CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:20140723174500 GitHash:6c2d896 ModuleCount:13 HasImagePlaneDetails:False Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] : Filter images?:Images only Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Extract metadata?:Yes Metadata data type:Text Metadata types:{} Extraction method count:1 Metadata extraction method:Extract from image file headers Metadata source:File name Regular expression:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ Extract metadata from:All images Select the filtering criteria:and (file does contain "") Metadata file location: Match file and image metadata:\x5B\x5D Use case insensitive matching?:No NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Assign a name to:All images Select the image type:Color image Name to assign these images:h2AXcells Match metadata:\x5B\x5D Image set matching method:Order Set intensity range from:Image metadata Assignments count:1 Single images count:0 Select the rule criteria:and (file does contain "") Name to assign these images:DNA Name to assign these objects:Cell Select the image type:Grayscale image Set intensity range from:Image metadata Retain outlines of loaded objects?:No Name the outline image:LoadedOutlines Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Do you want to group your images?:No grouping metadata count:1 Metadata category:None ColorToGray:[module_num:5|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:h2AXcells Conversion method:Split Image type:RGB Name the output image:OrigGray Relative weight of the red channel:1.0 Relative weight of the green channel:1.0 Relative weight of the blue channel:1.0 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 Convert hue to gray?:Yes Name the output image:OrigHue Convert saturation to gray?:Yes Name the output image:OrigSaturation Convert value to gray?:Yes Name the output image:OrigValue Channel count:1 Channel number:Red\x3A 1 Relative weight of the channel:1.0 Image name:Channel1 Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigBlue Name the output image:FilteredBlue Select smoothing method:Smooth Keeping Edges Calculate artifact diameter automatically?:Yes Typical artifact diameter:16.0 Edge intensity difference:0.1 Clip intensities to 0 and 1?:Yes IdentifyPrimaryObjects:[module_num:7|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:FilteredBlue Name the primary objects to be identified:Nuclei Typical diameter of objects, in pixel units (Min,Max):10,40000 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: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?: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:Global Thresholding method:MCT 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?:0.15 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 Smooth:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:OrigGreen Name the output image:SmoothedGreen Select smoothing method:Smooth Keeping Edges Calculate artifact diameter automatically?:Yes Typical artifact diameter:16.0 Edge intensity difference:0.1 Clip intensities to 0 and 1?:Yes EnhanceOrSuppressFeatures:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input image:SmoothedGreen Name the output image:SharpenedGreen Select the operation:Enhance Feature size:5 Feature type:Speckles Range of hole sizes:1,10 Smoothing scale:2.0 Shear angle:0.0 Decay:0.95 Enhancement method:Tubeness IdentifyPrimaryObjects:[module_num:10|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:SharpenedGreen Name the primary objects to be identified:speckles 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 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:7.0 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:Adaptive 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:0.0 Select the measurement to threshold with:None Select binary image:None Masking objects:Nuclei 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 RelateObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Select the input child objects:speckles Select the input parent objects:Nuclei Calculate child-parent distances?:None Calculate per-parent means for all child measurements?:No Calculate distances to other parents?:No Parent name:None ClassifyObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] Make each classification decision on how many measurements?:Single measurement Hidden:1 Select the object to be classified:Nuclei Select the measurement to classify by:Children_speckles_Count Select bin spacing:Custom-defined bins Number of bins:1 Lower threshold:0.0 Use a bin for objects below the threshold?:No Upper threshold:5 Use a bin for objects above the threshold?:Yes Enter the custom thresholds separating the values between bins:-1,5.1 Give each bin a name?:No Enter the bin names separated by commas:zerotofive, overfive Retain an image of the classified objects?:Yes Name the output image:ClassifiedNuclei Select 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 classified objects?:No Enter the image name:None ExportToSpreadsheet:[module_num:13|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?:Yes 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?:Yes Calculate the per-image median values for object measurements?:No Calculate the per-image standard deviation values for object measurements?:Yes 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?:Yes Press button to select measurements to export: Representation of Nan/Inf:NaN Add a prefix to file names?:Yes Filename prefix\x3A:MyExpt_ Overwrite without warning?:Yes 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