Document Type : Review Paper
Authors
1 Computer Science,Computer science and information technology,Anbar University,Ramadi,Iraq
2 University of Anbar College of Computer Science and Information Technology Department of Computer Science
Abstract
Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated.
Keywords
Main Subjects
1] T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad Saad, "Performance analysis of image thresholding: Otsu technique," Meas. J. Int. Meas. Confed., vol. 114, pp. 298–307, Jan. 2018, doi: 10.1016/j. measurement. 2017.09.052.
[2] M. Azarbad, A. Ebrahimzade, and V. Izadian, "Segmentation of Infrared Images and Objectives Detection Using Maximum Entropy Method Based on the Bee Algorithm." [Online]. Available: http://www.mirlabs.org/ijcisim.
[3] S. Aja-Fernández, A. H. Curiale, and G. Vegas-Sánchez-Ferrero, "A local fuzzy thresholding methodology for multiregion image segmentation," Knowledge-Based Syst., vol. 83, no. 1, pp. 1–12, 2015, doi: 10.1016/j.knosys.2015.02.029.
[4] K. Tang, X. Yuan, T. Sun, J. Yang, and S. Gao, "An improved scheme for minimum cross entropy threshold selection based on genetic algorithm," Knowledge-Based Syst., vol. 24, no. 8, pp. 1131–1138, 2011, doi: 10.1016/j.knosys.2011.02.013.
[5] D. Mishra, I. Bose, U. C. De, and M. Das, "Medical Image Thresholding Using Particle Swarm Optimization," in Advances in Intelligent Systems and Computing, 2015, vol. 308 AISC, no. VOLUME 1, pp. 379–383, doi: 10.1007/978-81-322-2012-1_39.
[6] A. K. Bhandari, A. Kumar, S. Chaudhary, and G. K. Singh, "A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms," Expert Syst. Appl., vol. 63, pp. 112–133, Nov. 2016, doi: 10.1016/j.eswa.2016.06.044.
[7] M. A. El Aziz, A. A. Ewees, and A. E. Hassanien, "Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation," Expert Syst. Appl., vol. 83, pp. 242–256, Oct. 2017, doi: 10.1016/j.eswa.2017.04.023.
[8] A. Ahilan et al., "Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images," IEEE Access, vol. 7, pp. 89570–89580, 2019, doi: 10.1109/ACCESS.2019.2891632.
[9] A. Ramya, D. Murugan, G. Murugeswari, and N. Joseph, "Adaptive multi-threshold based de-noising filter for medical image applications," 2019.
[10] G. Ding, F. Dong, and H. Zou, "Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding," Appl. Soft Comput. J., vol. 84, p. 105704, 2019, doi: 10.1016/j.asoc.2019.105704.
[11] W. Liu et al., "Renyi's entropy based multilevel thresholding using a novel meta-heuristics algorithm," Appl. Sci., vol. 10, no. 9, 2020, doi: 10.3390/app10093225.
[12] S. Wang, K. Sun, W. Zhang, and H. Jia, "Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation," Math. Biosci. Eng., vol. 18, no. 4, pp. 3092–3143, 2021, doi: 10.3934/mbe.2021155.
[13] L. He and S. Huang, "An efficient krill herd algorithm for color image multilevel thresholding segmentation problem," Appl. Soft Comput. J., vol. 89, p. 106063, 2020, doi: 10.1016/j.asoc.2020.106063.
[14] F. Rezaei, H. Izadi, H. Memarian, and M. Baniassadi, "The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity," J. Pet. Sci. Eng., vol. 177, pp. 518–527, 2019, doi: 10.1016/j.petrol.2018.12.063.
[15] Z. Wang, E. Wang, and Y. Zhu, "Image segmentation evaluation: a survey of methods," Artif. Intell. Rev., vol. 53, no. 8, pp. 5637–5674, Dec. 2020, doi: 10.1007/s10462-020-09830-9.
[16] S. J. Mousavirad and H. Ebrahimpour-Komleh, "Human mental search-based multilevel thresholding for image segmentation," Appl. Soft Comput., vol. 97, Dec. 2020, doi: 10.1016/j.asoc.2019.04.002.
[17] J. Rahaman and M. Sing, "An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm," Expert Syst. Appl., vol. 174, no. January, p. 114633, 2021, doi: 10.1016/j.eswa.2021.114633.
[18] Y. Meraihi, A. B. Gabis, S. Mirjalili, and A. Ramdane-Cherif, "Grasshopper optimization algorithm: Theory, variants, and applications," IEEE Access, vol. 9, pp. 50001–50024, 2021, doi: 10.1109/ACCESS.2021.3067597.
[19] X. Bao, H. Jia, and C. Lang, "A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation," IEEE Access, vol. 7, pp. 76529–76546, 2019, doi: 10.1109/ACCESS.2019.2921545.
[20] V. Rajinikanth, N. Dey, S. C. Satapathy, and A. S. Ashour, "An approach to examine Magnetic Resonance Angiography based on Tsallis entropy and deformable snake model," Futur. Gener. Comput. Syst., vol. 85, pp. 160–172, 2018, doi: 10.1016/j.future.2018.03.025.
[21] A. K. Bhandari, A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation, vol. 32, no. 9. Springer London, 2020.
[22] S. Khan, D. H. Lee, M. A. Khan, A. R. Gilal, J. Iqbal, and A. Waqas, "Efficient and improved edge detection via a hysteresis thresholding method," Curr. Sci., vol. 118, no. 6, pp. 954–960, 2020, doi: 10.18520/cs/v118/i6/954-960.
[23] K. G. Dhal, S. Ray, A. Das, J. Gálvez, and S. Das, "Fuzzy Multilevel Color Satellite Image Segmentation Using Nature-Inspired Optimizers: A Comparative Study," J. Indian Soc. Remote Sens., vol. 47, no. 8, pp. 1391–1415, 2019, doi: 10.1007/s12524-019-01005-6.
[24] X. Li, Z. Zhao, and H. D. Cheng, "Fuzzy Entropy Threshold Approach to Breast Cancer Detection."
[25] H. Al-Hashimy, "Using Threshold Methods to Segment Highly Images beans without overlapping Saba Q."
[26] S. Khairnar, S. D. Thepade, and S. Gite, "Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade's SBTC," Intell. Syst. with Appl., vol. 10–11, p. 200046, 2021, doi: 10.1016/j.iswa.2021.200046.
[27] S. N and V. S, "Image Segmentation By Using Thresholding Techniques For Medical Images," Comput. Sci. Eng. An Int. J., vol. 6, no. 1, pp. 1–13, 2016, doi: 10.5121/cseij.2016.6101.
[28] J. M. García, C. A. Acosta, and M. J. Mesa, "Genetic algorithms for mathematical optimization," J. Phys. Conf. Ser., vol. 1448, no. 1, 2020, doi: 10.1088/1742-6596/1448/1/012020.
[29] S. Pare, A. K. Bhandari, A. Kumar, G. K. Singh, and S. Khare, "Satellite image segmentation based on different objective functions using genetic algorithm: A comparative study," Int. Conf. Digit. Signal Process. DSP, vol. 2015-Septe, no. 1, pp. 730–734, 2015, doi: 10.1109/ICDSP.2015.7251972.
[30] S. Pang, W. Li, H. He, Z. Shan, and X. Wang, "An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing," IEEE Access, vol. 7, pp. 146379–146389, 2019, doi: 10.1109/ACCESS.2019.2946216.
[31] A. Pradhan, S. K. Bisoy, and A. Das, "A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment," Journal of King Saud University - Computer and Information Sciences. King Saud bin Abdulaziz University, 2021, doi: 10.1016/j.jksuci.2021.01.003.
[32] R. Radha and R. Gopalakrishnan, "A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization," Microprocess. Microsyst., vol. 79, Nov. 2020, doi: 10.1016/j.micpro.2020.103283.
[33] S. Pare, A. K. Bhandari, A. Kumar, and G. K. Singh, Rényi's entropy and bat algorithm based color image multilevel thresholding, vol. 748. Springer Singapore, 2019.
[34] H. Liang, H. Jia, Z. Xing, J. Ma, and X. Peng, "Modified grasshopper algorithm-based multilevel thresholding for color image segmentation," IEEE Access, vol. 7, pp. 11258–11295, 2019, doi: 10.1109/ACCESS.2019.2891673.
[35] H. Wang et al., "Firefly algorithm with neighborhood attraction," Inf. Sci. (Ny)., vol. 382–383, pp. 374–387, 2017, doi: 10.1016/j.ins.2016.12.024.
[36] A. Sharma, R. Chaturvedi, U. K. Dwivedi, S. Kumar, and S. Reddy, "Firefly algorithm based effective gray scale image segmentation using multilevel thresholding and entropy function," Int. J. Pure Appl. Math., vol. 118, no. 5 Special Issue, pp. 437–443, 2018.
[37] H. Peng, Z. Zeng, C. Deng, and Z. Wu, "Multi-strategy serial cuckoo search algorithm for global optimization," Knowledge-Based Syst., vol. 214, p. 106729, 2021, doi: 10.1016/j.knosys.2020.106729.
[38] S. Pare, A. Kumar, V. Bajaj, and G. K. Singh, "An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy," Appl. Soft Comput. J., vol. 61, pp. 570–592, Dec. 2017, doi: 10.1016/j.asoc.2017.08.039.
[39] A. Bagheri, M. Bagheri, and A. Lorestani, "Optimal reconfiguration and DG integration in distribution networks considering switching actions costs using tabu search algorithm," J. Ambient Intell. Humaniz. Comput., 2020, doi: 10.1007/s12652-020-02511-z.
[40] G. Sangeetha, M. Vijayalakshmi, S. Ganapathy, and A. Kannan, A heuristic path search for congestion control in WSN, vol. 11. 2018.
[41] J. Lee and D. Perkins, "A simulated annealing algorithm with a dual perturbation method for clustering," Pattern Recognit., vol. 112, p. 107713, 2021, doi: 10.1016/j.patcog.2020.107713.
[42] E. H. Houssein, A. G. Gad, and Y. M. Wazery, "Jaya Algorithm and Applications: A Comprehensive Review," in Lecture Notes in Electrical Engineering, vol. 696, Springer Science and Business Media Deutschland GmbH, 2021, pp. 3–24.
[43] E. H. Houssein, A. G. Gad, and Y. M. Wazery, Jaya Algorithm and Applications: A Comprehensive Review, vol. 696. Springer International Publishing, 2021.
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