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Research Journal of Pharmacy and Technology
Year : 2019, Volume : 12, Issue : 7
First page : ( 3564) Last page : ( 3568)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2019.00608.5

A Simple Approach to Automated Brain Tumor Segmentation and Classification

Ganesan P1, Sathish B.S.2, Murugesan R.3

1Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad

2Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, India

3Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India

Corresponding Author E-mail: gganeshnathan@gmail.com, subramanyamsathish@yahoo.co.in, rmurugesan61@gmail.com

Online published on 24 December, 2019.

Abstract

Brain tumor is the abnormal growth of superfluous cells in central nervous system or brain. It is fact that brain tumor is second most common of cancer death among young people. There are two key categories of brain tumor as cancerous and non cancerous. The cancerous brain tumor is called as Malignant. It spreads very quickly and difficult to remove. The non-cancerous tumor, called Benign, growth rate is very slow as compared to malignant one and easy to remove. The work proposes a simple but more efficient method to detect and segment the brain tumor from the MRI image. The proposed work based on the threshold segmentation for the segmentation of the brain tumor. The MRI image of the brain is taken and processed in such a way so that the tumor is extracted from the given MRI image and displays the segmented part of the image which contains the tumor. The otsu global threshold performs tumor segmentation and image area opening applies to remove the small components form the tumor portion. The gray level co-occurrence matrix and other image quality measures computes (extracts) the features from the segmented image. Support vector machine classifier is finally classifies the tumor, either benign or malignant, based on the extracted features.

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Keywords

Brain tumor, Threshold, Principal Component Analysis, Discrete wavelet transform, Gray level co-occurrence matrix, Support vector machine.

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