MR image denoising method for brain surface 3D modeling
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Tianjin Key Laboratory of Intelligent Computing & Novel Software Technology, Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China

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This work has been supported by the National Natural Science Foundation of China (No.61202169), the Tianjin Key Natural Science Foundation (No.13JCZDJC34600), the China Scholarship Council (CSC) Foundation (No.201308120010), and the Training Plan of Tianjin University Innovation Team (No.TD12-5016).

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    Abstract:

    Three-dimensional (3D) modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance (MR) images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.

    Reference
    [1]C. Shyam Anand and Jyotinder S. Sahambi, Magnetic Resonance Imaging 28, 842 (2010).
    [2]Mohan J., Krishnaveni V. and Guo Y., Biomedical Signal Processing and Control 8, 779 (2013).
    [3]Chen G. and Qian S. E., IEEE Transactions on Geoscience and Remote Sensing 49, 973 (2011).
    [4]José V. Manjón, Pierrick Coupé, Luis Martí‐Bonmatí, D. Louis Collins and Montserrat Robles, Journal of Magnetic Resonance Imaging 31, 192 (2010).
    [5]Ruikar S. D. and Doye D. D., International Journal of Advanced Computer Science and Applications 2, 49 (2011).
    [6]Wang Shengqian, Zhou Yuanhua and Zou Daowen, Journal of Infrared and Millimeter Waves 20, 387 (2001). (in Chinese)
    [7]Khare A., Tiwary U. S. and Pedrycz W., Imaging Science Journal 58, 340 (2010).
    [8]Sendur L. and Selesnick I. W., IEEE Signal Processing Letters 9, 438 (2002).
    [9]Jeny Rajan, Dirk Poot, Jaber Juntu and Jan Sijbers, Physics in Medicine and Biology 55, 441 (2010).
    [10]Florian Luisier, Cedric Vonesch, Thierry Blu and Michael Unser, Signal Processing 90, 415 (2010).
    [11]Ashish Khare, Manish Khare and Yongyeon Jeong, Signal Processing 90, 428 (2010).
    [12]Degan Zhang and Xiaodan Zhang, Enterprise Information Systems 6, 473 (2012).
    [13]Degan Zhang, Guang Li and Ke Zheng, IEEE Transaction on Industrial Informatics 10, 766 (2014).
    [14]Degan Zhang and Xuejing Kang, Journal on Advances in Signal Processing 110, 1 (2012).
    [15]Zhang De-Gan, Kang Xue-Jing and Wang Dong, Journal of Optoelectronics.Laser 23, 180 (2012). (in Chinese)
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ZHAO De-xin, LIU Peng-jie, ZHANG De-gan. MR image denoising method for brain surface 3D modeling[J]. Optoelectronics Letters,2014,10(6):477-480

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History
  • Received:July 07,2014
  • Online: October 06,2015
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