title: Laplacian based non-local means denoising of MR images with Rician noise.
abstract: Magnetic Resonance (MR) image is often corrupted with a complex white Gaussian noise (Rician noise) which is signal dependent. Considering the special characteristics of Rician noise, we carry out nonlocal means denoising on squared magnitude images and compensate the introduced bias. In this paper, we propose an algorithm which not only preserves the edges and fine structures but also performs efficient denoising. For this purpose we have used a Laplacian of Gaussian (LoG) filter in conjunction with a nonlocal means filter (NLM). Further, to enhance the edges and to accelerate the filtering process, only a few similar patches have been preselected on the basis of closeness in edge and inverted mean values. Experiments have been conducted on both simulated and clinical data sets. The qualitative and quantitative measures demonstrate the efficacy of the proposed method.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology, Bombay, Mumbai 400076, Maharashtra, India. Electronic address: hemalata@ee.iitb.ac.in.
authors.forename: Hemalata V
authors.initials: HV
authors.lastname: Bhujle
authors.forename: Subhasis
authors.initials: S
authors.lastname: Chaudhuri
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007717:Knee / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D016011:Normal Distribution; D015203:Reproducibility of Results
keywords: Laplacian of Gaussian; Magnetic Resonance Imaging; Nonlocal-means; Rician noise