Computational intelligence techniques for identifying the pectoral muscle region in mammograms

Abstract

Segmentation of the pectoral muscle is an imperative task in mammographic image analysis. The pectoral edge is specifically examined by radiologists for abnormal axillary lymph nodes, serves as one of the axes in 3-dimensional reconstructions, and is one of the fundamental landmarks in mammogram registration and comparison. However, this region interferes with intensity-based image processing methods and may bias cancer detection algorithms. The purpose of this study was to develop and evaluate computational intelligence techniques for identifying the pectoral muscle region in medio-lateral oblique (MLO) view mammograms. After removal of the background region, the mammograms were segmented using a K-clustered self-organizing map (SOM). Morphological operations were then applied to obtain an initial estimate of the pectoral muscle region. Shape-based analysis determined which of the K estimates to use in the final segmentation. The algorithm has been applied to 250 MLO-view Lumisys mammograms from the Digital Database for Screening Mammography (DDSM). Upon examination, it was discovered that three of the original mammograms did not contain the pectoral muscle and one contained a clear defect. Of the 246 remaining, 95.94% were considered to have successfully identified the pectoral muscle region. The results provide a compelling argument for the effectiveness of computational intelligence techniques for identifying the pectoral muscle region in MLO-view mammograms.