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110209CountGreenAnRedCellsWithMask.cp
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CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10415
LoadImages:[module_num:1|svn_version:\'10372\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
File type to be loaded:individual images
File selection method:Text-Regular expressions
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?:Yes
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:3
Text that these images have in common (case-sensitive):\\w*0000.tif
Position of this image in each group:1
Extract metadata from where?:None
Regular expression that finds metadata in the file name:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:2
Name this loaded image:Hoechst
Channel number:1
Text that these images have in common (case-sensitive):\\w*0001.tif
Position of this image in each group:2
Extract metadata from where?:None
Regular expression that finds metadata in the file name:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:2
Name this loaded image:GFP
Channel number:1
Text that these images have in common (case-sensitive):\\w*0002.tif
Position of this image in each group:3
Extract metadata from where?:None
Regular expression that finds metadata in the file name:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:3
Name this loaded image:PI
Channel number:1
RunImageJ:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Command or macro?:Macro
Command\x3A:None
Macro\x3A:run("Select All");\nrun("Fit Ellipse");\nrun("Create Mask");
Options\x3A:
Set the current image?:Yes
Current image\x3A:GFP
Get the current image?:Yes
Final image\x3A:ImageMask
Wait for ImageJ?:No
Run before each group?:Nothing
Command\x3A:None
Macro\x3A:run("Invert");
Options\x3A:
Run after each group?:Nothing
Command\x3A:None
Macro\x3A:run("Invert");
Options\x3A:
Save the selected image?:No
Image name\x3A:ImageJGroupImage
MaskImage:[module_num:3|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:Hoechst
Name the output image:MaskBlue
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:ImageMask
Invert the mask?:Yes
MaskImage:[module_num:4|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:GFP
Name the output image:MaskGreen
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:ImageMask
Invert the mask?:Yes
MaskImage:[module_num:5|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input image:PI
Name the output image:MaskRed
Use objects or an image as a mask?:Image
Select object for mask:None
Select image for mask:ImageMask
Invert the mask?:Yes
IdentifyPrimaryObjects:[module_num:6|svn_version:\'10372\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the input image:MaskBlue
Name the primary objects to be identified:Nuclei
Typical diameter of objects, in pixel units (Min,Max):10,45
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:0.000000,1.000000
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: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
IdentifySecondaryObjects:[module_num:7|svn_version:\'10300\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D]
Select the input objects:Nuclei
Name the objects to be identified:Cells
Select the method to identify the secondary objects:Distance - N
Select the input image:MaskGreen
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
Number of pixels by which to expand the primary objects:20
Regularization factor:0.05
Name the outline image:SecondaryOutlines
Manual threshold:0.0
Select binary image:None
Retain outlines of the identified secondary objects?:No
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
Discard secondary objects that touch the edge of the image?:No
Discard the associated primary objects?:No
Name the new primary objects:FilteredNuclei
Retain outlines of the new primary objects?:No
Name the new primary object outlines:FilteredNucleiOutlines
Select the measurement to threshold with:None
Fill holes in identified objects?:No
MeasureObjectIntensity:[module_num:8|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:MaskGreen
Select objects to measure:Cells
MeasureObjectIntensity:[module_num:9|svn_version:\'10300\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Hidden:1
Select an image to measure:MaskRed
Select objects to measure:Cells
FilterObjects:[module_num:10|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:InfectedCells
Select the object to filter:Cells
Filter using classifier rules or measurements?:Measurements
Select the filtering method:Limits
Select the objects that contain the filtered objects:Cells
Retain outlines of the identified objects?:No
Name the outline image:FilteredObjects
Rules file location:Default Input Folder\x7C.
Rules file name:rules.txt
Measurement count:1
Additional object count:0
Select the measurement to filter by:Intensity_MeanIntensity_MaskGreen
Filter using a minimum measurement value?:Yes
Minimum value:0.0025
Filter using a maximum measurement value?:No
Maximum value:1
FilterObjects:[module_num:11|svn_version:\'10300\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D]
Name the output objects:DeadCells
Select the object to filter:Cells
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:Intensity_MeanIntensity_MaskRed
Filter using a minimum measurement value?:Yes
Minimum value:0.003
Filter using a maximum measurement value?:No
Maximum value: