Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing

Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation...

Full description

Autores:
Chen, Jiaochen
Cai, Zhennao
Chen, Huiling
·Chen, Xiaowei
Escorcia-Gutierrez, José
Mansour, Romany F.
Ragab, Mahmoud
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10568
Acceso en línea:
https://hdl.handle.net/11323/10568
https://repositorio.cuc.edu.co/
Palabra clave:
Multi-threshold image segmentation
2D Rényi entropy
Renal pathology
Swarm intelligence algorithms
Bionic algorithm
Cuckoo search algorithm
Rights
embargoedAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_a6126e4e5072ab7f14070c49c804a269
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10568
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
title Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
spellingShingle Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
Multi-threshold image segmentation
2D Rényi entropy
Renal pathology
Swarm intelligence algorithms
Bionic algorithm
Cuckoo search algorithm
title_short Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
title_full Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
title_fullStr Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
title_full_unstemmed Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
title_sort Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing
dc.creator.fl_str_mv Chen, Jiaochen
Cai, Zhennao
Chen, Huiling
·Chen, Xiaowei
Escorcia-Gutierrez, José
Mansour, Romany F.
Ragab, Mahmoud
dc.contributor.author.none.fl_str_mv Chen, Jiaochen
Cai, Zhennao
Chen, Huiling
·Chen, Xiaowei
Escorcia-Gutierrez, José
Mansour, Romany F.
Ragab, Mahmoud
dc.subject.proposal.eng.fl_str_mv Multi-threshold image segmentation
2D Rényi entropy
Renal pathology
Swarm intelligence algorithms
Bionic algorithm
topic Multi-threshold image segmentation
2D Rényi entropy
Renal pathology
Swarm intelligence algorithms
Bionic algorithm
Cuckoo search algorithm
dc.subject.proposal.fra.fl_str_mv Cuckoo search algorithm
description Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-10-31T16:19:47Z
dc.date.available.none.fl_str_mv 2023-10-31T16:19:47Z
2024
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.citation.spa.fl_str_mv Chen, J., Cai, Z., Chen, H. et al. Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing. J Bionic Eng 20, 2240–2275 (2023). https://doi.org/10.1007/s42235-023-00365-7
dc.identifier.issn.spa.fl_str_mv 1672-6529
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10568
dc.identifier.doi.none.fl_str_mv 10.1007/s42235-023-00365-7
dc.identifier.eissn.spa.fl_str_mv 2543-2141
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Chen, J., Cai, Z., Chen, H. et al. Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing. J Bionic Eng 20, 2240–2275 (2023). https://doi.org/10.1007/s42235-023-00365-7
1672-6529
10.1007/s42235-023-00365-7
2543-2141
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10568
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Journal of Bionic Engineering
dc.relation.references.spa.fl_str_mv 1. Huo, Y. K., Deng, R. N., Liu, Q., Fogo, A. B., & Yang, H. C. (2021). AI applications in renal pathology. Kidney International, 99, 1309–1320.
2. Danila, M. I., Pons-Estel, G. J., Zhang, J., Vila, L. M., Reveille, J. D., & Alarcon, G. S. (2009). Renal damage is the most important predictor of mortality within the damage index: Data from LUMINA LXIV, a multiethnic US cohort. Rheumatology (Oxford), 48, 542–545.
3. James, J. A., Guthridge, J. M., Chen, H., Lu, R., Bourn, R. L., Bean, K., Munroe, M. E., Smith, M., Chakravarty, E., Baer, A. N., Noaiseh, G., Parke, A., Boyle, K., Keyes-Elstein, L., Coca, A., Utset, T., Genovese, M. C., Pascual, V., Utz, P. J., … St Clair, E. W. (2020). Unique Sjögren’s syndrome patient subsets defned by molecular features. Rheumatology (Oxford), 59, 860–868.
4. Fanouriakis, A., Kostopoulou, M., Cheema, K., Anders, H. J., Aringer, M., Bajema, I., Boletis, J., Frangou, E., Houssiau, F. A., Hollis, J., Karras, A., Marchiori, F., Marks, S. D., Moroni, G., Mosca, M., Parodis, I., Praga, M., Schneider, M., Smolen, J. S., … Boumpas, D. T. (2020). 2019 Update of the Joint European League Against Rheumatism and European Renal AssociationEuropean Dialysis and Transplant Association (EULAR/ERAEDTA) recommendations for the management of lupus nephritis. Annals of the Rheumatic Diseases, 79, 713–723.
5. Liu, Z. J., Su, W., Ao, J. P., Wang, M., Jiang, Q. L., He, J., Gao, H., Lei, S., Nie, J. S., Yan, X. F., Guo, X. J., Zhou, P. H., Hu, H., & Ji, M. B. (2022). Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nature Communications, 13, 4050.
6. Jin, K., Huang, X. R., Zhou, J. X., Li, Y. X., Yan, Y., Sun, Y. B., Zhang, Q. N., Wang, Y. Q., & Ye, J. (2022). FIVES: A fundus image dataset for artifcial intelligence based vessel segmentation. Scientifc Data, 9, 475.
7. Li, Q. H., Song, D. Q., Yuan, C. M., & Nie, W. (2022). An image recognition method for the deformation area of open-pit rock slopes under variable rainfall. Measurement, 188, 110544.
8. Kline, A., Chung, H. J., Rahmani, W., & Chun, J. (2021). Semisupervised segmentation of renal pathology: an alternative to manual segmentation and input to deep learning training. In: IEEE Engineering in Medicine and Biology Society. Annual International Conference, Mexico, pp. 2688–2691.
9. Albayrak, A., & Bilgin, G. (2019). Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Medical & Biological Engineering & Computing, 57, 653–665.
10. Yoruk, U., Hargreaves, B. A., & Vasanawala, S. S. (2018). Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magnetic Resonance in Medicine, 79, 1696–1707.
11. Gadermayr, M., Eschweiler, D., Jeevanesan, A., Klinkhammer, B. M., Boor, P., & Merhof, D. (2017). Segmenting renal whole slide images virtually without training data. Computers in Biology and Medicine, 90, 88–97.
12. Li, X. L., Chen, X. J., Yao, J. H., Zhang, X., & Tian, J. (2011). Renal cortex segmentation using optimal surface search with novel graph construction. Medical Image Computing Computing Assisted Intervention, 14, 387–394.
13. Yang, X., Le Minh, H., Tim Cheng, K. T., Sung, K. H., & Liu, W. (2016). Renal compartment segmentation in DCE-MRI images. Medical Image Analysis, 32, 269–280.
14. Luo, J., Yang, Y. S., & Shi, B. Y. (2019). Multi-threshold Image segmentation of 2D Otsu based on improved adaptive diferential evolution algorithm. Journal of Electronics & Information Technology, 41, 2017–2024.
15. Nguyen, T. T., Wang, H. J., Dao, T. K., Pan, J. S., Ngo, T. G., & Yu, J. (2020). A scheme of color image multithreshold segmentation based on improved moth-fame algorithm. Ieee Access, 8, 174142–174159.
16. Mirjalili, S. (2015). Moth-fame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
17. Zhou, Y. Q., Yang, X., Ling, Y., & Zhang, J. Z. (2018). Metaheuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimedia Tools and Applications, 77, 23699–23727.
18. Zhao, D., Liu, L., Yu, F. H., Heidari, A. A., Wang, M. J., Oliva, D., Muhammad, K., & Chen, H. L. (2021). Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation. Expert Systems with Applications, 167, 114122.
19. Sharma, S., Saha, A. K., Majumder, A., & Nama, S. (2021). MPBOA - A novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimedia Tools and Applications, 80, 12035–12076.
20. Chakraborty, S., Sharma, S., Saha, A. K., & Saha, A. (2022). A novel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artifcial Intelligence Review, 55, 4605–4716.
21. Chakraborty, S., Saha, A. K., Nama, S., & Debnath, S. (2021). COVID-19 X-ray image segmentation by modifed whale optimization algorithm with population reduction. Computers in Biology and Medicine, 139, 104984.
22. Li, R. H., Wu, X. L., Tian, H., Yu, N., & Wang, C. (2022). Hybrid Memetic pretrained factor analysis-based deep belief networks for transient electromagnetic inversion. Ieee Transactions on Geoscience and Remote Sensing, 60, 1–20.
23. Duan, H. B., & Luo, Q. N. (2015). New progresses in swarm intelligence-based computation. International Journal of BioInspired Computation, 7, 26–35.
24. Lin, Y., Song, H., Ke, F., Yan, W. Z., Liu, Z. K., & Cai, F. M. (2022). Optimal caching scheme in D2D networks with multiple robot helpers. Computer Communications, 181, 132–142.
25. Chen, W. N., Zhang, J., Lin, Y., Chen, N., Zhan, Z. H., Chung, H. S. H., Li, Y., & Shi, Y. H. (2013). Particle swarm optimization with an aging leader and challengers. Ieee Transactions on Evolutionary Computation, 17, 241–258.
26. Storn, R., & Price, K. (1997). Diferential evolution—A simple and efcient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
27. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
28. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.
29. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849–872.
30. Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
31. Holland, J. H. (1992). Genetic algorithms. Scientifc American, 267, 66–72.
32. Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29, 17–35.
33. Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.
34. Gharehchopogh, F. S. (2022). An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems. Journal of Bionic Engineering, 19, 1177–1202.
35. Gharehchopogh, F. S. (2022). Advances in tree seed algorithm: A comprehensive survey. Archives of Computational Methods in Engineering, 29, 3281–3304.
36. Kiran, M. S. (2015). TSA: Tree-seed algorithm for continuous optimization. Expert Systems with Applications, 42, 6686–6698.
37. Xue, J. K., & Shen, B. (2020). A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering, 8, 22–34.
38. Gharehchopogh, F. S., Namazi, M., Ebrahimi, L., & Abdollahzadeh, B. (2022). Advances in Sparrow Search Algorithm: A comprehensive survey. Archives of Computational Methods in Engineering, 30, 427–455.
39. Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112.
40. Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2020). A comprehensive survey on symbiotic organisms search algorithms. Artifcial Intelligence Review, 53, 2265–2312.
41. Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 48, 1–24.
42. Sahoo, S. K., & Saha, A. K. (2022). A hybrid moth fame optimization algorithm for global optimization. Journal of Bionic Engineering, 19, 1522–1543.
43. Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., & Elaziz, M. A. (2022). Binary Horse herd optimization algorithm with crossover operators for feature selection. Computers in Biology and Medicine, 141, 105152.
44. Dong, R. Y., Chen, H. L., Heidari, A. A., Turabieh, H., Mafarja, M., & Wang, S. S. (2021). Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge-Based Systems, 233, 107529.
45. Liu, Y., Heidari, A. A., Cai, Z. N., Liang, G. X., Chen, H. L., Pan, Z. F., Alsufyani, A., & Bourouis, S. (2022). Simulated annealing-based dynamic step shufed frog leaping algorithm: Optimal performance design and feature selection. Neurocomputing, 503, 325–362.
46. Piri, J., & Mohapatra, P. (2021). An analytical study of modifed multi-objective Harris Hawk Optimizer towards medical data feature selection. Computers in Biology and Medicine, 135, 104558.
47. Zhang, Y. N., Liu, R. J., Heidari, A. A., Wang, X., Chen, Y., Wang, M. J., & Chen, H. L. (2021). Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing, 430, 185–212.
48. Yang, X. S., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing & Applications, 24, 169–174
49. Fan, J. Y., Xu, W. J., Huang, Y., & Samuel, R. D. J. (2021). Application of Chaos Cuckoo Search algorithm in computer vision technology. Soft Computing, 25, 12373–12387.
50. Long, W., Zhang, W. Z., Huang, Y. F., & Chen, Y. X. (2014). A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization. Journal of Central South University, 21, 3197–3204.
51. Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). Improved cuckoo search algorithm for hybrid fow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93–101.
52. Rosli, R., & Mohamed, Z. (2021). Optimization of modifed Bouc-Wen model for magnetorheological damper using modifed cuckoo search algorithm. Journal of Vibration and Control, 27, 1956–1967.
53. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. Ieee Transactions on Evolutionary Computation, 1, 67–82.
54. Wu, G. H., Mallipeddi, R., & Suganthan, P. (2016). Problem defnitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective realparameter optimization, Nanyang Technological University, Singapore, Tech. Rep, 1–18.
55. Fister, I., Yang, X. S., Fister, D., & Fister, I. (2014). Cuckoo search: A brief literature review. In X.-S. Yang (Ed.), Cuckoo search and frefy algorithm: Theory and applications (pp. 49–62). Springer International Publishing.
56. Al-Betar, M. A., Aljarah, I., Awadallah, M. A., Faris, H., & Mirjalili, S. (2019). Adaptive β-hill climbing for optimization. Soft Computing, 23, 13489–13512.
57. Al-Betar, M. A. (2017). β-Hill climbing: An exploratory local search. Neural Computing & Applications, 28, 153–168.
58. Li, R. H., Yu, N., Wang, X. B., Liu, Y., Cai, Z. K., & Wang, E. C. (2022). Model-based synthetic geoelectric sampling for magnetotelluric inversion with deep neural networks. Ieee Transactions on Geoscience and Remote Sensing, 60, 1–14.
59. Liao, L. Y., Du, L., & Guo, Y. C. (2022). Semi-supervised SAR target detection based on an improved faster R-CNN. Remote Sensing, 14, 143.
60. Liu, R. J., Wang, X. S., Lu, H. M., Wu, Z. H., Fan, Q., Li, S. X., & Jin, X. (2021). SCCGAN: Style and characters inpainting based on CGAN. Mobile Networks & Applications, 26, 3–12.
61. Yang, B., Xu, S. Y., Chen, H. R., Zheng, W. F., & Liu, C. (2022). Reconstruct dynamic soft-tissue with stereo endoscope based on a single-layer network. Ieee Transactions on Image Processing, 31, 5828–5840.
62. Wang, K. N., Zhang, B. L., Alenezi, F., & Li, S. M. (2022). Communication-efcient surrogate quantile regression for nonrandomly distributed system. Information Sciences, 588, 425–441.
63. Dong, C. H., Li, Y. H., Gong, H. F., Chen, M. X., Li, J. X., Shen, Y., & Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55, 173.
64. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.
65. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems-the International Journal of Escience, 111, 300–323.
66. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-Verse Optimizer: A nature-inspired algorithm for global optimization. Neural Computing & Applications, 27, 495–513.
67. Wang, Y. J., & Chen, Y. (2020). An improved farmland fertility algorithm for global function optimization. Ieee Access, 8, 111850–111874.
68. Heidari, A. A., Abbaspour, R. A., & Chen, H. L. (2019). Efcient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Applied Soft Computing, 81, 105521.
69. Qu, C. W., Zeng, Z. L., Dai, J., Yi, Z. J., & He, W. (2018). A modifed sine-cosine algorithm based on neighborhood search and greedy Levy mutation. Computational Intelligence and Neuroscience, 2018, 4231647.
70. Zhu, A. J., Xu, C. P., Li, Z., Wu, J., & Liu, Z. B. (2015). Hybridizing grey wolf optimization with diferential evolution for global optimization and test scheduling for 3D stacked SoC. Journal of Systems Engineering and Electronics, 26, 317–328.
71. Chen, H., Heidari, A. A., Zhao, X. H., Zhang, L. J., & Chen, H. L. (2020). Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies. Expert Systems with Applications, 144, 113113.
72. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Ieee Transactions on Evolutionary Computation, 10, 281–295.
73. Ling, Y., Zhou, Y. Q., & Luo, Q. F. (2017). Levy fight trajectorybased whale optimization algorithm for global optimization. Ieee Access, 5, 6168–6186.
74. Chen, H. L., Yang, C. J., Heidari, A. A., & Zhao, X. H. (2020). An efcient double adaptive random spare reinforced whale optimization algorithm. Expert Systems with Applications, 154, 113018.
75. Wei, Y., Lv, H. J., Chen, M. X., Wang, M. J., Heidari, A. A., Chen, H. L., & Li, C. Y. (2020). Predicting entrepreneurial intention of students: An extreme learning machine with Gaussian Barebone Harris hawks optimizer. Ieee Access, 8, 76841–76855.
76. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29, 464–483.
77. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.
78. Chen, X., Tianfeld, H., Mei, C. L., Du, W. L., & Liu, G. H. (2017). Biogeography-based learning particle swarm optimization. Soft Computing, 21, 7519–7541.
79. Mostafa Bozorgi, S., & Yazdani, S. (2019). IWOA: An improved whale optimization algorithm for optimization problems. Journal of Computational Design and Engineering, 6, 243–259.
80. Zhuang, Y., Chen, S., Jiang, N., Hu, H. (2022). An efective WSSENet-based similarity retrieval method of large lung CT image databases. KSII Transactions on Internet & Information Systems, 16, 2359–2376.
81. Zhuang, Y., Jiang, N., & Xu, Y. M. (2022). Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks. Wireless Communications & Mobile Computing, 2022, 13.
82. Lu, S. Y., Yang, B., Xiao, Y., Liu, S., Liu, M. Z., Yin, L. R., & Zheng, W. F. (2023). Iterative reconstruction of low-dose CT based on diferential sparse. Biomedical Signal Processing and Control, 79, 104204.
83. Qin, X. M., Ban, Y. X., Wu, P., Yang, B., Liu, S., Yin, L. R., Liu, M. Z., & Zheng, W. F. (2022). Improved image fusion method based on sparse decomposition. Electronics, 11, 2321.
84. Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44, 800-U835.
85. Zhang, L., Zhang, L., Mou, X. Q., & Zhang, D. (2011). FSIM: A Feature Similarity Index for Image Quality Assessment. Ieee Transactions on Image Processing, 20, 2378–2386.
86. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. Ieee Transactions on Image Processing, 13, 600–612.
87. Pare, S., Kumar, A., Bajaj, V., & Singh, G. K. (2016). A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Applied Soft Computing, 47, 76–102.
88. Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13, 146–168.
89. Agrawal, S., Panda, R., Choudhury, P., & Abraham, A. (2022). Dominant color component and adaptive whale optimization algorithm for multilevel thresholding of color images. KnowledgeBased Systems., 240, 108172.
90. Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image denoising. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (vol. 2, pp. 60–65) San Diego, CA, USA.
91. Abutaleb, A. S. (1989). Automatic thresholding of gray-level pictures using two-dimensional entropy. Computer Vision, Graphics, and Image Processing, 47, 22–32.
92. Liu, L., Zhao, D., Yu, F., Heidari, A. A., Ru, J., Chen, H., Mafarja, M., Turabieh, H., & Pan, Z. (2021). Performance optimization of diferential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Computers in Biology and Medicine, 138, 104910.
93. Kim, K., Choi, J., & Lee, Y. (2020). Efectiveness of non-local means algorithm with an industrial 3 MeV linac high-energy X-ray system for non-destructive testing. Sensors, 20, 2634.
94. Zhao, S., Wang, P., Heidari, A. A., Chen, H., Turabieh, H., Mafarja, M., & Li, C. (2021). Multilevel threshold image segmentation with difusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Computers in Biology and Medicine, 134, 104427.
95. Liu, W., Huang, Y. K., Ye, Z. W., Cai, W. C., Yang, S., Cheng, X. C., & Frank, I. (2020). Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Applied SciencesBasel, 10, 3225.
96. Golshani, L., Pasha, E., & Yari, G. (2009). Some properties of Renyi entropy and Renyi entropy rate. Information Sciences, 179, 2426–2433.
97. Rényi, A. (1985). On measures of entropy and information. Virology, 142, 158–174.
98. Bovik, H. (2006). Image information and visual quality. Ieee Transactions on Image Processing a Publication of the Ieee Signal Processing Society, 15, 430.
99. Yim, C., & Bovik, A. C. (2011). Quality assessment of deblocked images. IEEE Transactions on Image Processing, 20, 88–98.
dc.relation.citationendpage.spa.fl_str_mv 2275
dc.relation.citationstartpage.spa.fl_str_mv 2240
dc.relation.citationvolume.spa.fl_str_mv 20
dc.rights.eng.fl_str_mv © 2023 Springer Nature
dc.rights.license.spa.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/embargoedAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_f1cf
rights_invalid_str_mv Atribución 4.0 Internacional (CC BY 4.0)
© 2023 Springer Nature
https://creativecommons.org/licenses/by/4.0/
http://purl.org/coar/access_right/c_f1cf
eu_rights_str_mv embargoedAccess
dc.format.extent.spa.fl_str_mv 36 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Science Press
dc.publisher.place.spa.fl_str_mv China
dc.source.spa.fl_str_mv https://link.springer.com/article/10.1007/s42235-023-00365-7
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/cf278525-d882-4944-80c5-1a50549ce78e/download
https://repositorio.cuc.edu.co/bitstreams/f8ddd760-6022-4cdd-a793-e34508f32e90/download
https://repositorio.cuc.edu.co/bitstreams/a6786ab4-6c20-4d1e-886b-9341a4cccc78/download
https://repositorio.cuc.edu.co/bitstreams/e9f7cd47-ba48-4b12-ba97-83a915fa51d6/download
bitstream.checksum.fl_str_mv f1ed7ecb0a378752f8c4e64364f2fe4f
2f9959eaf5b71fae44bbf9ec84150c7a
55c3ac3e35c2471ffe2296abd6085d3a
cebd8347b5642509dcf10ddca5cddce2
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760683033821184
spelling Atribución 4.0 Internacional (CC BY 4.0)© 2023 Springer Naturehttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfChen, JiaochenCai, ZhennaoChen, Huiling ·Chen, XiaoweiEscorcia-Gutierrez, JoséMansour, Romany F.Ragab, Mahmoud2023-10-31T16:19:47Z20242023-10-31T16:19:47Z2023Chen, J., Cai, Z., Chen, H. et al. Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing. J Bionic Eng 20, 2240–2275 (2023). https://doi.org/10.1007/s42235-023-00365-71672-6529https://hdl.handle.net/11323/1056810.1007/s42235-023-00365-72543-2141Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.36 páginasapplication/pdfengScience PressChinahttps://link.springer.com/article/10.1007/s42235-023-00365-7Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbingArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Journal of Bionic Engineering1. Huo, Y. K., Deng, R. N., Liu, Q., Fogo, A. B., & Yang, H. C. (2021). AI applications in renal pathology. Kidney International, 99, 1309–1320.2. Danila, M. I., Pons-Estel, G. J., Zhang, J., Vila, L. M., Reveille, J. D., & Alarcon, G. S. (2009). Renal damage is the most important predictor of mortality within the damage index: Data from LUMINA LXIV, a multiethnic US cohort. Rheumatology (Oxford), 48, 542–545.3. James, J. A., Guthridge, J. M., Chen, H., Lu, R., Bourn, R. L., Bean, K., Munroe, M. E., Smith, M., Chakravarty, E., Baer, A. N., Noaiseh, G., Parke, A., Boyle, K., Keyes-Elstein, L., Coca, A., Utset, T., Genovese, M. C., Pascual, V., Utz, P. J., … St Clair, E. W. (2020). Unique Sjögren’s syndrome patient subsets defned by molecular features. Rheumatology (Oxford), 59, 860–868.4. Fanouriakis, A., Kostopoulou, M., Cheema, K., Anders, H. J., Aringer, M., Bajema, I., Boletis, J., Frangou, E., Houssiau, F. A., Hollis, J., Karras, A., Marchiori, F., Marks, S. D., Moroni, G., Mosca, M., Parodis, I., Praga, M., Schneider, M., Smolen, J. S., … Boumpas, D. T. (2020). 2019 Update of the Joint European League Against Rheumatism and European Renal AssociationEuropean Dialysis and Transplant Association (EULAR/ERAEDTA) recommendations for the management of lupus nephritis. Annals of the Rheumatic Diseases, 79, 713–723.5. Liu, Z. J., Su, W., Ao, J. P., Wang, M., Jiang, Q. L., He, J., Gao, H., Lei, S., Nie, J. S., Yan, X. F., Guo, X. J., Zhou, P. H., Hu, H., & Ji, M. B. (2022). Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nature Communications, 13, 4050.6. Jin, K., Huang, X. R., Zhou, J. X., Li, Y. X., Yan, Y., Sun, Y. B., Zhang, Q. N., Wang, Y. Q., & Ye, J. (2022). FIVES: A fundus image dataset for artifcial intelligence based vessel segmentation. Scientifc Data, 9, 475.7. Li, Q. H., Song, D. Q., Yuan, C. M., & Nie, W. (2022). An image recognition method for the deformation area of open-pit rock slopes under variable rainfall. Measurement, 188, 110544.8. Kline, A., Chung, H. J., Rahmani, W., & Chun, J. (2021). Semisupervised segmentation of renal pathology: an alternative to manual segmentation and input to deep learning training. In: IEEE Engineering in Medicine and Biology Society. Annual International Conference, Mexico, pp. 2688–2691.9. Albayrak, A., & Bilgin, G. (2019). Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Medical & Biological Engineering & Computing, 57, 653–665.10. Yoruk, U., Hargreaves, B. A., & Vasanawala, S. S. (2018). Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magnetic Resonance in Medicine, 79, 1696–1707.11. Gadermayr, M., Eschweiler, D., Jeevanesan, A., Klinkhammer, B. M., Boor, P., & Merhof, D. (2017). Segmenting renal whole slide images virtually without training data. Computers in Biology and Medicine, 90, 88–97.12. Li, X. L., Chen, X. J., Yao, J. H., Zhang, X., & Tian, J. (2011). Renal cortex segmentation using optimal surface search with novel graph construction. Medical Image Computing Computing Assisted Intervention, 14, 387–394.13. Yang, X., Le Minh, H., Tim Cheng, K. T., Sung, K. H., & Liu, W. (2016). Renal compartment segmentation in DCE-MRI images. Medical Image Analysis, 32, 269–280.14. Luo, J., Yang, Y. S., & Shi, B. Y. (2019). Multi-threshold Image segmentation of 2D Otsu based on improved adaptive diferential evolution algorithm. Journal of Electronics & Information Technology, 41, 2017–2024.15. Nguyen, T. T., Wang, H. J., Dao, T. K., Pan, J. S., Ngo, T. G., & Yu, J. (2020). A scheme of color image multithreshold segmentation based on improved moth-fame algorithm. Ieee Access, 8, 174142–174159.16. Mirjalili, S. (2015). Moth-fame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.17. Zhou, Y. Q., Yang, X., Ling, Y., & Zhang, J. Z. (2018). Metaheuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimedia Tools and Applications, 77, 23699–23727.18. Zhao, D., Liu, L., Yu, F. H., Heidari, A. A., Wang, M. J., Oliva, D., Muhammad, K., & Chen, H. L. (2021). Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation. Expert Systems with Applications, 167, 114122.19. Sharma, S., Saha, A. K., Majumder, A., & Nama, S. (2021). MPBOA - A novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimedia Tools and Applications, 80, 12035–12076.20. Chakraborty, S., Sharma, S., Saha, A. K., & Saha, A. (2022). A novel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artifcial Intelligence Review, 55, 4605–4716.21. Chakraborty, S., Saha, A. K., Nama, S., & Debnath, S. (2021). COVID-19 X-ray image segmentation by modifed whale optimization algorithm with population reduction. Computers in Biology and Medicine, 139, 104984.22. Li, R. H., Wu, X. L., Tian, H., Yu, N., & Wang, C. (2022). Hybrid Memetic pretrained factor analysis-based deep belief networks for transient electromagnetic inversion. Ieee Transactions on Geoscience and Remote Sensing, 60, 1–20.23. Duan, H. B., & Luo, Q. N. (2015). New progresses in swarm intelligence-based computation. International Journal of BioInspired Computation, 7, 26–35.24. Lin, Y., Song, H., Ke, F., Yan, W. Z., Liu, Z. K., & Cai, F. M. (2022). Optimal caching scheme in D2D networks with multiple robot helpers. Computer Communications, 181, 132–142.25. Chen, W. N., Zhang, J., Lin, Y., Chen, N., Zhan, Z. H., Chung, H. S. H., Li, Y., & Shi, Y. H. (2013). Particle swarm optimization with an aging leader and challengers. Ieee Transactions on Evolutionary Computation, 17, 241–258.26. Storn, R., & Price, K. (1997). Diferential evolution—A simple and efcient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.27. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.28. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.29. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849–872.30. Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67.31. Holland, J. H. (1992). Genetic algorithms. Scientifc American, 267, 66–72.32. Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29, 17–35.33. Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.34. Gharehchopogh, F. S. (2022). An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems. Journal of Bionic Engineering, 19, 1177–1202.35. Gharehchopogh, F. S. (2022). Advances in tree seed algorithm: A comprehensive survey. Archives of Computational Methods in Engineering, 29, 3281–3304.36. Kiran, M. S. (2015). TSA: Tree-seed algorithm for continuous optimization. Expert Systems with Applications, 42, 6686–6698.37. Xue, J. K., & Shen, B. (2020). A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering, 8, 22–34.38. Gharehchopogh, F. S., Namazi, M., Ebrahimi, L., & Abdollahzadeh, B. (2022). Advances in Sparrow Search Algorithm: A comprehensive survey. Archives of Computational Methods in Engineering, 30, 427–455.39. Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112.40. Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2020). A comprehensive survey on symbiotic organisms search algorithms. Artifcial Intelligence Review, 53, 2265–2312.41. Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 48, 1–24.42. Sahoo, S. K., & Saha, A. K. (2022). A hybrid moth fame optimization algorithm for global optimization. Journal of Bionic Engineering, 19, 1522–1543.43. Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., & Elaziz, M. A. (2022). Binary Horse herd optimization algorithm with crossover operators for feature selection. Computers in Biology and Medicine, 141, 105152.44. Dong, R. Y., Chen, H. L., Heidari, A. A., Turabieh, H., Mafarja, M., & Wang, S. S. (2021). Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge-Based Systems, 233, 107529.45. Liu, Y., Heidari, A. A., Cai, Z. N., Liang, G. X., Chen, H. L., Pan, Z. F., Alsufyani, A., & Bourouis, S. (2022). Simulated annealing-based dynamic step shufed frog leaping algorithm: Optimal performance design and feature selection. Neurocomputing, 503, 325–362.46. Piri, J., & Mohapatra, P. (2021). An analytical study of modifed multi-objective Harris Hawk Optimizer towards medical data feature selection. Computers in Biology and Medicine, 135, 104558.47. Zhang, Y. N., Liu, R. J., Heidari, A. A., Wang, X., Chen, Y., Wang, M. J., & Chen, H. L. (2021). Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing, 430, 185–212.48. Yang, X. S., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing & Applications, 24, 169–17449. Fan, J. Y., Xu, W. J., Huang, Y., & Samuel, R. D. J. (2021). Application of Chaos Cuckoo Search algorithm in computer vision technology. Soft Computing, 25, 12373–12387.50. Long, W., Zhang, W. Z., Huang, Y. F., & Chen, Y. X. (2014). A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization. Journal of Central South University, 21, 3197–3204.51. Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). Improved cuckoo search algorithm for hybrid fow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93–101.52. Rosli, R., & Mohamed, Z. (2021). Optimization of modifed Bouc-Wen model for magnetorheological damper using modifed cuckoo search algorithm. Journal of Vibration and Control, 27, 1956–1967.53. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. Ieee Transactions on Evolutionary Computation, 1, 67–82.54. Wu, G. H., Mallipeddi, R., & Suganthan, P. (2016). Problem defnitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective realparameter optimization, Nanyang Technological University, Singapore, Tech. Rep, 1–18.55. Fister, I., Yang, X. S., Fister, D., & Fister, I. (2014). Cuckoo search: A brief literature review. In X.-S. Yang (Ed.), Cuckoo search and frefy algorithm: Theory and applications (pp. 49–62). Springer International Publishing.56. Al-Betar, M. A., Aljarah, I., Awadallah, M. A., Faris, H., & Mirjalili, S. (2019). Adaptive β-hill climbing for optimization. Soft Computing, 23, 13489–13512.57. Al-Betar, M. A. (2017). β-Hill climbing: An exploratory local search. Neural Computing & Applications, 28, 153–168.58. Li, R. H., Yu, N., Wang, X. B., Liu, Y., Cai, Z. K., & Wang, E. C. (2022). Model-based synthetic geoelectric sampling for magnetotelluric inversion with deep neural networks. Ieee Transactions on Geoscience and Remote Sensing, 60, 1–14.59. Liao, L. Y., Du, L., & Guo, Y. C. (2022). Semi-supervised SAR target detection based on an improved faster R-CNN. Remote Sensing, 14, 143.60. Liu, R. J., Wang, X. S., Lu, H. M., Wu, Z. H., Fan, Q., Li, S. X., & Jin, X. (2021). SCCGAN: Style and characters inpainting based on CGAN. Mobile Networks & Applications, 26, 3–12.61. Yang, B., Xu, S. Y., Chen, H. R., Zheng, W. F., & Liu, C. (2022). Reconstruct dynamic soft-tissue with stereo endoscope based on a single-layer network. Ieee Transactions on Image Processing, 31, 5828–5840.62. Wang, K. N., Zhang, B. L., Alenezi, F., & Li, S. M. (2022). Communication-efcient surrogate quantile regression for nonrandomly distributed system. Information Sciences, 588, 425–441.63. Dong, C. H., Li, Y. H., Gong, H. F., Chen, M. X., Li, J. X., Shen, Y., & Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55, 173.64. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.65. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems-the International Journal of Escience, 111, 300–323.66. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-Verse Optimizer: A nature-inspired algorithm for global optimization. Neural Computing & Applications, 27, 495–513.67. Wang, Y. J., & Chen, Y. (2020). An improved farmland fertility algorithm for global function optimization. Ieee Access, 8, 111850–111874.68. Heidari, A. A., Abbaspour, R. A., & Chen, H. L. (2019). Efcient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Applied Soft Computing, 81, 105521.69. Qu, C. W., Zeng, Z. L., Dai, J., Yi, Z. J., & He, W. (2018). A modifed sine-cosine algorithm based on neighborhood search and greedy Levy mutation. Computational Intelligence and Neuroscience, 2018, 4231647.70. Zhu, A. J., Xu, C. P., Li, Z., Wu, J., & Liu, Z. B. (2015). Hybridizing grey wolf optimization with diferential evolution for global optimization and test scheduling for 3D stacked SoC. Journal of Systems Engineering and Electronics, 26, 317–328.71. Chen, H., Heidari, A. A., Zhao, X. H., Zhang, L. J., & Chen, H. L. (2020). Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies. Expert Systems with Applications, 144, 113113.72. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Ieee Transactions on Evolutionary Computation, 10, 281–295.73. Ling, Y., Zhou, Y. Q., & Luo, Q. F. (2017). Levy fight trajectorybased whale optimization algorithm for global optimization. Ieee Access, 5, 6168–6186.74. Chen, H. L., Yang, C. J., Heidari, A. A., & Zhao, X. H. (2020). An efcient double adaptive random spare reinforced whale optimization algorithm. Expert Systems with Applications, 154, 113018.75. Wei, Y., Lv, H. J., Chen, M. X., Wang, M. J., Heidari, A. A., Chen, H. L., & Li, C. Y. (2020). Predicting entrepreneurial intention of students: An extreme learning machine with Gaussian Barebone Harris hawks optimizer. Ieee Access, 8, 76841–76855.76. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29, 464–483.77. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.78. Chen, X., Tianfeld, H., Mei, C. L., Du, W. L., & Liu, G. H. (2017). Biogeography-based learning particle swarm optimization. Soft Computing, 21, 7519–7541.79. Mostafa Bozorgi, S., & Yazdani, S. (2019). IWOA: An improved whale optimization algorithm for optimization problems. Journal of Computational Design and Engineering, 6, 243–259.80. Zhuang, Y., Chen, S., Jiang, N., Hu, H. (2022). An efective WSSENet-based similarity retrieval method of large lung CT image databases. KSII Transactions on Internet & Information Systems, 16, 2359–2376.81. Zhuang, Y., Jiang, N., & Xu, Y. M. (2022). Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks. Wireless Communications & Mobile Computing, 2022, 13.82. Lu, S. Y., Yang, B., Xiao, Y., Liu, S., Liu, M. Z., Yin, L. R., & Zheng, W. F. (2023). Iterative reconstruction of low-dose CT based on diferential sparse. Biomedical Signal Processing and Control, 79, 104204.83. Qin, X. M., Ban, Y. X., Wu, P., Yang, B., Liu, S., Yin, L. R., Liu, M. Z., & Zheng, W. F. (2022). Improved image fusion method based on sparse decomposition. Electronics, 11, 2321.84. Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44, 800-U835.85. Zhang, L., Zhang, L., Mou, X. Q., & Zhang, D. (2011). FSIM: A Feature Similarity Index for Image Quality Assessment. Ieee Transactions on Image Processing, 20, 2378–2386.86. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. Ieee Transactions on Image Processing, 13, 600–612.87. Pare, S., Kumar, A., Bajaj, V., & Singh, G. K. (2016). A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Applied Soft Computing, 47, 76–102.88. Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13, 146–168.89. Agrawal, S., Panda, R., Choudhury, P., & Abraham, A. (2022). Dominant color component and adaptive whale optimization algorithm for multilevel thresholding of color images. KnowledgeBased Systems., 240, 108172.90. Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image denoising. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (vol. 2, pp. 60–65) San Diego, CA, USA.91. Abutaleb, A. S. (1989). Automatic thresholding of gray-level pictures using two-dimensional entropy. Computer Vision, Graphics, and Image Processing, 47, 22–32.92. Liu, L., Zhao, D., Yu, F., Heidari, A. A., Ru, J., Chen, H., Mafarja, M., Turabieh, H., & Pan, Z. (2021). Performance optimization of diferential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Computers in Biology and Medicine, 138, 104910.93. Kim, K., Choi, J., & Lee, Y. (2020). Efectiveness of non-local means algorithm with an industrial 3 MeV linac high-energy X-ray system for non-destructive testing. Sensors, 20, 2634.94. Zhao, S., Wang, P., Heidari, A. A., Chen, H., Turabieh, H., Mafarja, M., & Li, C. (2021). Multilevel threshold image segmentation with difusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Computers in Biology and Medicine, 134, 104427.95. Liu, W., Huang, Y. K., Ye, Z. W., Cai, W. C., Yang, S., Cheng, X. C., & Frank, I. (2020). Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Applied SciencesBasel, 10, 3225.96. Golshani, L., Pasha, E., & Yari, G. (2009). Some properties of Renyi entropy and Renyi entropy rate. Information Sciences, 179, 2426–2433.97. Rényi, A. (1985). On measures of entropy and information. Virology, 142, 158–174.98. Bovik, H. (2006). Image information and visual quality. Ieee Transactions on Image Processing a Publication of the Ieee Signal Processing Society, 15, 430.99. Yim, C., & Bovik, A. C. (2011). Quality assessment of deblocked images. IEEE Transactions on Image Processing, 20, 88–98.2275224020Multi-threshold image segmentation2D Rényi entropyRenal pathologySwarm intelligence algorithmsBionic algorithmCuckoo search algorithmPublicationORIGINALRenal Pathology Images Segmentation Based on Improved Cuckoo.pdfRenal Pathology Images Segmentation Based on Improved Cuckoo.pdfArtículoapplication/pdf11987522https://repositorio.cuc.edu.co/bitstreams/cf278525-d882-4944-80c5-1a50549ce78e/downloadf1ed7ecb0a378752f8c4e64364f2fe4fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/f8ddd760-6022-4cdd-a793-e34508f32e90/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTRenal Pathology Images Segmentation Based on Improved Cuckoo.pdf.txtRenal Pathology Images Segmentation Based on Improved Cuckoo.pdf.txtExtracted texttext/plain117010https://repositorio.cuc.edu.co/bitstreams/a6786ab4-6c20-4d1e-886b-9341a4cccc78/download55c3ac3e35c2471ffe2296abd6085d3aMD53THUMBNAILRenal Pathology Images Segmentation Based on Improved Cuckoo.pdf.jpgRenal Pathology Images Segmentation Based on Improved Cuckoo.pdf.jpgGenerated Thumbnailimage/jpeg14272https://repositorio.cuc.edu.co/bitstreams/e9f7cd47-ba48-4b12-ba97-83a915fa51d6/downloadcebd8347b5642509dcf10ddca5cddce2MD5411323/10568oai:repositorio.cuc.edu.co:11323/105682024-09-16 16:47:50.334https://creativecommons.org/licenses/by/4.0/© 2023 Springer Natureopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares