Manyetik rezonans beyin i̇mgelerinden alzheimer hastaligi tanisinda gabor dalgaciklari kullanimi

Translated title of the contribution: Detecting alzheimer disease in magnetic resonance brain images using gabor wavelets

Ulaş Baǧci*, Li Bai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A novel method for classification of Magnetic Resonance brain images is presented in this paper. We construct a computational framework for discriminative image feature subspaces. Magnetic Resonance Images of patients in Alzheimer's Disease and normal brain MR images are classified with Support Vector Machines. The framework for the novel method bases on the extraction of gabor features from 2D-Magnetic Resonance images in different scales and orientations. Experiments show that Gabor wavelets can significantly improve classification performance with respect to other popular approaches reported recently in the literature. Combination of gabor features in 3 scales and 8 orientations give 100% classification performance. Experimental results with promising improvements and comparison to related studies are provided.

Translated title of the contributionDetecting alzheimer disease in magnetic resonance brain images using gabor wavelets
Original languageTurkish
Title of host publication2007 IEEE 15th Signal Processing and Communications Applications, SIU
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 IEEE 15th Signal Processing and Communications Applications, SIU - Eskisehir, Turkey
Duration: Jun 11 2007Jun 13 2007

Publication series

Name2007 IEEE 15th Signal Processing and Communications Applications, SIU

Conference

Conference2007 IEEE 15th Signal Processing and Communications Applications, SIU
CountryTurkey
CityEskisehir
Period6/11/076/13/07

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication
  • Signal Processing

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