Brain Tumor Segmentation using FCM and Symbolic Feature
Manjunath S1, Sanja Pande M B2, Raveesh B N3
1Manjunath S, Research Scholar, Department of Computer Science, Jain University, Bangalore (Karnataka), India.
2Dr. Sanja Pande M B, Professor and Head, Department of Computer Science, GMIT, Davangere (Karnataka), India.
3Dr. Raveesh B N, Professor and Head, Department of Psychiatry, Mysore Medical College, Mysuru (Karnataka), India.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 654-658 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11300886S19/19©BEIESP | DOI: 10.35940/ijeat.F1130.0886S19
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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: The brain tumor segmentation from image is interesting and challenging in the field of image processing and pattern recognition. An early detection of a brain tumor region helps the patient to take the correct medicine and increase the rate of the survival. The brain tumor segmentation is a process of differentiating the abnormal tissues and normal tissues. most common types of brain tumors are Benign and Malignant tumors. In this paper, the Fuzzy C-Means (FCM) approach is used to cluster the abnormal cells region and normal cells region in the brain image. The possible noises are removed by employing the median filter and morphological function is applied to extract the possible tumor region. The true tumor region is extracted with the help of symbolic features. Finally, the proposed methods is tested on T2- weighted MR brain images.
Keywords: Tumor, Fuzzy C Means, Symbolic, Segmentation.
Scope of the Article: Fuzzy Logics