Volume 4 Number 1 (Feb. 2014)
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IJEEEE 2014 Vol.4(1): 15-18 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2014.V4.293

Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

Farag H. Alhsnony, Maher G. M. Abdolrasol, and Samei G. M. Abadelrsool
Abstract— For the time being Mammography is the most effective imaging modality used by radiologists for screening of breast tumor. Resulting precise, robust and efficient breast segmentation technique still remains a challenging problem in digital mammography. Extraction of the pectoral muscle and breast region is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for irregularities to be eliminated from the region of the breast tissue without undue influence from the background of the mammogram. This paper represents a new automated technique for segmenting a digital mammogram image and identifies the region of pectoral muscle by using a bit depth and edge processes techniques.

Index Terms— Digital Mammography, pectoral Muscle detection, breast border, breast cancer.

Farag H. Alhsnony is with the The Higher Institute of Technology and Polytechnics (HITP)/Tobruk (e-mail: Alhsnony@yahoo.com ).
Maher G. M. Abdolrasol was with University of Malaya, Kuala Lumpur, Malaysia. He is now with Faculty of Engineering & Built Environment National University of Malaysia UKM, Selangor Darul Ehsan UKM Bangi, Malaysia (e-mail: maher.abdolrasol@gmail.com).
Sami G. M. Abadelrsool is with Tobruk Medical Centre in Radiology Department and Faculty of Medical/Tobruk at Omer Al-Mukhtar University Tobruk, Libya (e-mail: samei.g.m.abadelrsool@gmail.com).

Cite: Farag H. Alhsnony, Maher G. M. Abdolrasol, and Samei G. M. Abadelrsool, " Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 4, no. 1, pp. 15-18, 2013.

General Information

ISSN: 2010-3654 (Online)
Frequency: Quarterly (Since 2015)
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, EI (INSPEC, IET)
E-mail: ijeeee@iap.org
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