Gabor phase response based scheme for accurate pectoral muscle boundary detection. Issue 5 (25th March 2019)

• Record Type: Journal Article
• Authors: Rahman, Mohammad Akhlaqur; Jha, Rajib Kumar; Gupta, Abhishek Kumar
• Is Part Of: IET image processing. Volume 13:Issue 5(2019)
• Page Start: 771
• Page End: 778
• Publication Date: 2019-03-25
• Subjects: mammography -- image segmentation -- cancer -- feature extraction -- edge detection -- medical image processing -- muscle -- image texture -- CAD -- Gabor filters -- Hough transforms; Gabor phase response based scheme -- accurate pectoral muscle boundary detection -- pectoral muscle segmentation -- detection system -- automatic detection -- mammographic image -- Gabor filter -- particular direction -- important image processing tool; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5290
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Pectoral muscle segmentation is a very important step in any Computer Aided Detection (CAD) system, for automatic detection of different possible signs of malignancy in the mammographic image. Presence of high‐density glandular tissues overlapping pectoral muscle boundary, presence of pectoral muscle of small size and low density, presence of pectoralis minor etc., poses a great challenge for researchers working on the problem of automatic pectoral muscle boundary detection. Gabor filter has been used by many researchers to detect textural edges. The ability that it can be tuned along a particular direction, makes it a very important image processing tool. In the proposed research work a Gabor filter tuned in the direction of pectoral muscle boundary is applied on the region of interest (ROI) containing pectoral muscle. Finally, the magnitude and phase response of Gabor filtered ROI image along with an edge‐connect and region merge algorithm is used to detect pectoral muscle boundary. The proposed method is validated using 200 mammograms from the miniMIAS database and 200 mammograms from INbreast database. With an accuracy of 97.50% for miniMIAS database and 94.50% for INbreast database, the proposed method outperforms many state‐of‐the‐art methods which show its suitability for CAD Systems.