title: Denoising MR images using non-local means filter with combined patch and pixel similarity.
abstract: Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details in MR magnitude images. However, the Rician NLM (RNLM) filter usually blurs small high-contrast particle details which might be clinically relevant information. In this paper, we investigated the reason of this particle blurring problem and proposed a novel particle-preserving RNLM filter with combined patch and pixel (RNLM-CPP) similarity. The results of experiments on both synthetic and real MR data demonstrate that the proposed RNLM-CPP filter can preserve small high-contrast particle details better than the original RNLM filter while denoising MR images.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Dermatology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
authors.forename: Guirong
authors.initials: G
authors.lastname: Hou
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Department of Diagnostic Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
authors.forename: Bingquan
authors.initials: B
authors.lastname: Lin
authors.affiliation: Department of Diagnostic Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
authors.forename: Yikai
authors.initials: Y
authors.lastname: Xu
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059629:Signal-To-Noise Ratio