Abstract
This paper concentrates on the analyses of medical images of patients with brain tumor obtained via magnetic resonance imaging (MRI). Clustering algorithms, statistical techniques and various distribution functions are used to find patterns in the computed parameters to help finding the desired clustering technique. In this paper, Fuzzy C Means clustering algorithm is applied for each patient, and the negative functions and the iteration counts are noted. All simulations are carried out in MATLAB. The results reveal the objective functions and the iteration count of pre-operation and post-operation stages of MRI image frames. The thresholding method and multiple histograms are used to observe the images. An overview of how imaging techniques and MRI functions work is also reviewed along with the different medical image processing established methods.
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References
Paul, G., Varghese, T., Purushothaman, K.V., Singh, N.A.: A Fuzzy C Mean clustering algorithm for automated segmentation of brain MRI. Adv. Intell. Syst. Comput. 247, 59–65 (2013)
Ahmed, M.N., et al.: A modified Fuzzy C-Means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21, 193–199 (2002)
Li, M., et al.: An improved Fuzzy C-Means algorithm for brain MRI image segmentation. In: 2016 IEEE International Conference on Progress in Informatics and Computing, pp. 336–339, Shanghai (2016)
Sheela, C.J.J., Suganthi, G.: Automatic brain tumor segmentation from MRI using Greedy Snake Model and Fuzzy C-Means optimization, J. King Saud Univ. Comput. Inform. Sci. https://doi.org/10.1016/j.jksuci.2019.04.006
Huang, H., et al.: Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7, 12386–12396 (2019)
Wan, C., Ye, M., Yao, C., Wu, C.: Brain MR image segmentation based on Gaussian filtering and improved FCM clustering algorithm. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Shanghai, pp. 1–5 (2017)
Selvakumar, J., Lakshmi, A., Arivoli, T.: Brain tumor segmentation and its area calculation in brain MR images using K-Mean clustering and Fuzzy C-Mean algorithm. In: IEEE International Conference on Advanced Management Science, Tamil Nadu, pp. 186–190 (2012)
Rajini, N.H., Bhavani, R.: Enhancing K-means and Kernelized Fuzzy C-Means clustering with cluster center initialization in segmenting MRI brain images. In: 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, pp. 259–263 (2011)
Benson, C.C., Deepa, V., Lajish, V.L., Rajamani, K.: Brain tumor segmentation from MR brain images using improved Fuzzy C-Means clustering and watershed algorithm. In: 2016 International Conference on Advances in Computing, Communications and Informatics, Jaipur, pp. 187–192 (2016)
Chetih, N., Messali, Z., Serir, A., Ramou, N.: Robust Fuzzy C-Means clustering algorithm using non-parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation. IET Image Process. 12, 652–660 (2018)
Dunn, J.C.: A Fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernetics 3, 32–57 (2008)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceeding 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Mercier, L., et al.: Online database of clinical MR and ultrasound images of brain tumour. Am. Assoc. Phys. Med. 39, 3253–3261 (2012)
Thresholding, https://www.cse.unr.edu/~bebis/CS791E/Notes/Thresholding.pdf
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Habib, M.R. et al. (2022). Clustering of MRI in Brain Images Using Fuzzy C Means Algorithm. In: Chen, J.IZ., Wang, H., Du, KL., Suma, V. (eds) Machine Learning and Autonomous Systems. Smart Innovation, Systems and Technologies, vol 269. Springer, Singapore. https://doi.org/10.1007/978-981-16-7996-4_31
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DOI: https://doi.org/10.1007/978-981-16-7996-4_31
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