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Brain tumor segmentation using DE embedded OTSU method and neural network

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Abstract

In the past few decades, medical imaging and soft computing have shown a symbolic growth in brain tumor segmentation. Research in medical imaging is becoming quite popular field, particularly in magnetic resonance images of brain tumor, because of the tremendous need of efficient and effective technique for evaluation of large amount of data. Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being. In considering brain tumor image segmentation, manually with an expert, it is more likely that the errors are present in it. To automate image segmentation, we have proposed an algorithm to obtain a global thresholding value for a particular image. To find out an optimal threshold value we have used Differential Evolution algorithm embedded with OTSU method and trained neural network for future use. Proposed Methodology provides classification of the images successfully for brain tumors. Results show its efficiency over other methods.

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(Reproduced with permission from Drevelegas and Papanikolaou 2011)

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References

  • Bauer, S., Wiest, R., Nolte, L. P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Journal of Physics in Medicine and Biology, 58(13), 97–129.

    Google Scholar 

  • Bloch, F. (1946). Nuclear induction. Physical Review, 70(7–8), 460–473.

    Google Scholar 

  • Bloch, F., Hansen, W., & Packard, M. (1946). The nuclear induction experiment. Physical Review, 70(7–8), 474–485.

    Google Scholar 

  • Chumchob, N., & Chen, K. (2009). A robust affine image registration method. International journal of numerical analysis and modeling, 6(2), 311–334.

    MathSciNet  MATH  Google Scholar 

  • Comaniciu, D., & Meer, P. (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.

    Google Scholar 

  • Demirhan, A., & Guler, I. (2010). Image segmentation using self-organizing maps and gray level co-occurrence matrices. Journal of the Faculty of Engineering and Architecture of Gazi University, 25(2), 285–291.

    Google Scholar 

  • Demirhan, A., Toru, M., & Guler, I. (2015). Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE Journal of biomedical and health informatics, 19(4), 1451–1458.

    Google Scholar 

  • Drevelegas, A., & Papanikolaou, N. (2011). Imaging modalities in brain tumors. In A. Drevelegas (Ed.), Imaging of brain tumors with histological correlations (pp. 13–33). Berlin: Springer.

    Google Scholar 

  • Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32–57.

    MathSciNet  MATH  Google Scholar 

  • Grau, V., Mewes, A. U. J., Alcaniz, M., Kikinis, R., & Warfield, S. K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23(4), 447–458.

    Google Scholar 

  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621.

    Google Scholar 

  • Havaei, M., Davy, A., Farley, D. W., Biard, A., Courville, A., Bengio, Y., et al. (2017). Brain tumor segmentation with deep neural networks. Journal of Medical Image Analysis, 35, 18–31.

    Google Scholar 

  • Howlader, N., Noone, A. M., Krapcho, M., Miller, D., Bishop, K., Altekruse, S. F., et al. (2016). SEER cancer statistics review, 1975–2013. Bethesda, MD: National Cancer Institute. http://seer.cancer.gov/csr/1975_2013/.

  • Jothi, G., & Hannah, I. H. (2016). Hybrid tolerance rough set-firefly based supervised feature selection for MRI brain tumor image classification. Journal of Applied Soft Computing, 46, 639–651.

    Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University.

  • Kaya, I. E., Pehlivanlı, A. C., Sekizkardes, E. G., & Ibrikci, T. (2017). PCA based clustering for brain tumor segmentation of T1w MRI images. Journal of Computer Methods and Programs in Biomedicine, 140, 19–28.

    Google Scholar 

  • Kleihues, P., Burger, P. C., & Scheithauer, B. W. (1993). The new WHO classification of brain tumours. Brain Pathology, 3, 255–268.

    Google Scholar 

  • Kumar, S., Kumar, P., Sharma, T. K., & Pant, M. (2013). Bi-level thresholding using PSO, Artificial Bee Colony and MRLDE embedded with Otsu method. Journal of Memetic Computing, 5(4), 323–334.

    Google Scholar 

  • Kumar, S., Pant, M., Kumar, M., & Dutt, A. (2015). Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms. International Journal of Machine Learning and Cybernetics, 9, 1–21.

    Google Scholar 

  • Kumar, S., Pant, M., & Ray, A. K. (2011). Differential evolution embedded Otsu’s method for optimized image thresholding. IEEE International conference on world congress on information and communication technologies (pp. 325–329).

  • Langleben, D. D., & Segall, G. M. (2000). PET in differentiation of recurrent brain tumor from radiation injury. Journal of Nuclear Medicine, 41, 1861–1867.

    Google Scholar 

  • Lauterbur, P. (1973). Image formation by induced local interactions: Examples employing nuclear magnetic resonance. Journal of Nature, 242, 190–191.

    Google Scholar 

  • Lemieux, L., Hagemann, G., Krakow, K., & Woermann, F. G. (1999). Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Journal of Magnetic Resonance in Medicine, 42, 127–135.

    Google Scholar 

  • Li, C., Gore, J. C., & Davatzikos, C. (2014). Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Journal of Magnetic Resonance Imaging, 32(7), 913–923.

    Google Scholar 

  • Li, C., Huang, R., Ding, Z., Gatenby, C., Metaxas, D. N., & Gore, J. C. (2011). A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing, 20(7), 2007–2016.

    MathSciNet  MATH  Google Scholar 

  • Loizou, C. P., Kyriacou, E. C., Seimenis, I., Pantziaris, M., Petroudi, S., Karaolis, M., et al. (2013a). Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability. Journal of Intelligent Decision Technologies (IDT), 7, 3–10.

    Google Scholar 

  • Loizou, C. P., Murray, V., Pattichis, M. S., Seimenis, I., Pantziaris, M., & Pattichis, C. S. (2011). Multi-scale amplitude modulation–frequency modulation (AM–FM) texture analysis of multiple sclerosis in brain MRI images. IEEE Transactions on Information Technology in Biomedicine, 15(1), 119–129.

    Google Scholar 

  • Loizou, C. P., Pantziaris, M., Pattichis, C. S., & Seimenis, I. (2013b). Brain MRI Image normalization in texture analysis of multiple sclerosis. Journal of Biomedical Graphics and Computing, 3(1), 20–34.

    Google Scholar 

  • Loizou, C. P., Petroudi, S., Seimenis, I., Pantziaris, M., & Pattichis, C. S. (2015). Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. Journal of Neuroradiology, 42(2), 99–114.

    Google Scholar 

  • McCulloch, W., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics., 5(4), 115–133.

    MathSciNet  MATH  Google Scholar 

  • Meng, L., Ding, S., & Xue, Y. (2017). Research on denoising sparse autoencoder. International Journal of Machine Learning and Cybernetics, 8(5), 1719–1729.

    Google Scholar 

  • Menze, B. (2015). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transaction on Medical Imaging, 34(10), 1993–2024.

    Google Scholar 

  • Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3(5), 1–5.

    Google Scholar 

  • Neri, F., & Tirronen, V. (2009). Scale factor local search in differential evolution. Journal of Memetic Computing, 1, 153–171.

    Google Scholar 

  • Nyul, L. G., Udupa, J. K., & Zhang, X. (2000). New variants of a method of MRI scale standardization. IEEE Transaction on Medical Imaging, 19(2), 143–150.

    Google Scholar 

  • Ostrom, O. T., Gittleman, H., & Xu, J. (2016). CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2009–2013. Journal of Neuro Oncology, 18(suppl_5), iv1–iv76.

    Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on System, Man and Cybernetics, 9(1), 62–66.

    Google Scholar 

  • Pan, Z., & Lu, J. (2007). A Bayes-based region-growing algorithm for medical image segmentation. Journal of Computing in Science and Engineering, 9(4), 32–38.

    Google Scholar 

  • Park, J. G., & Lee, C. (2009). Skull stripping based on region growing for magnetic resonance brain images. Journal of NeuroImage, 47, 1394–1407.

    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.

    Google Scholar 

  • Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation 1. Annual Review of Biomedical Engineering, 2(1), 315–337.

    Google Scholar 

  • Price, K. (1999). An introduction to differential evolution. In D. Corne, M. Dorigo, & F. Glover (Eds.), New ideas in optimization (pp. 79–108). New York City: McGraw Hill.

    Google Scholar 

  • Roslan, R., Jamil, N., & Mahmud, R. (2011). Skull stripping magnetic resonance images brain images: Region growing versus mathematical morphology. International Journal of Computer Information Systems and Industrial Management Applications, 3, 150–158.

    Google Scholar 

  • Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transaction on Medical Imaging, 18(8), 712–721.

    Google Scholar 

  • Sathya, P. D., & Kayalvizhi, R. (2011). Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Journal of Neurocomputing, 74(14–15), 2299–2313.

    Google Scholar 

  • Segonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D., Hahn, H. K., et al. (2004). A hybrid approach to the skull stripping problem in MRI. Journal of NeuroImage, 22(3), 1060–1075.

    Google Scholar 

  • Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165.

    Google Scholar 

  • Sharma, N., & Aggarwal, L. M. (2010). Automated medical image segmentation techniques. Journal of Medical Physics, 35(1), 3–14.

    Google Scholar 

  • Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., & Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. Journal of NeuroImage, 13, 856–876.

    Google Scholar 

  • Shen, S., Sandham, W., Granat, M., & Sterr, A. (2005). MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Transactions on Information Technology in Biomedicine, 9(3), 459–467.

    Google Scholar 

  • Smith, S. M. (2002). Fast robust automated brain extraction. Journal of Human Brain Mapping, 17, 143–155.

    Google Scholar 

  • Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.

    MathSciNet  MATH  Google Scholar 

  • Straka, M., Cruz, A. L., Kochl, A., Sramek, M., Groller, M. E., & Fleischmann, D. (2003). 3D watershed transform combined with a probabilistic atlas for medical image segmentation. In Proceeding MIT2003 (pp. 1–8).

  • Subudhi, B. N., Thangaraj, V., Sankaralingam, E., & Ghosh, A. (2016). Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation. Journal of Magnetic Resonance Imaging, 34, 1292–1304.

    Google Scholar 

  • Suetens, P. (2009). Fundamentals of medical imaging (2nd ed.). Cambridge: Cambridge University Press. (BookFi.org).

    Google Scholar 

  • Suzuki, H., & Toriwaki, J. (1991). Automatic segmentation of head MRI images by knowledge guided thresholding. Journal of Computerized Medical Imaging and Graphics, 15(4), 233–240.

    Google Scholar 

  • Tustison, N. J. (2010). N4ITK: Improved n3 bias correction. IEEE Transaction on Medical Imaging, 29(6), 1310–1320.

    Google Scholar 

  • Vasupradha, V., Kavitha, A. R., & Rebecca, S. R. (2016). Automated brain tumor segmentation and detection in MRI using enhanced darwinian particle swarm optimization (EDPSO). Proceedings of the International Conference on Intelligent Computing, Communication & Convergence, 92, 475–480.

    Google Scholar 

  • Weickert, J. (1998). Anisotropic diffusion in image processing. Stuttgart: B.G. Teubner.

    MATH  Google Scholar 

  • Wong, K. P., & Dong, Z. Y. (2007). Differential evolution, an alternative approach to evolutionary algorithm. In K. Y. Lee & M. A. E. Sharkawi (Eds.), Modern heuristic optimization techniques: Theory and applications to power systems (pp. 171–187). New York City: Wiley.

    Google Scholar 

  • Wu, J., & Chung, A. C. (2007). A segmentation model using compound Markov random fields based on a boundary model. IEEE Transactions on Image Processing, 16(1), 241–252.

    MathSciNet  Google Scholar 

  • Yang, X. S., & Deb, S. (2009). Cuckoo search via levy flights. In IEEE publications on nature and biologically inspired computing (pp. 210–214).

  • Yin, P. Y. (2007). Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Journal of Applied Mathematics and Computing, 184(2), 503–513.

    MathSciNet  MATH  Google Scholar 

  • You, Y. L., Xu, W., Tannenbaum, A., & Kaveh, M. (1996). Behavioral analysis of anisotropic diffusion in image processing. IEEE Transactions on Image Processing, 5(11), 1539–1553.

    Google Scholar 

  • Zhang, N., Ding, S., & Shi, Z. (2016). Denoising Laplacian multi-layer extreme learning machine. Neurocomputing, 171, 1066–1074.

    Google Scholar 

  • Zhang, L., Ding, S., Zhang, J., & Xue, Y. (2018). An overview on restricted Boltzmann machines, neurocomputing. New York City: Elsevier.

    Google Scholar 

  • Zhang, Y., & Wu, L. (2011). Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach. Journal of Entropy, 13(4), 841–859.

    MathSciNet  MATH  Google Scholar 

  • Zhiwei, Y., Zhaobao, Z., Xin, Y., & Xiaogang, N. (2006). Automatic threshold selection based on ant colony optimization algorithm. In Proceedings of the international conference on neural networks and brain, Beijing (pp. 728–732).

  • Zhuang, A. H., Valentino, D. J., & Toga, A. W. (2006). Skull stripping magnetic resonance brain images using a model based level set. Journal of NeuroImage, 32(1), 79–92.

    Google Scholar 

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Acknowledgements

We are very thankful to the department of Computer Science and Engineering of National Institute of Technology Warangal, Warangal, India and Amity University, Noida, India for their valuable time and support for making this research a success.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

    Anshika Sharma & Shailendra Narayan Singh

  2. Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana, India

    Sushil Kumar

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  1. Anshika Sharma
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Correspondence to Anshika Sharma.

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Sharma, A., Kumar, S. & Singh, S.N. Brain tumor segmentation using DE embedded OTSU method and neural network. Multidim Syst Sign Process 30, 1263–1291 (2019). https://doi.org/10.1007/s11045-018-0603-3

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