By Topic

MR brain image segmentation by growing hierarchical SOM and probability clustering

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 $33
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

4 Author(s)
A. Ortiz ; Departamento de Ingenieria de Comunicaciones, Universidad de Malaga, Spain ; J. M. Górriz ; J. Ramírez ; D. Salas-Gonzalez

A fully automatic tool to assist the segmentation of brain magnetic resonance images (MRI) is presented. Thus, the figured out regions can be evaluated for the diagnosis of brain disorders. The main problem to be handled consists in discovering different regions on the image without using apriori information. The new approach consists in hybridising multiobjective optimisation for feature selection with a growing hierarchical self-organising map (GHSOM) classifier and a probability clustering method. The segmentation results yield average overlap metric values of 0.32, 0.75 and 0.69 for white matter, grey matter and cerebrospinal fluid, respectively, over the Internet Brain Segmentation Repository database. These results mean an improvement over the values reached by other existing techniques.

Published in:

Electronics Letters  (Volume:47 ,  Issue: 10 )