Brain tumor segmentation using hierarchical combination of fuzzy logic and cellular automata

Roqaie Kalantari, Roqaie Moqadam, Nazila Loghmani, Armin Allahverdy, Mohammad Bagher Shiran, Arash Zare-Sadeghi

DOI: 10.4103/jmss.jmss_128_21


Background: Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning. Methods: In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA). Results: The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%. Conclusion: The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.


Cellular automata, fuzzy, glioma, segmentation

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Wadhwa A, Bhardwaj A, Singh Verma V. A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019;61:247-59.

Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 2018;43:98-111.

Goetz M, Weber C, Bloecher J, Stieltjes B, Meinzer HP, Maier-Hein K. “Extremely Randomized Trees Based Brain Tumor Segmentation,” Proceeding of BRATS Challenge-MICCAI; 2014. p. 006-11.

Goetz M, Weber C, Binczyk F, Polanska J, Tarnawski R, Bobek-Billewicz B, et al. DALSA: Domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans Med Imaging 2016;35:184-96.

Allahverdi A, Akbarzadeh S, Moghaddam AK, Allahverdy A. Differentiating tumor and edema in brain magnetic resonance images using a convolutional neural network. Front Biomed Technol 2018;5:44-50.

Kapur T, Eric W, Grimson L, Kikinis R, Wells WM. “Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery,” In International Conference on Medical Image Computing and Computer-Assisted Intervention; 1998. p. 457-68.

Bullmore E, Brammer M, Rouleau G, Everitt B, Simmons A, Sharma T, et al. Computerized brain tissue classification of magnetic resonance images: A new approach to the problem of partial volume artifact. Neuroimage 1995;2:133-47.

Sujan M, Alam N, Noman SA, Islam MJ. A segmentation based automated system for brain tumor detection. Int J Comput Appl 2016;153:41-9.

Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci 2017;120:580-7.

Salman YM. Modified technique for volumetric brain tumor measurements. J Biomed Sci Eng 2009;2:16.

Bajwa IS, Asghar MN, Naeem MA. Learning-based improved seeded region growing algorithm for brain tumor identification. Proc Pakistan Acad Sci 2017;54:127-33.

Deng W, Xiao W, Deng H, Liu J. MRI Brain Tumor Segmentation with Region Growing Method Based on the Gradients and Variances Along and Inside of the Boundary Curve,” in 2010 3rd International Conference on Biomedical Engineering and Informatics; 2010. p. 393-6.

Kavitha A, Chellamuthu C, Rupa K. “An Efficient Approach for Brain Tumour Detection Based on Modified Region Growing and Neural Network in MRI Images,” in 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET); 2012. p. 1087-95.

Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62-6.

Taheri S, Ong SH, Chong V. Level-set segmentation of brain tumors using a threshold-based speed function. Image Vis Comput 2010;28:26-37.

Park JW. Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images. Image Vis Comput 2005;23:1277-87.

Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994;16:641-7.

Sato M, Lakare S, Wan M, Kaufman A, Nakajima M. “A Gradient Magnitude Based Region Growing Algorithm for Accurate Segmentation,” In Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101); 2000. p. 448-51.

Lakare S, Kaufman A. “3D Segmentation Techniques for Medical Volumes.” Vol. 2000. Center for Visual Computing, Department of Computer Science, State University of New York; 2000. p. 59-68.

Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013;31:1426-38.

Khalid NE, Ibrahim S, Haniff P. MRI brain abnormalities segmentation using K-nearest neighbors (k-NN). Int J Comput Sci Eng 2011;3:980-90.

Steenwijk MD, Pouwels PJ, Daams M, van Dalen JW, Caan MW, Richard E, et al. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). Neuroimage Clin 2013;3:462-9.

Kharrat A, Gasmi K, Messaoud MB, Benamrane N, Abid M. A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J Sci 2010;17:71-82.

Chaddad A. Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. Int J Biomed Imaging 2015;2015:868031.

Koley S, Sadhu AK, Mitra P, Chakraborty B, Chakraborty C. Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest. Appl Soft Comput 2016;41:453-65.

Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, et al. Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 2015;25:153-64.

Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int Arab J Inf Technol 2015;12:42-52.

Vijay J, Subhashini J. “An Efficient Brain Tumor Detection Methodology Using K-means Clustering Algoriftnn,” In 2013 International Conference on Communication and Signal Processing; 2013. p. 653-7.

Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys 1993;20:1033-48.

Chang H, Chen Z, Huang Q, Shi J, Li X. Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing 2015;151:632-44.

Pratondo A, Chui CK, Ong SH. Robust edge-stop functions for edge-based active contour models in medical image segmentation. IEEE Signal Proc Lett 2015;23:222-6.

Singh A. “Detection of Brain Tumor in MRI Images, Using Combination of Fuzzy c-Means and SVM,” In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN); 2015. p. 98-102.

Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques. Indian J Sci Technol 2016;9:1-12.

Li C, Liu L, Sun X, Zhao J, Yin J. Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP J Image Video Proc 2019;2019:1-11.

Rundo L, Militello C, Russo G, Vitabile S, Gilardi MC, Mauri G. GTV cut for neuro-radiosurgery treatment planning: An MRI brain cancer seeded image segmentation method based on a cellular automata model. Nat Comput 2018;17:521-36.

Sompong C, Wongthanavasu S. An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst Appl 2017;72:231-44.

Barik R, Naskar MN, Chowdhury S, Pal S. “Cancer Detection Using Cellular Automata Based Segmentation Techniques,” in 2021 Asian Conference on Innovation in Technology (ASIANCON); 2021. p. 1-6.

Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015;34:1993-2024.

Selvapandian A, Manivannan K. Fusion based Glioma brain tumor detection and segmentation using ANFIS classification. Comput Methods Programs Biomed 2018;166:33-8.

Anitha R, Raja DS. Segmentation of glioma tumors using convolutional neural networks. Int J Imaging Syst Technol 2017;27:354-60.

Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016;35:1240-51.

Urban G, Bendszus M, Hamprecht F, Kleesiek J. “Multi-Modal Brain Tumor Segmentation Using Deep Convolutional Neural Networks,” MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, Winning Contribution; 2014. p. 31-5.

Islam A, Reza SM, Iftekharuddin KM. Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng 2013;60:3204-15.


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