Ganesan P, B. S. Sathish, V. Elamaran, R. Murugesan
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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.
Volume - 13,
Issue - 6,
Year - 2020
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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9. P. Ganesan, B. S. Sathish and G. Sajiv, "A comparative approach of identification and segmentation of forest fire region in high resolution satellite images," 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, 2016, pp. 1-6. doi: 10.1109/STARTUP.2016.7583959
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22. Ganesan P, M.Ganesh , L.M. I. Leo Joseph and V. Kalist, “ Central Retinal Vein Occlusion: An Approach for the Detection and Extraction of Retinal Blood Vessels”, J. Pharm. Sci. & Res. Vol. 10(1), 2018, 192-195.
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25. Ganesan P, “Detection and Segmentation of Retinal Blood Vessel in Digital RGB and CIELUV color space Fundus Images”, Research J. Pharm. and Tech. 11(6): 2018, 2326-2330.
26. Sajiv G and Ganesan P. Comparative Study of Possiblistic Fuzzy C-Means Clustering based Image Segmentation in RGB and CIELuv Color Space. International Journal of Pharmacy & Technology. 8(1); 2016; 10899-10909.
27. Ganesan P and Sajiv G. Unsupervised Clustering of Satellite Images in CIELab Color Space using Spatial Information Incorporated FCM Clustering Method. International Journal of Applied Engineering Research. 10(20); 2015.
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.