By Topic

Neural network based segmentation of magnetic resonance images of the brain

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Alirezaie, J. ; Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada ; Jernigan, M.E. ; Nahmias, C.

The potential of artificial neural networks (ANNs) for the classification and segmentation of magnetic resonance (MR) images of the human brain is investigated. In this study, we present the application of a Learning Vector Quantization (LVQ) Artificial Neural Network (ANN) for the multispectral supervised classification of MR images. We have modified the LVQ for better and more accurate classification. We have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, our method is insensitive to the gray-level variation of MR images between different slices. It shows that tissue segmentation using LVQ ANN also performs better and faster than that using back-propagation ANN

Published in:

Nuclear Science Symposium and Medical Imaging Conference Record, 1995., 1995 IEEE  (Volume:3 )

Date of Conference:

21-28 Oct 1995