Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model

The denoising step for Computed Tomography (CT) images is an important challenge in the medical image processing. These images are degraded by low resolution and noise. In this paper, we propose a new method for 3D CT denoising based on Coherence Enhancing Diffusion model. Quantitative measures such as PSNR, SSIM and RMSE are computed to a phantom CT image in order to improve the efficiently of our proposed model, compared to a number of denoising algorithms. Furthermore, experimental results on a real 3D CT data show that this approach is effective and promising in removing noise and preserving details.

[1]  Joachim Weickert,et al.  Coherence-Enhancing Diffusion Filtering , 1999, International Journal of Computer Vision.

[2]  Cornelis H. Slump,et al.  Optimized Anisotropic Rotational Invariant Diffusion Scheme on Cone-Beam CT , 2010, MICCAI.

[3]  Lucas J. van Vliet,et al.  Confidence and curvature estimation of curvilinear structures in 3-D , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Lucas J. van Vliet,et al.  The application of a Local Dimensionality Estimator to the analysis of 3-D microscopic network structures , 1999 .

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  H. Amiri,et al.  3D medical images denoising , 2014, International Image Processing, Applications and Systems Conference.

[8]  Philippe Montesinos,et al.  Half Gaussian Kernels Based Shock Filter for Image Deblurring and Regularization , 2013, VISAPP.

[9]  David Tschumperlé,et al.  Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's , 2006, International Journal of Computer Vision.

[10]  O. Lavialle,et al.  A New Partial Differential Equation-based approach for 3D data denoising and edge preserving , 2007 .

[11]  A S Frangakis,et al.  Noise reduction in electron tomographic reconstructions using nonlinear anisotropic diffusion. , 2001, Journal of structural biology.

[12]  Max A. Viergever,et al.  Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: Results of an in-vitro evaluation , 2002, Medical Image Anal..

[13]  Max A. Viergever,et al.  Noise Reduction in Computed Tomography Scans Using 3-D Anisotropic Hybrid Diffusion With Continuous Switch , 2009, IEEE Transactions on Medical Imaging.