title: Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.
abstract: Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.
authors: Department of Pathology, Liuhuaqiao Hospital, Guangzhou, Guangdong, China. wangzcmail@163.com
authors: Zhuocai
authors: Z
authors: Wang
authors: Xiangmin
authors: X
authors: Xu
authors: Xiaojun
authors: X
authors: Ding
authors: Hui
authors: H
authors: Xiao
authors: Yusheng
authors: Y
authors: Huang
authors: Jian
authors: J
authors: Liu
authors: Xiaofen
authors: X
authors: Xing
authors: Hua
authors: H
authors: Wang
authors: D Joshua
authors: DJ
authors: Liao
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D002137:Calculi / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D011467:Prostate / Q000473:pathology*; D011470:Prostatic Hyperplasia / Q000473:pathology; D011471:Prostatic Neoplasms / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity