By Topic

Stability study of some neural networks applied to tissue characterization of brain magnetic resonance images

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
$33 $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

5 Author(s)
A. Stocker ; Inst. of Neruoinf., Swiss Federal Inst. of Technol., Zurich, Switzerland ; O. Sipila ; A. Visa ; O. Salonen
more authors

This study investigates the segmentation ability of unsupervised clustering of the image feature space. A self-organizing map, a feed-forward neural network and a k-nearest neighbor classifier were compared in labeling brain slices from magnetic resonance imaging. Qualitative and quantitative tests were carried out using brain images of a patient with an infarction. Five different tissue classes were partitioned: white matter, gray matter, cerebrospinal fluid, fluid in the infarct region and gray matter in the infarct region. The SOM based method performed best in all the cases that were investigated. Especially, the stability of the method concerning the influence of the training set was superior

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996