Title: SVM classification of brain images from MRI scans using morphological transformation and GLCM texture features
Authors: R. Usha; K. Perumal
Addresses: Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, Tamilnadu, India ' Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, Tamilnadu, India
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.
Keywords: magnetic resonance images; MRI; classification; texture feature extraction; grey level co-occurrence matrix; support vector machine; SVM; top hat transform; bottom hat transform.
International Journal of Computational Systems Engineering, 2019 Vol.5 No.1, pp.18 - 23
Available online: 06 Mar 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article