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First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images

Author(s): Namita Aggarwal | R. K. Agrawal

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 03;
Issue: 02;
Start page: 146;
Date: 2012;
Original page

Keywords: Alzheimer’s Disease | Magnetic Resonance Imaging | Feature Extraction | Discrete Wavelet Transform | First and Second Order Statistical Features

In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
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