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Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

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Abstract

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick’s texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.

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Acknowledgments

This project was funded by grants from the Canadian Breast Cancer Foundation: Prairies/NWT Chapter, the Alberta Heritage Foundation for Medical Research (AHFMR), and the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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  1. Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary, AB, Canada, T2N 1N4

    Rangaraj M. Rangayyan, Shantanu Banik & J. E. Leo Desautels

  2. Department of Radiology, University of Calgary, Calgary, AB, Canada, T2N 1N4

    Rangaraj M. Rangayyan

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Correspondence to Rangaraj M. Rangayyan.

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Rangayyan, R.M., Banik, S. & Desautels, J.E.L. Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. J Digit Imaging 23, 611–631 (2010). https://doi.org/10.1007/s10278-009-9257-x

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