Author(s): Ganesan P, B. S. Sathish, V. Elamaran, R. Murugesan

Email(s): gganeshnathan@gmail.com , subramanyamsathish@yahoo.co.in

DOI: 10.5958/0974-360X.2020.00458.8   

Address: Ganesan P1*, B. S. Sathish2, V. Elamaran3, R. Murugesan4
1Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, India.
2Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, India.
3Department of Electronics and Communication Engineering, School of EEE, SASTRA Deemed University, Thanjavur, India.
4Department of Electronics and Communication Engineering, Narsimha Reddy Engineering College, Secunderabad, Telangana, India.
*Corresponding Author

Published In:   Volume - 13,      Issue - 6,     Year - 2020


ABSTRACT:
In medical image processing, the detection and segmentation of brain tumor from the test image is the challenging but most important task. According to WHO, the brain tumor is second most common of cancer death among young people. The proposed approach for the brain tumor segmentation is worked on the principle of threshold and classification using support vector machine. The brain image of the brain is processed in such a way so that the tumor is extracted and displayed the segmented the tumor portion of the image. The gray level co-occurrence matrix and other image quality measures are utilized to calculate the statistical features of the extracted tumor portion of brain. Based on the extracted features, the category of tumor, either benign or malignant, is classified using SVM classifier.


Cite this article:
Ganesan P, B. S. Sathish, V. Elamaran, R. Murugesan. Brain Tumour Segmentation and Measurement Based on Threshold and Support Vector Machine Classifier. Research J. Pharm. and Tech 2020; 13(6):2573-2577. doi: 10.5958/0974-360X.2020.00458.8


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24.    https://www.mathworks.com/help/matlab/
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28.  Sathish BS, Ganesan P and Khamar Basha. Shaik. Color Image Segmentation based on Genetic Algorithm and Histogram Threshold. International Journal of Applied Engineering Research. 10(6); 2015; 123-127.

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