Denoising MR images using non-local means filter with combined patch and pixel similarity

• Published in: PloS one Vol. 9; no. 6; p. e100240
• Main Authors: Zhang, Xinyuan, Hou, Guirong, Ma, Jianhua, Yang, Wei, Lin, Bingquan, Xu, Yikai, Chen, Wufan, Feng, Yanqiu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.06.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Bias; Biomedical engineering; Blurring; Brain - anatomy & histology; Engineering; Engineering and Technology; Humans; Image acquisition; Image contrast; Image Enhancement; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine and Health Sciences; Noise; Noise control; Noise reduction; Pixels; Quantitative analysis; Reliability analysis; Signal processing; Signal-To-Noise Ratio; Similarity; Stroke
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0100240

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.