A spatial shape constrained clustering method for mammographic mass segmentation.
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.
School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Jian-Yong
JY
Lou
Department of Computing Science, Institute of High Performance Computing, A STAR, Singapore 138632.
Xu-Lei
XL
Yang
Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Ai-Ze
AZ
Cao
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D016000:Cluster Analysis; D016208:Databases, Factual; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results