Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Elements of information theory
Elements of information theory
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
International Journal of Computer Vision
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
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In this work, we propose novel results for the optimization of divergences within the framework of region-based active contours. We focus on parametric statistical models where the region descriptor is chosen as the probability density function (pdf) of an image feature (e.g. intensity) inside the region and the pdf belongs to the exponential family. The optimization of divergences appears as a flexible tool for segmentation with and without intensity prior. As far as segmentation without reference is concerned, we aim at maximizing the discrepancy between the pdf of the inside region and the pdf of the outside region. Moreover, since the optimization framework is performed within the exponential family, we can cope with difficult segmentation problems including various noise models (Gaussian, Rayleigh, Poisson, Bernoulli ...). We also experimentally show that the maximisation of the KL divergence offers interesting properties compare to some other data terms (e.g. minimization of the anti-log-likelihood). Experimental results on medical images (brain MRI, contrast echocardiography) confirm the applicability of this general setting.