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

Chuan Yu Chang, Yuh-Shyan Tsai, I. Lien Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Prostate hyperplasia usually affects male adults in developed countries. Transrectal ultrasound (TRUS) imaging is widely used to diagnose prostate disease. Ultrasonic images have primitive echo perturbations and speckle noise, which may confuse physicians during inspection. Therefore, this study proposes an automatic prostate segmentation system for TRUS images to eliminate the process of manual outlining the prostate region. The proposed automatic segmentation system combines the active contour model (ACM) with a prostate classifier. The prostate classifier consists of a validation incremental neural network (VINN) and a radial-basis function neural network (RBFNN). Experimental results show that the proposed method has higher accuracy than that of the regular ACM method.

Original languageEnglish
Pages (from-to)3035-3046
Number of pages12
JournalInternational Journal of Innovative Computing, Information and Control
Volume7
Issue number6
Publication statusPublished - 2011 Jun 1

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Active Contour Model
Ultrasound Image
Radial Basis Function Neural Network
Segmentation
Ultrasonics
Classifier
Neural Networks
Neural networks
Speckle Noise
Classifiers
Ultrasound
Inspection
High Accuracy
Eliminate
Imaging
Speckle
Perturbation
Acoustic noise
Experimental Results
Imaging techniques

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Information Systems
  • Software
  • Theoretical Computer Science

Cite this

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