DocumentCode :
3850401
Title :
MR brain image segmentation by growing hierarchical SOM and probability clustering
Author :
A. Ortiz;J.M. Gorriz;J. Ramirez;D. Salas-Gonzalez
Author_Institution :
Departamento de Ingenier?a de Comunicaciones, Universidad de Malaga, Spain
Volume :
47
Issue :
10
fYear :
2011
fDate :
5/12/2011 12:00:00 AM
Firstpage :
585
Lastpage :
586
Abstract :
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.
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el.2011.0322
Filename :
5767234
Link To Document :
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