Abstract
Image enhancement technique is able to enhance structure of a lesion and filter out irrelevant information through an image processing method. It can make contrast of an image stronger, and therefore enhance diagnostic accuracy. Current image enhancement techniques apply global enhancement with some strategies like histogram equalization. However, local contrast information may be lost because of the use of a global enhancement. And, global enhancement may bring unnecessary information on irrelevant background tissues. For this reason, local contrast information needs to be incorporated in the global enhancement procedure. We propose a multi-view learning approach for glioblastoma image contrast enhancement. Each local contrast is accomplished by a single-view learning and therefore final enhancement is a result of multi-view learning. Experimental results demonstrate that the proposed method is able to outperform traditional global contrast enhancement techniques.
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References
Haacke, M., Thompson, M., Venkatesan, R., Brown, R., and Cheng, Y.: Magnetic resonance imaging: physical principles and sequence design, 1st edn. Wiley-Liss (1999)
Larkman, D.J., Nunes, R.G.: Parallel magnetic resonance imaging. Phys. Med. Biol. 52, 15–55 (2007)
Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
Chauhan, R., Bhadoria, S.: An improved image contrast enhancement based on histogram equalization and brightness preserving weight clustering histogram equalization. In: International Conference on Communication Systems and Network Technologies (2011)
Ghita, O., Ilea, D.E., Whelan, P.F.: Texture enhanced histogram equalization using TV-L-1 image decomposition. IEEE Trans. Image Process. 22(8), 3133–3144 (2013)
Zhao, J., Xie, X., Xu, X., Sun, S.: Multi-view learning overview. Inf Fusion 38, 43–54 (2017)
BRATS 2013 (2013). https://www.smir.ch/BRATS/Start
Gonzalez, R., Woods, R.: Digital Image Processing. Pearson Education (2004)
Coltuc, D., Bolon, P., Chassery, J.M.: Exact histogram specification. IEEE Trans. Image Process. 15(5), 1143–1152 (2006)
Wang, X., An, Z., Wang, H., Chang, Y.: MR brain image enhancement via learning ensemble. In: The 2018 IEEE International Conference on Intelligence and Safety for Robotics. ISBN. 978-1-5386-5546-7, pp. 282–285 (2018)
Dey, N., Ashour, A.S., Beagum, S., Pistola, D.S., Gospodinov, M., Gospodinova, E.P., Tavares, J.M.R.S.: Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: An application for brain MRI image de-noising. J. Imaging 1(1), 60–84 (2015)
Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. Int. J. Ambient Comput. Intell. (IJACI) 7(2), 75–93 (2016)
Beagum, S., Dey, N., Ashour, A.S., Sifaki-Pistolla, D., Balas, V.E.: Nonparametric de-noising filter optimization using structure-based microscopic image classification. Microsc. Res. Tech. 80(4), 419–429 (2017)
Wang, H., Wang, X., Chang, Y.: Automatic clustering of natural scene using color spatial envelope feature. In: Proceedings of the International Conference on Computing and Artificial Intelligence (ICCAI), pp. 144–148 (2018)
Ashourab, A.S., Dey, N.: Adaptive window bandwidth selection for direction of arrival estimation of uniform velocity moving targets based relative intersection confidence interval technique. Ain Shams Eng. J. 9(4), 835–843 (2018)
Wang, H., Zhou, Y., Su, S., Hu, Z., Liao, J., Chang, Y.: Adaptive volterra filter for parallel MRI reconstruction. EURASIP J. Adv. Signal Process. 34, 2019 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No.61563055, No.61871373, and No.81729003).
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Wang, X., An, Z., Zhou, J., Chang, Y. (2020). A Multi-view Learning Approach for Glioblastoma Image Contrast Enhancement. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3867-4_18
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DOI: https://doi.org/10.1007/978-981-15-3867-4_18
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