CELLULAR AUTOMATA-BASED IDENTIFICATION OF THE PECTORAL MUSCLE IN MAMMOGRAMS

The pectoral muscle represents a predominant density region in most medio-lateral oblique (MLO) views of mammograms. Its inclusion can affect the results of intensity-based image processing methods. This paper presents a new method on the basis of cellular automata model for the identification of the pectoral muscle in MLO mammograms. A dataset of 84 MLO mammograms from the MIAS (Mammographic Image Analysis Society, London, U.K.) database was implemented throughout for evaluation. In this respect, the pectoral muscle edge detected in the mammograms was carried out based upon the percentage of falsepositive (FP) and false-negative (FN) pixels determined by comparison between the numbers of pixels enclosed in the regions delimited by the edges identified by a radiologist and by the proposed method. The proposed CA-based method provides the promising results.