[1] Kouhi, A et al, “Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation,” Expert Systems with Applications, vol. 146, pp. 113159, 2020.
[2] Emam, M. M et al, “A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images,” Computers in biology and medicine, vol. 152, pp. 106404, 2023.
[3] Bandyopadhyay, R et al, “Segmentation of brain MRI using an altruistic Harris Hawks’ Optimization algorithm,” Knowledge-Based Systems, vol. 232, pp. 107468, 2021.
[4] Li, C et al, “Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation,” Magnetic resonance imaging, vol. 32, no. 7, pp. 913-923, 2014.
[5] Hassan, M et al, “Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images,” Computer methods and programs in biomedicine, vol. 175, pp. 179-192, 2019.
[6] Singh, C et al, “An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation,” Journal of Imaging Informatics in Medicine, pp. 1-24., 2024.
[7] Jafrasteh, B et al, “Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images,” Neuroinformatics, pp. 1-14, 2024.
[8] Singh, C et al, “A novel approach for brain MRI segmentation and image restoration under intensity inhomogeneity and noisy conditions,” Biomedical Signal Processing and Control, vol. 87, pp. 105348, 2024.
[9] Natarajan, S et al, “Minimally parametrized segmentation framework with dual metaheuristic optimisation algorithms and FCM for detection of anomalies in MR brain images,” Biomedical Signal Processing and Control, vol. 78, pp. 103866, 2022.
[10] Houssein, E. H et al, “Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease,” Computers in Biology and Medicine, vol. 169, pp. 107922, 2024.
[11] H. Essam et al, “An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm,” Expert Systems with Applications, vol. 185, pp. 115651, 2021.
[12] M. Guoyuan and X. Yue, “An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method,” Engineering Applications of Artificial Intelligence, vol. 113, pp. 104960, 2022.
[13] Lang, T and Sauer, T, “Feature-Adaptive Interactive Thresholding of Large 3D Volumes. arXiv preprint arXiv:2210.06961, 2022.
[14] D. Bin et al, “An active contour model based on shadow image and reflection edge for image segmentation,” Expert Systems with Applications, vol. 238, pp. 122330, 2024.
[15] C. Yiyang et al, “An active contour model for image segmentation using morphology and nonlinear Poisson’s equation,” Optik, vol. 287, pp. 170997, 2023.
[16] Zia, H et al, “Active Contour Model for Image Segmentation,” Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE), pp. 13-17, 2022.
[17] Adhikari, S. K et al, “Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images,” Applied soft computing, vol. 34, pp. 758-769, 2015.
[18] Elazab, A et al, “Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel‐based fuzzy C‐means clustering,” Computational and mathematical methods in medicine, vol. 2015, no. 1, pp. 485495, 2015.
[19] Bakhshali, M. A, “Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory,” Soft Computing, vol. 21, pp. 6633-6640., 2017.
[20] Moeskops, P et al, “Automatic segmentation of MR brain images with a convolutional neural network,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1252-1261, 2016.
[21] Ghosh, P et al, “Chaotic firefly algorithm-based fuzzy C-means algorithm for segmentation of brain tissues in magnetic resonance images,” Journal of Visual Communication and Image Representation, vol. 54, pp. 63-79., 2018.
[22] Verma, H et al, “A population-based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image,” Expert systems with applications, vol. 167, pp. 114121, 2021.
[23] Tongbram, S and et al, “A novel image segmentation approach using fcm and whale optimization algorithm,” Journal of ambient intelligence and humanized computing, pp. 1-15., 2021.
[24] Chighoub, F and Saouli, R, “Fully integrated spatial information to improve FCM algorithm for brain MRI Image segmentation,” Automatic Control and Computer Sciences, vol. 56, no. 1, pp. 67-82., 2022.
[25] Kumar, D et al, “Kernel picture fuzzy clustering with spatial neighborhood information for MRI image segmentation,” Soft Computing, vol. 26, no. 22, pp. 12717-12740, 2022.
[26] Khaled, A et al, “Learning to detect boundary information for brain image segmentation,” BMC bioinformatics, vol. 23, no. 1, pp. 332, 2022.
[27] Khatri, I et al, “A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentation,” Applied Intelligence, vol. 53, no. 13, pp. 16487-16518, 2023.
[28] Kumar, D et al, “Bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information approach for human brain MRI image segmentation,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 3, pp. 687-700, 2020.
[29] Kollem, S et al, “Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage,” Multimedia Tools and Applications, vol. 82, no. 14, pp. 20741-20770, 2023.
[30] Solanki, R and Kumar, D, “Probabilistic intuitionistic fuzzy c-means algorithm with spatial constraint for human brain MRI segmentation,” Multimedia Tools and Applications, vol. 82, no. 22, pp. 33663-33692, 2023.
[31] Alagarsamy, S and et al, “Automated brain tumor segmentation for MR brain images using artificial bee colony combined with interval type-II fuzzy technique,” IEEE Transactions on Industrial Informatics, vol. 19, no. 11, pp. 11150-11159, 2023.
[32] Kalti, K and Touil, A, “A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation,” Journal of Classification, vol. 40, no. 3, pp. 488-512, 2023.
[33] Mohammadi, S et al, “Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm,” International Journal of Surgery Case Reports, vol. 111, pp. 108818, 2023.
[34] Shekari, M and Rostamian, M, “Brain tumor segmentation from MRI using FCM clustering, morphological reconstruction, and active contour,” Multimedia Tools and Applications, vol. 83, no. 14, pp. 42973-42998, 2024.
[35] Tian, Z and Wang, S, “A level set model with shape prior constraint for intervertebral disc MRI image segmentation,” Multimedia Tools and Applications, pp. 1-29, 2024.
[36] Dunning, P. D and Kim, H. A, “Introducing the sequential linear programming level-set method for topology optimization,” Structural and Multidisciplinary Optimization, vol. 51, pp. 631-643, 2015.
[37] Jafargholkhanloo, A. F. and Shamsi, M, “Cephalometry analysis of facial soft tissue based on two orthogonal views applicable for facial plastic surgeries,” Multimedia Tools and Applications, vol. 82, no. 20, pp. 30643-30668, 2023.
[38] J. C. Bezdek et al, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci, vol. 10, no. 2-3, pp. 191-203, 1984.
[39] K. Raghu and J. Kim, “A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms,” IEEE Transactions on Fuzzy systems, vol. 7, no. 4, pp. 453-461, 1999.
[40] D. Dejan and I. Škrjanc, “Recursive clustering based on a Gustafson–Kessel algorithm,” Evolving systems, vol. 2, no. 1, pp. 15-24, 2011.
[41] B. R. P. J. Veen and U. Kaymak, “Improved covariance estimation for Gustafson-Kessel clustering,” IEEE International Conference on Fuzzy Systems, vol. 2, 2002.
[42] G. Donald and W. C. Kessel, “Fuzzy clustering with a fuzzy covariance matrix,” IEEE conference on decision and control including the 17th symposium on adaptive processes, 1979.
[43] S. László et al, “MR brain image segmentation using an enhanced fuzzy c-means algorithm,” Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, vol. 1, 2003.
[44] L. Tao et al, “Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 3027-3041, 2018.
[45] Göreke, V, “A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images,” Biomedical Signal Processing and Control, vol. 79, pp. 104031, 2023.
[46] Shreyamsha Kumar, B. K, “Image denoising based on non-local means filter and its method noise thresholding,” Signal, image and video processing, vol. 7, pp. 1211-1227, 2013.
[47] W. Cong et al, “Residual-driven fuzzy C-means clustering for image segmentation,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 876-889, 2020.
[48] Jafargholkhanloo, A. F and Shamsi, M, “Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery,” Signal Processing: Image Communication, 121, 117086, 2024.
[49] https://brainweb.bic.mni.mcgill.ca/cgi/brainweb1
[50] M. Shah et al., “Evaluating intensity normalization on MRIs of human brain with multiple sclerosis,” Med. Image Anal, vol. 15, no. 2, pp. 267–282, 2011.
[51] R. T. Shinohara et al., “Statistical normalization techniques for magnetic resonance imaging,” NeuroImage Clin, vol. 6, pp. 9–19, 2014.
[52] Fortin, Jean-Philippe et al, “Harmonization of cortical thickness measurements across scanners and sites,” Neuroimage, vol. 167, pp. 104-120, 2018.
[53] M. Jenkinson et al, “FSL,” Neuroimage, vol. 62, no. 2, pp. 782–790, 2012.
[54] R. R. Edelman, J. R. Hesselink, M. B. Zlatkin, and J. V. Crues, Clinical magnetic resonance imaging, vol. 1, WB Saunders, 2006.
[55] C. Westbrook, C. K. Roth, and J. Talbot, MRI in Practice, 4th ed. Chichester, England: Wiley-Blackwell, 2011.
[56] R. Wang et al, “Medical image segmentation using deep learning: A survey,” IET Image Process, vol. 16, no. 5, pp. 1243–1267, 2022.
[57] B. Billot et al, “SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining,” Med. Image Anal, vol. 86, no. 102789, p. 102789, 2023.
[58] B. Billot et al, “Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets,” Proc. Natl. Acad. Sci. U. S. A, vol. 120, no. 9, p. e2216399120, 2023.