A Comparison of Daubechies and Gabor Wavelets for Classification of MR Images

Author

Bagci, Ulas ; Bai, Li

Author_Institution

Collaborative Med. Image Anal. Group, Univ. of Nottingham, Nottingham, UK

fYear

2007

fDate

24-27 Nov. 2007

Firstpage

676

Lastpage

679

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.

Keywords

biomedical MRI; brain; feature extraction; image classification; medical image processing; support vector machines; wavelet transforms; Alzheimer´s Disease; Daubechies wavelets; Gabor wavelet feature extraction; SVM kernel functions; magnetic resonance image classification; Alzheimer´s disease; Brain; Computer science; Discrete wavelet transforms; Feature extraction; Fourier transforms; Kernel; Magnetic resonance imaging; Support vector machine classification; Support vector machines; Alzheimer´s Disease; Classification; Gabor Wavelets; MRI; Support Vector Machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on

Conference_Location

Dubai

Print_ISBN

978-1-4244-1235-8

Electronic_ISBN

978-1-4244-1236-5

Type

conf

DOI

10.1109/ICSPC.2007.4728409

Filename

4728409