A E E B Selecting filter CONCITIONS eccccceeeeeeeeees t PO MIEN errei i IHI Automated batch analysis cece IV Statistical analyses cccccccccccccccceeeeeeeeeees Pe BE E B Assignment of images to treatment groups a Manual assignment cccccssssesesseeeeeeees b Semi automated assignment eee c Fully automated assignment ccceeee d File naming for fully automated assignment C Execute and plot results of statistical analyses a Comparison SUMMALIES ccccceeeeseeeeeeeeees b Group SUMMAaries ocd csacesennscdedgesiascdtecondsiens c Group assignment summaries 006 V Contact information ccccccecceceecesceees VI Appendix es cseseessnncccacasnnsagasanesnasacorntoniontanvnouseens 2 25 10 AutoSpots by Jason Cumbie and Rebecca Pankow I Image selection After successful installation double click on the file autospots pl in the installation directory You will see a screen similar to the one depicted below AutoSpots can be used to automatically batch process JPEG images To select which sets of JPEG images to analyze browse the directory tree in the text box under the heading Upload pictures
14. le Select the Use Grayscale checkbox above the Preview Filters button AutoSpots by Jason Cumbie and Rebecca Pankow If the user elects to use grayscale do not use RGB Ratio filters since all RGB ratios will equal one This will change the RGB values If you entered RGB values previously re adjust RGB values accordingly for use in grayscale converted JPEG images B Selecting filter conditions Once filters are defined the user selects which filters to use by highlighting them in the Existing Filters list by clicking and dragging over all filters or holding down the ctrl button while left clicking to select individual filters To edit a saved filter select the filter from the Existing Filters list and click Edit Filter Make the necessary changes click the Save Changes button and click Done to finish To remove a filter select a filter from the Existing Filters list and click the Delete Filter button C Previewing filters Once the filters are set it is highly recommended that the user previews several images analyzed by AutoSpots Click on the Preview Filters button The features identified by AutoSpots as a callose deposit based on user defined and selected filters will be displayed in the viewing window The total number of identified callose deposits will be displayed above the image Examine several images and adjust filter settings accordingly or try differen
15. n be rerun with different parameters and or treatment groups by closing the graph window modifying the desired parameters and clicking Plot Run Stats again The graph shows all treatment groups x axis versus their average number of callose deposits y axis The values are calculated by averaging the number of callose deposits per JPEG image per sample per treatment Standard error or deviation is shown as defined by the user To save the graph to a post script file right click the graph and click Save as PostScript Do not add a file extension since this is added automatically A summary of the statistical analyses is presented in the Summary text box To save the summary report right click the Summary window and left click the Save Summary to file menu item Do not add a file extension since this 1s added automatically The statistical summary is divided into 3 sections a Comparison summaries b Group summaries c Group assignment summaries a Comparison summaries These statistics summarize comparisons of each treatment group to the user defined control Each treatment to control comparison lists 1 F statistic and cutoff F Statistic to test the null hypothesis that the treatment groups and the control group have the same variance 2 A statement of whether or not the null hypothesis of equal variance was accepted or rejected 3 The total degrees of freedom DF a T statistic and the p value for th
16. nt 01 02 jpg Treatment 01 03 jpg Treatment 01 01 jpq Treatment 02 04 jpg Treatment 02 05 jpg Treatment 02 01 jog Treatment 02 02 jpg Add Image Set Treatment 02 03 jpg Toi t 02 04 ipg Uploaded Sets Load All Images fH 02 05 ipg Mame Treatment 1 i Cancel To remove any of the uploaded sets highlight the set in the Uploaded Sets list right click the selection and left click the Delete pop up menu item Once all the sets of JPEG images have been defined click the Filter Settings tab to setup the filters II Image filters AutoSpots identifies callose deposits by searching JPEG images for pixels for those that pass the user defined and selected Color or RGB Ratio filter if both filters are defined then a pixel must pass at least one filter AutoSpots then distinguishes one callose deposit from another by expanding outwards from identified pixels to their adjacent pixels and then terminating at the edges of the callose deposit again using the user defined and selected filter AutoSpots then examines the sum of the pixels within each of the defined callose deposits to determine whether the identified callose deposits passes the user defined and selected Size filter User selected filters are critical for determining the accuracy and sensitivity of AutoSpots in distinguishing callose deposits from other features that may be stained by aniline blue e g trichomes or leaf
17. roup names will appear in the drop down menu item Treatment Groups Add Results Groups fx Results AutoSpots by Jason Cumbie and Rebecca Pankow For manual or semi automated assignment samples and JPEG images need to be manually assigned to their corresponding groups Select a treatment group from the drop down menu item right click on the box directly below the treatment group drop down menu and left click on the Add Samples pop up menu item Enter how many samples you will add Click the Add button Treatment Groups Treatment Groups Treatment Sample s Images Treatment Sample s Images Add Samples Ed of Samples d Add Samples P Add Cancel E asle C alant anntbral Treatment Groups Treatment Sample s To assign JPEG images to their corresponding samples 1 Select sample to be added to a Select the corresponding group name from the drop down menu item b Highlight the corresponding sample 2 Select JPEG images to assign to samples a Select the results set b Highlight the JPEG images in the results set Pacis Bathe 3 Assign the JPEG images to the selected sample o Teamen Treatme a Right click to bring up the pop up menu item Treatment2 01 01 ipg 14 b Click Add Images 4 Repeat until all samples have assigned JPEG images A minimum of one assigned JPEG image per sample is required r Error bar ges Treatment2 02 03
18. should be saved in directories labeled according to treatment groups AutoSpots will then automatically assign JPEG images to their samples and treatment groups without any significant user input See Fully automated assignment Otherwise the user will have to manually assign To make enumeration easier convert all JPEG images to grayscale See Use of grayscale the conversion of all JPEG images to grayscale is an automated function if checked by the user AutoSpots has a function to help the user define filter settings for average sensitivity and accuracy in enumeration of callose deposits The user must create a size filter and at least one color based filter color or RGB ratio filter for AutoSpots to analyze JPEG images For the size filter start with 25 for the minimum and 1000 for the maximum For the color filter do not use RGB ratio in grayscale load an image click on multiple pixels record their values repeat with other representative images and calculate the average sum values number of pixels examined Input those values See Color selection assistant Run AutoSpots Do not stray too far from your computer AutoSpots will be done in a flash Publish ill 2 25 10 AutoSpots by Jason Cumbie and Rebecca Pankow Table of Contents I eo Coo een ee A PW FS eeii i B Loading images ccccccssssssssssssssseeeeeeeeeeeeeees U Ee DE econ nausea A Defining filters 20 0 cccssssesssssseseeeeeeeeeeeeees a Settin
19. sults to a file You will also need to select an output directory Do not make this the same directory you uploaded the images from since you will overwrite your original images All analyzed JPEG images with outlined callose deposits will be saved in this directory for the user to examine Click Run and AutoSpots will begin automated batch enumeration of all images in the user defined directories AutoSpots Upload Filter Settings Find Spots Compare Sets Directories Images Treatment 01 04 jpq Treatment 02 05 jpq Current Image Progess Done Total Spots 350 Output file C Documents and Settings cumbie Desktop spol Browse Output dir C Documents and Settings cumbiej Desktop spol Browse IV Statistical analyses The Compare Sets tab has three different tasks A Quick view B Assignment of JPEG images to treatment groups C Execute and plot results of statistical analyses AutoSpots by Jason Cumbie and Rebecca Pankow A Quick view For a quick view of the results for all JPEG images in a data set select the set from the Results drop down menu item A list of all analyzed JPEG images and their corresponding number of enumerated callose deposits will appear in the text box located below the Results drop down menu item AutoSpots Upload Filter Settings Find Spots Compare Sets Treatment Groups Summar y Sample s Images reatment1 01 01 jpg 22 A tment1
20. t filter combinations until AutoSpots meets with the desired level of sensitivity and accuracy AutoSpots HER Upload Filter Settings Find Spots Compare Sets Name Add New Filter Existing Filters Pixel rgb values are compared with the distance formula Filter based on Color RGB C RGB Ratio Min Distance C Size Trip Distance Drop Distance Edit Filter Delete Filter Preview Progress Done Total Spots 339 V Use Grayscale Preview Filters Treatment onl Treatment1 2 Treatment1 01 03 jpaq Treatment 01 04 jpg Treatment 01 05 jpaq Image analysis is not trivial and some callose deposits may be missed especially when there are multiple focal planes in the image It is important to set the filters so that the majority of callose deposits within a focal plane are correctly identified and that the exact same filter settings are used to analyze across all images of a dataset to ensure useful comparisons between treatments Overly sensitive settings may compromise accuracy and cause AutoSpots to incorrectly identify and enumerate other features or portions thereof to be callose deposits AutoSpots by Jason Cumbie and Rebecca Pankow III Automated batch analysis Click the Find Spots tab to begin automated batch enumeration You will be prompted to enter a name for your output file AutoSpots will automatically add the txt extension when writing the re
21. the distance formula Filter based on Color Reef C RGE Ratio Min Distance Size Trip Distance Drop Distance a Settings for color filter The user can use the Color selection assistance feature of AutoSpots to help determine average values of callose deposits for the settings described in this section RGB Red Green Blue values These settings define an idealized pixel of a callose deposit based on the expected amount of red green and blue respectively in each color channel The user inputs three integers between 0 and 255 for each color channel Min Distance Minimum distance This setting defines the allowable minimum distance a pixel can be from the idealized pixel as defined by RGB setting to be considered a pixel of the same callose deposit The recommended setting is 0 AutoSpots by Jason Cumbie and Rebecca Pankow Trip Distance This setting defines the maximum distance a pixel can deviate from the idealized pixel as defined by the RGB setting to have AutoSpots expand and include the pixel as part of the same callose deposit In other words if a pixel is below the maximum distance it trips AutoSpots into expanding the callose deposit Drop Distance This setting defines the maximum distance a pixel must exceed from the idealized pixel as defined by the RGB setting for AutoSpots to terminate expansion to define a single callose deposit In other words if a pixel exceeds the ma
22. ximum distance it drops AutoSpots from expanding the callose deposit Name Add New Filter Rato between e e Filter based on Color Min valid ratio Trip ratio Drop ratio AGB Ratio l Size b Settings for ratio filter The user can use the Color selection assistance feature of AutoSpots to help determine average values of callose deposits for the settings described in this section Ratio between This setting allows the user to define the two color channels R G or B do not use the same channel twice of a pixel to calculate the ratio Min valid ratio minimum valid ratio This setting defines the minimum ratio that must be exceeded between the two user defined color channels of a pixel for AutoSpots to consider the pixel as a part of a callose deposit Trip ratio This setting defines the maximum ratio that must be exceeded between the two user defined color channels of a pixel for AutoSpots to expand and include a pixel as part of the same callose deposit Drop ratio This setting defines the maximum ratio that must be exceeded between the two user defined color channels of a pixel for AutoSpots to terminate expansion to define the edges of a single callose deposit AutoSpots by Jason Cumbie and Rebecca Pankow Name Add New Filter Min Spot Size Filter based on Max Spot Size f Color C RGE Ratio Size c Settings for size filter Min Spot Si
23. ze Minimum Spot Size This setting defines the minimum number of adjacent pixels necessary for AutoSpots to identify a callose deposit Recommended starting value 25 Max Spot Size Maximum Spot Size This setting defines the maximum number of adjacent pixels allowed for AutoSpots to identify a callose deposit Recommended starting value 1000 d Color selection assistance AutoSpots can be used to measure values for a given pixel in the Color and RGB ratio filter to help the user define appropriate color filter settings Select a representative preferable multiple image from an image set Mouse over and click a pixel in the image Values for that pixel will be displayed Jt is highly recommended to determine the average values from multiple pixels of multiple images Upland Fiter Settings Find Spots Compare Sets Name Add Mew Filler Ensing Filbers Bare mgh wet ore simge mh thet mia amida Fillor bsid om F Cily Feed 30 12 EE C AGE Antia Mri Delanoe Sere Tip Diiia Drap Diran Edit Fiter Delete Finer T Use Grayscale Preview Filters Treatment Did T ANT gpg resis 01 lt rg GEEET Poewerss Proge ESETE LST T Trestreert 01 th ing Tresirent O14 pg e Use of grayscale Callose deposits of grayscale converted JPEG images are generally more consistent and easier to identify AutoSpots can automatically convert all JPEG images within selected directories to graysca
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