Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network

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

Abstract

Methods of 3-D visualization of the brain based on fuzzy c-means (FCM) classified magnetic resonance (MR) images and a neural network trained on the FCM data are presented. A 3-D MR scan of a volunteer serves as the basis for the unsupervised classification techniques. The images were first classified into different tissue types by using FCM. The classified images were then reconstructed for 3-D display. Results show that individual tissue types can be discriminated during the 3-D rendering process. A neural network trained on the fuzzy classification data was also implemented. By using the cascade correlation algorithm during the network training, much of the tedious training work was avoided. The preliminary results from the neural network approach are quite encouraging.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsAndrew G. Tescher
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages636-643
Number of pages8
ISBN (Print)0819416223
StatePublished - 1994
EventApplications of Digital Image Processing XVII - San Diego, CA, USA
Duration: Jul 26 1994Jul 29 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2298
ISSN (Print)0277-786X

Other

OtherApplications of Digital Image Processing XVII
CitySan Diego, CA, USA
Period7/26/947/29/94

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network'. Together they form a unique fingerprint.

Cite this