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Proceedings Paper

Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter
Author(s): Ruriha Yoshikawa; Atsushi Teramoto; Tomoko Matsubara; Hiroshi Fujita
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Paper Abstract

Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.

Paper Details

Date Published: 18 March 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701Z (18 March 2013); doi: 10.1117/12.2007853
Show Author Affiliations
Ruriha Yoshikawa, Fujita Health Univ. (Japan)
Atsushi Teramoto, Fujita Health Univ. (Japan)
Tomoko Matsubara, Nagoya Bunri Univ. (Japan)
Hiroshi Fujita, Gifu Univ. School of Medicine (Japan)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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