Image thresholding based on Pareto multiobjective optimization

A new image thresholding method based on multiobjective optimization following the Pareto approach is presented. This method allows to optimize several segmentation criteria simultaneously, in order to improve the quality of the segmentation. To obtain the Pareto front and then the optimal Pareto solution, we adapted the evolutionary algorithm NSGA-II (Deb et al., 2002). The final solution or Pareto solution corresponds to that allowing a compromise between the different segmentation criteria, without favouring any one. The proposed method was evaluated on various types of images. The obtained results show the robustness of the method, and its non dependence towards the kind of the image to be segmented.

[1]  Shu-Kai S. Fan,et al.  Optimal multi-thresholding using a hybrid optimization approach , 2005, Pattern Recognit. Lett..

[2]  Kalyanmoy Deb,et al.  On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods , 2007, Eur. J. Oper. Res..

[3]  Joseph Moysan,et al.  Bscan image segmentation by thresholding using cooccurrence matrix analysis , 1996, Pattern Recognit..

[4]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Richard F. Hartl,et al.  Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection , 2004, Ann. Oper. Res..

[6]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[7]  H. Schwefel,et al.  Evolutionary approaches to solve three challenging engineering tasks , 2000 .

[8]  Prasanna K. Sahoo,et al.  Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy , 2006, Pattern Recognit. Lett..

[9]  Kim Fung Man,et al.  Multiobjective Optimization , 2011, IEEE Microwave Magazine.

[10]  Paola Campadelli,et al.  Quantitative evaluation of color image segmentation results , 1998, Pattern Recognit. Lett..

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

[12]  Lorenzo Bruzzone,et al.  Image thresholding based on the EM algorithm and the generalized Gaussian distribution , 2007, Pattern Recognit..

[13]  S. Pal,et al.  Object-background segmentation using new definitions of entropy , 1989 .

[14]  Hui-Fuang Ng,et al.  Automatic thresholding for defect detection , 2004, Third International Conference on Image and Graphics (ICIG'04).

[15]  B. Bhanu,et al.  Adaptive image segmentation using genetic and hybrid search methods , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[17]  Ahmed S. Abutableb.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

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

[19]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[20]  A. Nakib,et al.  Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.