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Identification of Blood Vessel Clot Region using Fuzzy C Means Clustering Based Artificial Bee Colony Algorithm
Kottaimalai Ramaraj1, Vishnuvarthanan Govindaraj2, Pallikonda Rajasekaran Murugan3, Yudong Zhang4, Shuihua Wang5, Arunprasath Thiyagarajan6
1Kottaimalai Ramaraj, Department of ICE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
2Vishnuvarthanan Govindaraj, Department of BME, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
3Pallikonda Rajasekaran Murugan, Department of ECE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
4Yudong Zhang, Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK.
5 Shuihua Wang, Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK.
6Arunprasath Thiyagarajan, Department of ECE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 741-746 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11351291S419/19©BEIESP | DOI: 10.35940/ijeat.A1135.1291S419
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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: Medical image segmentation results in the multiple fractioning of an input image for a deeper analysis/insight. Localization of objects and detection of boundaries are the core-theme of using segmentation for medical images. It elucidates the process of finding the anatomic structures in medical images. In this paper, we put forth a technique that has Fuzzy C-Means clustering and Artificial Bee Colony (ABC) Optimization has delivered the segmentation of MRA brain image. Artificial Bee Colony (ABC) has been used by many researchers as it is a population-based stochastic approach that has better search-inspace abilities for various optimization problems. The unsupervised clustering FCM has produced candidate outcomes in medical image processing. FCM is mostly preferable for segmenting the soft tissues in brain model, and it provides better output when compared to some of the competitive clustering techniques like KM, EM and KNN. The output of the suggested techniques is verified by using real MRA brain images. The results of Statistical parameters show that our method is notably better compared to other algorithms.
Keywords: Magnetic Resonance Angiography (MRA) Image Segmentation, Fuzzy C– Means Clustering (FCM), Artificial Bee Colony Optimization (ABC), Blood Clot Identification.
Scope of the Article: Clustering