CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:11000
LoadImages:[module_num:1|svn_version:\'10951\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D]
File type to be loaded:individual images
File selection method:Text-Exact match
Number of images in each group?:3
Type the text that the excluded images have in common:thumnails
Analyze all subfolders within the selected folder?:All
Input image file location:Default Input Folder\x7CNone
Check image sets for missing or duplicate files?:Yes
Group images by metadata?:No
Exclude certain files?:No
Specify metadata fields to group by:animal
Select subfolders to analyze:
Image count:1
Text that these images have in common (case-sensitive):A488_Data
Position of this image in each group:1
Extract metadata from where?:File name
Regular expression that finds metadata in the file name:^(?P.{8,10})_(?P.{2,20})_(?P.{1,8})_(?P.{1,2})_(?P.{2,5})_Data_x10.tif
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:3
Load the input as images or objects?:Images
Name this loaded image:original-A488
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:LoadedImageOutlines
Channel number:1
Rescale intensities?:No
LoadSingleImage:[module_num:2|svn_version:\'10884\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Input image file location:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):\\g_\\g_\\g_\\g_A546_Data_x10.tif
Load as images or objects?:Images
Name the image that will be loaded:original-A546
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outlines:NucleiOutlines
Rescale intensities?:No
Filename of the image to load (Include the extension, e.g., .tif):\\g_\\g_\\g_\\g_A594_Data_x10.tif
Load as images or objects?:Images
Name the image that will be loaded:original-A594
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outlines:NucleiOutlines
Rescale intensities?:No
Filename of the image to load (Include the extension, e.g., .tif):\\g_\\g_\\g_\\g_POPO1_Data_x10.tif
Load as images or objects?:Images
Name the image that will be loaded:original-POPO
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outlines:NucleiOutlines
Rescale intensities?:No
ApplyThreshold:[module_num:3|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Select the input image:original-A488
Name the output image:Threshgreen
Select the output image type:Binary (black and white)
Set pixels below or above the threshold to zero?:Below threshold
Subtract the threshold value from the remaining pixel intensities?:No
Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0
Select the thresholding method:Otsu Global
Manual threshold:0.0
Lower and upper bounds on threshold:0.001,1.0
Threshold correction factor:1
Approximate fraction of image covered by objects?:0.01
Select the input objects:None
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Select the measurement to threshold with:None
MaskImage:[module_num:4|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:original-A488
Name the output image:total green
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:Threshgreen
Invert the mask?:No
ApplyThreshold:[module_num:5|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Select the input image:original-A594
Name the output image:Threshred
Select the output image type:Binary (black and white)
Set pixels below or above the threshold to zero?:Below threshold
Subtract the threshold value from the remaining pixel intensities?:No
Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0
Select the thresholding method:Otsu Global
Manual threshold:0.0
Lower and upper bounds on threshold:0.001,1.0
Threshold correction factor:1
Approximate fraction of image covered by objects?:0.01
Select the input objects:None
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select the measurement to threshold with:None
ApplyThreshold:[module_num:6|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Select the input image:original-POPO
Name the output image:ThreshPOPO
Select the output image type:Binary (black and white)
Set pixels below or above the threshold to zero?:Below threshold
Subtract the threshold value from the remaining pixel intensities?:No
Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0
Select the thresholding method:Otsu Global
Manual threshold:0.0
Lower and upper bounds on threshold:0.001,1.0
Threshold correction factor:1
Approximate fraction of image covered by objects?:0.01
Select the input objects:None
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select the measurement to threshold with:None
ApplyThreshold:[module_num:7|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Select the input image:original-A546
Name the output image:A546-thresh
Select the output image type:Binary (black and white)
Set pixels below or above the threshold to zero?:Below threshold
Subtract the threshold value from the remaining pixel intensities?:No
Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0
Select the thresholding method:Otsu Global
Manual threshold:0.0
Lower and upper bounds on threshold:0.001,1.0
Threshold correction factor:1
Approximate fraction of image covered by objects?:0.01
Select the input objects:None
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select the measurement to threshold with:None
ImageMath:[module_num:8|svn_version:\'10718\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Operation:Add
Raise the power of the result by:1
Multiply the result by:1
Add to result:0
Set values less than 0 equal to 0?:Yes
Set values greater than 1 equal to 1?:Yes
Ignore the image masks?:No
Name the output image:total
Image or measurement?:Image
Select the first image:Threshgreen
Multiply the first image by:1
Measurement:
Image or measurement?:Image
Select the second image:ThreshPOPO
Multiply the second image by:1
Measurement:
Image or measurement?:Image
Select the third image:A546-thresh
Multiply the third image by:1
Measurement:
MaskImage:[module_num:9|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B"By applying the Mask of the total image...you get the \'pure\' signal of Red"\x5D]
Select the input image:original-A546
Name the output image:total yellow
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:total
Invert the mask?:No
MaskImage:[module_num:10|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B"By applying the Mask of the total image...you get the \'pure\' signal of POPO = all nuclei in image"\x5D]
Select the input image:original-POPO
Name the output image:total nuclei
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:total
Invert the mask?:No
MaskImage:[module_num:11|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B"By applying the Mask of the total image...you get the \'pure\' signal of Green"\x5D]
Select the input image:original-A488
Name the output image:total green
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:total
Invert the mask?:No
ConserveMemory:[module_num:12|svn_version:\'9401\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Specify which images?:Images to remove
Select image to remove:Threshgreen
Select image to remove:A546-thresh
Select image to remove:ThreshPOPO
IdentifyPrimaryObjects:[module_num:13|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Threshred
Name the primary objects to be identified:594
Typical diameter of objects, in pixel units (Min,Max):500,9000000
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Global
Threshold correction factor:.5
Lower and upper bounds on threshold:0.0,.5
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:None
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:PrimaryOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
ConvertObjectsToImage:[module_num:14|svn_version:\'10807\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select the input objects:594
Name the output image:594EPI
Select the color type:Binary (black & white)
Select the colormap:Default
MaskImage:[module_num:15|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total nuclei
Name the output image:Epithelium
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:No
MaskImage:[module_num:16|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total green
Name the output image:Epithelium-green
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:No
IdentifyPrimaryObjects:[module_num:17|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Epithelium
Name the primary objects to be identified:positive nuclei-epithelium
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:Yes
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
IdentifyPrimaryObjects:[module_num:18|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Epithelium-green
Name the primary objects to be identified:green-epithelium
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:No
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
RelateObjects:[module_num:19|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\'How many POPO stained nuclei are also stained for PR...\'\x5D]
Select the input child objects:green-epithelium
Select the input parent objects:positive nuclei-epithelium
Calculate distances?:None
Calculate per-parent means for all child measurements?:No
Calculate distances to other parents?:No
Parent name:None
ClassifyObjects:[module_num:20|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-epithelium
Select the measurement to classify by:Children_green-epithelium_Count
Select bin spacing:Custom-defined bins
Number of bins:2
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.25
Give each bin a name?:Yes
Enter the bin names separated by commas:noBrdU_epi, someBrdU_epi
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_noBrdU\x3Aepi someBrdU\x3Aepi
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
MeasureObjectIntensity:[module_num:21|svn_version:\'10816\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:Epithelium-green
Select objects to measure:positive nuclei-epithelium
MeasureObjectSizeShape:[module_num:22|svn_version:\'1\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select objects to measure:green-epithelium
Calculate the Zernike features?:Yes
DisplayDataOnImage:[module_num:23|svn_version:\'10412\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Display object or image measurements?:Object
Select the input objects:positive nuclei-epithelium
Measurement to display:Intensity_MedianIntensity_Epithelium-green
Select the image on which to display the measurements:total green
Text color:red
Name the output image that has the measurements displayed:DisplayImage
Font size (points):8
Number of decimals:5
Image elements to save:Image
ClassifyObjects:[module_num:24|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\'need to define intensity threshold\'\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-epithelium
Select the measurement to classify by:Intensity_MedianIntensity_Epithelium-green
Select bin spacing:Custom-defined bins
Number of bins:3
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.001
Give each bin a name?:Yes
Enter the bin names separated by commas:greenNEG_epi, greenPOS_epi
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_greenNEG\x3Aepi greenPOS\x3Aepi
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
SaveImages:[module_num:25|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the type of image to save:Image
Select the image to save:ClassifiedNuclei_greenNEG\x3Aepi greenPOS\x3Aepi
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:original-A488
Enter single file name:4
Do you want to add a suffix to the image file name?:Yes
Text to append to the image name:Epi_BrdUNEG_BrdUPOS
Select file format to use:bmp
Output file location:Default Output Folder\x7CNone
Image bit depth:8
Overwrite existing files without warning?:No
Select how often to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:Spectral
Store file and path information to the saved image?:Yes
Create subfolders in the output folder?:Yes
FilterObjects:[module_num:26|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:greenPOS-epi
Select the object to filter:positive nuclei-epithelium
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:1
Additional object count:0
Select the measurement to filter by:Children_green-epithelium_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:27|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent positive_green_epi
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_greenPOS-epi
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-epithelium
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
MaskImage:[module_num:28|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total yellow
Name the output image:Epithelium-yellow
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:No
IdentifyPrimaryObjects:[module_num:29|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Epithelium-yellow
Name the primary objects to be identified:yellow-epithelium
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:No
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
RelateObjects:[module_num:30|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Select the input child objects:yellow-epithelium
Select the input parent objects:positive nuclei-epithelium
Calculate distances?:None
Calculate per-parent means for all child measurements?:No
Calculate distances to other parents?:No
Parent name:None
ClassifyObjects:[module_num:31|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-epithelium
Select the measurement to classify by:Children_yellow-epithelium_Count
Select bin spacing:Custom-defined bins
Number of bins:2
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.25
Give each bin a name?:Yes
Enter the bin names separated by commas:noKi67_epi, someKi67_epi
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_noKI67\x3Aepi someKi67\x3Aepi
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
MeasureObjectIntensity:[module_num:32|svn_version:\'10816\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:Epithelium-yellow
Select objects to measure:positive nuclei-epithelium
MeasureObjectSizeShape:[module_num:33|svn_version:\'1\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select objects to measure:yellow-epithelium
Calculate the Zernike features?:Yes
DisplayDataOnImage:[module_num:34|svn_version:\'10412\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Display object or image measurements?:Object
Select the input objects:positive nuclei-epithelium
Measurement to display:Intensity_MedianIntensity_Epithelium-yellow
Select the image on which to display the measurements:total yellow
Text color:green
Name the output image that has the measurements displayed:DisplayImage
Font size (points):8
Number of decimals:5
Image elements to save:Image
ClassifyObjects:[module_num:35|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-epithelium
Select the measurement to classify by:Intensity_MedianIntensity_Epithelium-yellow
Select bin spacing:Custom-defined bins
Number of bins:3
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.0005
Give each bin a name?:Yes
Enter the bin names separated by commas:yellowNEG_epi, yellowPOS_epi
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_NEGyellow\x3Aepi POSyellow\x3Aepi
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
SaveImages:[module_num:36|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the type of image to save:Image
Select the image to save:ClassifiedNuclei_NEGyellow\x3Aepi POSyellow\x3Aepi
Select the objects to save:None
Select the module display window to save:23\x3A ClassifyObjects
Select method for constructing file names:From image filename
Select image name for file prefix:original-A546
Enter single file name:546_Epi_Ki67NEG_POS
Do you want to add a suffix to the image file name?:Yes
Text to append to the image name:Epi_Ki67NEG_Ki67POS
Select file format to use:bmp
Output file location:Default Output Folder\x7CNone
Image bit depth:8
Overwrite existing files without warning?:No
Select how often to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:Spectral
Store file and path information to the saved image?:Yes
Create subfolders in the output folder?:Yes
FilterObjects:[module_num:37|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:yellowPOS-epi
Select the object to filter:positive nuclei-epithelium
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:1
Additional object count:0
Select the measurement to filter by:Children_yellow-epithelium_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:38|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent positive_yellow_epi
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_yellowPOS-epi
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-epithelium
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
FilterObjects:[module_num:39|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:GreenYellow epithelial nuclei
Select the object to filter:positive nuclei-epithelium
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:2
Additional object count:0
Select the measurement to filter by:Children_greenPOS-epi_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
Select the measurement to filter by:Children_yellowPOS-epi_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:40|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent postive_Coloc gr/rd_epi
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_GreenYellow epithelial nuclei
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-epithelium
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
MaskImage:[module_num:41|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total nuclei
Name the output image:Stroma
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:Yes
MaskImage:[module_num:42|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total green
Name the output image:Stroma-green
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:Yes
IdentifyPrimaryObjects:[module_num:43|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Stroma
Name the primary objects to be identified:positive nuclei-stroma
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:Yes
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
IdentifyPrimaryObjects:[module_num:44|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Stroma-green
Name the primary objects to be identified:green-stroma
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:No
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
RelateObjects:[module_num:45|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Select the input child objects:green-stroma
Select the input parent objects:positive nuclei-stroma
Calculate distances?:None
Calculate per-parent means for all child measurements?:No
Calculate distances to other parents?:No
Parent name:None
ClassifyObjects:[module_num:46|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-stroma
Select the measurement to classify by:Children_green-stroma_Count
Select bin spacing:Custom-defined bins
Number of bins:2
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.25
Give each bin a name?:Yes
Enter the bin names separated by commas:noBrdU_stroma, someBrdU_stroma
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_noBrdU\x3Ast someBrdU\x3Ast
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
MeasureObjectIntensity:[module_num:47|svn_version:\'10816\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:Stroma-green
Select objects to measure:positive nuclei-stroma
MeasureObjectSizeShape:[module_num:48|svn_version:\'1\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select objects to measure:green-stroma
Calculate the Zernike features?:Yes
DisplayDataOnImage:[module_num:49|svn_version:\'10412\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Display object or image measurements?:Object
Select the input objects:positive nuclei-stroma
Measurement to display:Intensity_MedianIntensity_Stroma-green
Select the image on which to display the measurements:total green
Text color:red
Name the output image that has the measurements displayed:DisplayImage
Font size (points):8
Number of decimals:5
Image elements to save:Image
ClassifyObjects:[module_num:50|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-stroma
Select the measurement to classify by:Intensity_MedianIntensity_Stroma-green
Select bin spacing:Custom-defined bins
Number of bins:3
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.001
Give each bin a name?:Yes
Enter the bin names separated by commas:greenNEG_stroma, greenPOS_stroma
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_greenNEG\x3Ast greenPOS_st
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
SaveImages:[module_num:51|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the type of image to save:Image
Select the image to save:ClassifiedNuclei_greenNEG\x3Ast greenPOS_st
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:original-A488
Enter single file name:488_ST_BrdUNEG_POS
Do you want to add a suffix to the image file name?:Yes
Text to append to the image name:ST_BrdUNEG_BrdUPOS
Select file format to use:bmp
Output file location:Default Output Folder\x7CNone
Image bit depth:8
Overwrite existing files without warning?:No
Select how often to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:Spectral
Store file and path information to the saved image?:Yes
Create subfolders in the output folder?:Yes
FilterObjects:[module_num:52|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:stroma_greenPOS
Select the object to filter:positive nuclei-stroma
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:1
Additional object count:0
Select the measurement to filter by:Children_green-stroma_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:53|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent Positive greenPOS_stroma
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_stroma_greenPOS
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-stroma
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
MaskImage:[module_num:54|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:total yellow
Name the output image:Stroma-yellow
Use objects or an image as a mask?:Image
Select object for mask:594
Select image for mask:594EPI
Invert the mask?:Yes
IdentifyPrimaryObjects:[module_num:55|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D]
Select the input image:Stroma-yellow
Name the primary objects to be identified:yellow-stroma
Typical diameter of objects, in pixel units (Min,Max):15,115
Discard objects outside the diameter range?:No
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Global
Threshold correction factor:1
Lower and upper bounds on threshold:0.00,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:positive nuclei
Fill holes in identified objects?:No
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified objects?:No
Automatically calculate the threshold using the Otsu method?:Yes
Enter Laplacian of Gaussian threshold:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter:5
Handling of objects if excessive number of objects identified:Continue
Maximum number of objects:500
Select the measurement to threshold with:None
RelateObjects:[module_num:56|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Select the input child objects:yellow-stroma
Select the input parent objects:positive nuclei-stroma
Calculate distances?:None
Calculate per-parent means for all child measurements?:No
Calculate distances to other parents?:No
Parent name:None
ClassifyObjects:[module_num:57|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-stroma
Select the measurement to classify by:Children_yellow-stroma_Count
Select bin spacing:Custom-defined bins
Number of bins:2
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.25
Give each bin a name?:Yes
Enter the bin names separated by commas:noKi67_st, someKi67_st
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_noKi67\x3Ast someKi67\x3Ast
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
MeasureObjectIntensity:[module_num:58|svn_version:\'10816\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:Stroma-yellow
Select objects to measure:positive nuclei-stroma
MeasureObjectSizeShape:[module_num:59|svn_version:\'1\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select objects to measure:yellow-stroma
Calculate the Zernike features?:Yes
DisplayDataOnImage:[module_num:60|svn_version:\'10412\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Display object or image measurements?:Object
Select the input objects:positive nuclei-stroma
Measurement to display:Intensity_MedianIntensity_Stroma-yellow
Select the image on which to display the measurements:total yellow
Text color:green
Name the output image that has the measurements displayed:DisplayImage
Font size (points):8
Number of decimals:5
Image elements to save:Image
ClassifyObjects:[module_num:61|svn_version:\'10720\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Should each classification decision be based on a single measurement or on the combination of a pair of measurements?:Single measurement
Hidden:1
Select the object to be classified:positive nuclei-stroma
Select the measurement to classify by:Intensity_MedianIntensity_Stroma-yellow
Select bin spacing:Custom-defined bins
Number of bins:3
Lower threshold:0
Use a bin for objects below the threshold?:Yes
Upper threshold:1
Use a bin for objects above the threshold?:Yes
Enter the custom thresholds separating the values between bins:.0005
Give each bin a name?:Yes
Enter the bin names separated by commas:stroma_yellowNEG, stroma_yellowPOS
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:Yes
Name the output image:ClassifiedNuclei_NEGyellow\x3Ast POSyellow\x3Ast
Enter the object name:None
Select the first measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Select the second measurement:None
Method to select the cutoff:Mean
Enter the cutoff value:0.5
Use custom names for the bins?:No
Enter the low-low bin name:low_low
Enter the low-high bin name:low_high
Enter the high-low bin name:high_low
Enter the high-high bin name:high_high
Retain an image of the objects classified by their measurements, for use later in the pipeline (for example, in SaveImages)?:No
Enter the image name:None
SaveImages:[module_num:62|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the type of image to save:Image
Select the image to save:ClassifiedNuclei_NEGyellow\x3Ast POSyellow\x3Ast
Select the objects to save:None
Select the module display window to save:None
Select method for constructing file names:From image filename
Select image name for file prefix:original-A546
Enter single file name:546_ST_Ki67NEG_POS
Do you want to add a suffix to the image file name?:Yes
Text to append to the image name:ST_Ki67NEG_Ki67POS
Select file format to use:bmp
Output file location:Default Output Folder\x7CNone
Image bit depth:8
Overwrite existing files without warning?:No
Select how often to save:Every cycle
Rescale the images? :No
Save as grayscale or color image?:Grayscale
Select colormap:Spectral
Store file and path information to the saved image?:Yes
Create subfolders in the output folder?:Yes
FilterObjects:[module_num:63|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:stroma_yellowPOS
Select the object to filter:positive nuclei-stroma
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:1
Additional object count:0
Select the measurement to filter by:Children_yellow-stroma_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:64|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent Positive_yellow_stroma
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_stroma_yellowPOS
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-stroma
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
FilterObjects:[module_num:65|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:GreenYellow stromal nuclei
Select the object to filter:positive nuclei-stroma
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:None
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7CNone
Rules file name:rules.txt
Measurement count:2
Additional object count:0
Select the measurement to filter by:Children_stroma_greenPOS_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
Select the measurement to filter by:Children_stroma_yellowPOS_Count
Filter using a minimum measurement value?:Yes
Minimum value:1
Filter using a maximum measurement value?:No
Maximum value:1
CalculateMath:[module_num:66|svn_version:\'10905\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Name the output measurement:Percent Positive_Coloc gr/yellow_stroma
Operation:Divide
Select the numerator measurement type:Image
Select the numerator objects:None
Select the numerator measurement:Count_GreenYellow stromal nuclei
Multiply the above operand by:1
Raise the power of above operand by:1
Select the denominator measurement type:Image
Select the denominator objects:None
Select the denominator measurement:Count_positive nuclei-stroma
Multiply the above operand by:1
Raise the power of above operand by:1
Take log10 of result?:No
Multiply the result by:100
Raise the power of result by:1
ExportToDatabase:[module_num:67|svn_version:\'10962\'|variable_revision_number:20|show_window:False|notes:\x5B\x5D]
Database type:SQLite
Database name:cleanp63DB
Add a prefix to table names?:No
Table prefix:Expt_
SQL file prefix:SQL_
Output file location:Default Output Folder\x7CNone
Create a CellProfiler Analyst properties file?:Yes
Database host:
Username:
Password:
Name the SQLite database file:DefaultDB.db
Calculate the per-image mean values of object measurements?:Yes
Calculate the per-image median values of object measurements?:Yes
Calculate the per-image standard deviation values of object measurements?:Yes
Calculate the per-well mean values of object measurements?:No
Calculate the per-well median values of object measurements?:No
Calculate the per-well standard deviation values of object measurements?:No
Export measurements for all objects to the database?:Select...
Select the objects:GreenYellow epithelial nuclei,GreenYellow stromal nuclei,green-epithelium,green-stroma,greenPOS-epi,positive nuclei-epithelium,positive nuclei-stroma,stroma_greenPOS,stroma_yellowPOS,yellow-epithelium,yellow-stroma,yellowPOS-epi
Maximum # of characters in a column name:64
Create one table per object or a single object table?:Single object table
Enter an image url prepend if you plan to access your files via http:
Write image thumbnails directly to the database?:No
Select the images you want to save thumbnails of:A546-thresh,ClassifiedNuclei_NEGyellow\x3Aepi POSyellow\x3Aepi,ClassifiedNuclei_NEGyellow\x3Ast POSyellow\x3Ast,ClassifiedNuclei_greenNEG\x3Aepi greenPOS\x3Aepi,ClassifiedNuclei_greenNEG\x3Ast greenPOS_st,ClassifiedNuclei_noBrdU\x3Aepi someBrdU\x3Aepi,ClassifiedNuclei_noBrdU\x3Ast someBrdU\x3Ast,ClassifiedNuclei_noKI67\x3Aepi someKi67\x3Aepi,ClassifiedNuclei_noKi67\x3Ast someKi67\x3Ast,DisplayImage,Epithelium ,Epithelium-green,Epithelium-yellow,Stroma,Stroma-green,Stroma-yellow,ThreshPOPO,Threshgreen,Threshred,original-A488,original-A546,original-A594,original-POPO,total,total green,total nuclei,total yellow
Auto-scale thumbnail pixel intensities?:No
Select the plate type:None
Select the plate metadata:None
Select the well metadata:None
Include information for all images, using default values?:Yes
Hidden:2
Hidden:1
Hidden:1
Select an image to include:red
Use the image name for the display?:Yes
Image name:Channel1
Channel color:red
Select an image to include:OrigBlue
Use the image name for the display?:Yes
Image name:Channel2
Channel color:green
Do you want to add group fields?:Yes
Enter the name of the group:animals
Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_animal, Image_Metadata_channel, Image_Metadata_imgnumber
Do you want to add filter fields?:No
Automatically create a filter for each plate?:No
Enter the name of the filter:
Enter the MySQL WHERE clause to define a filter:
ExportToSpreadsheet:[module_num:68|svn_version:\'10880\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select or enter the column delimiter:Comma (",")
Prepend the output file name to the data file names?:Yes
Add image metadata columns to your object data file?:Yes
Limit output to a size that is allowed in Excel?:No
Select the columns of measurements to export?:Yes
Calculate the per-image mean values for object measurements?:Yes
Calculate the per-image median values for object measurements?:No
Calculate the per-image standard deviation values for object measurements?:No
Output file location:Default Output Folder\x7CNone
Create a GenePattern GCT file?:No
Select source of sample row name:Metadata
Select the image to use as the identifier:None
Select the metadata to use as the identifier:None
Export all measurements, using default file names?:Yes
Press button to select measurements to export:positive nuclei-epithelium\x7CIntensity_StdIntensityEdge_Epithelium-green,positive nuclei-epithelium\x7CIntensity_StdIntensityEdge_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MinIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MinIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_IntegratedIntensityEdge_Epithelium-green,positive nuclei-epithelium\x7CIntensity_IntegratedIntensityEdge_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_StdIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_StdIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MassDisplacement_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MassDisplacement_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_UpperQuartileIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_UpperQuartileIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_LowerQuartileIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_LowerQuartileIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MinIntensityEdge_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MinIntensityEdge_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MeanIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MeanIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MeanIntensityEdge_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MeanIntensityEdge_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MaxIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MaxIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MedianIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MedianIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_IntegratedIntensity_Epithelium-green,positive nuclei-epithelium\x7CIntensity_IntegratedIntensity_Epithelium-yellow,positive nuclei-epithelium\x7CIntensity_MaxIntensityEdge_Epithelium-green,positive nuclei-epithelium\x7CIntensity_MaxIntensityEdge_Epithelium-yellow,positive nuclei-epithelium\x7CChildren_yellow-epithelium_Count,positive nuclei-epithelium\x7CChildren_GreenYellow epithelial nuclei_Count,positive nuclei-epithelium\x7CChildren_greenPOS-epi_Count,positive nuclei-epithelium\x7CChildren_green-epithelium_Count,positive nuclei-epithelium\x7CChildren_yellowPOS-epi_Count,positive nuclei-epithelium\x7CClassify_greenPOS_epi,positive nuclei-epithelium\x7CClassify_greenNEG_epi,positive nuclei-epithelium\x7CClassify_yellowNEG_epi,positive nuclei-epithelium\x7CClassify_yellowPOS_epi,yellow-epithelium\x7CAreaShape_Perimeter,yellow-epithelium\x7CAreaShape_FormFactor,yellow-epithelium\x7CAreaShape_Orientation,yellow-epithelium\x7CAreaShape_Area,yellow-epithelium\x7CAreaShape_Solidity,yellow-epithelium\x7CAreaShape_Zernike_1_1,yellow-epithelium\x7CAreaShape_Zernike_0_0,yellow-epithelium\x7CAreaShape_Zernike_3_1,yellow-epithelium\x7CAreaShape_Zernike_3_3,yellow-epithelium\x7CAreaShape_Zernike_2_0,yellow-epithelium\x7CAreaShape_Zernike_2_2,yellow-epithelium\x7CAreaShape_Zernike_5_1,yellow-epithelium\x7CAreaShape_Zernike_5_5,yellow-epithelium\x7CAreaShape_Zernike_5_3,yellow-epithelium\x7CAreaShape_Zernike_4_0,yellow-epithelium\x7CAreaShape_Zernike_4_2,yellow-epithelium\x7CAreaShape_Zernike_4_4,yellow-epithelium\x7CAreaShape_Zernike_7_1,yellow-epithelium\x7CAreaShape_Zernike_7_5,yellow-epithelium\x7CAreaShape_Zernike_7_3,yellow-epithelium\x7CAreaShape_Zernike_7_7,yellow-epithelium\x7CAreaShape_Zernike_6_0,yellow-epithelium\x7CAreaShape_Zernike_6_2,yellow-epithelium\x7CAreaShape_Zernike_6_4,yellow-epithelium\x7CAreaShape_Zernike_6_6,yellow-epithelium\x7CAreaShape_Zernike_9_1,yellow-epithelium\x7CAreaShape_Zernike_9_3,yellow-epithelium\x7CAreaShape_Zernike_9_5,yellow-epithelium\x7CAreaShape_Zernike_9_7,yellow-epithelium\x7CAreaShape_Zernike_9_9,yellow-epithelium\x7CAreaShape_Zernike_8_0,yellow-epithelium\x7CAreaShape_Zernike_8_2,yellow-epithelium\x7CAreaShape_Zernike_8_4,yellow-epithelium\x7CAreaShape_Zernike_8_6,yellow-epithelium\x7CAreaShape_Zernike_8_8,yellow-epithelium\x7CAreaShape_EulerNumber,yellow-epithelium\x7CAreaShape_Compactness,yellow-epithelium\x7CAreaShape_Extent,yellow-epithelium\x7CAreaShape_Eccentricity,yellow-epithelium\x7CAreaShape_MinorAxisLength,yellow-epithelium\x7CAreaShape_MajorAxisLength,yellow-epithelium\x7CAreaShape_Center_Y,yellow-epithelium\x7CAreaShape_Center_X,positive nuclei-stroma\x7CIntensity_StdIntensityEdge_Stroma-green,positive nuclei-stroma\x7CIntensity_StdIntensityEdge_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MinIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_MinIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_IntegratedIntensityEdge_Stroma-green,positive nuclei-stroma\x7CIntensity_IntegratedIntensityEdge_Stroma-yellow,positive nuclei-stroma\x7CIntensity_StdIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_StdIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MassDisplacement_Stroma-green,positive nuclei-stroma\x7CIntensity_MassDisplacement_Stroma-yellow,positive nuclei-stroma\x7CIntensity_UpperQuartileIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_UpperQuartileIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_LowerQuartileIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_LowerQuartileIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MinIntensityEdge_Stroma-green,positive nuclei-stroma\x7CIntensity_MinIntensityEdge_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MeanIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_MeanIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MeanIntensityEdge_Stroma-green,positive nuclei-stroma\x7CIntensity_MeanIntensityEdge_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MaxIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_MaxIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MedianIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_MedianIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_IntegratedIntensity_Stroma-green,positive nuclei-stroma\x7CIntensity_IntegratedIntensity_Stroma-yellow,positive nuclei-stroma\x7CIntensity_MaxIntensityEdge_Stroma-green,positive nuclei-stroma\x7CIntensity_MaxIntensityEdge_Stroma-yellow,positive nuclei-stroma\x7CChildren_stroma_greenPOS_Count,positive nuclei-stroma\x7CChildren_stroma_yellowPOS_Count,positive nuclei-stroma\x7CChildren_GreenYellow stromal nuclei_Count,positive nuclei-stroma\x7CChildren_green-stroma_Count,positive nuclei-stroma\x7CChildren_yellow-stroma_Count,positive nuclei-stroma\x7CClassify_greenPOS_stroma,positive nuclei-stroma\x7CClassify_stroma_yellowNEG,positive nuclei-stroma\x7CClassify_stroma_yellowPOS,positive nuclei-stroma\x7CClassify_greenNEG_stroma,Image\x7CCount_positive nuclei-epithelium,Image\x7CCount_yellow-epithelium,Image\x7CCount_positive nuclei-stroma,Image\x7CCount_GreenYellow epithelial nuclei,Image\x7CCount_yellow-stroma,Image\x7CCount_stroma_greenPOS,Image\x7CCount_stroma_yellowPOS,Image\x7CCount_greenPOS-epi,Image\x7CCount_GreenYellow stromal nuclei,Image\x7CCount_green-epithelium,Image\x7CCount_green-stroma,Image\x7CCount_yellowPOS-epi,Image\x7CFileName_original-A594,Image\x7CFileName_original-A546,Image\x7CFileName_ClassifiedNuclei_greenNEG\x3Ast greenPOS_st,Image\x7CFileName_ClassifiedNuclei_NEGyellow\x3Aepi POSyellow\x3Aepi,Image\x7CFileName_ClassifiedNuclei_NEGyellow\x3Ast POSyellow\x3Ast,Image\x7CFileName_ClassifiedNuclei_greenNEG\x3Aepi greenPOS\x3Aepi,Image\x7CFileName_original-A488,Image\x7CFileName_original-POPO,Image\x7CMath_Percent postive_Coloc gr/rd_epi,Image\x7CMath_Percent positive_green_epi,Image\x7CMath_Percent positive_yellow_epi,Image\x7CMath_Percent Positive greenPOS_stroma,Image\x7CMath_Percent Positive_Coloc gr/yellow_stroma ,Image\x7CMath_Percent Positive_yellow_stroma,Image\x7CThreshold_OrigThreshold_Threshgreen,Image\x7CThreshold_OrigThreshold_positive nuclei-epithelium,Image\x7CThreshold_OrigThreshold_yellow-epithelium,Image\x7CThreshold_OrigThreshold_594,Image\x7CThreshold_OrigThreshold_positive nuclei-stroma,Image\x7CThreshold_OrigThreshold_yellow-stroma,Image\x7CThreshold_OrigThreshold_ThreshPOPO,Image\x7CThreshold_OrigThreshold_Threshred,Image\x7CThreshold_OrigThreshold_green-epithelium,Image\x7CThreshold_OrigThreshold_A546-thresh,Image\x7CThreshold_OrigThreshold_green-stroma,Image\x7CThreshold_SumOfEntropies_Threshgreen,Image\x7CThreshold_SumOfEntropies_positive nuclei-epithelium,Image\x7CThreshold_SumOfEntropies_yellow-epithelium,Image\x7CThreshold_SumOfEntropies_594,Image\x7CThreshold_SumOfEntropies_positive nuclei-stroma,Image\x7CThreshold_SumOfEntropies_yellow-stroma,Image\x7CThreshold_SumOfEntropies_ThreshPOPO,Image\x7CThreshold_SumOfEntropies_Threshred,Image\x7CThreshold_SumOfEntropies_green-epithelium,Image\x7CThreshold_SumOfEntropies_A546-thresh,Image\x7CThreshold_SumOfEntropies_green-stroma,Image\x7CThreshold_WeightedVariance_Threshgreen,Image\x7CThreshold_WeightedVariance_positive nuclei-epithelium,Image\x7CThreshold_WeightedVariance_yellow-epithelium,Image\x7CThreshold_WeightedVariance_594,Image\x7CThreshold_WeightedVariance_positive nuclei-stroma,Image\x7CThreshold_WeightedVariance_yellow-stroma,Image\x7CThreshold_WeightedVariance_ThreshPOPO,Image\x7CThreshold_WeightedVariance_Threshred,Image\x7CThreshold_WeightedVariance_green-epithelium,Image\x7CThreshold_WeightedVariance_A546-thresh,Image\x7CThreshold_WeightedVariance_green-stroma,Image\x7CThreshold_FinalThreshold_Threshgreen,Image\x7CThreshold_FinalThreshold_positive nuclei-epithelium,Image\x7CThreshold_FinalThreshold_yellow-epithelium,Image\x7CThreshold_FinalThreshold_594,Image\x7CThreshold_FinalThreshold_positive nuclei-stroma,Image\x7CThreshold_FinalThreshold_yellow-stroma,Image\x7CThreshold_FinalThreshold_ThreshPOPO,Image\x7CThreshold_FinalThreshold_Threshred,Image\x7CThreshold_FinalThreshold_green-epithelium,Image\x7CThreshold_FinalThreshold_A546-thresh,Image\x7CThreshold_FinalThreshold_green-stroma,Image\x7CClassify_greenPOS_stroma_NumObjectsPerBin,Image\x7CClassify_greenPOS_stroma_PctObjectsPerBin,Image\x7CClassify_greenPOS_epi_NumObjectsPerBin,Image\x7CClassify_greenPOS_epi_PctObjectsPerBin,Image\x7CClassify_stroma_yellowNEG_NumObjectsPerBin,Image\x7CClassify_stroma_yellowNEG_PctObjectsPerBin,Image\x7CClassify_stroma_yellowPOS_NumObjectsPerBin,Image\x7CClassify_stroma_yellowPOS_PctObjectsPerBin,Image\x7CClassify_greenNEG_stroma_NumObjectsPerBin,Image\x7CClassify_greenNEG_stroma_PctObjectsPerBin,Image\x7CClassify_greenNEG_epi_NumObjectsPerBin,Image\x7CClassify_greenNEG_epi_PctObjectsPerBin,Image\x7CClassify_yellowNEG_epi_NumObjectsPerBin,Image\x7CClassify_yellowNEG_epi_PctObjectsPerBin,Image\x7CClassify_yellowPOS_epi_NumObjectsPerBin,Image\x7CClassify_yellowPOS_epi_PctObjectsPerBin,Image\x7CMetadata_image,Image\x7CMetadata_spectra,Image\x7CMetadata_base,Image\x7CMetadata_mag,Image\x7CMetadata_region,yellow-stroma\x7CAreaShape_Perimeter,yellow-stroma\x7CAreaShape_FormFactor,yellow-stroma\x7CAreaShape_Orientation,yellow-stroma\x7CAreaShape_Area,yellow-stroma\x7CAreaShape_Solidity,yellow-stroma\x7CAreaShape_Zernike_1_1,yellow-stroma\x7CAreaShape_Zernike_0_0,yellow-stroma\x7CAreaShape_Zernike_3_1,yellow-stroma\x7CAreaShape_Zernike_3_3,yellow-stroma\x7CAreaShape_Zernike_2_0,yellow-stroma\x7CAreaShape_Zernike_2_2,yellow-stroma\x7CAreaShape_Zernike_5_1,yellow-stroma\x7CAreaShape_Zernike_5_5,yellow-stroma\x7CAreaShape_Zernike_5_3,yellow-stroma\x7CAreaShape_Zernike_4_0,yellow-stroma\x7CAreaShape_Zernike_4_2,yellow-stroma\x7CAreaShape_Zernike_4_4,yellow-stroma\x7CAreaShape_Zernike_7_1,yellow-stroma\x7CAreaShape_Zernike_7_5,yellow-stroma\x7CAreaShape_Zernike_7_3,yellow-stroma\x7CAreaShape_Zernike_7_7,yellow-stroma\x7CAreaShape_Zernike_6_0,yellow-stroma\x7CAreaShape_Zernike_6_2,yellow-stroma\x7CAreaShape_Zernike_6_4,yellow-stroma\x7CAreaShape_Zernike_6_6,yellow-stroma\x7CAreaShape_Zernike_9_1,yellow-stroma\x7CAreaShape_Zernike_9_3,yellow-stroma\x7CAreaShape_Zernike_9_5,yellow-stroma\x7CAreaShape_Zernike_9_7,yellow-stroma\x7CAreaShape_Zernike_9_9,yellow-stroma\x7CAreaShape_Zernike_8_0,yellow-stroma\x7CAreaShape_Zernike_8_2,yellow-stroma\x7CAreaShape_Zernike_8_4,yellow-stroma\x7CAreaShape_Zernike_8_6,yellow-stroma\x7CAreaShape_Zernike_8_8,yellow-stroma\x7CAreaShape_EulerNumber,yellow-stroma\x7CAreaShape_Compactness,yellow-stroma\x7CAreaShape_Extent,yellow-stroma\x7CAreaShape_Eccentricity,yellow-stroma\x7CAreaShape_MinorAxisLength,yellow-stroma\x7CAreaShape_MajorAxisLength,yellow-stroma\x7CAreaShape_Center_Y,yellow-stroma\x7CAreaShape_Center_X,green-epithelium\x7CAreaShape_Perimeter,green-epithelium\x7CAreaShape_FormFactor,green-epithelium\x7CAreaShape_Orientation,green-epithelium\x7CAreaShape_Area,green-epithelium\x7CAreaShape_Solidity,green-epithelium\x7CAreaShape_Zernike_1_1,green-epithelium\x7CAreaShape_Zernike_0_0,green-epithelium\x7CAreaShape_Zernike_3_1,green-epithelium\x7CAreaShape_Zernike_3_3,green-epithelium\x7CAreaShape_Zernike_2_0,green-epithelium\x7CAreaShape_Zernike_2_2,green-epithelium\x7CAreaShape_Zernike_5_1,green-epithelium\x7CAreaShape_Zernike_5_5,green-epithelium\x7CAreaShape_Zernike_5_3,green-epithelium\x7CAreaShape_Zernike_4_0,green-epithelium\x7CAreaShape_Zernike_4_2,green-epithelium\x7CAreaShape_Zernike_4_4,green-epithelium\x7CAreaShape_Zernike_7_1,green-epithelium\x7CAreaShape_Zernike_7_5,green-epithelium\x7CAreaShape_Zernike_7_3,green-epithelium\x7CAreaShape_Zernike_7_7,green-epithelium\x7CAreaShape_Zernike_6_0,green-epithelium\x7CAreaShape_Zernike_6_2,green-epithelium\x7CAreaShape_Zernike_6_4,green-epithelium\x7CAreaShape_Zernike_6_6,green-epithelium\x7CAreaShape_Zernike_9_1,green-epithelium\x7CAreaShape_Zernike_9_3,green-epithelium\x7CAreaShape_Zernike_9_5,green-epithelium\x7CAreaShape_Zernike_9_7,green-epithelium\x7CAreaShape_Zernike_9_9,green-epithelium\x7CAreaShape_Zernike_8_0,green-epithelium\x7CAreaShape_Zernike_8_2,green-epithelium\x7CAreaShape_Zernike_8_4,green-epithelium\x7CAreaShape_Zernike_8_6,green-epithelium\x7CAreaShape_Zernike_8_8,green-epithelium\x7CAreaShape_EulerNumber,green-epithelium\x7CAreaShape_Compactness,green-epithelium\x7CAreaShape_Extent,green-epithelium\x7CAreaShape_Eccentricity,green-epithelium\x7CAreaShape_MinorAxisLength,green-epithelium\x7CAreaShape_MajorAxisLength,green-epithelium\x7CAreaShape_Center_Y,green-epithelium\x7CAreaShape_Center_X,green-stroma\x7CAreaShape_Perimeter,green-stroma\x7CAreaShape_FormFactor,green-stroma\x7CAreaShape_Orientation,green-stroma\x7CAreaShape_Area,green-stroma\x7CAreaShape_Solidity,green-stroma\x7CAreaShape_Zernike_1_1,green-stroma\x7CAreaShape_Zernike_0_0,green-stroma\x7CAreaShape_Zernike_3_1,green-stroma\x7CAreaShape_Zernike_3_3,green-stroma\x7CAreaShape_Zernike_2_0,green-stroma\x7CAreaShape_Zernike_2_2,green-stroma\x7CAreaShape_Zernike_5_1,green-stroma\x7CAreaShape_Zernike_5_5,green-stroma\x7CAreaShape_Zernike_5_3,green-stroma\x7CAreaShape_Zernike_4_0,green-stroma\x7CAreaShape_Zernike_4_2,green-stroma\x7CAreaShape_Zernike_4_4,green-stroma\x7CAreaShape_Zernike_7_1,green-stroma\x7CAreaShape_Zernike_7_5,green-stroma\x7CAreaShape_Zernike_7_3,green-stroma\x7CAreaShape_Zernike_7_7,green-stroma\x7CAreaShape_Zernike_6_0,green-stroma\x7CAreaShape_Zernike_6_2,green-stroma\x7CAreaShape_Zernike_6_4,green-stroma\x7CAreaShape_Zernike_6_6,green-stroma\x7CAreaShape_Zernike_9_1,green-stroma\x7CAreaShape_Zernike_9_3,green-stroma\x7CAreaShape_Zernike_9_5,green-stroma\x7CAreaShape_Zernike_9_7,green-stroma\x7CAreaShape_Zernike_9_9,green-stroma\x7CAreaShape_Zernike_8_0,green-stroma\x7CAreaShape_Zernike_8_2,green-stroma\x7CAreaShape_Zernike_8_4,green-stroma\x7CAreaShape_Zernike_8_6,green-stroma\x7CAreaShape_Zernike_8_8,green-stroma\x7CAreaShape_EulerNumber,green-stroma\x7CAreaShape_Compactness,green-stroma\x7CAreaShape_Extent,green-stroma\x7CAreaShape_Eccentricity,green-stroma\x7CAreaShape_MinorAxisLength,green-stroma\x7CAreaShape_MajorAxisLength,green-stroma\x7CAreaShape_Center_Y,green-stroma\x7CAreaShape_Center_X
Data to export:Do not use
Combine these object measurements with those of the previous object?:No
File name:DATA.csv
Use the object name for the file name?:Yes