Morphological-based adaptive segmentation and quantification of cell assays in high content screening

In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloidin) on nanodrops cell-on-chip format.