Computational and Mathematical Methods in Medicine
Volume 2011 (2011), Article ID 831278, 6 pages
http://dx.doi.org/10.1155/2011/831278
Research Article

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

Zhuocai Wang,1 Xiangmin Xu,2 Xiaojun Ding,1 Hui Xiao,1 Yusheng Huang,2 Jian Liu,1 Xiaofen Xing,2 Hua Wang,3 and D. Joshua Liao4

1Department of Pathology, Liuhuaqiao Hospital, Guangzhou, Guangdong 510010, China
2School of Electronic and Information Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
3Institute of Electronics and Information, Guangdong Institute of Science and Technology, Guangzhou, Guangdong 510640, China
4Hormel Institute, University of Minnesota, Austin, MN 55912, USA

Received 25 May 2010; Accepted 17 January 2011

Academic Editor: Philip Biggin

Copyright © 2011 Zhuocai Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.