I looked at your images, and attached is a pipeline that you can try and get started with. The biggest issue is “What do you judge is a pigmented/non-pigmented area that you call a node in the cyclic graph?”. This judgement is hard and best done by you, the expert.
So there are two approaches I suggest.
(1) Standard image analysis pipeline, as you have done already. To run the pipeline, use File > Import Pipeline. Then you need to drag/drop your images
NOTE:the Metadata module expects the folder name to be the “Condition” like “Atypical”.
ALSO NOTE: Many modules have comments in the Module Notes at the top that I wrote, so please refer to my thoughts there.
The first module (ClassifyPixels) is disabled (click on the green checkmark to enable/disable. This is used in the Approach #2 below.
(2) Use ilastik, a pixel-based machine learning classifier, that is included in the CellProfiler download, as a ‘seed’ for CellProfiler. The benefit is that the classifier would hopefully learn your pixel regions as you see them intuitively (a tough image analysis problem!), and might help to be robust against lighting and local intensity variations in background, etc. I zipped my attempt at an ilastik classifier (.ilp project file) as well as its exported file which gets imported into CellProfiler.
You would need to run ilastik and train the pixel classes, as shown in this screenshot: cl.ly/image/1g2k0p2K1w19 Then you would import use the ClassifyPixels module in CellProfiler (just enable it in the attached pipeline). Then you could add IdentifyPrimaryObjects to identify the objects on the classified images.
Hope that helps!
ilastik_files.zip (4.51 MB)
DL_melanoma_pipe_ilastik.cppipe (11.7 KB)