CellProfiler Pipeline: http://www.cellprofiler.org Version:1 SVNRevision:9978 LoadImages:[module_num:1|svn_version:\'9976\'|variable_revision_number:6|show_window:True|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:Do not use Analyze all subfolders within the selected folder?:No 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: Image count:1 Text that these images have in common (case-sensitive):tif Position of this image in each group:1 Extract metadata from where?:None Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ Channel count:1 Name this loaded image:OrigColor Channel number\x3A:1 ColorToGray:[module_num:2|svn_version:\'9559\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D] Select the input image:OrigColor Conversion method:Split Name the output image:OrigGray Relative weight of the red channel:1 Relative weight of the green channel:1 Relative weight of the blue channel:1 Convert red to gray?:Yes Name the output image:OrigRed Convert green to gray?:Yes Name the output image:OrigGreen Convert blue to gray?:Yes Name the output image:OrigBlue ImageMath:[module_num:3|svn_version:\'9898\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D] Operation:Invert 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 Name the output image:InvertedBlue Select the first image:OrigBlue Multiply the first image by:1 Select the second image: Multiply the second image by:1 IdentifyPrimaryObjects:[module_num:4|svn_version:\'9856\'|variable_revision_number:7|show_window:True|notes:\x5B\x5D] Select the input image:InvertedBlue Name the primary objects to be identified:Plate Typical diameter of objects, in pixel units (Min,Max):10,40 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:0.000000,1.000000 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?: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 ExpandOrShrinkObjects:[module_num:5|svn_version:\'9941\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D] Select the input objects:Plate Name the output objects:ShrunkenPlate Select the operation:Shrink objects by a specified number of pixels Number of pixels by which to expand or shrink:2 Fill holes in objects so that all objects shrink to a single point?:No Retain the outlines of the identified objects for use later in the pipeline (for example, in SaveImages)?:No Name the outline image:ShrunkenNucleiOutlines MaskImage:[module_num:6|svn_version:\'9448\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D] Select the input image:InvertedBlue Name the output image:MaskInvertBlue Use objects or an image as a mask?:Objects Select object for mask:ShrunkenPlate Select image for mask:None Invert the mask?:No IdentifyPrimaryObjects:[module_num:7|svn_version:\'9856\'|variable_revision_number:7|show_window:True|notes:\x5B\x5D] Select the input image:MaskInvertBlue Name the primary objects to be identified:BlueSpots Typical diameter of objects, in pixel units (Min,Max):5,40 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 PerObject Threshold correction factor:1 Lower and upper bounds on threshold:0.000000,1.000000 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:4 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?:No 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 ExpandOrShrinkObjects:[module_num:8|svn_version:\'9941\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D] Select the input objects:BlueSpots Name the output objects:ExpandedBlueSpots Select the operation:Expand objects by a specified number of pixels Number of pixels by which to expand or shrink:1 Fill holes in objects so that all objects shrink to a single point?:No Retain the outlines of the identified objects for use later in the pipeline (for example, in SaveImages)?:No Name the outline image:ShrunkenNucleiOutlines MaskImage:[module_num:9|svn_version:\'9448\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D] Select the input image:OrigBlue Name the output image:MaskBlue Use objects or an image as a mask?:Objects Select object for mask:ShrunkenPlate Select image for mask:None Invert the mask?:No MaskImage:[module_num:10|svn_version:\'9448\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D] Select the input image:MaskBlue Name the output image:MaskBlueSpots Use objects or an image as a mask?:Objects Select object for mask:ExpandedBlueSpots Select image for mask:None Invert the mask?:Yes EnhanceOrSuppressFeatures:[module_num:11|svn_version:\'9922\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D] Select the input image:MaskBlueSpots Name the output image:EnhancedBlue Select the operation:Enhance Feature size:15 Feature type:Speckles Range of hole sizes:1,10 IdentifyPrimaryObjects:[module_num:12|svn_version:\'9856\'|variable_revision_number:7|show_window:True|notes:\x5B\x5D] Select the input image:EnhancedBlue Name the primary objects to be identified:WhiteSpots Typical diameter of objects, in pixel units (Min,Max):5,40 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:Background PerObject Threshold correction factor:1 Lower and upper bounds on threshold:0.000000,1.000000 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:4 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?:No Automatically calculate size of smoothing filter?:No 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