Feature selection for MR image classification

Tamer Olmez*, Zumray Dokur, Ertugrul Yazgan

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
YayınlayanIEEE
Sayfalar1134
Sayfa sayısı1
ISBN (Basılı)0780356756
Yayın durumuYayınlandı - 1999
EtkinlikProceedings 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
Süre: 13 Eki 199916 Eki 1999

Yayın serisi

AdıAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Hacim2
ISSN (Basılı)0589-1019

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???event.eventtypes.event.conference???Proceedings 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)
ŞehirAtlanta, GA, USA
Periyot13/10/9916/10/99

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