IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation

Mingzhu Li; Ping Li; Yao Liu; Mingzhu Li; Ping Li; Yao Liu

doi:10.3934/mbe.2023227

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4896-4911. doi: 10.3934/mbe.2023227

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IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation

Mingzhu Li ¹,
Ping Li ²,
Yao Liu ^{3
,
,}

1.
Department of Thyroid and Breast Surgery, East Branch of Quanzhou First Hospital, Fujian 362000, China
2.
Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian 362000, China
3.
College of Medicine, Huaqiao University, Quanzhou, Fujian 362021, China

Academic Editor: Andrei S. Rodin

Received: 28 November 2022 Revised: 21 December 2022 Accepted: 26 December 2022 Published: 04 January 2023

Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.
- gland segmentation,
- breast mammography,
- IDE algorithm,
- fuzzy entropy
Citation: Mingzhu Li, Ping Li, Yao Liu. IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4896-4911. doi: 10.3934/mbe.2023227

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Abstract

Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.

References

[1]	R. L. Siegel, K. D. Miller, H. E. Fuchs, A. Jemal, Cancer statistics, 2022, Ca A Cancer J. Clin., 72 (2022), 7–33. https://doi.org/10.3322/caac.21708 doi: 10.3322/caac.21708
[2]	S. Lei, R. Zheng, S. Zhang, S. Wang, R. Chen, K. Sun, et al., Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020, Cancer Commun., 41 (2021), 1183–1194. https://doi.org/10.1002/cac2.12207 doi: 10.1002/cac2.12207
[3]	W. Cai, B. Zhai, Y. Liu, R. Liu, X. Ning, Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation, Displays, 70 (2021), 102106. https://doi.org/10.1016/j.displa.2021.102106 doi: 10.1016/j.displa.2021.102106
[4]	H. Yazid, S. N. Basah, S. A. Rahim, M. J. A. Safar, K. S. Basaruddin, Performance analysis of entropy thresholding for successful image segmentation, Multimed. Tools Appl., 81 (2022), 6433–6450. https://doi.org/10.1007/s11042-021-11813-z doi: 10.1007/s11042-021-11813-z
[5]	A. Ciaramella, D. Nardone, A. Staiano, Data integration by fuzzy similarity-based hierarchical clustering, BMC Bioinf., 21 (2020), 350–350. https://doi.org/10.1186/s12859-020-03567-6 doi: 10.1186/s12859-020-03567-6
[6]	N. Petrick, H. P. Chan, B. Sahiner, M. A. Helvie, Combined adaptive enhancement and region growing segmentation of breast masses on digitized mammograms, Med. Phys., 26 (1999), 1642–1654. https://doi.org/10.1118/1.598658 doi: 10.1118/1.598658
[7]	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
[8]	D. Tran, M. Wagner, Fuzzy entropy clustering, in Ninth IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, 1 (2000), 152–157. https://doi.org/10.1109/FUZZY.2000.838650
[9]	A. Yezzi, S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, A geometric snake model for segmentation of medical imagery, IEEE Trans. Med. Imaging, 16 (1997), 199–209. https://doi.org/10.1109/42.563665 doi: 10.1109/42.563665
[10]	M. Versaci, F. C. Morabito, Image edge detection: A new approach based on fuzzy entropy and fuzzy divergence, Int. J. Fuzzy Syst., 23 (2021), 918–936. https://doi.org/10.1007/s40815-020-01030-5 doi: 10.1007/s40815-020-01030-5
[11]	N. Mu, H. Wang, Y. Zhang, J. Jiang, J. Tang, Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images, Pattern Recognit., 120 (2021), 108168. https://doi.org/10.1016/j.patcog.2021.108168 doi: 10.1016/j.patcog.2021.108168
[12]	J. He, Q. Zhu, K. Zhang, P. Yu, J. Tang, An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation, Appl. Soft Comput., 113 (2021), 107947. https://doi.org/10.1016/j.asoc.2021.107947 doi: 10.1016/j.asoc.2021.107947
[13]	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
[14]	Z. W. Ye, M. W. Wang, W. Liu, S. B. Chen, Fuzzy entropy based optimal thresholding using bat algorithm, Appl. Soft Comput., 31 (2015), 381–395. https://doi.org/10.1016/j.asoc.2015.02.012 doi: 10.1016/j.asoc.2015.02.012
[15]	M. S. R. Naidu, P. R. Kumar, Multilevel image thresholding for image segmentation by optimizing fuzzy entropy using Firefly algorithm, Int. J. Eng. Technol., 9 (2017), 472–488. https://doi.org/10.21817/ijet/2017/v9i2/170902013 doi: 10.21817/ijet/2017/v9i2/170902013
[16]	S. Dhar, M. K. Kundu, A novel method for image thresholding using interval type-2 fuzzy sets and bat algorithm, Appl. Soft Comput., 63 (2018), 154–166. https://doi.org/10.1016/j.asoc.2017.11.032 doi: 10.1016/j.asoc.2017.11.032
[17]	C. W. Lin, T. P. Hong, A survey of fuzzy web mining, WIRE Data Min. Knowl. Discovery, 3 (2013), 190–199. https://doi.org/10.1002/widm.1091 doi: 10.1002/widm.1091
[18]	C. W. Lin, T. P. Hong, W. H. Lu, Linguistic data mining with fuzzy FP-trees, Expert Syst. Appl., 37 (2010), 4560–4567. https://doi.org/10.1016/j.eswa.2009.12.052 doi: 10.1016/j.eswa.2009.12.052
[19]	J. Li, W. Tang, J. Wang, X. Zhang, Multilevel thresholding selection based on variational mode decomposition for image segmentation, Signal Process., 147 (2018), S0165168418300306. https://doi.org/10.1016/j.sigpro.2018.01.022 doi: 10.1016/j.sigpro.2018.01.022
[20]	R. Storn, K. Price, Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
[21]	Y. Wang, Z. Cai, Q. Zhang, Enhancing the search ability of differential evolution through orthogonal crossover, Inf. Sci., 185 (2012), 153–177. https://doi.org/10.1016/j.ins.2011.09.001 doi: 10.1016/j.ins.2011.09.001
[22]	S. M. Guo, C. C. Yang, Enhancing differential evolution utilizing eigenvector-based crossover operator, IEEE Trans. Evol. Comput., 19 (2015), 31–49. https://doi.org/10.1109/TEVC.2014.2375933 doi: 10.1109/TEVC.2014.2375933
[23]	Z. J. Wang, Z. H. Zhan, Y. Lin, W. J. Yu, H. Wang, S. Kwong, et al., Automatic niching differential evolution with contour prediction approach for multimodal optimization problems, IEEE Trans. Evol. Comput., 24 (2019), 114–128. https://doi.org/10.1109/TEVC.2019.2910721 doi: 10.1109/TEVC.2019.2910721
[24]	Y. Wang, Z. Cai, Q. Zhang, Differential evolution with composite trial vector generation strategies and control parameters, IEEE Trans. Evol. Comput., 15 (2011), 55–66. https://doi.org/10.1109/TEVC.2010.2087271 doi: 10.1109/TEVC.2010.2087271
[25]	G. Wu, R. Mallipeddi, P. N. Suganthan, R. Wang, H. Chen, Differential evolution with multipopulation based ensemble of mutation strategies, Inf. Sci., 329 (2015), 329–345. https://doi.org/10.1016/j.ins.2015.09.009 doi: 10.1016/j.ins.2015.09.009
[26]	Y. Fan, P. Liu, J. Tang, Y. Luo, Y. Du, Fuzzy entropy based on differential evolution for breast gland segmentation, Australas Phys. Eng. Sci. Med., 41 (2018), 1101–1114. https://doi.org/10.1007/s13246-018-0672-5 doi: 10.1007/s13246-018-0672-5
[27]	S. G. Orel, N. Kay, C. Reynolds, D. C. Sullivan, BI-RADS categorization as a predictor of malignancy, Radiology, 211 (1999), 845–850. https://doi.org/10.1148/radiology.211.3.r99jn31845 doi: 10.1148/radiology.211.3.r99jn31845

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