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Feature Extraction and Classification of Brain Tumor Using MRI

Author(s):

Ananya S B , VVIET, Mysuru; Lalitha N, VVIET, Mysuru

Keywords:

Brain Tumor, K-means Segmentation, Feature Extraction (GLCM), SVM classifier

Abstract

MRI scan of brain tumor gives the detailed information of brain compared to other scans. Brain tumor is an uncontrolled and an unwanted multiplication and growing of cells in the body Image processing in MRI of brain is highly essential due to accurate detection of the type of brain abnormality which can reduce the chance of fatal result. Segmentation technique plays a major role in the brain tumor detection. This paper outlines an efficient image segmentation technique for the different ventricles affected brain tumor images. K-means segmentation is used for the brain tumor detection and extraction. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). By selecting the significant features the Classification has been performed to labeling the images into normal and abnormal (tumor detected) using Support Vector Machine (SVM).

Other Details

Paper ID: IJSRDV3I60615
Published in: Volume : 3, Issue : 6
Publication Date: 01/09/2015
Page(s): 1371-1376

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