“Multiscale feature-preserving smoothing of tomographic data” by Jibai, Soler, Subr and Holzschuch

  • ©Nassim Jibai, Cyril Soler, Kartic Subr, and Nicolas Holzschuch




    Multiscale feature-preserving smoothing of tomographic data



    Computer tomography (CT) has wide application in medical imaging and reverse engineering. Due to the limited number of projections used in reconstructing the volume, the resulting 3D data is typically noisy. Contouring such data, for surface extraction, yields surfaces with localised artifacts of complex topology. To avoid such artifacts, we propose a method for feature-preserving smoothing of CT data. The smoothing is based on anisotropic diffusion, with a diffusion tensor designed to smooth noise up to a given scale, while preserving features. We compute these diffusion kernels from the directional histograms of gradients around each voxel, using a fast GPU implementation.


    1. Frangakis, A. S., and Hegerl, R. 2001. Noise reduction in electron tomographic reconstructions using nonlinear anisotropic diffusion. Journal of Structural Biology 135, 3, 239–250.
    2. Kass, M., and Solomon, J. 2010. Smoothed local histogram filters. ACM Trans. Graph. 29 (July), 100:1–100:10.
    3. Schaap, M., Schilham, A., Zuiderveld, K., Prokop, M., Vonken, E.-J., and Niessen, W. 2008. Fast noise reduction in computed tomography for improved 3-d visualization. Medical Imaging, IEEE Transactions on 27, 8, 1120–1129.

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