Fractional Entropy Based Active Contour Segmentation of Cell Nuclei in Actin-Tagged Confocal Microscopy Images

In the framework of cell structure characterization for predictive oncology, we propose in this paper an unsupervised statistical region based active contour approach integrating an original fractional entropy measure for single channel actin tagged fluorescence confocal microscopy image segmentation. Following description of statistical based active contour segmentation and the mathematical definition of the proposed fractional entropy descriptor, we demonstrate comparative segmentation results between the proposed approach and standard Shannon’s entropy obtained for nuclei segmentation. We show that the unsupervised proposed statistical based approach integrating the fractional entropy measure leads to very satisfactory segmentation of the cell nuclei from which shape characterization can be subsequently used for the therapy progress assessment

Statistical Region-Based Active Contour Using Optimization of Alpha-Divergence Family For Image Segmentation

International audienceThis article deals with statistical region-based active contour segmentation using the alpha-divergence family as similarity measure between the density probability functions of the background and the object regions of interest. Following previous publications on that topic, main originality of this contribution is in the proposed joint optimization of the energy steering the evolution of the active curve and the parameter alpha related to the metric of the divergence and closely related to the statistical luminance distribution of the data. Experiments are shown on both synthetic noisy and textured data as well as on real images (natural and medical ones). We show that the joint optimization process leads to satisfying results for every targeted tasks: above all, it is shown that the proposed approach overcome classic statistical-based region active contour approach using Kullback-Leibler divergence as similarity measure, that can stuck in local extrema during the usual optimization process

Confocal microscopy segmentation using active contour based on alpha(α)-divergence

This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (PDFs) describing foreground and background regions. The most common techniques use χ2, Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated