Integrating the Validation Incremental Neural Network and Radial-Basis Function Neural Network for Segmenting Prostate in Ultrasound Images

摘要：

Prostate hyperplasia is usually found affecting male adults in developed countries. Transrectal ultrasoundgraphy (TRUS) imaging is widely used to diagnose prostate disease. Ultrasonic images are often argued with their primitive echo perturbations and speckle noise, which may confuse the physicians in inspection. Therefore, in this paper, we propose an automatic prostate segmentation system in TRUS images. The automatic segmentation system utilizes a prostate classifier which consists of Validation Incremental Neural Network and Radial-Basis Function Neural Networks for prostate segmentation. Experimental results show that the proposed method has higher accuracy than Active Contour Model (ACM).

关键词： TRUS images RBFNN Active Contour Model

作者: Chuan-Yu Chang Yi-Lian Wu Yuh-Shyan Tsai

作者单位: Department of Computer Science and Information Engineering, National Yunlin University of Science & Department of Urology, National Cheng Kung University Hospital Douliou Branch, Taiwan

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）