A Novel Image Segmentation Algorithm Based on Fuzzy C-means Algorithm and Neutrosophic Set

DOI

10.2991/jcis.2008.44How to use a DOI?

Keywords

Image segmentation, fuzzy c-means, Neutrsophic set, Entropy.

Abstract

Image segment is an important step in image processing, pattern recognition and computer vision. Numerous algorithms have been proposed to in this field for last twenty years. However, a generalized segmentation method, especial for noisy image, are not studied greatly. A neutrosophic set (NS), a part of neu-trosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a formal framework that has been recently pro-posed. However, the neutrosophic set needs to be specified from a technical point of view for a given application or field. We apply the neutrosophic set in image domain and define some concepts and operations for image segmentation. The image G is transformed into NS do-main. Then, the entropy in neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, -mean operation is proposed to reduce the set indeterminancy. Finally, a new fuzzy c-means algorithm, -fuzzy-c-means (-FCM) is proposed to segment the image on NS domain. We have conducted ex-periments on a variety of images. The ex-perimental results demonstrate that the proposed approach can segment the im-ages automatically and effectively. Espe-cially, it can process the “clean” images and the images with noise without know-ing its type, which is the most difficult task for image segmentation.

Copyright

© 2008, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanhui Guo
AU  - H.D. Cheng
AU  - Wei Zhao
AU  - Yingtao Zhang
PY  - 2008/12
DA  - 2008/12
TI  - A Novel Image Segmentation Algorithm Based on Fuzzy C-means Algorithm and Neutrosophic Set
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 256
EP  - 261
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.44
DO  - 10.2991/jcis.2008.44
ID  - Guo2008/12
ER  -