Abstract
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. To reduce noise, the non-local means (NLM) filter has been shown to yield state-of-the-art denoising performance. However, NLM relies heavily on the existence of recurring structural patterns and this condition might not always be satisfied especially within a single image, where complex patterns might not recur. In this paper, we propose to leverage common structures from multiple images to collaboratively denoise an image. The assumption is that, although the human brain is structurally complex, common structures can be found with greater probability from multiple scans than from a single scan. More specifically, to denoise an image, multiple images from different individuals are spatially aligned to the image and NLM-like block matching is performed on these aligned images with the image as the reference. Experiments on synthetic and real data indicate that the proposed approach - collaborative non-local means (CNLM) - outperforms the classic NLM and yields results with markedly improved structural details.
Original language | English |
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Title of host publication | 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 |
Publisher | IEEE Computer Society |
Pages | 564-567 |
Number of pages | 4 |
ISBN (Electronic) | 9781479923748 |
DOIs | |
Publication status | Published - 2015 Jul 21 |
Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 2015 Apr 16 → 2015 Apr 19 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2015-July |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Other
Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
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Country/Territory | United States |
City | Brooklyn |
Period | 15/4/16 → 15/4/19 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- MRI denoising
- edge-preserving denoising
- non-local means filter
- patch-based approach
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging