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