TY - JOUR
T1 - Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform
AU - Zhang, Yu Dong
AU - Wang, Shui Hua
AU - Liu, Ge
AU - Yang, Jiquan
N1 - Publisher Copyright:
© 2016 The Author(s).
PY - 2016/2/24
Y1 - 2016/2/24
N2 - 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.
AB - 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.
KW - Fractional Fourier transform
KW - abnormal breast
KW - computer-aided diagnosis
KW - k-nearest neighbors
KW - mammogram
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84959371434&partnerID=8YFLogxK
U2 - 10.1177/1687814016634243
DO - 10.1177/1687814016634243
M3 - Article
AN - SCOPUS:84959371434
SN - 1687-8132
VL - 8
SP - 1
EP - 11
JO - Advances in Mechanical Engineering
JF - Advances in Mechanical Engineering
IS - 2
ER -