Spine Segmentation in Medical Image Processing using Unsupervised learning
B. Suresh Kumar1, B. L. Shivakumar2

1B. Suresh Kumar, Assistant Professor, Department of Computer Science, CBM College, Coimbatore (Tamil Nadu), India.
2Dr. B. L. Shivakumar, Director, Department of Computer Application, Sri Ramakrishna College of Arts and Science Nava India Road, Peelamedu, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 December 2014 | Revised Manuscript received on 20 December 2014 | Manuscript Published on 30 December 2014 | PP: 47-50 | Volume-4 Issue-7, December 2014 | Retrieval Number: G1905124714/14©BEIESP
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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: Image segmentation may be a method of segmenting a picture into teams of pixels supported some criterions. The aim of image segmentation is to alter or change the image illustration for the aim of straightforward understanding or faster analysis. Previously the fuzzy C-means (FCM) cluster algorithmic program was for the most part utilized in numerous medical image segmentation approaches. The normal two-component MRF model for segmentation needs coaching knowledge to estimate necessary model parameters and is therefore unsuitable for unsupervised segmentation. In order to beat the disadvantages of as sorted segmentation processes a brand new methodology of unattended segmentation is projected victimization ROR (Robust Outlyingness Ratio). The advantages of proposed method is to improve accuracy level and speed of time.
Keywords: Adaptive Fuzzy K-Means(AFKM), Centrum, Fuzzy-C-Means (FCM), Spinal Cord, Unattended Segmentation, Vertebral.

Scope of the Article: Image Processing and Pattern Recognition