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Segmentation of cellular structures is of primary interest in cell imaging for a 3D reconstruction of cell shape. Such an analysis provides crucial information about cell morphology and is instrumental in understanding of biological processes leading to development of a particular pathology. The work presented in this paper reports on a novel method for segmentation of cellular structures (nuclei and cell boundaries) from 3D single channel actin tagged fluorescence confocal microscopy images. The proposed segmentation method uses histogram-based image similarity measure in a level-set active-contour framework. The novelty of the method is in application of the alpha-divergence distance measure which can be seen as a generalization of classic Kullback-Leibler and Chi 2 measures. The resulting alpha-divergence level-set formulation leads to a single front evolution formula for both nuclei and cell boundaries segmentation, with no requirements for any enhancement or preprocessing of acquired cell images (a monolayer of human cells (PNT2) culture).

3D CONFOCAL MICROSCOPY DATA ANALYSIS USING LEVEL-SET SEGMENTATION WITH ALPHA DIVERGENCE SIMILARITY MEASURE