Ubiquitous Computing and Communication Journal
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Abstract
Title: Classification of MC Clusters in Digital Mammography via Haralick Descriptors and Heuristic Embedded Feature Selection Method
Authors: Dr. Imad Zyout, Dr. Ikhlas Abdel-Qader, Dr. Christina Jacobs
Abstract:
Characterizing the texture of mammographic tissue is an efficient and robust tool for the diagnosis of microcalcification (MC) clusters in mammography because it does not require a prior MC segmentation stage. This work is not only intended to validate MCs’ surrounding tissue hypothesis 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 dataset, our results demonstrate the effectiveness of the proposed characterization and feature selection methods.