Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis
Senthilkumar Natarajan1, Vishnuvarthanan Govindaraj2, Kannapiran Balasubramanian3, Pallikonda Rajasekaran Murugan4, Arunprasath Thiyagarajan5, Anitha Narayanan6, Deny John Samuvel7
1Senthilkumar Natarajan, Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar (Tamil Nadu), India.
2Vishnuvarthanan Govindaraj, Department of Biomedical Engineering, Kalasalingam Academy of Research and Education Virudhunagar (Tamil Nadu), India.
3Kannapiran Balasubramanian, Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology Pollachi (Tamil Nadu), India.
4Pallikonda Rajasekaran Murugan, Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar (Tamil Nadu), India.
5Arunprasath Thiyagarajan, Department of Biomedical Engineering, Kalasalingam Academy of Research and Education Virudhunagar (Tamil Nadu), India.
6Anitha Narayanan, Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar (Tamil Nadu), India.
7Deny John Samuvel, Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar (Tamil Nadu), India.
Manuscript received on 06 December 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 642-647 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11591292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1159.1292S219
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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: Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.
Keywords: Jaya Algorithm (JA), Tumor detection, Fuzzy C Means Clustering, Meta-heuristic Optimisation.
Scope of the Article: Clustering