A novel image thresholding method based on membrane computing and fuzzy entropy

Authors:
Hong Peng;Jun Wang;Mario J. Pérez-Jiménez;Peng Shi
Affiliations:
School of Mathematics and Computer Engineering, Xihua University, Chengdu, China and Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of ...;School of Electrical and Information Engineering, Xihua University, Chengdu, China;Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, Spain;Department of Computing and Mathematical Sciences, University of Glamorgan, Pontypridd, UK and School of Engineering and Science, Victoria University, Melbourne, Australia
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Year:
2013

Citing 19
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Special issue: Computational intelligence models for image processing and information reasoning

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning

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Abstract

Multi-level thresholding methods are a class of most popular image segmentation techniques, however, they are not computationally efficient since they exhaustively search the optimal thresholds to optimize the objective function. In order to eliminate the shortcoming, a novel multi-level thresholding method for image segmentation based on tissue P systems is proposed in this paper. The fuzzy entropy is used as the evaluation criterion to find optimal segmentation thresholds. The presented method can effectively search the optimal thresholds for multi-level thresholding based on fuzzy entropy due to parallel computing ability and particular mechanism of tissue P systems. Experimental results of both qualitative and quantitative comparisons for the proposed method and several existing methods illustrate its applicability and effectiveness.