analysis_loaddata_2.1.1.-janssen.cppipe

CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20141021164117
GitHash:00c0426
ModuleCount:51
HasImagePlaneDetails:False

LoadData:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:6|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Input data file location:Default Input Folder\x7C
    Name of the file:filelist_analysis.csv
    Load images based on this data?:Yes
    Base image location:None\x7C
    Process just a range of rows?:No
    Rows to process:1,100000
    Group images by metadata?:No
    Select metadata tags for grouping:
    Rescale intensities?:Yes

MeasureImageQuality:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'This module is needed to collect the 2-class and 3-class thresholds needed for the next step.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Calculate metrics for which images?:Select...
    Image count:2
    Scale count:1
    Threshold count:2
    Scale count:1
    Threshold count:1
    Select the images to measure:OrigHoechst
    Include the image rescaling value?:No
    Calculate blur metrics?:No
    Spatial scale for blur measurements:20
    Calculate saturation metrics?:No
    Calculate intensity metrics?:No
    Calculate thresholds?:Yes
    Use all thresholding methods?:No
    Select a thresholding method:Otsu
    Typical fraction of the image covered by objects:0.1
    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?:Background
    Select a thresholding method:Otsu
    Typical fraction of the image covered by objects:0.1
    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 images to measure:OrigCellMask
    Include the image rescaling value?:No
    Calculate blur metrics?:No
    Spatial scale for blur measurements:20
    Calculate saturation metrics?:No
    Calculate intensity metrics?:No
    Calculate thresholds?:Yes
    Use all thresholding methods?:No
    Select a thresholding method:Otsu
    Typical fraction of the image covered by objects:0.1
    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

CalculateMath:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'An attempt to set a debris threshold by taking the sum of the Otsu 2-class and 3-class w/background thresholds. The idea is to dial up the threshold to a point where the detected objects are more likely to be separated since the brigher portions will be found. Using the 3-class w/background is an added boost since it is biased towards higher values in the presence of bright debris.This value will be used later.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:BrightThreshold
    Operation:Add
    Select the first operand measurement type:Image
    Select the first operand objects:None
    Select the first operand measurement:ImageQuality_ThresholdOtsu_OrigHoechst_2W
    Multiply the above operand by:1
    Raise the power of above operand by:1
    Select the second operand measurement type:Image
    Select the second operand objects:None
    Select the second operand measurement:ImageQuality_ThresholdOtsu_OrigHoechst_3BW
    Multiply the above operand by:1
    Raise the power of above operand by:1
    Take log10 of result?:No
    Multiply the result by:1
    Raise the power of result by:1
    Add to the result:0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1

IdentifyPrimaryObjects:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Threshold the image using the value just calculated. No declumping is needed since we want to keep the debris as a contiguous objetc. Then set the min size filter to a value larger than most nuclei.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:OrigHoechst
    Name the primary objects to be identified:BrightROI
    Typical diameter of objects, in pixel units (Min,Max):40,1000
    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
    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:DebrisOutlines
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Retain outlines of the identified objects?:No
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    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
    Threshold setting version:1
    Threshold strategy:Measurement
    Thresholding method:MCT
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1
    Threshold correction factor:1
    Lower and upper bounds on threshold:0.001,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:Math_BrightThreshold
    Select binary image:None
    Masking objects:From image
    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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

MeasureImageIntensity:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Measure the intensity of the bright ROI against the CellMask channel.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the image to measure:OrigCellMask
    Measure the intensity only from areas enclosed by objects?:Yes
    Select the input objects:BrightROI

FlagImage:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Flag the image as containing debris if (a) a bright ROI was found, and (b) the CellMask intensity underneath is low. Flag it as containing a cell clump if   (a) a bright ROI was found, and (b) the CellMask intensity underneath is high.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Hidden:2
    Hidden:2
    Name the flag\'s category:Metadata
    Name the flag:isDebris
    Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if all fail
    Skip image set if flagged?:No
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Intensity_LowerQuartileIntensity_OrigCellMask_BrightROI
    Flag images based on low values?:Yes
    Minimum value:0.2
    Flag images based on high values?:No
    Maximum value:0.5
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Count_BrightROI
    Flag images based on low values?:No
    Minimum value:0.0
    Flag images based on high values?:Yes
    Maximum value:0.0
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:
    Hidden:2
    Name the flag\'s category:Metadata
    Name the flag:isCellClump
    Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if all fail
    Skip image set if flagged?:No
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Intensity_LowerQuartileIntensity_OrigCellMask_BrightROI
    Flag images based on low values?:No
    Minimum value:0.5
    Flag images based on high values?:Yes
    Maximum value:0.2
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Count_BrightROI
    Flag images based on low values?:No
    Minimum value:0.0
    Flag images based on high values?:Yes
    Maximum value:0.0
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:

IdentifySecondaryObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B"Expand the bright ROI object to capture the large mass in the CellMask channel,if a cell clump. If it\'s debris, this won\'t expand much beyond the primary object. Lower bound on threshold is set based on noise level from empty wells."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Select the input objects:BrightROI
    Name the objects to be identified:ExpandedBrightROI
    Select the method to identify the secondary objects:Propagation
    Select the input image:OrigCellMask
    Number of pixels by which to expand the primary objects:10
    Regularization factor:0.05
    Name the outline image:BrightROIOutlines
    Retain outlines of the identified secondary objects?:Yes
    Discard secondary objects touching the border 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
    Fill holes in identified objects?:Yes
    Threshold setting version:1
    Threshold strategy:Global
    Thresholding method:Otsu
    Select the smoothing method for thresholding:No smoothing
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.002,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Mask the Hoechst image with the expanded bright regions.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:OrigHoechst
    Name the output image:MaskedHoechst
    Use objects or an image as a mask?:Objects
    Select object for mask:ExpandedBrightROI
    Select image for mask:None
    Invert the mask?:Yes

IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify nuclei from the masked Hoechst image. Lower bound on threshold is set based on noise level from empty wells.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:MaskedHoechst
    Name the primary objects to be identified:UnsegmentedNuclei
    Typical diameter of objects, in pixel units (Min,Max):10,100
    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
    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:7.0
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:PrimaryOutlines
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Retain outlines of the identified objects?:No
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5.0
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Threshold setting version:1
    Threshold strategy:Global
    Thresholding method:Otsu
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.001,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

ConvertObjectsToImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'Convert nuclei to a binary image of the foreground.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input objects:UnsegmentedNuclei
    Name the output image:Foreground
    Select the color format:Binary (black & white)
    Select the colormap:Default

Morph:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert the pixel intensities of the foreground image, and take the distance transform (DT). This new image labels each pixel of the image with the distance to the nearest nucleus. The idea is that scratches/well edges will appear as contiguous regions of unusually large distances.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Foreground
    Name the output image:DistanceTransform
    Select the operation to perform:invert
    Number of times to repeat operation:Once
    Repetition number:2
    Scale:3.0
    Structuring element:Disk
    X offset:1.0
    Y offset:1.0
    Angle:0.0
    Width:3.0
    Height:3.0
    Custom:5,5,1111111111111111111111111
    Rescale values from 0 to 1?:Yes
    Select the operation to perform:distance
    Number of times to repeat operation:Once
    Repetition number:2
    Scale:3.0
    Structuring element:Disk
    X offset:1.0
    Y offset:1.0
    Angle:0.0
    Width:3.0
    Height:3.0
    Custom:5,5,1111111111111111111111111
    Rescale values from 0 to 1?:No

MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Mask the DT so that the 0-labeled nuclei will not be considered in measurements from this image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:DistanceTransform
    Name the output image:MaskedDistanceTransform
    Use objects or an image as a mask?:Objects
    Select object for mask:UnsegmentedNuclei
    Select image for mask:None
    Invert the mask?:Yes

MeasureImageIntensity:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Measure the intensity (i.e, distance) statistics from the DT image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the image to measure:MaskedDistanceTransform
    Measure the intensity only from areas enclosed by objects?:No
    Select the input objects:None

CalculateMath:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B"What is an unsually large distance to indicate a scratch/well edge? I\'ve chosen to mark outliers based on the upper fence of the DT distribution. First, calculate the interquartile range (IQR)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:DistanceTransformIQR
    Operation:Subtract
    Select the minuend measurement type:Image
    Select the minuend objects:None
    Select the minuend measurement:Intensity_UpperQuartileIntensity_MaskedDistanceTransform
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Select the subtrahend measurement type:Image
    Select the subtrahend objects:None
    Select the subtrahend measurement:Intensity_LowerQuartileIntensity_MaskedDistanceTransform
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Take log10 of result?:No
    Multiply the result by:1.0
    Raise the power of result by:1.0
    Add to the result:0.0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0.0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1.0

CalculateMath:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B"The upper inner fence is officially Q3 + 1.5*IQR. I\'ve chosen 0.75*IQR since it produces better results."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:UpperInnerFence
    Operation:Add
    Select the first operand measurement type:Image
    Select the first operand objects:None
    Select the first operand measurement:Intensity_UpperQuartileIntensity_MaskedDistanceTransform
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Select the second operand measurement type:Image
    Select the second operand objects:None
    Select the second operand measurement:Math_DistanceTransformIQR
    Multiply the above operand by:0.75
    Raise the power of above operand by:1.0
    Take log10 of result?:No
    Multiply the result by:1.0
    Raise the power of result by:1.0
    Add to the result:0.0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0.0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1.0

ImageMath:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'In CellProfiler, any thresholding is performed only on the \x5B0,1\x5D range, so I need to workaround it in order to threshold the DT. First, subtract the DT cutoff from the DT pixel values, so that the cutoff distance is set to 0.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Operation:Subtract
    Raise the power of the result by:1.0
    Multiply the result by:1.0
    Add to result:0.0
    Set values less than 0 equal to 0?:No
    Set values greater than 1 equal to 1?:Yes
    Ignore the image masks?:No
    Name the output image:DistanceCutoff
    Image or measurement?:Image
    Select the first image:DistanceTransform
    Multiply the first image by:1.0
    Measurement:
    Image or measurement?:Measurement
    Select the second image:
    Multiply the second image by:1.0
    Measurement:Math_UpperInnerFence

ApplyThreshold:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'From the cutoff image, threshold manually with a value of 0. Regions above 0 are candidates for scratches or well edges.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:DistanceCutoff
    Name the output image:BinaryDistanceCutoff
    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
    Threshold setting version:1
    Threshold strategy:Manual
    Thresholding method:Otsu
    Select the smoothing method for thresholding:No smoothing
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

Morph:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B"Skeletonize the binary cutoff image. Using \'skelPE\' has a few less spurs than the \'skel\' operation alone, but a few still remain. Spur the line segments to reduce them. I\'ve chosen 25 as the repetition number since that seems to be the average half-distance between nuclei."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:BinaryDistanceCutoff
    Name the output image:DistanceTransformSkeleton
    Select the operation to perform:skelpe
    Number of times to repeat operation:Once
    Repetition number:2
    Scale:100
    Structuring element:Disk
    X offset:1.0
    Y offset:1.0
    Angle:0.0
    Width:3.0
    Height:3.0
    Custom:5,5,1111111111111111111111111
    Rescale values from 0 to 1?:Yes
    Select the operation to perform:spur
    Number of times to repeat operation:Custom
    Repetition number:25
    Scale:3.0
    Structuring element:Disk
    X offset:1.0
    Y offset:1.0
    Angle:0.0
    Width:3.0
    Height:3.0
    Custom:5,5,1111111111111111111111111
    Rescale values from 0 to 1?:Yes

IdentifyPrimaryObjects:[module_num:19|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify all skeletons from the binary image as objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:DistanceTransformSkeleton
    Name the primary objects to be identified:AllSkeletons
    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
    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:7.0
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:AllSkeletonOutlines
    Fill holes in identified objects?:Never
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Retain outlines of the identified objects?:Yes
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5.0
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Threshold setting version:1
    Threshold strategy:Manual
    Thresholding method:Otsu
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

MeasureObjectSizeShape:[module_num:20|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'Measure the morphological features of the skeleton objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select objects to measure:AllSkeletons
    Calculate the Zernike features?:No

ImageMath:[module_num:21|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B"Since I need to define the scratch region for exclusion, I found that simply using the upper inner fence doesn\'t work well for well edges, especially when they are large. Since I set the distance cutoff to 25 pixels for any region defined as a scratch."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Operation:None
    Raise the power of the result by:1.0
    Multiply the result by:1.0
    Add to result:-25
    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:DistanceManualCutoff
    Image or measurement?:Image
    Select the first image:DistanceTransform
    Multiply the first image by:1.0
    Measurement:
    Image or measurement?:Image
    Select the second image:
    Multiply the second image by:1.0
    Measurement:

IdentifySecondaryObjects:[module_num:22|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\'Define the scratch/well edge region as an object based on the skeletons.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input objects:AllSkeletons
    Name the objects to be identified:AllScratches
    Select the method to identify the secondary objects:Propagation
    Select the input image:DistanceManualCutoff
    Number of pixels by which to expand the primary objects:10
    Regularization factor:0.05
    Name the outline image:ScratchOutlines
    Retain outlines of the identified secondary objects?:No
    Discard secondary objects touching the border 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
    Fill holes in identified objects?:No
    Threshold setting version:1
    Threshold strategy:Manual
    Thresholding method:Otsu
    Select the smoothing method for thresholding:No smoothing
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.5
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

FilterObjects:[module_num:23|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'Filter the skeletons based on the two measurements and cutoffs which I determined using CellProfiler Analyst. The scratch region assocaited with any filtered skeletons are also retained.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output objects:FilteredSkeletons
    Select the object to filter:AllSkeletons
    Select the filtering mode: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:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:1
    Measurement count:2
    Additional object count:1
    Assign overlapping child to:Both parents
    Select the measurement to filter by:AreaShape_Compactness
    Filter using a minimum measurement value?:Yes
    Minimum value:220
    Filter using a maximum measurement value?:No
    Maximum value:1.0
    Select the measurement to filter by:AreaShape_MaxFeretDiameter
    Filter using a minimum measurement value?:Yes
    Minimum value:710
    Filter using a maximum measurement value?:No
    Maximum value:0.3
    Select additional object to relabel:AllScratches
    Name the relabeled objects:Scratches
    Retain outlines of relabeled objects?:Yes
    Name the outline image:ScratchOutlines

MeasureImageAreaOccupied:[module_num:24|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure the image area occupied by the scratch region.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Hidden:1
    Measure the area occupied in a binary image, or in objects?:Objects
    Select objects to measure:Scratches
    Retain a binary image of the object regions?:No
    Name the output binary image:Stain
    Select a binary image to measure:ScratchThreshDist

CalculateMath:[module_num:25|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The image area that counts as covered is the total image minus the scratch/well edge area, if any.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:CoveredArea
    Operation:Subtract
    Select the minuend measurement type:Image
    Select the minuend objects:None
    Select the minuend measurement:AreaOccupied_TotalArea_Scratches
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Select the subtrahend measurement type:Image
    Select the subtrahend objects:None
    Select the subtrahend measurement:AreaOccupied_AreaOccupied_Scratches
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Take log10 of result?:No
    Multiply the result by:1.0
    Raise the power of result by:1.0
    Add to the result:0.0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0.0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1.0

FlagImage:[module_num:26|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Flag the image if there are any skeletons that survived the filtering.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Hidden:1
    Hidden:1
    Name the flag\'s category:Metadata
    Name the flag:isScratchOrWell
    Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
    Skip image set if flagged?:No
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Count_FilteredSkeletons
    Flag images based on low values?:No
    Minimum value:1
    Flag images based on high values?:Yes
    Maximum value:0
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:

MaskImage:[module_num:27|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Mask the Hoechst image with the nuclei objects to create a foreground image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:OrigHoechst
    Name the output image:ForegroundHoechst
    Use objects or an image as a mask?:Objects
    Select object for mask:UnsegmentedNuclei
    Select image for mask:None
    Invert the mask?:No

MaskImage:[module_num:28|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Mask the Hoechst image with the inverted nuclei objects to create a background image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:OrigHoechst
    Name the output image:BackgroundHoechst
    Use objects or an image as a mask?:Objects
    Select object for mask:UnsegmentedNuclei
    Select image for mask:None
    Invert the mask?:Yes

MeasureImageIntensity:[module_num:29|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Measure the intensity statistics from the foreground and background images.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True]
    Select the image to measure:ForegroundHoechst
    Measure the intensity only from areas enclosed by objects?:No
    Select the input objects:Nuclei
    Select the image to measure:BackgroundHoechst
    Measure the intensity only from areas enclosed by objects?:No
    Select the input objects:None

CalculateMath:[module_num:30|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Now we calculate the image signal-to-nose ratio (SNR). Rather than using the standard (mean foreground)/(variance background), I use the more-robust median/IQR. First, calculate the IQR of the background pixels.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:BackgroundHoechstIQR
    Operation:Subtract
    Select the minuend measurement type:Image
    Select the minuend objects:None
    Select the minuend measurement:Intensity_UpperQuartileIntensity_BackgroundHoechst
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Select the subtrahend measurement type:Image
    Select the subtrahend objects:None
    Select the subtrahend measurement:Intensity_LowerQuartileIntensity_BackgroundHoechst
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Take log10 of result?:No
    Multiply the result by:1.0
    Raise the power of result by:1.0
    Add to the result:0.0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0.0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1.0

CalculateMath:[module_num:31|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Calculate the SNR of the Hoechst image as the (median foreground)/(IQR background)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Name the output measurement:RobustHoechstSNR
    Operation:Divide
    Select the numerator measurement type:Image
    Select the numerator objects:None
    Select the numerator measurement:Intensity_MedianIntensity_ForegroundHoechst
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Select the denominator measurement type:Image
    Select the denominator objects:None
    Select the denominator measurement:Math_BackgroundHoechstIQR
    Multiply the above operand by:1.0
    Raise the power of above operand by:1.0
    Take log10 of result?:No
    Multiply the result by:1.0
    Raise the power of result by:1.0
    Add to the result:0.0
    Constrain the result to a lower bound?:No
    Enter the lower bound:0.0
    Constrain the result to an upper bound?:No
    Enter the upper bound:1.0

FlagImage:[module_num:32|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B"Based on the SNR distribution, I\'ve set the cutoff as 30."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Hidden:1
    Hidden:1
    Name the flag\'s category:Metadata
    Name the flag:isLowIntensity
    Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
    Skip image set if flagged?:No
    Flag is based on:Whole-image measurement
    Select the object to be used for flagging:None
    Which measurement?:Math_RobustHoechstSNR
    Flag images based on low values?:Yes
    Minimum value:30
    Flag images based on high values?:No
    Maximum value:1.0
    Rules file location:Elsewhere...\x7C
    Rules file name:rules.txt
    Class number:

CorrectIlluminationApply:[module_num:33|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Select the input image:OrigHoechst
    Name the output image:Hoechst
    Select the illumination function:IllumHoechst
    Select how the illumination function is applied:Divide
    Select the input image:OrigAlexa568
    Name the output image:Alexa568
    Select the illumination function:IllumAlexa568
    Select how the illumination function is applied:Divide
    Select the input image:OrigCellMask
    Name the output image:CellMask
    Select the illumination function:IllumCellMask
    Select how the illumination function is applied:Divide

MaskImage:[module_num:34|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:Hoechst
    Name the output image:MaskedCorrectedHoechst
    Use objects or an image as a mask?:Objects
    Select object for mask:ExpandedBrightROI
    Select image for mask:None
    Invert the mask?:Yes

IdentifyPrimaryObjects:[module_num:35|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:MaskedCorrectedHoechst
    Name the primary objects to be identified:AllNuclei
    Typical diameter of objects, in pixel units (Min,Max):10,100
    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
    Method to distinguish clumped objects:Shape
    Method to draw dividing lines between clumped objects:Shape
    Size of smoothing filter:25
    Suppress local maxima that are closer than this minimum allowed distance:30
    Speed up by using lower-resolution image to find local maxima?:Yes
    Name the outline image:PrimaryOutlines
    Fill holes in identified objects?:After both thresholding and declumping
    Automatically calculate size of smoothing filter for declumping?:Yes
    Automatically calculate minimum allowed distance between local maxima?:Yes
    Retain outlines of the identified objects?:No
    Automatically calculate the threshold using the Otsu method?:Yes
    Enter Laplacian of Gaussian threshold:0.5
    Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
    Enter LoG filter diameter:5.0
    Handling of objects if excessive number of objects identified:Continue
    Maximum number of objects:500
    Threshold setting version:1
    Threshold strategy:Global
    Thresholding method:Otsu
    Select the smoothing method for thresholding:Automatic
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.001,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

MaskImage:[module_num:36|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input image:CellMask
    Name the output image:MaskedCorrectedCellMask
    Use objects or an image as a mask?:Objects
    Select object for mask:ExpandedBrightROI
    Select image for mask:None
    Invert the mask?:Yes

IdentifySecondaryObjects:[module_num:37|svn_version:\'Unknown\'|variable_revision_number:9|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the input objects:AllNuclei
    Name the objects to be identified:Cells
    Select the method to identify the secondary objects:Watershed - Image
    Select the input image:MaskedCorrectedCellMask
    Number of pixels by which to expand the primary objects:10
    Regularization factor:0.05
    Name the outline image:CellOutlines
    Retain outlines of the identified secondary objects?:Yes
    Discard secondary objects touching the border of the image?:Yes
    Discard the associated primary objects?:Yes
    Name the new primary objects:Nuclei
    Retain outlines of the new primary objects?:Yes
    Name the new primary object outlines:NucleiOutlines
    Fill holes in identified objects?:Yes
    Threshold setting version:1
    Threshold strategy:Global
    Thresholding method:Otsu
    Select the smoothing method for thresholding:No smoothing
    Threshold smoothing scale:1.0
    Threshold correction factor:1.0
    Lower and upper bounds on threshold:0.0006,1.0
    Approximate fraction of image covered by objects?:0.01
    Manual threshold:0.0
    Select the measurement to threshold with:None
    Select binary image:None
    Masking 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?:Foreground
    Method to calculate adaptive window size:Image size
    Size of adaptive window:10

IdentifyTertiaryObjects:[module_num:38|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:True]
    Select the larger identified objects:Cells
    Select the smaller identified objects:Nuclei
    Name the tertiary objects to be identified:Cytoplasm
    Name the outline image:CytoplasmOutlines
    Retain outlines of the tertiary objects?:No
    Shrink smaller object prior to subtraction?:Yes

MeasureCorrelation:[module_num:39|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Hidden:4
    Hidden:3
    Select an image to measure:Hoechst
    Select an image to measure:Alexa568
    Select an image to measure:CellMask
    Select an image to measure:Alexa568
    Select where to measure correlation:Within objects
    Select an object to measure:Nuclei
    Select an object to measure:Cells
    Select an object to measure:Cytoplasm

MeasureObjectIntensity:[module_num:40|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Hidden:3
    Select an image to measure:Hoechst
    Select an image to measure:Alexa568
    Select an image to measure:CellMask
    Select objects to measure:Nuclei
    Select objects to measure:Cytoplasm
    Select objects to measure:Cells

MeasureObjectNeighbors:[module_num:41|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Select objects to measure:Cells
    Select neighboring objects to measure:Cells
    Method to determine neighbors:Within a specified distance
    Neighbor distance:5
    Retain the image of objects colored by numbers of neighbors?:No
    Name the output image:ObjectNeighborCount
    Select colormap:Default
    Retain the image of objects colored by percent of touching pixels?:No
    Name the output image:PercentTouching
    Select a colormap:Default

MeasureObjectNeighbors:[module_num:42|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Select objects to measure:Nuclei
    Select neighboring objects to measure:Nuclei
    Method to determine neighbors:Within a specified distance
    Neighbor distance:1
    Retain the image of objects colored by numbers of neighbors?:No
    Name the output image:ObjectNeighborCount
    Select colormap:Default
    Retain the image of objects colored by percent of touching pixels?:No
    Name the output image:PercentTouching
    Select a colormap:Default

MeasureObjectNeighbors:[module_num:43|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Select objects to measure:Cells
    Select neighboring objects to measure:Cells
    Method to determine neighbors:Adjacent
    Neighbor distance:5
    Retain the image of objects colored by numbers of neighbors?:No
    Name the output image:ObjectNeighborCount
    Select colormap:Default
    Retain the image of objects colored by percent of touching pixels?:No
    Name the output image:PercentTouching
    Select a colormap:Default

MeasureObjectRadialDistribution:[module_num:44|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Hidden:3
    Hidden:3
    Hidden:1
    Select an image to measure:Hoechst
    Select an image to measure:CellMask
    Select an image to measure:Alexa568
    Select objects to measure:Cells
    Object to use as center?:These objects
    Select objects to use as centers:None
    Select objects to measure:Nuclei
    Object to use as center?:These objects
    Select objects to use as centers:None
    Select objects to measure:Cytoplasm
    Object to use as center?:These objects
    Select objects to use as centers:None
    Scale the bins?:Yes
    Number of bins:4
    Maximum radius:100

MeasureObjectSizeShape:[module_num:45|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Select objects to measure:Cells
    Select objects to measure:Nuclei
    Select objects to measure:Cytoplasm
    Calculate the Zernike features?:Yes

MeasureTexture:[module_num:46|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\'\', \n      dtype=\'\x7CS1\')|enabled:True|wants_pause:False]
    Hidden:3
    Hidden:3
    Hidden:3
    Select an image to measure:Hoechst
    Select an image to measure:CellMask
    Select an image to measure:Alexa568
    Select objects to measure:Cells
    Select objects to measure:Cytoplasm
    Select objects to measure:Nuclei
    Texture scale to measure:3
    Angles to measure:Horizontal
    Texture scale to measure:5
    Angles to measure:Horizontal
    Texture scale to measure:10
    Angles to measure:Horizontal
    Measure Gabor features?:Yes
    Number of angles to compute for Gabor:4
    Measure images or objects?:Objects

SaveImages:[module_num:47|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save an outline image of the expanded bright ROI outlines, organized by plate barcode.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:BrightROIOutlines
    Select the objects to save:None
    Select the module display window to save:None
    Select method for constructing file names:Single name
    Select image name for file prefix:OrigHoechst
    Enter single file name:\\\\g<Well>_s\\\\g<Site>_BrightROI
    Number of digits:4
    Append a suffix to the image file name?:No
    Text to append to the image name:
    Saved file format:png
    Output file location:Default Output Folder sub-folder\x7Coutlines/\\\\g<Barcode>
    Image bit depth:8
    Overwrite existing files without warning?:Yes
    When to save:Every cycle
    Rescale the images? :No
    Save as grayscale or color image?:Grayscale
    Select colormap:gray
    Record the file and path information to the saved image?:Yes
    Create subfolders in the output folder?:No
    Base image folder:Elsewhere...\x7C
    Saved movie format:avi

SaveImages:[module_num:48|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save an outline image of the scratch/well edge outlines, organized by plate barcode.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:ScratchOutlines
    Select the objects to save:None
    Select the module display window to save:None
    Select method for constructing file names:Single name
    Select image name for file prefix:OrigHoechst
    Enter single file name:\\\\g<Well>_s\\\\g<Site>_Scratch
    Number of digits:4
    Append a suffix to the image file name?:No
    Text to append to the image name:
    Saved file format:png
    Output file location:Default Output Folder sub-folder\x7Coutlines/\\\\g<Barcode>
    Image bit depth:8
    Overwrite existing files without warning?:Yes
    When to save:Every cycle
    Rescale the images? :No
    Save as grayscale or color image?:Grayscale
    Select colormap:gray
    Record the file and path information to the saved image?:Yes
    Create subfolders in the output folder?:No
    Base image folder:Elsewhere...\x7C
    Saved movie format:avi

SaveImages:[module_num:49|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:NucleiOutlines
    Select the objects to save:None
    Select the module display window to save:None
    Select method for constructing file names:Single name
    Select image name for file prefix:OrigHoechst
    Enter single file name:\\\\g<Well>_s\\\\g<Site>_NucleiOutlines
    Number of digits:4
    Append a suffix to the image file name?:Yes
    Text to append to the image name:_nuclei
    Saved file format:png
    Output file location:Default Output Folder sub-folder\x7Coutlines/\\\\g<Barcode>
    Image bit depth:8
    Overwrite existing files without warning?:Yes
    When to save:Every cycle
    Rescale the images? :No
    Save as grayscale or color image?:Grayscale
    Select colormap:gray
    Record the file and path information to the saved image?:Yes
    Create subfolders in the output folder?:No
    Base image folder:Default Input Folder
    Saved movie format:avi

SaveImages:[module_num:50|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the type of image to save:Image
    Select the image to save:CellOutlines
    Select the objects to save:None
    Select the module display window to save:None
    Select method for constructing file names:Single name
    Select image name for file prefix:OrigHoechst
    Enter single file name:\\\\g<Well>_s\\\\g<Site>_CellOutlines
    Number of digits:4
    Append a suffix to the image file name?:Yes
    Text to append to the image name:_nuclei
    Saved file format:png
    Output file location:Default Output Folder sub-folder\x7Coutlines/\\\\g<Barcode>
    Image bit depth:8
    Overwrite existing files without warning?:Yes
    When to save:Every cycle
    Rescale the images? :No
    Save as grayscale or color image?:Grayscale
    Select colormap:gray
    Record the file and path information to the saved image?:Yes
    Create subfolders in the output folder?:No
    Base image folder:Default Input Folder
    Saved movie format:avi

ExportToSpreadsheet:[module_num:51|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
    Select the column delimiter:Comma (",")
    Add image metadata columns to your object data file?:No
    Limit output to a size that is allowed in Excel?:No
    Select the measurements to export:No
    Calculate the per-image mean values for object measurements?:No
    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\x7C
    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 measurement types?:No
    Press button to select measurements to export:
    Representation of Nan/Inf:NaN
    Add a prefix to file names?:No
    Filename prefix\x3A:MyExpt_
    Overwrite without warning?:No
    Data to export:Image
    Combine these object measurements with those of the previous object?:No
    File name:image.csv
    Use the object name for the file name?:No
    Data to export:Cells
    Combine these object measurements with those of the previous object?:No
    File name:object.csv
    Use the object name for the file name?:No
    Data to export:Cytoplasm
    Combine these object measurements with those of the previous object?:Yes
    File name:DATA.csv
    Use the object name for the file name?:Yes
    Data to export:Nuclei
    Combine these object measurements with those of the previous object?:Yes
    File name:DATA.csv
    Use the object name for the file name?:Yes