Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

MR image segmentation using a fuzzy-based neural network

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

2 Author(s)
Ma, J.C. ; Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA ; Rodriguez, J.J.

Most techniques for segmentation of magnetic resonance images of the brain are extremely time consuming and/or require extensive user interaction. An automated segmentation procedure is presented, whereby the fuzzy c-means classification results are used to train a feedforward neural network. The cascade correlation algorithm is used to optimize the network training process. After applying a brain-extraction technique, the segmented images are then used for rendering computer-generated images of the brain's surface. Experimental results using real, 3D magnetic resonance images are presented, demonstrating the performance of the segmentation as well as the final surface rendering

Published in:

Neural Networks, 1995. Proceedings., IEEE International Conference on  (Volume:5 )

Date of Conference:

Nov/Dec 1995