Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter

Ruriha Yoshikawa, Atsushi Teramoto, Tomoko Matsubara, Hiroshi Fujita

研究成果: Conference contribution

1 引用 (Scopus)

抄録

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.

元の言語English
ホスト出版物のタイトルMedical Imaging 2013
ホスト出版物のサブタイトルComputer-Aided Diagnosis
8670
DOI
出版物ステータスPublished - 05-06-2013
イベントMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
継続期間: 12-02-201314-02-2013

Other

OtherMedical Imaging 2013: Computer-Aided Diagnosis
United States
Lake Buena Vista, FL
期間12-02-1314-02-13

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mammary glands
Gabor filters
Gabor Filter
Mammogram
Adaptive Filter
Adaptive filters
Mammography
False Positive
Computer-aided Detection
Microcalcifications
Binarization
Labeling
Support vector machines
Screening
calcification
glands
Breast Cancer
Post-processing
Health
chest

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

これを引用

Yoshikawa, R., Teramoto, A., Matsubara, T., & Fujita, H. (2013). Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter. : Medical Imaging 2013: Computer-Aided Diagnosis (巻 8670). [86701Z] https://doi.org/10.1117/12.2007853
Yoshikawa, Ruriha ; Teramoto, Atsushi ; Matsubara, Tomoko ; Fujita, Hiroshi. / Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter. Medical Imaging 2013: Computer-Aided Diagnosis. 巻 8670 2013.
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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.",
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Yoshikawa, R, Teramoto, A, Matsubara, T & Fujita, H 2013, Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter. : Medical Imaging 2013: Computer-Aided Diagnosis. 巻. 8670, 86701Z, Medical Imaging 2013: Computer-Aided Diagnosis, Lake Buena Vista, FL, United States, 12-02-13. https://doi.org/10.1117/12.2007853

Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter. / Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi.

Medical Imaging 2013: Computer-Aided Diagnosis. 巻 8670 2013. 86701Z.

研究成果: Conference contribution

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Yoshikawa R, Teramoto A, Matsubara T, Fujita H. Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter. : Medical Imaging 2013: Computer-Aided Diagnosis. 巻 8670. 2013. 86701Z https://doi.org/10.1117/12.2007853