Let me clarify a bit the working context. The microscopic slides contains nasal mucosa cytotypes (essentially: ciliated, muciparous, neutrophils, eosinophils, lymphocitye, mast cells). I have two datasets of microscopic slides images, one dataset contains images of slides processed with direct smearing (SM) and mgg staining and the other one processed with cytocentrifugation (CYT) and mgg staining. My task is to visually compare (objectively) the similarity of these the two dataset from multiple visual features (spatial distribution, chromaticity, texture, etc…). What’s the purpose? These images are processed automatically by python code that extract cells from the images and using a CNN , classify them into the cytotypes already cited. So measuring the spatial distribution I can assess the performance of the extraction process for the SM and CYT dataset.
From a quick subjective analysis, I noticed that:
The number of cells cointained in the CYT images are much lower than SM images.
The dispersion in CYT images are much higher than SM images, ie the distance between cells is higher.
For (1) I’ve already found and computed a measure (directly on pixels, without any segmentation) that seems pretty robust. For (2) the measure is the agglomeration measure that we’re are talking about that from a conceptual point of view seems more fuzzy and complex to define.
Now for answer your question, as you said, I think that’s true, non circular cells have more impact due to the equivalent disk approximation. I don’t understand the second part of your answer, the segmentation step is executed before the “disk approximation” step, so “I suspect this method will make segmentation errors where watershed fails to separate two cells much more heavily weighted, due to the size of the circle created.” seems wrong to me or maybe I don’t understand