Detection of architectural distortion in prior mammograms using measures of angular distribution

Rangaraj M. Rangayyan; Shantanu Banik; J. E. L. Desautels

doi:10.1117/12.877587

4 March 2011 Detection of architectural distortion in prior mammograms using measures of angular distribution

Rangaraj M. Rangayyan, Shantanu Banik, J. E. L. Desautels

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796308 (2011) https://doi.org/10.1117/12.877587
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

We present methods for the detection of architectural distortion in mammograms of interval-cancer cases taken prior to the diagnosis of breast cancer using measures of angular distribution derived from Gabor filter responses in magnitude and angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum. A total of 4224 regions of interest (ROIs) were automatically obtained using Gabor filters and phase portrait analysis from 106 prior mammograms of 56 interval-cancer cases with 301 ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. Images of coherence and orientation strength were derived from the Gabor responses in magnitude and orientation. Each ROI was represented by the entropy of the angular histogram composed with the Gabor magnitude response, angle, coherence, and orientation strength; the entropy of the angular spread of power in the Fourier spectrum was also computed. Using stepwise logistic regression for feature selection and the leave-one-image-out method in feature selection and pattern classification, the area under the receiver operating characteristic curve of 0.76 was obtained with an artificial neural network based on radial basis functions. Analysis of the free-response receiver operating characteristics indicated 82% sensitivity at 7.2 false positives per image.

Citation Download Citation

Rangaraj M. Rangayyan, Shantanu Banik, and J. E. L. Desautels "Detection of architectural distortion in prior mammograms using measures of angular distribution", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796308 (4 March 2011); https://doi.org/10.1117/12.877587

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