By Topic

Neural networks based segmentation of 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

3 Author(s)
R. Sammouda ; Sch. of Med. Sci., Tokushima Univ., Japan ; N. Niki ; H. Nishitani

Segmentation of the images obtained from magnetic resonance imaging (MRI) is an important step in the visualization of soft tissues in the human body. The new emerging field of artificial neural networks (ANNs) promises to provide unique solutions for the pattern classification of medical images. In this preliminary study, we report an application of Hopfield neural network (HNN) for the multispectral unsupervised classification of magnetic resonance (MR) images. We formulate the problem as minimization of an energy function constructed with two terms, the cost-term which is the sum of squares errors, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be close to the global minimum. We present results from subjects with normal and abnormal physiological conditions obtained using HNN with two and three channels data segmentation

Published in:

Nuclear Science Symposium and Medical Imaging Conference, 1994., 1994 IEEE Conference Record  (Volume:4 )

Date of Conference:

30 Oct-5 Nov 1994