SVM classification of brain images from MRI scans using morphological transformation and GLCM texture features Online publication date: Fri, 22-Mar-2019
by R. Usha; K. Perumal
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 5, No. 1, 2019
Abstract: This paper introduces a novel HTT-based GLCM texture feature extraction procedure for an automatic magnetic resonance images (MRI) brain image classification. The method has three phases: 1) hierarchical transformation technique (HTT); 2) texture feature extraction; 3) classification. The new proposed HTT method incorporates optimum disk-shaped mask selection, top-hat and bottom-hat morphological operations, and some mathematical operation for both image pre-processing and enhancement. The gray level co-occurrence matrix is computed to extract statistical texture features such as contrast, correlation, energy, entropy, and homogeneity from an image. And these extracted images features of co-occurrence matrix can very well be fed into support vector machine (SVM) for further MRI brain normal and abnormal image classification. The alternate approach of the HTT-based GLCM also compared with conventional GLCM texture feature extraction method.
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