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Research Article

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Author(s): Dharmendra Kumar*, Anil Kumar Solanki and Anil Kumar Ahlawat

Volume 17, Issue 5, 2024

Published on: 20 October, 2023

Article ID: e201023222441 Pages: 12

DOI: 10.2174/0126662558248113231012055802

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Abstract

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work.

Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm.

Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency.

Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Keywords: Medical image segmentation, image analysis, classification, hybrid optimization, multilevel thresholding.

Graphical Abstract

[1]
A. Khadidos, V. Sanchez, and C.T. Li, "Weighted level set evolution based on local edge features for medical image segmentation", IEEE Trans. Image Process., vol. 26, no. 4, pp. 1979-1991, 2017.
[http://dx.doi.org/10.1109/TIP.2017.2666042] [PMID: 28186897]
[2]
Y. Li, Y. Zhang, W. Cui, B. Lei, X. Kuang, and T. Zhang, "Dual encoder-based dynamic channel graph convolutional network with edge enhancement for retinal vessel segmentation", IEEE Trans. Med. Imaging, vol. 41, no. 8, pp. 1975-1989, 2022.
[http://dx.doi.org/10.1109/TMI.2022.3151666] [PMID: 35167444]
[3]
W. Yan, Y. Wang, M. Xia, and Q. Tao, "Edge-guided output adaptor: Highly efficient adaptation module for cross-vendor medical image segmentation", IEEE Signal Process. Lett., vol. 26, no. 11, pp. 1593-1597, 2019.
[http://dx.doi.org/10.1109/LSP.2019.2940926]
[4]
L. Su, X. Fu, X. Zhang, X. Cheng, Y. Ma, Y. Gan, and Q. Hu, "Delineation of carpal bones from hand x-ray images through prior model, and integration of region-based and boundary-based segmentations", IEEE Access, vol. 6, pp. 19993-20008, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2815031]
[5]
Yan W., Wang Y., Xia M., and Tao Q., "(2019) Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation", In IEEE Signal Process. Lett. 26: 1593-1597.
[6]
S. Nitkunanantharajah, G. Zahnd, M. Olivo, N. Navab, P. Mohajerani, and V. Ntziachristos, "Skin surface detection in 3D optoacoustic mesoscopy based on dynamic programming", IEEE Trans. Med. Imaging, vol. 39, no. 2, pp. 458-467, 2020.
[http://dx.doi.org/10.1109/TMI.2019.2928393] [PMID: 31329549]
[7]
D. Chen, J. Zhu, X. Zhang, M. Shu, and L.D. Cohen, "Geodesic paths for image segmentation with implicit region-based homogeneity enhancement", IEEE Trans. Image Process., vol. 30, pp. 5138-5153, 2021.
[http://dx.doi.org/10.1109/TIP.2021.3078106] [PMID: 34014824]
[8]
F. Riaz, S. Rehman, M. Ajmal, R. Hafiz, A. Hassan, N.R. Aljohani, R. Nawaz, R. Young, and M. Coimbra, "Gaussian mixture model based probabilistic modeling of images for medical image segmentation", IEEE Access, vol. 8, pp. 16846-16856, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2967676]
[9]
M. Kim, and B.D. Lee, "A simple generic method for effective boundary extraction in medical image segmentation", IEEE Access, vol. 9, pp. 103875-103884, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3099936]
[10]
J.L. Bruse, S. Schievano, M.A. Zuluaga, A. Khushnood, K. McLeod, H.N. Ntsinjana, T-Y. Hsia, M. Sermesant, X. Pennec, and A.M. Taylor, "Detecting clinically meaningful shape clusters in medical image data: Metrics analysis for hierarchical clustering applied to healthy and pathological aortic arches", IEEE Trans. Biomed. Eng., vol. 64, no. 10, pp. 2373-2383, 2017.
[http://dx.doi.org/10.1109/TBME.2017.2655364] [PMID: 28221991]
[11]
X. Bai, Y. Zhang, H. Liu, and Y. Wang, "Intuitionistic center-free FCM clustering for MR brain image segmentation", IEEE J. Biomed. Health Inform., vol. 23, no. 5, pp. 2039-2051, 2019.
[http://dx.doi.org/10.1109/JBHI.2018.2884208] [PMID: 30507540]
[12]
Z. Guo, W. Tan, L. Wang, L. Xu, X. Wang, B. Yang, and Y. Yao, "Local Motion Intensity Clustering (LMIC) model for segmentation of right ventricle in cardiac MRI images", IEEE J. Biomed. Health Inform., vol. 23, no. 2, pp. 723-730, 2019.
[http://dx.doi.org/10.1109/JBHI.2018.2821709] [PMID: 29994105]
[13]
J. Duan, S. Mao, J. Jin, Z. Zhou, L. Chen, and C.L.P. Chen, "A novel GA-based optimized approach for regional multimodal medical image fusion with superpixel segmentation", IEEE Access, vol. 9, pp. 96353-96366, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3094972]
[14]
A. Mayer, and H. Greenspan, "An adaptive mean-shift framework for MRI brain segmentation", IEEE Trans. Med. Imaging, vol. 28, no. 8, pp. 1238-1250, 2009.
[http://dx.doi.org/10.1109/TMI.2009.2013850] [PMID: 19211339]
[15]
S. Pang, Q. Feng, Z. Lu, J. Jiang, L. Zhao, L. Lin, X. Li, T. Lian, M. Huang, and W. Yang, "Hippocampus segmentation based on iterative local linear mapping with representative and local structure- preserved feature embedding", IEEE Trans. Med. Imaging, vol. 38, no. 10, pp. 2271-2280, 2019.
[http://dx.doi.org/10.1109/TMI.2019.2906727] [PMID: 30908202]
[16]
H. Cai, Z. Yang, X. Cao, W. Xia, and X. Xu, "A new iterative triclass thresholding technique in image segmentation", IEEE Trans. Image Process., vol. 23, no. 3, pp. 1038-1046, 2014.
[http://dx.doi.org/10.1109/TIP.2014.2298981] [PMID: 24474373]
[17]
S.P. Awate, Zhang Hui, and J.C. Gee, "A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI-tract extraction", IEEE Trans. Med. Imaging, vol. 26, no. 11, pp. 1525-1536, 2007.
[http://dx.doi.org/10.1109/TMI.2007.907301] [PMID: 18041267]
[18]
K.J. Batenburg, and J. Sijbers, "Optimal threshold selection for tomogram segmentation by projection distance minimization", IEEE Trans. Med. Imaging, vol. 28, no. 5, pp. 676-686, 2009.
[http://dx.doi.org/10.1109/TMI.2008.2010437] [PMID: 19272989]
[19]
J.E. Lutton, S. Collier, and T. Bretschneider, "A curvature- enhanced random walker segmentation method for detailed capture of 3D cell surface membranes", IEEE Trans. Med. Imaging, vol. 40, no. 2, pp. 514-526, 2021.
[http://dx.doi.org/10.1109/TMI.2020.3031029] [PMID: 33052849]
[20]
G. Toz, and P. Erdogmus, "A novel hybrid image segmentation method for detection of suspicious regions in mammograms based on adaptive multi-thresholding (HCOW)", IEEE Access, vol. 9, pp. 85377-85391, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3089077]
[21]
G.A. Wachs-Lopes, R.M. Santos, N.T. Saito, and P.S. Rodrigues, "Recent nature-Inspired algorithms for medical image segmentation based on tsallis statistics", Commun. Nonlinear Sci. Numer. Simul., vol. 88, p. 105256, 2020.
[http://dx.doi.org/10.1016/j.cnsns.2020.105256]
[22]
A.F. Ali, A. Mostafa, G.I. Sayed, M.A. Elfattah, and A.E. Hassanien, "Nature inspired optimization algorithms for CT liver segmentation", Stud. Comput. Intell., vol. 651, pp. 431-460, 2016.
[http://dx.doi.org/10.1007/978-3-319-33793-7_19]
[23]
R. Sumathi, and M. Venkatesulu, "Segmenting MRI brain tumor images using modified cuckoo search optimization with morphological reconstruction filters", In IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)Tamilnadu, India, 11-13 April, 2019, pp. 1-4.
[http://dx.doi.org/10.1109/INCOS45849.2019.8951331]
[24]
B. Chaudhari, P. Shetiye, and A. Gulve, "Image segmentation using hybrid ant colony optimization: A review", In Sixth International Conference on Image Information Processing (ICIIP)
Shimla, India, 26-28 Nov, pp.461-466. [http://dx.doi.org/10.1109/ICIIP53038.2021.9702695]
[25]
T. Dang, T.T. Nguyen, C.F. Moreno-García, E. Elyan, and J. McCall, "Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation", In IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, 28 June - 01 July, 2021pp. 744-751
[26]
B. Sridhar, S. Sridhar, V. Nanchariah, and K. Gayatri, "Cluster medical image segmentation using morphological adaptive bilateral filter based BSA algorithm", In 5th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 03-05 June,, 2021, pp. 726-731
[http://dx.doi.org/10.1109/ICOEI51242.2021.9452816]
[27]
D. Kumar, A.K. Solanki, and A.K. Ahlawat, "Luminosity control and contrast enhancement of digital mammograms using combined application of adaptive gamma correction and DWT-SVD", J. Sens., vol. 2022, pp. 1-18, 2022.
[http://dx.doi.org/10.1155/2022/4433197]
[28]
K.G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, "Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation", Arch. Comput. Methods Eng., vol. 27, no. 3, pp. 855-888, 2020.
[http://dx.doi.org/10.1007/s11831-019-09334-y]
[29]
S. Ray, A. Das, K.G. Dhal, J. Gálvez, and P.K. Naskar, "Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation", Neural Comput. Appl., vol. 33, no. 11, pp. 5917-5949, 2021.
[http://dx.doi.org/10.1007/s00521-020-05368-7]
[30]
F. Marini, and B. Walczak, "Particle swarm optimization (PSO). A tutorial", Chemom. Intell. Lab. Syst., vol. 149, pp. 153-165, 2015.
[http://dx.doi.org/10.1016/j.chemolab.2015.08.020]
[31]
X.S. Yang, and S. Deb, "Cuckoo search: Recent advances and applications", Neural Comput. Appl., vol. 24, no. 1, pp. 169-174, 2014.
[http://dx.doi.org/10.1007/s00521-013-1367-1]
[32]
X.S. Yang, and S. Deb, "Engineering optimisation by cuckoo search", Int. J. Math. Model. Numer. Optim., vol. 1, pp. 330-343, 2010.
[33]
Asmaa S. Alsolami, "King abdulaziz university breast cancer mammogram dataset (KAU-BCMD)", Data, vol. 6, no. 11, p. 111, 2021.
[34]
J. Suckling, "et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica", In International Congress Series, vol. 1069, pp. 375-378
[35]
K.A. Santhos, A. Kumar, V. Bajaj, and G.K. Singh, "McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation", Multimedia Tools Appl., vol. 79, no. 41-42, pp. 30453-30488, 2020.
[http://dx.doi.org/10.1007/s11042-020-09310-w]
[36]
S. Subasree, N.K. Sakthivel, V.R. Balasaraswathi, and A.K. Tyagi, "Selection of optimal thresholds in multi-level thresholding using multi-objective emperor penguin optimization for precise segmentation of mammogram images", J. Circuits Syst. Comput., vol. 31, no. 7, p. 2250131, 2022.
[http://dx.doi.org/10.1142/S0218126622501316]
[37]
O. Christiana Abikoye, R. Oluwaseun Ogundokun, S. Misra, and A. Agrawal, "Analytical study on lsb-based image steganography approach", Lecture Notes Elec. Eng., vol. 834, pp. 451-457, 2022.
[http://dx.doi.org/10.1007/978-981-16-8484-5_43]
[38]
O.O. Abayomi-Alli, R. Damaševičius, S. Misra, R. Maskeliūnas, and A. Abayomi-Alli, "Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower- dimensional embedding manifold", Turk. J. Electr. Eng. Comput. Sci., vol. 29, no. SI-1, pp. 2600-2614, 2021.
[http://dx.doi.org/10.3906/elk-2101-133]
[39]
DO Oyewola, EG Dada, S Misra, and R Damaševičius, "A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images", Appl. Artificial Intell., vol. 36, no. 1, p. 2033473, 2022.
[http://dx.doi.org/10.1080/08839514.2022.2033473]
[40]
JB Awotunde, S Misra, D Obagwu, and H Florez, "Multiple colour detection of rgb images using machine learning algorithm", Commun. Comput. Inf. Sci., vol. 1643, pp. 60-74, 2022.
[http://dx.doi.org/10.1007/978-3-031-19647-8_5]
[41]
T. Olaleye, O. Arogundade, C. Adenusi, S. Misra, and A. Bello, "Evaluation of image filtering parameters for plant biometrics improvement using machine learning", Commun. Comput. Inf. Sci., vol. 1374, pp. 301-315, 2021.
[http://dx.doi.org/10.1007/978-981-16-0708-0_25]
[42]
J. Wang, D. Xiaolei, and P. Zhou, "Current situation and review of image segmentation", Recent Pat. Comput. Sci., vol. 10, no. 1, p. 2213275910666170111151203, 2017.
[http://dx.doi.org/10.2174/2213275910666170111151203]
[43]
S. Shirly, and K. Ramesh, "Review on 2D and 3D MRI Image segmentation techniques", Curr. Med. Imaging Rev., vol. 15, no. 2, pp. 150-160, 2019.
[http://dx.doi.org/10.2174/1573405613666171123160609] [PMID: 31975661]
[44]
Y. Chai, J. Qiu, L. Yin, L. Zhang, B.B. Gupta, and Z. Tian, "From data and model levels: improve the performance of few-shot malware classification", IEEE Trans. Netw. Serv. Manag., vol. 19, no. 4, pp. 4248-4261, 2022.
[http://dx.doi.org/10.1109/TNSM.2022.3200866]
[45]
S. Ait-Aoudia, E-H. Guerrout, and R. Mahiou, "Medical image segmentation using particle swarm optimization", In 18th International Conference on Information Visualisation
Paris, France, 16-18 July, 2014, pp.287-291. [http://dx.doi.org/10.1109/IV.2014.68]
[46]
A. Khare, and S. Rangnekar, "A review of particle swarm optimization and its applications in Solar Photovoltaic system", Appl. Soft Comput., vol. 13, no. 5, pp. 2997-3006, 2013.
[http://dx.doi.org/10.1016/j.asoc.2012.11.033]
[47]
A. Sharma, R. Chaturvedi, U. Dwivedi, and S. Kumar, "Multi-level image segmentation of color images using opposition based improved firefly algorithm", Recent Adv. Comput. Sci. Commun., vol. 14, no. 2, pp. 521-539, 2021.
[http://dx.doi.org/10.2174/2213275912666190716165024]
[48]
Bowen Wu, "A hybrid preaching optimization algorithm based on Kapur entropy for multilevel thresholding color image segmentation", Entropy, vol. 23, no. 12, p. 1599, 2021.
[http://dx.doi.org/10.3390/e23121599]
[49]
J. Munoz-Minjares, "Alternative thresholding technique for image segmentation based on cuckoo search and generalized gaussians", Mathematics, vol. 18, no. 9, p. 2287, 2021.
[http://dx.doi.org/10.3390/math9182287]

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