Karim Kalti

Learn 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)
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 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 pixel(More)
Mammography constitutes a credible technique for the detection of breast cancer. Early detection of microcalcifications in breast tissue, which is an indication of developing breast cancer, facilitates prompt intervention averting fatalities associated with this type of disease. It is, however, difficult for practitioners to pinpoint effectively the(More)
Content-Based Image Retrieval (CBIR) is becoming one of the most vivid research area in computer vision. It is widely used in medical applications especially in computer aided diagnostic systems (CAD). CBIR systems in digital mammography take an important part of these works. The work presented in this paper aims to propose a CBIR approach based on inexact(More)
We propose in this paper a Bayesian model for the retrieving of MRI (magnetic resonance imaging) exams that contain cerebral tumors. Bayesian network proved its efficiency and reliability in several AI (Artificial Intelligence) problems and especially in aid-decision applications. To diagnose a cerebral tumor in a MRI exam, we need to interpret diverse(More)
This paper presents a semantic image segmentation approach that combines a fuzzy region classification and a contextual region-growing. First image is over-segmented and a domain knowledge based fuzzy classification is applied on obtained regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of(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)