Abstract
Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.
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References
Arifin, A.Z.; Asano, A.: Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recogn. Lett. 27(13), 1515–1521 (2006)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Revol, C.; Jourlin, M.: A new minimum variance region growing algorithm for image segmentation. Pattern Recogn. Lett. 18(3), 249–258 (1997)
Sezgin, M.; et al.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)
Weszka, J.S.: A survey of threshold selection techniques. Comput. Graph. Image Process. 7(2), 259–265 (1978)
Kapur, J.N.; Sahoo, P.K.; Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)
Du, J.: Property of Tsallis entropy and principle of entropy increase. ArXiv preprint arXiv:0802.3424 (2008)
Wong, A.K.; Sahoo, P.K.: A gray-level threshold selection method based on maximum entropy principle. IEEE Trans. Syst. Man Cybern. 19(4), 866–871 (1989)
Li, C.H.; Lee, C.: Minimum cross entropy thresholding. Pattern Recogn. 26(4), 617–625 (1993)
Li, C.; Tam, P.K.S.: An iterative algorithm for minimum cross entropy thresholding. Pattern Recogn. Lett. 19(8), 771–776 (1998)
Pal, N.R.: On minimum cross-entropy thresholding. Pattern Recogn. 29(4), 575–580 (1996)
Al-Ajlan, A.; El-Zaart, A.: Image segmentation using minimum cross-entropy thresholding. In: IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009, pp. 1776–1781. IEEE (2009)
Sathya, P.; Kayalvizhi, R.: Image segmentation using minimum cross entropy and bacterial foraging optimization algorithm. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 500–506. IEEE (2011)
Perez, A.; Gonzalez, R.C.: An iterative thresholding algorithm for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 6, 742–751 (1987)
Tao, W.; Jin, H.; Liu, L.: Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recogn. Lett. 28(7), 788–796 (2007)
Arora, S.; Acharya, J.; Verma, A.; Panigrahi, P.K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn. Lett. 29(2), 119–125 (2008)
Cao, L.; Bao, P.; Shi, Z.: The strongest schema learning GA and its application to multilevel thresholding. Image Vis. Comput. 26(5), 716–724 (2008)
Pare, S.; Bhandari, A.K.; Kumar, A.; Singh, G.K.; Khare, S.: Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), pp. 730–734. IEEE (2015)
Naidu, M.; Kumar, P.R.; Chiranjeevi, K.: Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alex. Eng. J. (2017)
Horng, M.H.: Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl. 38(11), 13785–13791 (2011)
Karaboga, D.; Gorkemli, B.; Ozturk, C.; Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
Ma, M.; Liang, J.; Guo, M.; Fan, Y.; Yin, Y.: Sar image segmentation based on artificial bee colony algorithm. Appl. Soft Comput. 11(8), 5205–5214 (2011)
Suresh, S.; Lal, S.: An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst. Appl. 58, 184–209 (2016)
Chao, Y.; Dai, M.; Chen, K.; Chen, P.; Zhang, Z.: A novel gravitational search algorithm for multilevel image segmentation and its application on semiconductor packages vision inspection. Optik Int. J. Light Electron Opt. 127(14), 5770–5782 (2016)
Chander, A.; Chatterjee, A.; Siarry, P.: A new social and momentum component adaptive pso algorithm for image segmentation. Expert Syst. Appl. 38(5), 4998–5004 (2011)
Gao, H.; Xu, W.; Sun, J.; Tang, Y.: Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans. Instrum. Meas. 59(4), 934–946 (2010)
Önüt, S.; Tuzkaya, U.R.; Doğaç, B.: A particle swarm optimization algorithm for the multiple-level warehouse layout design problem. Comput. Ind. Eng. 54(4), 783–799 (2008)
Sathya, P.; Kayalvizhi, R.: Pso-based tsallis thresholding selection procedure for image segmentation. Int. J. Comput. Appl. 5(4), 39–46 (2010)
Ye, Z.; Ye, Y.; Yin, H.: Qualitative and quantitative study of gas and PSO based evolutionary intelligence for multilevel thresholding. In: 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 812–817. IEEE (2017)
Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)
Civicioglu, P.; Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39(4), 315–346 (2013)
Pal, S.K.; Rai, C.; Singh, A.P.: Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int. J. Intell. Syst. Appl. 4(10), 50 (2012)
Mukhopadhyay, S.; Banerjee, S.: Global optimization of an optical chaotic system by chaotic multi swarm particle swarm optimization. Expert Syst. Appl. 39(1), 917–924 (2012)
Zheng, H.; Jie, J.; Hou, B.; Fei, Z.: A multi-swarm particle swarm optimization algorithm for tracking multiple targets. In: 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA), pp. 1662–1665. IEEE (2014)
Sarkar, S.; Das, S.; Chaudhuri, S.S.: A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn. Lett. 54, 27–35 (2015)
Yin, P.Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)
Oliva, D.; Hinojosa, S.; Osuna-Enciso, V.; Cuevas, E.; Pérez-Cisneros, M.; Sanchez-Ante, G.: Image segmentation by minimum cross entropy using evolutionary methods. Soft Comput. 1–20 (2017)
Pare, S.; Kumar, A.; Bajaj, V.; Singh, G.: An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy. Appl. Soft Comput. 61, 570–592 (2017)
Horng, M.H.; Liou, R.J.: Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst. Appl. 38(12), 14805–14811 (2011)
Bhandari, A.K.; Kumar, A.; Singh, G.K.: Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using kapurs, otsu and tsallis functions. Expert Syst. Appl. 42(3), 1573–1601 (2015)
Zhang, L.; Zhang, L.; Mou, X.; Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Sampat, M.P.; Wang, Z.; Gupta, S.; Bovik, A.C.; Markey, M.K.: Complex wavelet structural similarity: a new image similarity index. IEEE Trans. Image Process. 18(11), 2385–2401 (2009)
Kullback, S.: Information Theory and Statistics. Courier Corporation, Chelmsford (1997)
Tang, K.; Yuan, X.; Sun, T.; Yang, J.; Gao, S.: An improved scheme for minimum cross entropy threshold selection based on genetic algorithm. Knowl. Based Syst. 24(8), 1131–1138 (2011)
Hammouche, K.; Diaf, M.; Siarry, P.: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput. Vis. Image Underst. 109(2), 163–175 (2008)
Gao, B.; Li, X.; Woo, W.L.; yun Tian, G.: Physics-based image segmentation using first order statistical properties and genetic algorithm for inductive thermography imaging. IEEE Trans. Image Process. 27(5), 2160–2175 (2018)
Rafiee, G.; Dlay, S.S.; Woo, W.L.: Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches. Pattern Recogn. 46(10), 2685–2699 (2013)
Sulistyo, S.B.; Woo, W.; Dlay, S.: Ensemble neural networks and image analysis for on-site estimation of nitrogen content in plants. In: Proceedings of SAI Intelligent Systems Conference, pp. 103–118. Springer (2016)
Sulistyo, S.; Woo, W.L.; Dlay, S.; Gao, B.: Building a globally optimized computational intelligent image processing algorithm for on-site nitrogen status analysis in plants. IEEE Intell. Syst. (2018)
Alkassar, S.; Woo, W.L.; Dlay, S.S.; Chambers, J.A.: Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)
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Chakraborty, R., Sushil, R. & Garg, M.L. An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding. Arab J Sci Eng 44, 3005–3020 (2019). https://doi.org/10.1007/s13369-018-3400-2
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DOI: https://doi.org/10.1007/s13369-018-3400-2