Cart (Loading....) | Create Account
Close category search window
 

A comparison of Hopfield neural network and Boltzmann machine in segmenting MR 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)
Sammouda, R. ; Dept. of Opt. Sci. & Technol., Tokushima Univ., Japan ; Niki, N. ; Nishitani, H.

Presents contributions to improve a previously published approach for the segmentation of magnetic resonance images of the human brain, based on an unsupervised Hopfield neural network. The authors formulate the segmentation problem as the minimization of an energy function constructed with two terms: the cost-term as a sum of squared errors and the second term temporary noise added to the cost-term as an excitation to the network to escape certain local minima, with the result of being closer to the global minimum. Also, to ensure the convergence of the network and its utilization in the clinic with useful results, the minimization is achieved with a step function that permits the network to reach stability corresponding to a local minimum close to the global minimum in a prespecified period of time. The authors present segmentation results of their approach for data of patient diagnosed with a metastatic tumor in the brain, and they compare them to those obtained from previous work using Hopfield neural networks, the Boltzmann machine, and the conventional ISODATA clustering technique

Published in:

Nuclear Science, IEEE Transactions on  (Volume:43 ,  Issue: 6 )

Date of Publication:

Dec 1996

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.