Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering
Priya B S1, Basavaraj N Jagadale2, Swaroopa H N3, Vijayalaxmi Hegde4
1Priya B S*, Department of PG studies in Electronics, Kuvempu University, Shimoga, India.
2Basavaraj N Jagadale, Department of Electronics, Kuvempu University, Shimoga, India.
3Swaroopa H N, Department of Electronics, Kuvempu University, Shimoga, India.
4Vijayalaxmi Hegde, Department of Electronics, MESMM Arts and Science College, Sirsi, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9548-9551 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9918118419/2019©BEIESP | DOI: 10.35940/ijrte.D9918.118419
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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: Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.
Keywords: Fuzzy c-means Clustering, Segmentation, Stationary Wavelet Transform.
Scope of the Article: Clustering.