- Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science. 2016; 102: 317-24.
- Khosravanian A. Automatic Brain Lesion Segmentation in Magnetic Resonance Images Using Deformable models [dissertation]. [Iran]: University of Semnan; 2021. 170 pp.
- Wu M-N, Lin C-C, Chang C-C, editors. Brain tumor detection using color-based k-means clustering segmentation. Third international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2007); 2007: IEEE.
- Juang L-H, Wu M-N. MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement. 2010; 43(7): 941-9.
- Selvakumar J, Lakshmi A, Arivoli T, editors. Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm. IEEE-international conference on advances in engineering, science and management (ICAESM-2012); 2012: IEEE.
- Madhukumar S, Santhiyakumari N. Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain. The Egyptian Journal of Radiology and Nuclear Medicine. 2015; 46(2): 475-9.
- Dhanachandra N, Manglem K, Chanu YJ. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science. 2015; 54: 764-71.
- Vijay J, Subhashini J, editors. An efficient brain tumor detection methodology using K-means clustering algoriftnn. 2013 International conference on communication and signal processing; 2013: IEEE.
- Mahata N, Sing JK. A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation. Applied Soft Computing. 2020; 90: 106171.
- Kanniappan S, Samiayya D, Vincent PM DR, Srinivasan K, Jayakody DNK, Reina DG, et al. An efficient hybrid fuzzy-clustering driven 3D-modeling of magnetic resonance imagery for enhanced brain tumor diagnosis. Electronics. 2020; 9(3): 475.
- Kouhi A, Seyedarabi H, Aghagolzadeh A. Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation. Expert Systems with Applications. 2020; 146: 113159.
- Ilunga–Mbuyamba E, Avina–Cervantes JG, Cepeda–Negrete J, Ibarra–Manzano MA, Chalopin C. Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Computers in biology and medicine. 2017; 91: 69-79.
- Guo X, Schwartz L, Zhao B. Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal brain tumor segmentation. 2013; 27: 27-30.
- Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE transactions on image processing. 2011; 20(7): 2007-16.
- Li C, Wang X, Eberl S, Fulham M, Feng DD. Robust model for segmenting images with/without intensity inhomogeneities. IEEE transactions on image processing. 2013; 22(8): 3296-309.
- Zhang K, Zhang L, Lam K-M, Zhang D. A level set approach to image segmentation with intensity inhomogeneity. IEEE transactions on cybernetics. 2015; 46(2): 546-57.
- Huang G, Ji H, Zhang W. A fast level set method for inhomogeneous image segmentation with adaptive scale parameter. Magnetic resonance imaging. 2018; 52: 33-45.
- Wang L, Zhu J, Sheng M, Cribb A, Zhu S, Pu J. Simultaneous segmentation and bias field estimation using local fitted images. Pattern recognition. 2018; 74: 145-55.
- Feng C, Zhao D, Huang M. Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): a region-based level set method. Neurocomputing. 2017; 219: 107-29.
- Li C, Kao C-Y, Gore JC, Ding Z. Minimization of region-scalable fitting energy for image segmentation. IEEE transactions on image processing. 2008; 17(10): 1940-9.
- Zhao Y, Guo S, Luo M, Shi X, Bilello M, Zhang S, et al. A level set method for multiple sclerosis lesion segmentation. Magnetic resonance imaging. 2018; 49: 94-100.
- Zhang C, Shen X, Chen H. BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER. Journal of Mechanics in Medicine and Biology. 2020; 20(06): 2050032.
- Sheela C, Suganthi G. Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model. Multimedia Tools and Applications. 2020; 79(33): 23793-819.
- Radha R, Gopalakrishnan R. A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization. Microprocessors and Microsystems. 2020; 79: 103283.
- Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging. 2014; 34(10): 1993-2024.
- Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data. 2017; 4(1): 1-13.
- Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, Van Ginneken B, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:190209063. 2019.
- Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:181102629. 2018.
- Saha BN, Ray N, Greiner R, Murtha A, Zhang H. Quick detection of brain tumors and edemas: A bounding box method using symmetry. Computerized medical imaging and graphics. 2012 Mar 1; 36(2): 95-107.
- Fukunaga K. Chapter 3-hypothesis testing.
- Zhang D-Q, Chen S-C. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial intelligence in medicine. 2004; 32(1): 37-50.
- Zhang D-Q, Chen S-C, Pan Z-S, Tan K-R, editors. Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat No 03EX693); 2003: IEEE.
- Osher S, Fedkiw RP. Level set methods: an overview and some recent results. Journal of Computational physics. 2001; 169(2): 463-502.
- Sethian JA. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science: Cambridge university press; 1999.
- Alipour S, Shanbehzadeh J. Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method. Machine vision and applications. 2014; 25(6): 1469-88.
- Shahvaran Z, Kazemi K, Fouladivanda M, Helfroush MS, Godefroy O, Aarabi A. Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images. Journal of neuroscience methods. 2021 Oct 1; 362: 109296.
- Ali H, Rada L, Badshah N. Image segmentation for intensity inhomogeneity in presence of high noise. IEEE Transactions on Image Processing. 2018; 27(8): 3729-38.
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