Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform

Abstract

Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts. Our dataset contains 200 mammogram images with size of 1024 × 1024. First, we segmented the region of interest from mammogram images. Second, the fractional Fourier transform was employed to obtain the unified time–frequency spectrum. Third, spectrum coefficients were reduced by principal component analysis. Finally, both support vector machine and k-nearest neighbors were used and compared. The proposed “weighted-type fractional Fourier transform+principal component analysis+support vector machine” achieved sensitivity of 92.22% ± 4.16%, specificity of 92.10% ± 2.75%, and accuracy of 92.16% ± 3.60%. It is better than both the proposed “weighted-type fractional Fourier transform+principal component analysis+ k-nearest neighbors” and other five state-of-the-art approaches in terms of sensitivity, specificity, and accuracy. The proposed computer-aided diagnosis system is effective in detecting abnormal breasts.