Clustering Techniques for Medical Imaging
Divya D. J.1, Prakasha S.2
1Divya D. J, Department of Computer Science and Engineering, VTU, Belagavi (Karnataka), India.
2Dr. Prakasha S, Department of Information Science and Engineering, RNSIT, Bengaluru (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 510-516 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11331292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1133.1292S19
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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: Nowadays medical imaging is becoming one of the popular techniques used to monitor human body to diagnose diseases, detect and treat injuries so that it can be treated. It helps in fetching desired information from the medical images. Clustering techniques in medical imaging is used to assist image based analysis of heterogeneous ailments by creating clusters of given population into homogeneous sub populations which helps in better understanding of the disease within each sub population. In this paper, we have discussed and compared various clustering techniques such as Fuzzy C Means clustering (FCM), Spatial Fuzzy C Means clustering(SFCM), K-Means and Particle Swarm Optimization Incorporative Fuzzy C Means clustering (PSOFCM), Gustafson Kessel (GK) clustering and Density Based Clustering of Applications with Noise (DBSCAN) to detect a tumor in human brain based on various image segmentation parameters. Accuracy of these algorithms is tested using MRI brain image.
Keywords: Clustering Techniques, Medical Imaging, FCM, SFCM, K-means, PSOFCM, DBSCAN, Gustafson Kessel, Multiple Clustering, Brain Tumor.
Scope of the Article: Clustering