MRI Denoising and Artefact Removal Using Self-Organizing Maps for Fast Global Block-Matching

  • Lee B. ReidEmail author
  • Ashley Gillman
  • Alex M. Pagnozzi
  • José V. Manjón
  • Jurgen Fripp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)


Image noise and motion degrade the quality of MR images. Block-matching methods are a well-demonstrated means of improving signal-to-noise ratios in such images. Ideally, block-matching methods would search within the entire image for matching patches to a target, leveraging an image’s full informational redundancy, but this carries impractical computational costs. A well-known workaround, implemented in the traditional Non-Local Means (NLM) filter, is to search for matching patches only within a local neighborhood. Here, we detail a Global Approximate Block-matching (GAB) method that, via a self-organizing map, rapidly searches an entire image for patches similar to a target. Four sets of five T1 + five FLAIR images were acquired. GAB and NLM both denoised the T1s; the results were compared to subject-wise mean images with very low noise. GAB reliably produced images that were more similar to these ‘templates’ than NLM. This was repeated for the same images with motion-like artefacts artificially added. GAB, again, outperformed NLM. For this task, GAB further improved with multichannel inputs, even if the FLAIR image contained artefacts. GAB’s competitive performance appeared to be due to a better balance between preserving image features and removing noise/artefacts. The performance of GAB and NLM variants hinted that GAB’s advantage was not brute-force processing, but its ability to effectively search the whole image.


Block-matching Self-organizing map Image noise MRI artefacts Non-local means 


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Copyright information

© Crown Copyright is asserted by the Australian Government 2018

Authors and Affiliations

  1. 1.The Australian e-Health Research CentreCommonwealth Scientific and Industrial Research OrganisationBrisbaneAustralia
  2. 2.Faculty of MedicineThe University of QueenslandSt LuciaAustralia
  3. 3.Universitat Politècnia de València, ITACAValenciaSpain

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