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Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

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Abstract

In this article is introduced an innovative segmentation methodology that is based on a two-dimensional (2D) histogram that permits to increase the quality of the segmented images. The 2D histogram is constructed using the Lateral Inhibition (LI) that helps maintain and remark different image features. To segment the image in the proposed approach, the 2D Rényi entropy is used, which is a multi-level thresholding technique. Since the complexity of the 2D Rényi entropy increases with the number of thresholds, it is necessary to use an efficient search mechanism. To perform this task, it is also proposed an improved version of the Electromagnetic Field Optimization (EFO) algorithm that employs the High Disruptive Polynomial Mutation (HDPM) to exploit the search space intensively. The proposed metaheuristic is called MEFO. In combination with the 2D Rényi entropy creates a robust mechanism able to find the optimal configuration of thresholds that permits an accurate classification of the information contained in the 2D histogram generated using the (LI). The performance of the MEFO is tested over the Berkeley Segmentation Dataset (BSDS100) that contains 100 images with different complexities. The experiments include quantitative, qualitative, and statistical tests that permit the MEFO's efficiency in both senses for image segmentation and for solving multidimensional real optimization problems. Moreover, different comparisons validate the capabilities of the proposed algorithms to segment the images properly.

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References

  1. Abed-Alguni BH, Paul DJ (2020) Hybridizing the cuckoo search algorithm with different mutation operators for numerical optimization problems. J Intell Syst 29:1043–1062.https://doi.org/10.1515/jisys-2018-0331

    Article  Google Scholar 

  2. Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22.https://doi.org/10.1016/j.swevo.2015.07.002

    Article  Google Scholar 

  3. Abutaleb AS (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput Vis Graph Image Process 47:22–32.https://doi.org/10.1016/0734-189X(89)90051-0

    Article  Google Scholar 

  4. Aja-Fernandez S, Estepar RSJ, Alberola-Lopez C, Westin C-F (2006) Image quality assessment based on local variance. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 4815–4818

  5. Anita, Yadav A (2019) AEFA: Artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108.https://doi.org/10.1016/j.swevo.2019.03.013

    Article  Google Scholar 

  6. Anitha J, Immanuel Alex Pandian S, Akila Agnes S (2021) An efficient multi-level color image thresholding based on modified whale optimization algorithm. Expert Syst Appl 178:115003.https://doi.org/10.1016/j.eswa.2021.115003

    Article  Google Scholar 

  7. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12.https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  8. Bouchekara HREH, Zellagui M, Abido MA (2017) Optimal coordination of directional overcurrent relays using a modified electromagnetic field optimization algorithm. Appl Soft Comput J 54:267–283.https://doi.org/10.1016/j.asoc.2017.01.037

    Article  Google Scholar 

  9. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society, pp 60–65

  10. Cheng HD, Chen YH, Jiang XH (2000) Thresholding using two-dimensional histogram and fuzzy entropy principle. IEEE Trans Image Process 9:732–735.https://doi.org/10.1109/83.841949

    Article  Google Scholar 

  11. Cuevas E, Rodríguez A, Alejo-Reyes A, Del-Valle-Soto C (2021) Blood vessel segmentation using differential evolution algorithm. Studies in computational intelligence. Springer Science and Business Media Deutschland GmbH, pp 151–167

  12. Dai S, Liu Q, Li P et al (2015) Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network. Infrared Phys Technol 68:10–14

    Article  Google Scholar 

  13. David G (1989) Genetic algorithms in search, optimization, and machine learning, 1st edn. Addison-Wesley, Boston

    MATH  Google Scholar 

  14. De UC, Das M (2021) Lesion detection in brain MRI using PSO based segmentation. Mater Today Proc.https://doi.org/10.1016/j.matpr.2021.02.195

  15. Deb K, Tiwari S (2008) Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. Eur J Oper Res 185:1062–1087.https://doi.org/10.1016/j.ejor.2006.06.042

    Article MathSciNet MATH  Google Scholar 

  16. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68.https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  17. Hartline HK, Wagner HG (1956) Inhibition in the eye of Limulus. J Gen Physiol 39:651–673.https://doi.org/10.1085/jgp.39.5.651

    Article  Google Scholar 

  18. He L, Huang S (2020) An efficient krill herd algorithm for color image multi-level thresholding segmentation problem. Appl Soft Comput J 89:106063.https://doi.org/10.1016/j.asoc.2020.106063

    Article  Google Scholar 

  19. Jiang D, Li G, Tan C et al (2021) Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model. Futur Gener Comput Syst 123:94–104.https://doi.org/10.1016/j.future.2021.04.019

    Article  Google Scholar 

  20. Kalyani R, Sathya PD, Sakthivel VP (2021) Multi-level thresholding for image segmentation with exchange market algorithm. Multimed Tools Appl 1–39.https://doi.org/10.1007/s11042-021-10909-w

  21. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285

  22. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Neural Networks. Proc IEEE Int Conf 4:1942–1948.https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  23. Kotaridis I, Lazaridou M (2021) Remote sensing image segmentation advances: A meta-analysis. ISPRS J Photogramm Remote Sens 173:309–322

    Article  Google Scholar 

  24. Kucukoglu I (2019) Adaptive electromagnetic field optimization algorithm for the solar cell parameter identification problem. Int J Photoenergy 2019:1–16.https://doi.org/10.1155/2019/4692108

    Article  Google Scholar 

  25. Lan J, Zeng Y (2013) Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram. Optik (Stuttg) 124:3756–3760.https://doi.org/10.1016/j.ijleo.2012.11.023

    Article  Google Scholar 

  26. Lei B, Fan J (2019) Image thresholding segmentation method based on minimum square rough entropy. Appl Soft Comput J 84:105687.https://doi.org/10.1016/j.asoc.2019.105687

    Article  Google Scholar 

  27. Li CH, Lee CK (1993) Minimum cross entropy thresholding. Pattern Recognit 26:617–625.https://doi.org/10.1016/0031-3203(93)90115-D

    Article  Google Scholar 

  28. Li X-F, Liu H-Y, Yan M, Wei T-P (2017) Infrared image segmentation based on AAFSA and 2D-Renyi entropy threshold selection. DEStech Trans Comput Sci Eng.https://doi.org/10.12783/dtcse/aice-ncs2016/5692

    Article  Google Scholar 

  29. Livio M (2003) The golden ratio: the story of PHI, the world’s most astonishing number. In: Crown

  30. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165.https://doi.org/10.1016/j.chemolab.2015.08.020

    Article  Google Scholar 

  31. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE Comput. Soc, pp 416–423

  32. Matajira-Rueda D, Cruz Duarte J, Aviña Cervantes J, Correa Cely C (2018) Global optimization algorithms applied in a parameter estimation strategy. Rev UIS Ing 13:233–242.https://doi.org/10.18273/revuin.v17n1-2018023

    Article  Google Scholar 

  33. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67.https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  34. Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng Appl Artif Intell 71:226–235.https://doi.org/10.1016/j.engappai.2018.03.001

    Article  Google Scholar 

  35. Portes de Albuquerque M, Esquef IA, Gesualdi Mello AR, Portes de Albuquerque M (2004) Image thresholding using Tsallis entropy. Pattern Recognit Lett 25:1059–1065.https://doi.org/10.1016/j.patrec.2004.03.003

    Article  Google Scholar 

  36. Price K, Storn RM, Lampinen JA (2005) Differential Evolution. Springer-Verlag, Berlin/Heidelberg

    MATH  Google Scholar 

  37. Ray S, Das A, Dhal KG et al (2021) Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation. Neural Comput Appl 33:5917–5949.https://doi.org/10.1007/s00521-020-05368-7

    Article  Google Scholar 

  38. Reisenhofer R, Bosse S, Kutyniok G, Wiegand T (2018) A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Process Image Commun 61:33–43.https://doi.org/10.1016/J.IMAGE.2017.11.001

    Article  Google Scholar 

  39. Sahoo PK, Arora G (2004) A thresholding method based on two-dimensional Renyi’s entropy. Pattern Recognit 37:1149–1161.https://doi.org/10.1016/j.patcog.2003.10.008

    Article MATH  Google Scholar 

  40. Sankur B, Sankur B, Sayood K (2002) Statistical evaluation of image quality measures. J Electron Imaging 11:206.https://doi.org/10.1117/1.1455011

    Article  Google Scholar 

  41. Sarkar S, Das S, Chaudhuri SS (2016) Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution. Expert Syst Appl 50:120–129.https://doi.org/10.1016/j.eswa.2015.11.016

    Article  Google Scholar 

  42. Sathya PD, Sakthivel VP (2013) Multilevel Renyi’s entropy threshold selection based on bacterial foraging algorithm. Lecture Notes in Electrical Engineering. Springer, India, pp 53–65

    Google Scholar 

  43. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146.https://doi.org/10.1117/1.1631315

    Article  Google Scholar 

  44. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423.https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article MathSciNet MATH  Google Scholar 

  45. Talebi B, Dehkordi MN (2018) Sensitive association rules hiding using electromagnetic field optimization algorithm. Expert Syst Appl 114:155–172.https://doi.org/10.1016/j.eswa.2018.07.031

    Article  Google Scholar 

  46. Tariq Jamal A, Abdel-Khalek S, Ben Ishak A (2021) Multi-level segmentation of medical images in the framework of quantum and classical techniques. Multimed Tools Appl 1–14.https://doi.org/10.1007/s11042-020-10235-7

  47. Upadhyay P, Chhabra JK (2020) Kapur’s entropy based optimal multi-level image segmentation using Crow Search Algorithm. Appl Soft Comput 97:105522.https://doi.org/10.1016/j.asoc.2019.105522

    Article  Google Scholar 

  48. Vig G, Kumar S (2021) Comparison of different metaheuristic algorithms for multi-level non-local means 2d histogram thresholding segmentation. Advances in Intelligent Systems and Computing. Springer, Berlin, pp 563–572

  49. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1:80.https://doi.org/10.2307/3001968

    Article  Google Scholar 

  50. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82.https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  51. Wu J, Li H, Xia Z (2016) Image quality assessment based on Structure Similarity. In: ICSPCC 2016 - IEEE International Conference on Signal Processing, Communications and Computing, Conference Proceedings. Institute of Electrical and Electronics Engineers Inc

  52. Xue-guang W, Shu-hong C (2012) An improved image segmentation algorithm based on two-dimensional Otsu Method. Inf Sci Lett 1:77–83.https://doi.org/10.12785/isl/010202

    Article  Google Scholar 

  53. Yan M, Wang J, Li J et al (2020) Traffic scene semantic segmentation using self-attention mechanism and bi-directional GRU to correlate context. Neurocomputing 386:293–304.https://doi.org/10.1016/j.neucom.2019.12.007

    Article  Google Scholar 

  54. Yurtkuran A (2019) An improved electromagnetic field optimization for the global optimization problems. Comput Intell Neurosci.https://doi.org/10.1155/2019/6759106

    Article  Google Scholar 

  55. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20:2378–2386.https://doi.org/10.1109/TIP.2011.2109730

    Article MathSciNet MATH  Google Scholar 

  56. Zhao S, Wang P, Heidari AA et al (2021) Multi-level threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 134:104427.https://doi.org/10.1016/j.compbiomed.2021.104427

    Article  Google Scholar 

  57. Zhou Wang, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9:81–84.https://doi.org/10.1109/97.995823

    Article  Google Scholar 

  58. Życzkowski K (2003) Rényi extrapolation of Shannon entropy. Open Syst Inf Dyn 10:297–310.https://doi.org/10.1023/A:1025128024427

    Article MathSciNet MATH  Google Scholar 

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Authors and Affiliations

  1. División de Tecnologías para la Integración Ciber-Humana, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, 44430, Guadalajara, Jal, México

    Itzel Aranguren, Arturo Valdivia, Marco Pérez-Cisneros, Diego Oliva & Valentín Osuna-Enciso

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  1. Itzel Aranguren

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  2. Arturo Valdivia

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  3. Marco Pérez-Cisneros

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Aranguren, I., Valdivia, A., Pérez-Cisneros, M.et al. Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization.Multimed Tools Appl81, 10023–10049 (2022). https://doi.org/10.1007/s11042-022-11959-4

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