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23 February 2012 Detection of architectural distortion in prior mammograms using statistical measures of orientation of texture
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We present a method using statistical measures of the orientation of texture to characterize and detect architectural distortion in prior mammograms of interval-cancer cases. Based on the orientation field, obtained by the application of a bank of Gabor filters to mammographic images, two types of co-occurrence matrices were derived to estimate the joint occurrence of the angles of oriented structures. For each of the matrices, Haralick's 14 texture features were computed. From a total of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, 4,224 regions of interest (ROIs) were automatically obtained by applying Gabor filters and phase portrait analysis. For each ROI, statistical features were computed using the angle co-occurrence matrices. The performance of the features in the detection of architectural distortion was analyzed and compared with that of Haralick's features computed using the gray-level co-occurrence matrices of the ROIs. Using logistic regression for feature selection, an artificial neural network for classification, and the leave-one-image-out approach for cross-validation, the best result achieved was 0.77 in terms of the area under the receiver operating characteristic (ROC) curve. Analysis of the free-response ROC curve yielded a sensitivity of 80% at 5.4 false positives per image.
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Jayasree Chakraborty, Rangaraj M. Rangayyan, Shantanu Banik, Sudipta Mukhopadhyay, and J. E. L. Desautels "Detection of architectural distortion in prior mammograms using statistical measures of orientation of texture", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831521 (23 February 2012);

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