Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis

Shormistha Prajna; Rangaraj M. Rangayyan; Fábio J. Ayres; J. E. Leo Desautels M.D.

doi:10.1117/12.767669

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17 March 2008 Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis

Shormistha Prajna, Rangaraj M. Rangayyan, Fábio J. Ayres, J. E. Leo Desautels M.D.

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Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 691529 (2008) https://doi.org/10.1117/12.767669
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. A method based on Gabor filters and phase portrait analysis was used to detect initial candidates of sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 "prior mammograms", including 21 ROIs related to architectural distortion. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. The average fractal dimension of the normal (false-positive) ROIs was higher than that of the ROIs with architectural distortion. For the "prior mammograms", the best receiver operating characteristics (ROC) performance achieved was 0.74 with the fractal dimension and 0.70 with fourteen texture features, in terms of the area under the ROC curve.

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Shormistha Prajna, Rangaraj M. Rangayyan, Fábio J. Ayres, and J. E. Leo Desautels M.D. "Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691529 (17 March 2008); https://doi.org/10.1117/12.767669

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