Detecting Alzheimer disease in Magnetic Resonance brain images using Gabor wavelets

被引：0

作者：

Bagct, Ulas ^{[1
]}

Bai, Li ^{[1
]}

机构：

[1] Univ Nottingham, CMIAG, Sch Comp Sci & IT, Nottingham NG8 1BB, England

来源：

2007 IEEE 15TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1-3 | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：346 / 349

页数：4

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