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

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 x 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.