Automated Brain Tumor Segmentation and Identification using MR Images
Mahalakshmi1, Krishnappa H K2, Jayadevappa D3
1Ms. Mahalakshmi*, Research Scholar, Department of Computer Science & Engineering, R V College of Engineering (Autonomous), Bangalore, Karnataka, India.
2Dr. Krishnappa H K, Department of Computer Science & Engineering, R V College of Engineering (Autonomous), Bangalore, Karnataka, India.
3Dr. Jayadevappa D, Department of Electronics & Instrumentation Engineering, JSS Academy of Technical Education, Mysore, Karnataka, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2647-2651 | Volume-8 Issue-12, October 2019. | Retrieval Number: K22140981119//2019©BEIESP | DOI: 10.35940/ijitee.K2214.0981119
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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: Automatic identification of tumor in human brain is a challenging task due to its varying in size, shape and location. This paper proposes a multi-modality technique for the segmentation of brain tumor its classification to differentiate easily between cancerous and non-cancerous tumor from MR images of the human brain. To achieve this, different segmentation and classification techniques have been applied. The important stages involved in the proposed technique are pre-processing, segmentation and classification stages. The pre-processing step is carried out using wavelet transform, segmentation stage is done by applying modified Chan-Vese model and finally the extracted tumor can be classified as benign or malignant using Support Vector Machine (SVM) classifier. The experimental results on MR images prove that, the proposed method is efficient and robust to noise. Moreover, the comparisons with existing techniques also show that, the proposed method takes less computational time and classify the tumors very accurately.
Keywords: Segmentation, SVM, Active Contours, MRI, Wavelet Transform.
Scope of the Article: Image Processing and Pattern Recognition