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An effective tumor detection approach using denoised MRI based on fuzzy bayesian segmentation approach

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Abstract

In the cancer treatment, the accurate segmentation of the brain tumor is a major task for which the denoised image gains major significance. This paper proposes an effective segmentation of the brain tumor for accurate detection of the brain tumors using the MRI brain image. The tumor is segmented using the proposed fuzzy Bayesian cut brain tumor segmentation approach. Initially, the noise from the MRI images is removed using the image denoising algorithm, known as Taylor-Krill Herd-based SVM algorithm. The denoised MRI output from the filter is then subjected to tumor segmentation using the proposed Fuzzy Bayesian cut brain tumor segmentation approach, which is the inclusion of the fuzzy and Gaussian naive bayes concept in the tumor cut algorithm in order to enable effective segmentation. The experimentation is performed using the BraTS database and simulated BraTS database and the comparative analysis of the proposed Fuzzy Bayesian cut brain tumor segmentation approach is performed with the other state-of-the-art method based on the metrics, such as accuracy, Jaccard similarity, Sensitivity, Specificity, and Peak signal-to-noise ratio (PSNR). The simulation results reveal that the proposed method acquired a maximum accuracy of 0.9903, by considering the Rayleigh noise using simulated BRaTS database, which is 0.20%, 0.33%, and 0.02%, better than the existing methods, such as original tumor cut, wiener filter + fuzzy Bayesian cut algorithm, and median filter + fuzzy Bayesian cut algorithm, respectively.

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References

  • Akdemir Akar, S. (2016). Determination of optimal parameters for bilateral filter in brain MR image denoising. Applied Soft Computing, 43, 87–96.

    Article  Google Scholar 

  • Angelini, E. D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., & Duffau, H. (2007). Gliomadynamics and computational models: A review of segmentation, registration, and in silico growth algorithms and their clinical applications. Current Medical Imaging Reviews, 3(4), 262–276.

    Article  Google Scholar 

  • Bjoern, H. M., et al. (2015). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024.

    Article  Google Scholar 

  • BRATS database. Retrieved July, 2018, from http://www2.imm.dtu.dk/projects/BRATS2012/data.html.

  • Dennis, B., & Muthukrishnan, S. (2014). AGFS: Adaptive genetic fuzzy system for medical data classification. Applied Soft Computing, 25, 242–252.

    Article  Google Scholar 

  • Ding, Y., Chen, F., Zhao, Y., Wu, Z., Zhang, C., & Wu, D. (2019). A stacked multi-connection simple reducing net for brain tumor segmentation. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2926448.

    Article  Google Scholar 

  • Fowler, J. E. (2005). The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters, 12(9), 629–632.

    Article  Google Scholar 

  • Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.

    Article  MathSciNet  Google Scholar 

  • Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., et al. (2016). DALSA: Domain adaptation for supervised learning from sparsely annotated MR images. IEEE Transactions on Medical Imaging, 35(1), 184–196.

    Article  Google Scholar 

  • Goyal, B., Dogra, A., Agrawal, S., & Sohi, B. S. (2018). Two-dimensional gray scale image denoising via morphological operations in NSST domain & bitonic filtering. Future Generation Computer System, 82, 158–175.

    Article  Google Scholar 

  • Hamamci, A., Kucuk, N., Karaman, K., Engin, K., & Unal, G. (2011). Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Transactions on Medical Imaging, 31(3), 790–804.

    Article  Google Scholar 

  • Hu, K., Gan, Q., Zhang, Y., Deng, S., Xiao, F., Huang, W., et al. (2019). Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2927433.

    Article  Google Scholar 

  • Işın, A., DirekoÄŸlu, C., & Åžah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102, 317–324.

    Article  Google Scholar 

  • Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., et al. (2017). Efficient multi-scale 3DCNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61–78.

    Article  Google Scholar 

  • Kong, Z., Han, L., Liu, X., & Yang, X. (2018). A new 4-D nonlocal transform-domain filter for 3-D magnetic resonance images denoising. IEEE Transactions on Medical Imaging, 37(4), 941–954.

    Article  Google Scholar 

  • Kwan, H. K., & Cai, Y. (2002). Fuzzy filters for image filtering. In 45th Midwest Symp. Circuits Syst., Vol. 3, pp. 672–675.

  • Mangai, S. A., Sankar, B. R., & Alagarsamy, K. (2014). Taylor series prediction of time series data with error propagated by artificial neural network. International Journal of Computer Applications, 89(1), 44–47.

    Google Scholar 

  • McVeigh, E. R., Henkelman, R. M., & Bronskill, M. J. (1985). Noise and filtration in magneticresonance imaging. Medical Physics, 12, 586–591.

    Article  Google Scholar 

  • Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024.

    Article  Google Scholar 

  • Moghaddam, B., Nastar, C., & Pentland, A. (2001). A Bayesian similarity measure for deformable image matching. Image and Vision Computing, 19(5), 235–244.

    Article  Google Scholar 

  • Mohan, J., Krishnaveni, V., & Guo, Y. (2014). A survey on the magnetic resonance imagedenoising methods. Biomedical Signal Processing and Control, 9, 56–69.

    Article  Google Scholar 

  • Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in mri images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251.

    Article  Google Scholar 

  • Rajalakshmi, N., Narayanan, K., & Amudhavalli, P. (2018). Wavelet (2018) Based weighted median filter for image denoising of MRI brain images. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 10(1), 201–206.

    Article  Google Scholar 

  • Sheela, C. J. J., & Suganthi, G. (2019). Automatic brain tumor segmentation from MRI using greedy snake model and fuzzy C-means optimization. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.04.006.

    Article  Google Scholar 

  • Tustison, N. J., Shrinidhi, K. L., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., et al. (2015). Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplied) with ANTsR. Neuroinformatics, 13(2), 209–225.

    Article  Google Scholar 

  • Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., et al. (2019). DeepIGeoS: A deep interactive geodesic framework for medical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(7), 1559–1572.

    Article  Google Scholar 

  • Yang, J., Fan, J., Ai, D., Zhou, S., Tang, S., & Wang, Y. (2015). Brain M.R. image denoising for Rician noise using pre-smooth non-local means filter. Biomedical Engineering Online, 14(1), 2.

    Article  Google Scholar 

  • Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., et al. (2018). Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Transactions on Medical Imaging, 37(6), 1348–1357.

    Article  Google Scholar 

  • Zhan, T., Shen, F., Hong, X., Wang, X., Chen, Y., Lu, Z., et al. (2018). A glioma segmentation method using cotraining and superpixel-based spatial and clinical constraints. IEEE Access, 6, 57113–57122.

    Article  Google Scholar 

  • Zhang, F., & Ma, L. (2010). MRI denoising using the anisotropic coupled diffusion equations. In Proceedings of IEEE 3rd international conference on biomedical engineering and informatics, Yantai, pp. 397–401.

  • Zhang, F. & Ma, L. (2010 October) MRI denoising using the anisotropic coupled diffusion equations. In 3rd international conference on biomedical engineering and informatics (Vol. 1, pp. 397–401).

  • Zhang, Y., Yang, Z., Hu, J., Zou, S., & Fu, Y. (2019). MRI denoising using low rank prior and sparse gradient prior. IEEE Access, 7, 45858–45865.

    Article  Google Scholar 

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Authors and Affiliations

  1. SCOPE, Vellore Institute of Technology, Vellore, Tamilnadu, India

    C. Narasimha & A. Nagaraja Rao

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  1. C. Narasimha
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  2. A. Nagaraja Rao
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Correspondence to A. Nagaraja Rao.

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Narasimha, C., Rao, A.N. An effective tumor detection approach using denoised MRI based on fuzzy bayesian segmentation approach. Int J Speech Technol 24, 259–280 (2021). https://doi.org/10.1007/s10772-020-09782-z

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  • DOI: https://doi.org/10.1007/s10772-020-09782-z

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