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er_measure.cp
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er_measure.cp
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CellProfiler Pipeline: http://www.cellprofiler.org
Version:2
DateRevision:20130401200712
LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
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?:None
Input image file location:Default Input Folder\x7CNone
Check image sets for unmatched or duplicate files?:Yes
Group images by metadata?:No
Exclude certain files?:No
Specify metadata fields to group by:
Select subfolders to analyze:
Image count:1
Text that these images have in common (case-sensitive):er_mask.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:3
Load the input as images or objects?:Images
Name this loaded image:img_er_mask
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:LoadedImageOutlines
Channel number:1
Rescale intensities?:No
IdentifyPrimaryObjects:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:img_er_mask
Name the primary objects to be identified:obj_er
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:Manual
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:None
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?:No
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?:No
Manual threshold:0.1
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
Method to calculate adaptive window size:Image size
Size of adaptive window:10
ApplyThreshold:[module_num:3|svn_version:\'6746\'|variable_revision_number:6|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:img_er_mask
Name the output image:img_er_mask_bin
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:Manual
Manual threshold:0.1
Lower and upper bounds on threshold:0.000000,1.000000
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
Morph:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the input image:img_er_mask_bin
Name the output image:img_er_mask_bin_skel
Select the operation to perform:skel
Number of times to repeat operation:Once
Repetition number:2
Scale:3
MeasureNeurons:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Select the seed objects:obj_er
Select the skeletonized image:img_er_mask_bin_skel
Retain the branchpoint image?:No
Name the branchpoint image:BranchpointImage
Retain the branchpoint image?:No
Maximum hole size\x3A:10
Do you want the neuron graph relationship?:No
Intensity image\x3A:None
File output directory\x3A:Default Output Folder\x7CNone
Vertex file name\x3A :vertices.csv
Edge file name\x3A:edges.csv