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Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM

  • Research Article - Computer Engineering and Computer Science
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Abstract

Image segmentation is to recognize structures in the image that are expected to signify scene objects. It is widely used by the radiologists to segment the medical images into meaningful regions. Thus, various segmentation techniques in medical imaging depending on the region of interest had been proposed. In this article, a robust brain tumor classification method is proposed, which focuses on the structural analysis on both tumorous and normal tissues. The proposed system consists of preprocessing, segmentation, feature extraction and classification. In preprocessing steps, anisotropic filter is used to eliminate the noise and enhances the image quality for skull-stripping process. In feature extraction, some specific features are extracted using texture as well from intensity using modified multi-texton structure descriptor. The hybrid kernel is designed in the classification stage and applied to training of support vector machine to perform automatic classification of tumor in magnetic resonance imaging (MRI) images. For comparative analysis, the proposed method is compared with the existing works using k-fold cross-validation method. The accuracy level (93 %) for our proposed approach (αK1, K1 + K2, K1 * K2) proved is good at detecting the tumors in the brain MRI images.

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Authors and Affiliations

  1. Department of CSE, PSN College of Engineering and Technology, Tirunelveli, India

    A. Jayachandran

  2. Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India

    R. Dhanasekaran

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  1. A. Jayachandran
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  2. R. Dhanasekaran
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Correspondence to A. Jayachandran.

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Jayachandran, A., Dhanasekaran, R. Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM. Arab J Sci Eng 39, 7073–7086 (2014). https://doi.org/10.1007/s13369-014-1334-x

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  • DOI: https://doi.org/10.1007/s13369-014-1334-x

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