Karim Kalti

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— This paper introduces a Bayesian image segmentation algorithm based on finite mixtures. An EM algorithm is developed to estimate parameters of the Gaussian mixtures. The finite mixture is a flexible and powerful probabilistic modeling tool. It can be used to provide a model-based clustering in the field of pattern recognition. However, the application of(More)
The Expectation Maximization (EM) algorithm and the clustering method Fuzzy-C-Means (FCM) are widely used in image segmentation. However, the major drawback of these methods is their sensitivity to the noise. In this paper, we propose a variant of these methods which aim at resolving this problem. Our approaches proceed by the characterization of pixels by(More)
The clustering method " Fuzzy-C-Means " (FCM) is widely used in image segmentation. However, the major drawback of this method is its sensitivity to the noise. In this paper, we propose a variant of this method which aims at resolving this problem. Our approach is based on an adaptive distance which is calculated according to the spatial position of the(More)
In mammographic images, extracting different anatomical structures and tissues types is a critical requirement for the breast cancer diagnosis. For instance, separating breast and background regions increases the accuracy and efficiency of mammographic processing algorithms. In this paper, we propose a new region-based method to properly segment breast and(More)
We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used to perform a fuzzy classification of these regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at(More)