A comparison of daubechies and gabor wavelets for classification of mr images

Ulaş Baǧci*, Li Bai

*Corresponding author for this work

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

12 Scopus citations

Abstract

In this paper we report our experience using different types of wavelets and different SVM kernel functions for classification of Magnetic Resonance Images to identify those showing symptoms of Alzheimer's Disease. We have developed a novel computational framework for extracting discriminative Gabor wavelet features from the images for classification using Support Vector Machines with various kernel functions. Experiments show that Gabor wavelets perform better than Daubechies wavelets in classification. Our method outperformed other popular approaches recently reported in the literature. 100% classification accuracy has been achieved.

Original languageEnglish (US)
Title of host publicationICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Pages676-679
Number of pages4
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 - Dubai, United Arab Emirates
Duration: Nov 14 2007Nov 27 2007

Publication series

NameICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications

Conference

Conference2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007
CountryUnited Arab Emirates
CityDubai
Period11/14/0711/27/07

Keywords

  • Alzheimer's Disease
  • Classification
  • Gabor wavelets
  • MRI
  • Support Vector Machines

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Communication

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