Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter

• Published in: Journal of medical systems Vol. 41; no. 12; pp. 190 - 13
• Main Authors: Vikhe, P. S., Thool, V. R.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-12-2017; Springer Nature B.V
• Subjects: Breast cancer; Breasts; Cancer; Curve fitting; Diagnostic systems; Health Informatics; Health Sciences; Image analysis; Image processing; Image segmentation; Mammography; Medicine; Medicine & Public Health; Muscles; Performance evaluation; Statistics for Life Sciences; Transactional Processing Systems; Pectoral muscle; Segmentation; Detection; Mammograms; Enhancement mask
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-017-0839-8

The presence of predominant density region of the pectoral muscle in Medio-Lateral Oblique (MLO) view of the mammograms can affect or bias the results of mammograms processing for breast cancer detection using intensity based methods. Therefore, to improve the diagnostic performance of breast cancer detection using computer-aided system, identification and segmentation of pectoral muscle is an important task. This paper presents, an intensity based approach to identify the pectoral region in mammograms. In the presented approach enhancement mask and threshold technique is used to enhance and select the pectoral region and boundary points respectively, to find the boundary of pectoral muscle. Then curve fitting by Least Square Error (LSE) method is used to refine the rough initial boundaries. The proposed approach was applied on 320 mammograms from mini-Mammographic Image Analysis Society (mini-MIAS) database of 322 mammograms, with acceptable rate of 96.56% from radiologist experts. The performance evaluation for pectoral muscle segmentation, based on Hausdorff distance (H
d
), False Positive (FP) and False Negative (FN) rate, shows the usefulness and effectiveness of the proposed approach.