Feature selection for MR image classification

Tamer Olmez*, Zumray Dokur, Ertugrul Yazgan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, elements of the feature vectors are searched to increase the classification performance of MR images and to reduce the number of nodes of the neural network. Elements of a feature vector are determined by dynamic programming. This algorithm uses divergence analysis and orders the elements of the feature vector to give maximum divergence. The classification performance of new feature vectors is compared with features formed by the gray values at one neighborhood of the center pixel. MoRCE network, which gave satisfactory results in the previous study, is used as the classifier. MoRCE gives 97% classification performance with 7 nodes by using the new feature vectors.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
Pages1134
Number of pages1
ISBN (Print)0780356756
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA
Duration: 13 Oct 199916 Oct 1999

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2
ISSN (Print)0589-1019

Conference

ConferenceProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
CityAtlanta, GA, USA
Period13/10/9916/10/99

Fingerprint

Dive into the research topics of 'Feature selection for MR image classification'. Together they form a unique fingerprint.

Cite this