Multilevel thresholding using grey wolf optimizer for image segmentation

Abstract Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.

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

[2]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[3]  R. Kayalvizhi,et al.  Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images , 2011 .

[4]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[5]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[6]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[7]  Millie Pant,et al.  Multi-level image thresholding by synergetic differential evolution , 2014, Appl. Soft Comput..

[8]  Amitava Chatterjee,et al.  A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding , 2008, Expert Syst. Appl..

[9]  R. Kayalvizhi,et al.  Modified bacterial foraging algorithm based multilevel thresholding for image segmentation , 2011, Eng. Appl. Artif. Intell..

[10]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[11]  M. Maitra,et al.  A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging , 2008 .

[12]  Akshay K. Singh,et al.  Deformable models in medical image analysis , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[13]  Erik Valdemar Cuevas Jiménez,et al.  A multi-threshold segmentation approach based on Artificial Bee Colony optimization , 2012, Applied Intelligence.

[14]  Ling-Hwei Chen,et al.  A fast iterative scheme for multilevel thresholding methods , 1997, Signal Process..

[15]  Hao Gao,et al.  Multilevel Thresholding for Image Segmentation Through an Improved Quantum-Behaved Particle Swarm Algorithm , 2010, IEEE Transactions on Instrumentation and Measurement.

[16]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[17]  R. Kayalvizhi,et al.  Optimal multilevel thresholding using bacterial foraging algorithm , 2011, Expert Syst. Appl..

[18]  Ashish Kumar Bhandari,et al.  Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy , 2014, Expert Syst. Appl..

[19]  Pau-Choo Chung,et al.  A Fast Algorithm for Multilevel Thresholding , 2001, J. Inf. Sci. Eng..

[20]  S. Soltani,et al.  SURVEY A Survey of Thresholding Techniques , 1988 .

[21]  Swagatam Das,et al.  Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution , 2016, Expert Syst. Appl..

[22]  Marcin Ciecholewski,et al.  An edge-based active contour model using an inflation/deflation force with a damping coefficient , 2016, Expert Syst. Appl..

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

[24]  Janez Brest,et al.  A hybrid differential evolution for optimal multilevel image thresholding , 2016, Expert Syst. Appl..

[25]  Peng-Yeng Yin,et al.  A fast scheme for optimal thresholding using genetic algorithms , 1999, Signal Process..

[26]  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..

[27]  Milan Tuba,et al.  Improved Bat Algorithm Applied to Multilevel Image Thresholding , 2014, TheScientificWorldJournal.

[28]  Erik Valdemar Cuevas Jiménez,et al.  A novel multi-threshold segmentation approach based on differential evolution optimization , 2010, Expert Syst. Appl..

[29]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[30]  H. R. Keshavan,et al.  An optimal multiple threshold scheme for image segmentation , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[31]  Prasanta K. Panigrahi,et al.  Multilevel thresholding for image segmentation through a fast statistical recursive algorithm , 2006, Pattern Recognit. Lett..

[32]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[33]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

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

[35]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.