A local Rayleigh model with spatial scale selection for ultrasound image segmentation

12 pagesInternational audienceUltrasound images are very noisy, with poor contrast and the attenuation of the acoustic wave in the depth of the observed medium leads to strong inhomogeneities in the image. Segmentation methods using global image statistics give unsatisfactory results. The use of local image statistics can solve effectively the problem of attenuation. The contribution of this paper is two folds. First, we propose the study of the adaptation of the global model proposed by Sarti et al. We kept the variational framework and the Rayleigh model of the observed image statistics. Second, we propose an interesting and generic adaptive scale selection algorithm based on the Intersection of Confidence Interval rule. The latter is also applied to the local Gaussian segmentation model of Brox and Cremers. Results on realistic simulations of ultrasound images show the robustness and the superiority of the local Rayleigh model. The efficiency and the genericity of the proposed scale selection strategy is also demonstrated

Optimal spatial adaptation for local region-based active contours: An Intersection of Confidence Intervals Approach

Q.Yang, and D. Boukerroui. "Optimal spatial adaptation for local region-based active contours: An Intersection of Confidence Intervals Approach". International Conference on Imaging Theory and Applications (IMAGAPP'11), 5-7 March, Vilamoura, Portugal, 2011.In this paper, we propose within the level set framework a region-based segmentation method using local image statistics. An isotropic spatial kernel is used to define locality. We use the Intersection of Confidence Intervals (ICI) approach to define a pixel dependant local scale for the estimation of image statistics. The obtained scale is based on estimated optimal scales, in the sense of the mean-square error of a Local Polynomials Approximation of the observed image conditional on the current segmentation. In other words, the scale is 'optimal' in the sense that it gives the best trade-off between the bias and the variance of the estimates. The proposed approach performs very well, especially on images with intensity inhomogeneities

Contributions à la segmentation d'image : phase locale et modèles statistiques

Ce document presente une synthèse de mes travaux apres these, principalement sur la problematique de la segmentation d’images