Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

Abstract

Mammography is the primary modality that helped in the early detection and diagnosis of women b reast diseases. Further, the process of extracting the masses in mammogram represents a challenging task facing the radiologists, due to prob lems such as fuzzy or speculated b orders, low contrast and the presence of intensity inhomogeneities. Aims to help the radiologists in the diagnosis of b reast cancer, many approaches have b een conducted to automatically segment the masses in mammograms. Towards this aim, in this paper, we present a new approach for extraction of tumors from region-of-interest (ROI) using the algorithm of Fuzzy C-Means (FCM) setting two clusters for semi-automated segmentation. The proposed method meant to select as input data the set of pixels that enab le to get the meaningful information required to segment the masses with high accuracy. This could b e accomplished through eliminating unnecessary pixels, which influence on this process through separating it outside of the input data using an optimal thresho ld given b y monitoring the change of clusters rate during the process of threshold decrementing. The proposed methodology has successfully segmented the masses, with an average sensitivity of 82.02% and specificity of 98.23%.