Image Segmentation using a Fuzzy Roughness Measure
Sheeja T. K1, Sunny Kuriakose A.2
1Sheeja T. K., Department of Mathematics, T.M.J.M. Govt. College, Manimalakunnu, India.
2Dr. Sunny Kuriakose A., Professor and Dean,, FISAT Angamaly, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 307-311| Volume-8 Issue-6, August 2019. | Retrieval Number: E7648068519 /2019©BEIESP | DOI: 10.35940/ijeat.E7648.068519
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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: Measures of uncertainty are highly useful for determining the information content of a system. In this paper, new measures of information on fuzzy approximation spaces are introduced based on divergence measures of fuzzy sets. The proposed fuzzy rough uncertainty measure is used to develop an algorithm for histogram based foreground background segmentation of a grey level image and it is experimented with twelve standard test images. It is observed that the overlapping of the foreground background pixels in the images segmented using the proposed method is lesser than those produced by OTSU and FCM methods. The segmented images are compared using their root mean square error values.
Keywords: Rough set, Fuzzy rough set, Uncertainty measure, Image segmentation.