Ipsilateral-mammogram computer-aided detection of breast cancer.
In this paper, an ipsilateral multi-view computer-aided detection (CAD) scheme is presented for mass detection in digital mammograms by exploiting correlative information of suspicious lesions between mammograms of the same breast. After nonlinear tree-structured filtering for image noise suppression, two wavelet-based methods, directional wavelet transform and tree-structured wavelet transform for image enhancement, and adaptive fuzzy C-means algorithm for segmentation are employed on each mammograms of the same breast, respectively, concurrent analysis is developed for iterative analysis of ipsilateral multi-view mammograms by inter-projective feature matching analysis. A supervised artificial neural network is developed as a classifier, in which the back-propagation algorithm combined with Kalman filtering is used as training algorithm, and free-response receiver operating characteristic analysis is used to test the performance of the developed unilateral CAD system. Performance comparison has been conducted between the final ipsilateral multi-view CAD system and our previously developed single-mammogram-based CAD system. The study results demonstrate the advantages of ipsilateral multi-view CAD method combined with concurrent analysis over current single-view CAD system on false positive reduction.
Sun
Xuejun
X
Department of Interdisciplinary Oncology, College of Medicine, H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, 12902 Magnolia Drive, Tampa, FL 33612-9497, USA.
Qian
Wei
W
Song
Dansheng
D
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000379:methods*; D014481:United States