Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM

Zhuocai Wang, Xiangmin Xu, Xiaojun Ding, Hui Xiao, Yusheng Huang, Jian Liu, Xiaofen Xing, Hua Wang, D. Joshua Liao

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Article number831278
JournalComputational and mathematical methods in medicine
Volume2011
DOIs
StatePublished - Apr 28 2011

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