Abstract
For image segmentation, multilevel thresholding is treated as one of the widely used approach. However, this approach has a major issue that is it suffers of high computational complexity problem with the increase threshold levels. This paper presented a comparative performance analysis of some objective functions used for image segmentation using the concept of multilevel thresholding and a modified version of adaptive inertia weight Particle Swarm Optimization (PSO) technique. The PSO algorithm is applied to multilevel thresholding based image segmentation using either Otsu’s inter class variance, Kapur’s entropy or Masi entropy as an objective function. Each method is tested over various standard image dataset like Berkeley image database, USC-SIPI image dataset etc. The evaluated result of each method has been compared and the overall experimentation is performed in three different ways such as, PSO and Otsu’s method, PSO and Kapur’s entropy and PSO with Masi entropy. The performance and quality of the segmented images are measured using the following parameters such as, average Mean Structural Similarity Index (MSSIM), average Peak Signal to Noise Ratio (PSNR) values, average mean objective functions values and average CPU rum time values. The experimental analysis shows that the performance of Otsu’s inter-class variance function shows comparatively a better result than Kapur’s and Masi’s entropic method.
Similar content being viewed by others
Availability of data and materials
USC-SIPI image database https://sipi.usc.edu/database/database.php?volume=misc. BSD500 https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
References
Choy SK, Lam SY, Yu KW, Lee WY, Leung KT (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recogn 68:141–157
Nazir I, Haq IU, Khan MM, Qureshi MB, Ullah H, Butt S (2021) Efficient pre-processing and segmentation for lung cancer detection using fused CT images. Electronics 11(1):34
Rodríguez-Esparza E, Zanella-Calzada LA, Oliva D, Pérez-Cisneros M (2020) Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach. In: Medical imaging 2020: computer-aided diagnosis, vol 11314. International Society for Optics and Photonics, p 1131424
Montalvo M, Guijarro M, Ribeiro A (2018) A novel threshold to identify plant textures in agricultural images by Otsu and principal component analysis. J Intell Fuzzy Syst 34(6):4103–4111
Sengar SS, Mukhopadhyay S (2019) Motion segmentation-based surveillance video compression using adaptive particle swarm optimization. Neural Comput Appl 32:11443–11457
Vasantrao CP, Gupta N (2023) Wader hunt optimization based UNET model for change detection in satellite images. Int J Inf Technol 15(3):1611–1623
Sharma A, Chaturvedi R, Kumar S, Dwivedi UK (2020) Multi-level image thresholding based on Kapur and Tsallis entropy using firefly algorithm. J Interdiscip Math 23(2):563–571
Kaur P (2017) Intuitionistic fuzzy sets based credibilistic fuzzy C-means clustering for medical image segmentation. Int J Inf Technol 9(4):345–351
Li Y, Chi Z (2005) MR Brain image segmentation based on self-organizing map network. Int J Inf Technol 11(8):45–53
Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Galvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180
Guo Y, Ashour AS (2019) Neutrosophic sets in dermoscopic medical image segmentation. In: Neutrosophic set in medical image analysis. Academic Press, pp 229–243
Farshi TR, Drake JH, Özcan E (2020) A multimodal particle swarm optimization-based approach for image segmentation. Expert Syst Appl 149:113233
Dhal KG, Das A, Ray S, Gálvez J (2021) Randomly attracted rough firefly algorithm for histogram based fuzzy image clustering. Knowl-Based Syst 216:106814
Ahmad M, Alam MZ, Umayya Z, Khan S, Ahmad F (2018) An image encryption approach using particle swarm optimization and chaotic map. Int J Inf Technol 10:247–255
Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Masi M (2005) A step beyond Tsallis and Rényi entropies. Phys Lett A 338(3):217–224
Li CH, Lee CK (1993) Minimum cross entropy thresholding. Pattern Recogn 26(4):617–625
Sarkar S, Das S, Chaudhuri SS (2017) Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl Soft Comput 50:142–157
Jena B, Naik MK, Panda R, Abraham A (2021) Maximum 3D Tsallis entropy based multilevel thresholding of brain MR image using attacking Manta Ray foraging optimization. Eng Appl Artif Intell 103:104293
Wang HQ, Cheng XW, Chen GC (2021) A hybrid adaptive quantum behaved particle swarm optimization algorithm based multilevel thresholding for image segmentation. In: 2021 IEEE international conference on information communication and software engineering (ICICSE). IEEE, pp 97–102
Wang S, Jia H, Peng X (2020) Modified salp swarm algorithm based multilevel thresholding for color image segmentation. Math Biosci Eng 17(1):700–724
Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images—a study with teaching learning based optimization. Pattern Recogn Lett 94:87–95
Farnoush R, Zar PB (2008) Image segmentation using Gaussian mixture model, pp 29–32
Huang Z-K, Chau K-W (2008) A new image thresholding method based on Gaussian mixture model. Appl Math Comput 205(2):899–907
Nie F, Zhang P, Li J, Ding D (2017) A novel generalized entropy and its application in image thresholding. Signal Process 134:23–34
Priya A, Agrawal RK, Rana B (2022) Fusion-based multilevel thresholding for image segmentation using evolutionary algorithm. In: 2022 IEEE 9th Uttar Pradesh Section international conference on electrical, electronics and computer engineering (UPCON) 2022 Dec 2. IEEE, pp 1–7
Bhandari AK (2020) A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput Appl 32(9):4583–4613
Liu L, Zhao D, Yu F, Heidari AA, Ru J, Chen H et al (2021) Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 138:104910
Houssein EH, Mohamed GM, Ibrahim IA, Wazery YM (2023) An efficient multilevel image thresholding method based on improved heap-based optimizer. Sci Rep 13(1):9094
Abdel-Khalek S, Ishak AB, Omer OA, Obada A-S (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik 131:414–422
Abualigah L, Diabat A, Sumari P, Gandomi AH (2021) A novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of covid-19 CT images. Processes 9(7):1155
Khairuzzaman AKM, Chaudhury S (2019) Masi entropy based multilevel thresholding for image segmentation. Multimed Tools Appl 78(23):33573–33591
Kanadath A, Jothi JA, Urolagin S (2023) Multilevel colonoscopy histopathology image segmentation using particle swarm optimization techniques. SN Comput Sci 4(5):427
Tang K, Xiao X, Wu J, Yang J, Luo L (2017) An improved multilevel thresholding approach based modified bacterial foraging optimization. Appl Intell 46(1):214–226
Chouhan SS, Kaul A, Sinzlr UP (2019) Plants leaf segmentation using bacterial foraging optimization algorithm. In: 2019 International conference on communication and electronics systems (ICCES). IEEE, pp 1500–1505
Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76
Houssein EH, Helmy BE-D, Oliva D, Elngar AA, Shaban H (2021) A novel black widow optimization algorithm for multilevel thresholding image segmentation. Expert Syst Appl 167:114159
Abdel AM, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256
Anitha J, Pandian SIA, Agnes SA (2021) An efficient multilevel color image thresholding based on modified whale optimization algorithm. Expert Syst Appl 178:115003
Naik MK, Swain M, Panda R, Abraham A (2022) Novel square error minimization-based multilevel thresholding method for COVID-19 X-ray image analysis using fast cuckoo search. Int J Image Graph 30:2450004
Gupta S, Deep K (2020) Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Appl 32:9521–9543
Yue X, Zhang H (2020) Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation. Appl Soft Comput 90:106157
Xu L, Jia H, Lang C, Peng X, Sun K (2019) A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access 7:19502–19538
Singh S, Mittal N, Singh H (2021) A multilevel thresholding algorithm using HDAFA for image segmentation. Soft Comput 25(16):10677–10708
Rahkar Farshi T, Orujpour M (2019) Multi-level image thresholding based on social spider algorithm for global optimization. Int J Inf Technol 11(4):713–718
Upadhyay P, Chhabra JK (2021) Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm. J Ambient Intell Humaniz Comput 12:1081–1098
Yan Z, Zhang J, Yang Z, Tang J (2020) Kapur’s entropy for underwater multilevel thresholding image segmentation based on whale optimization algorithm. IEEE Access 9:41294–41319
El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256
Ji W, He X (2021) Kapur’s entropy for multilevel thresholding image segmentation based on moth-flame optimization. Math Biosci Eng 18:7110–7142
Khehra BS, Singh A, Kaur LM (2022) Masi entropy-and grey wolf optimizer-based multilevel thresholding approach for image segmentation. J Inst Eng (India) Ser B. 103(5):1619–1642
Khairuzzaman AK, Chaudhury S (2017) Moth-flame optimization algorithm based multilevel thresholding for image segmentation. Int J Appl Metaheuristic Comput (IJAMC) 8(4):58–83
Acknowledgements
This work is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under Grant No. EEQ/2019/000657.
Funding
Authors declare no funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare no competing interest.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ahmed, S., Biswas, A. & Khairuzzaman, A.K.M. An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization. Int. j. inf. tecnol. 16, 1717–1732 (2024). https://doi.org/10.1007/s41870-023-01606-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-023-01606-y