• Mohammed Zakir Bellary
• Dr B.Aziz Musthafa
• Dr Zahid Ansari

The Huge demand of Magnetic Resonance Imaging (MRI) in the world of medical field has helped the doctors to analyze and detect relevant disease. MRI is not only a reliable technique for the assessment of pathology but also a useful tool for monitoring the progression of disease. The capability of MRI to provide a fast three dimensional visualization has resulted in this technology being extensively used for the diagnosis and therapy of pathologies of various organs which are then used during surgery. Clustering techniques are widely used in image segmentation to capture similar interests and trends among users accessing a data image. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. This paper reviews some of the popularly used clustering techniques: k-Means, k-Medoids, Fuzzy C-Means, Leader and DBSCAN. Performance and validity results of each technique are presented and compared.

k-means clustering; k-medoids clustering; leader clustering; DBSCAN

"Segmentation of Pathological MR Images for Discovery of Useful Patterns Using Various Clustering Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.451-459, May 2019, Available :http://www.jetir.org/papers/JETIRCU06092.pdf

"Segmentation of Pathological MR Images for Discovery of Useful Patterns Using Various Clustering Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp451-459, May 2019, Available at : http://www.jetir.org/papers/JETIRCU06092.pdf