Image thresholding using type-2 fuzzy c-partition entropy and particle swarm optimization algorithm

The imprecision in an image can be expressed in terms of ambiguity of belonging of a pixel in the image or the bottom (if it is black or white), or at the in-definition of the form and the geometry of a region in the image, or the combination of the two previous factors. The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper, a multi-level thresholding method for image segmentation using type-2 fuzzy c-partition entropy is presented. Type-2 fuzzy sets represent fuzzy sets with fuzzy membership values. The procedure for finding the optimal combination of all the fuzzy parameters is implemented by a particle swarm optimization algorithm. Experimental results reveal that the proposed image thresholding approaches has good performances for images with low contrast and grayscale ambiguity.

[1]  Anil K. Jain,et al.  Goal-Directed Evaluation of Binarization Methods , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Wen-Hsiang Tsai,et al.  Moment-preserving thresolding: A new approach , 1985, Comput. Vis. Graph. Image Process..

[3]  Hamid R. Tizhoosh,et al.  Image thresholding using type II fuzzy sets , 2005, Pattern Recognit..

[4]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[5]  W. TanW.,et al.  Uncertain Rule-Based Fuzzy Logic Systems , 2007 .

[6]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[7]  K. Benmahammed,et al.  Fuzzy c-partition using particle swarm optimization algorithm , 2012, 2012 IEEE International Conference on Complex Systems (ICCS).

[8]  Wen-Hsiang Tsai,et al.  Moment-preserving thresholding: a new approach , 1995 .

[9]  Assas Ouarda,et al.  A Comparison of Evolutionary Algorithms: PSO, DE and GA for Fuzzy C-Partition , 2014 .

[10]  P. Bolon,et al.  Analyse d'images: filtrage et segmentation , 1995 .

[11]  Wesley E. Snyder,et al.  Optimal thresholding - A new approach , 1990, Pattern Recognit. Lett..

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[14]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[15]  H. D. Cheng,et al.  Threshold selection based on fuzzy c-partition entropy approach , 1998, Pattern Recognit..