Texture Characterization and Classification to Detect Brain Tumor
N. Kavitha1, Sai Sundara Amulya Ganti2, K. Sudha Rani3, K. Mani Kumari4, B. Bhagya Sree5

1N. Kavitha*, B.Tech, Department of ECE, Guru Nanak Engineering College, JNTUH, Hyderabad, Telangana, India.
2Sai Sundara Amulya Ganti, B.Tech, Department of EIE, VNR VJIET, Telangana, India.
3Dr. K Sudha Rani, Ph.D., Department of Biomedical Instrumentation, JNTU Kakinada, Andhra Pradesh, India.
4K. Mani Kumari, Pursing Ph.D., Osmania University, Hyderabad, Telangana, India.
5B. Bhagyasree, M. Tech, Department of Digital Systems, Osmania University, Hyderabad, Telangana, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1370-1372 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2470039520/2020©BEIESP | DOI: 10.35940/ijitee.E2470.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the field of medical sciences, brain tumor detection has immense significance. Extraction of peculiar tumor portion along with certain features is possible with the use of methods that come under image processing. In the recent years techniques like segmentation and morphological have been undertaken to detect the set of unusual cells that grow in the brain which might be malignant or benign. This paper deals with characterization of texture to obtain Haralick features, with texture being the principle attribute of an image and finds lot of application in image processing. This involves the use of SVM classifier in the algorithm to classify texture in order to detect brain tumor. It has been tested for 70 images and statistical parameters have been calculated and the obtained accuracy is 97.1%, precision is 98.4% and sensitivity is 98%. 
Keywords: GLCM, Haralick features, SVM Classifier, Texture Defect Detection
Scope of the Article: Classification