Integrating the validation incremental neural network and radial-basis function neural network for segmenting prostate in ultrasound images

Chuan Yu Chang, Yi Lian Wu, Yuh-Shyan Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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).

Original languageEnglish
Title of host publicationProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Pages198-203
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 27
Event2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009 - Shenyang, China
Duration: 2009 Aug 122009 Aug 14

Publication series

NameProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Volume1

Other

Other2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
CountryChina
CityShenyang
Period09-08-1209-08-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

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    Chang, C. Y., Wu, Y. L., & Tsai, Y-S. (2009). Integrating the validation incremental neural network and radial-basis function neural network for segmenting prostate in ultrasound images. In Proceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009 (pp. 198-203). [5254290] (Proceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009; Vol. 1). https://doi.org/10.1109/HIS.2009.47