title: [Diagnosis of prostate cancer using SVM-based ultrasound images].
abstract: This paper presents a computer-aided diagnosis method for prostate cancer detection using Trans-rectal ultrasound(TRUS) images. Firstly, statistical texture analysis is implemented in every ROI in segmented prostate images. From each ROI, grey level difference vector features, edge-frequency features and texture features in frequency domain are constructed. Then, the number of features is reduced using ANOVA statistics to select the optimal feature subset. Finally, SVM is applied to the selected subset for detecting the cancer regions. Experimental results show that the proposed algorithm can recognize and detect the cancer images effectively so as to supply essential information for a diagnosis.
authors.affiliation: Dept. of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui Provice 230027.
authors.forename: Zhen-Sen
authors.initials: ZS
authors.lastname: Yang
authors.forename: Chuan-Fu
authors.initials: CF
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Shi
authors.forename: Kang-Yuan
authors.initials: KY
authors.lastname: Zhou
authors.forename: Li
authors.initials: L
authors.lastname: He
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography