3D Confocal Microscopy data analysis using level-set segmentation with alpha-divergence similarity measure

Abstract : 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).
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Communication dans un congrès
International Conference on Computer Vision Theory and Applications, Feb 2012, Rome, Italy. pp.861-864, 2012
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Leila Meziou, Aymeric Histace, Frédéric Precioso, Bogdan Matuszewski, Franck Carreiras. 3D Confocal Microscopy data analysis using level-set segmentation with alpha-divergence similarity measure. International Conference on Computer Vision Theory and Applications, Feb 2012, Rome, Italy. pp.861-864, 2012. 〈hal-00656689〉

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