Automatic Detection of Pectoral Muscle with the Maximum Intensity Change Algorithm

Zhiyong Zhang, Joan Lu, Yau Jim Yip

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The accurate segmentation of pectoral muscle in mammograms is necessary to detect breast abnormalities in computer-aided diagnosis (CAD) of breast cancer. Based on morphological characteristics of pectoral muscle, a corner detector and the Maximum Intensity Change (MIC) algorithm were proposed in this research to detect the edge of pectoral muscle. The initial result shows that the proposed approach detected pectoral muscle with high quality.

Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVII
Subtitle of host publicationIncorporating Applications and Innovations in Intelligent Systems XVIII Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
EditorsMax Bramer, Miltos Petridis, Adrian Hopgood
PublisherSpringer
Pages489-494
Number of pages6
Edition1
ISBN (Electronic)9780857291301
ISBN (Print)9780857291295
DOIs
Publication statusPublished - 1 Dec 2011
Event30th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence - Cambridge, United Kingdom
Duration: 14 Dec 201016 Dec 2010
Conference number: 30

Conference

Conference30th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Abbreviated titleAI 2010
CountryUnited Kingdom
CityCambridge
Period14/12/1016/12/10

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    Zhang, Z., Lu, J., & Yip, Y. J. (2011). Automatic Detection of Pectoral Muscle with the Maximum Intensity Change Algorithm. In M. Bramer, M. Petridis, & A. Hopgood (Eds.), Research and Development in Intelligent Systems XXVII: Incorporating Applications and Innovations in Intelligent Systems XVIII Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (1 ed., pp. 489-494). Springer. https://doi.org/10.1007/978-0-85729-130-1_37