based on a set of shape-based features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes

that consists of two stages. The first one characterizes MC clusters using texture features from gray-level co-occurrence matrix (GLCM). In the second stage, an embedded feature selection based on particle swarm optimization and a k-nearest neighbor (KNN) classifier, called PSO-KNN, is applied to simultaneously

that reveals the potential of breast tissue surrounding MCs to diagnose microcalcifications, but to present an improvement over the existing methods by introducing a new heuristic feature selection based on particle swarm optimization and KNN classifier (PSO-KNN). Using MC clusters from mini-MIAS and a local

the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO

Abstract—The current study investigates whether texture properties of the tissue surrounding microcalcification (MC) clusters can contribute to breast cancer diagnosis. The case sample analyzed consists of 100 mammographic images, originating from the Digital Database for Screening Mammography

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters, which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification

with the Geometry of the Cluster? OBJECTIVE. The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system. MATERIALS

the important features of each cluster and (c) the classification stage, which classify between normal and microcalcifications’ patterns and then classify between benign and malignant microcalcifications. In classification stage, four methods were used, the voting K-Nearest Neighbor classifier (K-NN), Support

2010 to my wife, Joyce, and my family...- Résumé-

facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together