Novel Improved Fuzzy C-Mean Algorithm for MR-Image Segmentation
Brijesh Shah1, Satish Shah2, Y P Kosta3
1Brijesh Shah Electronics and Communication Department, Charotar University of Science and Technology, Changa, India.
2Satish Shah, Electrical Department, M.S. University, Vadodara, India.
3Yogeshwar P Kosta, Electronics and Communication Department, Marwardi Group of Institutions, Rajkot, India.
Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 355-357 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0803062312 /2012©BEIESP
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© The Authors. Published By: 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 is a very important part of image processing. This paper presents an image segmentation approach using improved fuzzy c-mean (FCM) algorithm. The improved fuzzy c-mean algorithm is formulated by modifying the distance measurement of the original fuzzy c-mean algorithm. The Euclidean distance in the fuzzy c-mean algorithm is replaced by the jaccard index and correlation distance, and thus the corresponding algorithm is derived and called as the improved fuzzy c-mean algorithm which is never earlier reported and that is shown to be more robust than original fuzzy c-mean algorithm. Experimental results are conducted on MR-images show that the proposed algorithms have better performance when noise and other artifacts are present than the original algorithms.
Keywords: Improved fuzzy c-mean algorithm, jaccard index, Medical image processing, image segmentation