Mass edge detection in mammography based on plane fitting and dynamic programming

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Mass edge detection in mammography based on plane fitting and dynamic programming

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Symposium on Applied Computing archive
Proceedings of the 2007 ACM symposium on Applied computing table of contents

Seoul, Korea

SESSION: Artificial intelligence, computational logic, and image analysis table of contents

Pages: 80 - 81

Year of Publication: 2007

ISBN:1-59593-480-4

Authors

Enmin Song	Huazhong University of Science and Technology, Wuhan, China
Luan Jiang	Huazhong University of Science and Technology, Wuhan, China
Bo Meng	Huazhong University of Science and Technology, Wuhan, China
Renchao Jin	Huazhong University of Science and Technology, Wuhan, China
Xiangyang Xu	Huazhong University of Science and Technology, Wuhan, China
Chih-Cheng Hung	Southern Polytechnic State University, Marietta, GA

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

In this paper an automatic and effective method was proposed for mass segmentation in mammography. Based on the facts that mass edges are continuous and closed curves consisted of points which have larger gradient transformation, a plane fitting method and a dynamic programming technique were applied. The regions of interest (ROIs) used in this study were extracted from DDSM. The preliminary experimental results show that the segmentation algorithm performs well for various types of masses.

REFERENCES

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	1	Boring CC., Squires TS., Tong T., and Montgomery S. Cancer statistics. Cancer, 44 (1994), 7--26.
	2	Rennie J. and Rusting R. Making headway against cancer. Scientific American, 275 (1996), 56--59.
	3	Miller AB. Mammography: reviewing the evidence Epidemiology aspects. Canad Fam Physician, 39 (1993), 85--90.
	4	Strax P. Detection of breast cancer. Cancer, 66 (1990), 1336--1340.
	5	Bin Zheng. Computer Detection of Masses in Digitized Mammograms Using Single-Image Segmentation and a Multilayer Topographic Feature Analysis. Acad Radial, 2 (1995), 959--966.
	6	Timp S. A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Medical Physics, 31 (2004), 958--971.
	7	Michael A. Wirth. Segmentation of the breast region in mammograms using active contours. In Proceeding of Visual Communications and Image Processing, SPIE, 2003.
	8	Tourassi G. D. Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information. Medical Physics, 30 (2003), 2123--2130.
	9	Masahito Aoyama, Qiang Li, Shigehiko Kasuragawa, Feng Li, Shusuke Sone, and Kunio Doi. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med. Phys. 30 (2003), 387--349.
	10	M. Heath, K. W. Bowyer, and D. Kopans. Current status of the Digital Database for Screening Mammography. Digital Mammography, Kluwer Academic Publishers, 1998, 457--460.