A Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding

Image entropy thresholding is one of the most widely used technique for multilevel thresholding. The endeavor of this paper is to focus on obtaining the optimal threshold points. Several meta-heuristics are being applied in literatures over the decade, for improving the accuracy and computational efficiency of Minimum Cross Entropy Thresholding (MCET) method. In this paper, we have incorporated a Differential Evolution (DE) based approach towards image segmentation. Results are also compared with modern state-of-art algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Further Mean Structural Similarity Index Measurement (SSIM) and Universal Image Quality Index (UIQI) are also being used for performance evaluation.

[1]  Swagatam Das,et al.  LINEAR ANTENNA ARRAY SYNTHESIS WITH CONSTRAINED MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION , 2010, Progress In Electromagnetics Research B.

[2]  Peng-Yeng Yin,et al.  Multilevel minimum cross entropy threshold selection based on particle swarm optimization , 2007, Appl. Math. Comput..

[3]  Shang Gao,et al.  An improved scheme for minimum cross entropy threshold selection based on genetic algorithm , 2011, Knowl. Based Syst..

[4]  Prasanna K. Sahoo,et al.  Threshold selection using Renyi's entropy , 1997, Pattern Recognit..

[5]  C. H. Li,et al.  An iterative algorithm for minimum cross entropy thresholding , 1998, Pattern Recognit. Lett..

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[8]  Ponnuthurai N. Suganthan,et al.  Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection , 2010, Inf. Sci..

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[11]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[12]  Patrick Siarry,et al.  A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem , 2010, Eng. Appl. Artif. Intell..

[13]  Nikhil R. Pal,et al.  On minimum cross-entropy thresholding , 1996, Pattern Recognit..

[14]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

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

[16]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .