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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 891692, 16 pages
http://dx.doi.org/10.1155/2015/891692
Research Article

A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation

1School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
2Department of Computing Science, Institute of High Performance Computing, A*STAR, Singapore 138632
3Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, USA

Received 26 September 2014; Revised 21 December 2014; Accepted 12 January 2015

Academic Editor: William Crum

Copyright © 2015 Jian-Yong Lou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in this study uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weighting parameters. As a result, pixels having similar intensity information but located in different regions can be differentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improved with optimal mass boundaries, less number of noisy patches, and computational efficiency. An average probability of segmentation error of 7.18% for well-defined masses (or 8.06% for ill-defined masses) was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55%) and 6.14% (or 5.27%) improvements as compared to the standard DA and fuzzy c-means methods.