title: A general strategy for anisotropic diffusion in MR image denoising and enhancement.
abstract: Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.
authors: Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore. g0901416@nus.edu.sg
authors: Chenchen
authors: C
authors: Tong
authors: Ying
authors: Y
authors: Sun
authors: Nicolas
authors: N
authors: Payet
authors: Sim-Heng
authors: SH
authors: Ong
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001331:Automation; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D004058:Diffusion; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy; D008334:Mandible / Q000473:pathology; D015233:Models, Statistical; D016011:Normal Distribution