The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.
Citation: Shenghan Li, Linlin Ye. Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9645-9669. doi: 10.3934/mbe.2023423
The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.
| [1] | C. Nayanakantha, Tapping panel dryness: The killer affecting the productivity of rubber plantations, 19 (2021), 26–29. |
| [2] |
R. Putranto, E. Herlinawati, M. Rio, J. Leclercq, P. Piyatrakul, E. Gohet, et al., Involvement of ethylene in the latex metabolism and tapping panel dryness of hevea brasiliensis, Int. J. Mol. Sci., 16 (2015), 17885–17908. https://doi.org/10.3390/ijms160817885 doi: 10.3390/ijms160817885
|
| [3] |
Z. Sun, J. Xing, H. Hun, X. Zhang, X. Dong, Y. Deng, Research on recognition and planning of tapping trajectory of natural rubber tree based on machine vision, J. Chin. Agric. Machanization, 43 (2022), 102–108. https://doi.org/10.13733/j.jcam.issn.20955553.2022.05.015 doi: 10.13733/j.jcam.issn.20955553.2022.05.015
|
| [4] | J. Zhang, Y. Liu, H. Xing, Application of improved 2-d entropy algorithm in rubber tree image segmentation, in 2019 2nd International Conference on Safety Produce Informatization (IICSPI), (2019), 311–314. https://doi.org/10.1109/IICSPI48186.2019.9096014 |
| [5] |
S. Li, J. Zhang, J. Zhang, L. Sun, Y. Liu, Study on the secant segmentation algorithm of rubber tree, J. Phys. Conf. Ser., 1004 (2018), 012033. https://doi.org/10.1088/1742-6596/1004/1/012033 doi: 10.1088/1742-6596/1004/1/012033
|
| [6] |
P. Parvati, B. Rao, M. Das, Image segmentation using gray-scale morphology and marker-controlled watershed transformation, Discrete Dyn. Nat. Soc., 2008 (2008). https://doi.org/10.1155/2008/384346 doi: 10.1155/2008/384346
|
| [7] |
P. Sathya, R. Kalyani, V. Sakthivel, Color image segmentation using kapur, otsu and minimum cross entropy functions based on exchange market algorithm, Expert Syst. Appl., 172 (2021), 114636. https://doi.org/10.1016/j.eswa.2021.114636 doi: 10.1016/j.eswa.2021.114636
|
| [8] |
X. Wang, S. Wang, Y. Guo, K. Hu, W. Wang, Coal gangue image segmentation method based on edge detection theory of star algorithm, Int. J. Coal Prep. Util., 43 (2023), 119–134. https://doi.org/10.1080/19392699.2021.2024173 doi: 10.1080/19392699.2021.2024173
|
| [9] |
H. Yu, P. Sun, F. He, Z. Hu, A weighted region-based level set method for image segmentation with intensity inhomogeneity, PLoS One, 16 (2021), e0255948. https://doi.org/10.1371/journal.pone.0255948 doi: 10.1371/journal.pone.0255948
|
| [10] |
D. Wei, Z. Wang, L. Si, C. Tan, X. Lu, An image segmentation method based on a modified local-information weighted intuitionistic fuzzy c-means clustering and gold-panning algorithm, Eng. Appl. Artif. Intell., 101 (2021), 104209. https://doi.org/10.1016/j.engappai.2021.104209 doi: 10.1016/j.engappai.2021.104209
|
| [11] |
P. Ghamisi, M. Couceiro, J. Benediktsson, N. Ferreira, An efficient method for segmentation of image based on fractional calculus and natural selection, Expert Syst. Appl., 39 (2012), 12407–12417. https://doi.org/10.1016/j.eswa.2012.04.078 doi: 10.1016/j.eswa.2012.04.078
|
| [12] |
K. Kumar, K. Venkatalakshmi, K. Krishnan, Lung cancer detection using image segmentation by means of various evolutionary algorithms, Comput. Math. Methods Med., 2019 (2019), 1–16. https://doi.org/10.1155/2019/4909846 doi: 10.1155/2019/4909846
|
| [13] |
Z. Wakaf, H. Jalab, Defect detection based on extreme edge of defective region histogram, J. King Saud Univ. Comput. Inf. Sci., 30 (2018), 33–40. https://doi.org/10.1016/j.jksuci.2016.11.001 doi: 10.1016/j.jksuci.2016.11.001
|
| [14] |
L. Zhang, A. Li, X. Li, S. Xu, X. Yang, Remote sensing image segmentation based on an improved 2-d gradient histogram and mmad model, IEEE Geosci. Remote Sens. Lett., 12 (2015), 58–62. https://doi.org/10.1109/LGRS.2014.2326008 doi: 10.1109/LGRS.2014.2326008
|
| [15] |
Y. Xie, L. Ning, M. Wang, C. Li, Image enhancement based on histogram equalization, J. Phys. Conf. Ser., 1314 (2019), 012161. https://doi.org/10.1088/1742-6596/1314/1/012161 doi: 10.1088/1742-6596/1314/1/012161
|
| [16] |
M. Abd Elaziz, A. A. Ewees, D. Oliva, Hyper-heuristic method for multilevel thresholding image segmentation, Expert Syst. Appl., 146 (2020), 113201. https://doi.org/10.1016/j.eswa.2020.113201 doi: 10.1016/j.eswa.2020.113201
|
| [17] |
S. Aja-Fernández, A. H. Curiale, G. Vegas-Sánchez-Ferrero, A local fuzzy thresholding methodology for multiregion image segmentation, Knowl.-Based Syst., 83 (2015), 1–12. https://doi.org/10.1016/j.knosys.2015.02.029 doi: 10.1016/j.knosys.2015.02.029
|
| [18] |
N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9 (1979), 62–66. https://doi.org/10.1109/TSMC.1979.4310076 doi: 10.1109/TSMC.1979.4310076
|
| [19] |
J. N. Kapur, P. K. Sahoo, A. K. Wong, A new method for gray-level picture thresholding using the entropy of the histogram, Comput. Vision, Graphics, Image Process., 29 (1985), 273–285. https://doi.org/10.1016/0734-189X(85)90125-2 doi: 10.1016/0734-189X(85)90125-2
|
| [20] |
S. Agrawal, R. Panda, S. Bhuyan, B. K. Panigrahi, Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm, Swarm Evol. Comput., 11 (2013), 16–30. https://doi.org/10.1016/j.swevo.2013.02.001 doi: 10.1016/j.swevo.2013.02.001
|
| [21] |
C. H. Li, C. Lee, Minimum cross entropy thresholding, Pattern Recognit., 26 (1993), 617–625. https://doi.org/10.1016/0031-3203(93)90115-D doi: 10.1016/0031-3203(93)90115-D
|
| [22] |
P. Y. Yin, Multilevel minimum cross entropy threshold selection based on particle swarm optimization, Appl. Math. Comput., 184 (2007), 503–513. https://doi.org/10.1016/j.amc.2006.06.057 doi: 10.1016/j.amc.2006.06.057
|
| [23] |
D. Wolpert, W. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67–82. https://doi.org/10.1109/4235.585893 doi: 10.1109/4235.585893
|
| [24] |
W. T. Pan, A new fruit fly optimization algorithm: Taking the financial distress model as an example, Knowl.-Based Syst., 26 (2012), 69–74. https://doi.org/10.1016/j.knosys.2011.07.001 doi: 10.1016/j.knosys.2011.07.001
|
| [25] |
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
|
| [26] |
S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst., 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
|
| [27] |
Y. Feng, S. Deb, G. G. Wang, A. H. Alavi, Monarch butterfly optimization: A comprehensive review, Expert Syst. Appl., 168 (2021), 114418. https://doi.org/10.1016/j.eswa.2020.114418 doi: 10.1016/j.eswa.2020.114418
|
| [28] |
J. Tu, H. Chen, M. Wang, A. Gandomi, The colony predation algorithm, J. Bionic Eng., 18 (2021), 674–710. https://doi.org/10.1007/s42235-021-0050-y doi: 10.1007/s42235-021-0050-y
|
| [29] |
G. G. Wang, Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems, Memet. Comput., 10 (2018), 151–164. https://doi.org/10.1007/s12293-016-0212-3 doi: 10.1007/s12293-016-0212-3
|
| [30] |
Y. Yang, H. Chen, A. A. Heidari, A. H. Gandomi, Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Syst. Appl., 177 (2021), 114864. https://doi.org/10.1016/j.eswa.2021.114864 doi: 10.1016/j.eswa.2021.114864
|
| [31] |
I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, A. H. Gandomi, Info: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 195 (2022), 116516. https://doi.org/10.1016/j.eswa.2022.116516 doi: 10.1016/j.eswa.2022.116516
|
| [32] |
I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, H. Chen, Run beyond the metaphor: An efficient optimization algorithm based on runge kutta method, Expert Syst. Appl., 181 (2021), 115079. https://doi.org/10.1016/j.eswa.2021.115079 doi: 10.1016/j.eswa.2021.115079
|
| [33] |
M. A. Awadallah, M. A. Al-Betar, M. S. Braik, A. I. Hammouri, I. A. Doush, R. A. Zitar, An enhanced binary rat swarm optimizer based on local-best concepts of pso and collaborative crossover operators for feature selection, Comput. Biol. Med., 147 (2022), 105675. https://doi.org/10.1016/j.compbiomed.2022.105675 doi: 10.1016/j.compbiomed.2022.105675
|
| [34] |
S. Thawkar, S. Sharma, M. Khanna, L. kumar Singh, Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer, Comput. Biol. Med., 139 (2021), 104968. https://doi.org/10.1016/j.compbiomed.2021.104968 doi: 10.1016/j.compbiomed.2021.104968
|
| [35] |
S. Chakraborty, A. K. Saha, S. Nama, S. Debnath, Covid-19 x-ray image segmentation by modified whale optimization algorithm with population reduction, Comput. Biol. Med., 139 (2021), 104984. https://doi.org/10.1016/j.compbiomed.2021.104984 doi: 10.1016/j.compbiomed.2021.104984
|
| [36] |
G. I. Sayed, M. M. Soliman, A. E. Hassanien, A novel melanoma prediction model for imbalanced data using optimized squeezenet by bald eagle search optimization, Comput. Biol. Med., 136 (2021), 104712. https://doi.org/10.1016/j.compbiomed.2021.104712 doi: 10.1016/j.compbiomed.2021.104712
|
| [37] |
M. A. Awadallah, A. I. Hammouri, M. A. Al-Betar, M. S. Braik, M. A. Elaziz, Binary horse herd optimization algorithm with crossover operators for feature selection, Comput. Biol. Med., 141 (2022), 105152. https://doi.org/10.1016/j.compbiomed.2021.105152 doi: 10.1016/j.compbiomed.2021.105152
|
| [38] |
J. K. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng., 8 (2020), 22–34. https://doi.org/10.1080/21642583.2019.1708830 doi: 10.1080/21642583.2019.1708830
|
| [39] |
S. Mirjalili, S. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
|
| [40] |
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
|
| [41] |
C. Huang, X. Li, Y. Wen, An otsu image segmentation based on fruitfly optimization algorithm, Alexandria Eng. J., 60 (2021), 183–188. https://doi.org/10.1016/j.aej.2020.06.054 doi: 10.1016/j.aej.2020.06.054
|
| [42] |
A. Bhandari, A. Kumar, G. Singh, Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, otsu and tsallis functions, Expert Syst. Appl., 42 (2015), 1573–1601. https://doi.org/10.1016/j.eswa.2014.09.049 doi: 10.1016/j.eswa.2014.09.049
|
| [43] |
E. Houssein, D. Abdelkareem, M. Emam, M. Hameed, M. Younan, An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm, Comput. Biol. Med., 149 (2022), 106075. https://doi.org/10.1016/j.compbiomed.2022.106075 doi: 10.1016/j.compbiomed.2022.106075
|
| [44] |
Z. Zhang, J. Yin, Bee foraging algorithm based multi-level thresholding for image segmentation, IEEE Access, 8 (2020), 16269–16280. https://doi.org/10.1109/ACCESS.2020.2966665 doi: 10.1109/ACCESS.2020.2966665
|
| [45] |
M. Abdel-Basset, R. Mohamed, N. AbdelAziz, M. Abouhawwash, Hwoa: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation, Expert Syst. Appl., 190 (2021), 116145. https://doi.org/10.1016/j.eswa.2021.116145 doi: 10.1016/j.eswa.2021.116145
|
| [46] |
G. Kang, S. Gao, L. Yu, D. Zhang, Deep architecture for high-speed railway insulator surface defect detection: Denoising autoencoder with multitask learning, IEEE Trans. Instrum. Meas., 68 (2018), 2679–2690. https://doi.org/10.1109/TIM.2018.2868490 doi: 10.1109/TIM.2018.2868490
|
| [47] |
Y. Zhan, G. Zhang, An improved otsu algorithm using histogram accumulation moment for ore segmentation, Symmetry, 11 (2019), 431. https://doi.org/10.3390/sym11030431 doi: 10.3390/sym11030431
|
| [48] |
X. Xu, S. Xu, L. Jin, E. Song, Characteristic analysis of otsu threshold and its applications, Pattern Recognit. Lett., 32 (2011), 956–961. https://doi.org/10.1016/j.patrec.2011.01.021 doi: 10.1016/j.patrec.2011.01.021
|
| [49] | S. Tripathi, K. Kumar, B. Singh, R. Singh, Image segmentation: A review, Int. J. Comput. Sci. Manage. Res., 1 (2012), 838–843. |
| [50] |
P. Sathya, R. Kayalvizhi, Optimal multilevel thresholding using bacterial foraging algorithm, Expert Syst. Appl., 38 (2011), 15549–15564. https://doi.org/10.1016/j.eswa.2011.06.004 doi: 10.1016/j.eswa.2011.06.004
|
| [51] |
F. A.Hashim, A. Hussien, Snake optimizer: A novel meta-heuristic optimization algorithm, Knowl.-Based Syst., 242 (2022), 108320. https://doi.org/10.1016/j.knosys.2022.108320 doi: 10.1016/j.knosys.2022.108320
|
| [52] |
L. Qingge, R. Zheng, X. Zhao, S. Wei, P. Yang, An improved otsu threshold segmentation algorithm, Int. J. Comput. Sci. Eng., 22 (2020), 146–153. https://doi.org/10.1504/IJCSE.2020.107266 doi: 10.1504/IJCSE.2020.107266
|
| [53] |
M. H. Horng, Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation, Expert Syst. Appl., 38 (2011), 13785–13791. https://doi.org/10.1016/j.eswa.2011.04.180 doi: 10.1016/j.eswa.2011.04.180
|
| [54] |
S. Sarkar, S. Das, S. Chaudhuri, A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution, Pattern Recognit. Lett., 54 (2015), 27–35. https://doi.org/10.1016/j.patrec.2014.11.009 doi: 10.1016/j.patrec.2014.11.009
|
| [55] |
D. Oliva, V. Osuna-Enciso, E. Cuevas, G. Pajares, M. Cisneros, D. Zaldivar, Improving segmentation velocity using an evolutionary method, Expert Syst. Appl., 42 (2015), 5874–5886. https://doi.org/10.1016/j.eswa.2015.03.028 doi: 10.1016/j.eswa.2015.03.028
|
Figures(18) / Tables(4)
Shenghan Li, Linlin Ye. Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9645-9669. doi: 10.3934/mbe.2023423
DownLoad: