Examining the Pathological Portions in MR Brain Slices using Automated Map and Improved Fuzzy K-Means Clustering
S. Vigneshwaran1, Vishnuvarthanan Govindaraj2, N. Anitha3, M. Pallikonda Rajasekaran4, T. Arunprasath5
1Vigneshwaran, Department of ECE, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
2Vishnuvarthanan, Department of BME, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
3Anitha, Department of ECE, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
4Pallikonda Rajasekaran, Department of ECE, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
5Arunprasath, Department of BME, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
Manuscript received on 10 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 30 December 2019 | PP: 942-946 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11541292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1154.1292S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Identification of pathological structures (tissue and tumor region) in brain MR images is executed by an automated algorithm, and it requires improvement in processing time and segmentation accuracy. Oncological experts have predicaments in detecting the tumor masses that have similar resemblance with the tissue matters. An innovative amalgamation of soft computing algorithms, such as the automated map and clustering technique is presented through this paper. The Self-Organizing Map (SOM), a subsection of map technique, and the clustering process named the Improved Fuzzy K-Means clustering (IFKM) are used for the automated segmentation of MR brain structures in this paper. The segmentation outcomes of the algorithm are accurate for brain MR image analysis, and it was evaluated using Jaccard index (TC), Mean Squared Error (MSE), Dice overlap Index (DOI) and Peak Signal to Nosie Ratio (PSNR) values in this paper. TC and DOI values were delivered as 84.43%, 91.43%, respectively. The efficiency of this algorithm is compared with other traditional approaches, and it has been confirmed that is better visualization of brain structures, which will greatly assist during Oncological treatment.
Keywords: Improved Fuzzy K-Means Clustering, Tumor Identification, Pathological Detection, Self-Organizing Map (Som), MR Brain Image Analysis.
Scope of the Article: Fuzzy Logics