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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2013 Vol.1(4): 344-349 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.74

Signal Dependent Rician Noise Denoising Using Nonlinear Filter

Isshaa Aarya, Danchi Jiang, and Timothy Gale
Abstract—MR images are increasingly used for diagnostic and surgical procedures, as they offer better soft tissue contrast and advanced imaging capabilities. Similar to other imaging modalities, MR images are also subjected to various forms of noises and artifacts. The noise affecting MRI images is known as Rician noise and displays a nonlinear and signal dependent behavior. In this paper we propose a nonlinear filtering method for Rician noise denoising. Nonlinear filters are more capable in addressing signal dependent behavior of noise and offer good denoising with better edge preserving capabilities. A nonlinear filter based on homomorphic filter characteristics has been designed to address Rician noise in MR images. The proposed filter has been implemented on synthetic images and MR images of the articular cartilage. The efficiency of the proposed filtering method is verified by computing the PSNR and SSIM index of the image. The proposed nonlinear filter performs good denoising with improvement in the image quality as observed from the PSNR values of the image. It also offers edge preservation and can be used for both structural MRI and soft tissue study effectively

Index Terms—Homomorphic filters, MRI denoising, Rician noise, signal dependent filtering.

The authors are with School of Engineering, University of Tasmania, Hobart, TAS-7001, Australia (e-mail: isaarya@ utas.edu.au, danchi.jiang@utas.edu.au, T.Gale@utas.edu.au).


Cite: Isshaa Aarya, Danchi Jiang, and Timothy Gale, "Signal Dependent Rician Noise Denoising Using Nonlinear Filter," Lecture Notes on Software Engineering vol. 1, no. 4, pp. 344-349, 2013.

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