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Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity

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  • Xinyuan Zhang
  • Guirong Hou
  • Jianhua Ma
  • Wei Yang
  • Bingquan Lin
  • Yikai Xu
  • Wufan Chen
  • Yanqiu Feng

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.

Suggested Citation

  • Xinyuan Zhang & Guirong Hou & Jianhua Ma & Wei Yang & Bingquan Lin & Yikai Xu & Wufan Chen & Yanqiu Feng, 2014. "Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    • Handle: RePEc:plo:pone00:0100240
    DOI: 10.1371/journal.pone.0100240
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