10 January 2014 Mammographic mass detection based on extended concentric morphology model
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Proceedings Volume 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013); 906902 (2014); doi: 10.1117/12.2049919
Event: Fifth International Conference on Graphic and Image Processing, 2013, Hong Kong, China
Abstract
Breast cancer occurs with high frequency among women. In most cases, the main early signs appear as mass and calcification. Distinguishing masses from normal tissues is still a challenging work as mass varies with shapes, margins and sizes. In this paper, a novel method for mass detection in mammograms was presented. First, morphology operators are employed to locate mass candidates. Then anisotropic diffusion was applied to make mass region display better multiple concentric layers (MCL). Finally an extended concentric morphology model (ECMM) criterion combining MCL criterion and template matching was proposed to detect masses. This method was examined on 170 images from Digital Database for Screening Mammography (DDSM) database. The detection rate is 93.92% at 1.88 false positives per image (FPs/I), demonstrating the effectiveness of the proposed method.
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Yanfeng Li, Houjin Chen, "Mammographic mass detection based on extended concentric morphology model", Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 906902 (10 January 2014); doi: 10.1117/12.2049919; http://dx.doi.org/10.1117/12.2049919
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KEYWORDS
Mammography

Anisotropic diffusion

Databases

Breast cancer

Image processing

Tissues

Associative arrays

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