DocumentCode :
2319815
Title :
MRI feature extraction using genetic algorithms
Author :
Velthuizen, Robert P. ; Hall, Lawrence O. ; Clarke, Laurence P.
Author_Institution :
Dept. of Radiol., Univ. of South Florida, Tampa, FL, USA
Volume :
3
fYear :
1996
fDate :
31 Oct-3 Nov 1996
Firstpage :
1138
Abstract :
Traditional machine vision techniques apply a feature extraction step before any classification, but this is not commonly done for magnetic resonance images. In this study the authors propose to discover optimal feature extractors for MRI to increase segmentation accuracy. Genetic algorithms are applied using a fitness function based on known class labels, and on a fitness function that can be applied to data without ground truth. Both fitness functions allow the discovery of good features, that can be applied outside the data that was used for the search. An increase in the tumor true positive rate for an MRI volume using fuzzy c-means (FCM) was found from 78.7% to 91.3% of all tumor pixels with constant false negative rate. This approach may lead to significantly improved MRI segmentation, which is needed in particular for multicenter trials for brain tumor treatment
Keywords :
biomedical NMR; feature extraction; genetic algorithms; image segmentation; medical image processing; MRI feature extraction; brain tumor treatment; fitness functions; fuzzy c-means; good features discovery; improved MRI segmentation; magnetic resonance imaging; medical diagnostic imaging; multicenter trials; segmentation accuracy increase; traditional machine vision techniques; tumor pixels; tumor true positive rate; Data analysis; Data mining; Error analysis; Feature extraction; Fuzzy logic; Genetic algorithms; Image segmentation; Magnetic resonance imaging; Neoplasms; Optimization methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-3811-1
Type :
conf
DOI :
10.1109/IEMBS.1996.652744
Filename :
652744
Link To Document :
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