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S. Chaplot, L. M. Patnaik and N. R. Jagannathan, “Classification of Magnetic Resonance Brain Images Using Wavelets as Input to Support Vector Machine and Neural Network,” Biomedical Signal Processing and Control, Vol. 1, No. 1, 2006, pp. 86-92. doi:10.1016/j.bspc.2006.05.002

has been cited by the following article:

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

  AUTHORS: Namita Aggarwal, R. K. Agrawal

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

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.3 No.2, May 30, 2012

  ABSTRACT: 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.