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Application of Noise Invalidation Denoising in MRI
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have beenused in clinical industry for diagnostic and research purposes. These images are subjectto noises while capturing the data that can eect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution andsignal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique isone of the common tools to remove the noise in the MRI images. The noise is eliminatedfrom the detailed coecients of the signal in the wavelet domain. This can be done byapplying thresholding methods. The main task here is to nd an optimal threshold andkeep all the coecients larger than this threshold as the noiseless ones. Noise InvalidationDenoising technique is a method in which the optimal threshold is found by comparingthe noisy signal to a noise signature (function of noise statistics). The original NIDeapproach is developed for one dimensional signals with additive Gaussian noise. In thiswork, the existing NIDe approach has been generalized for applications in MRI imageswith dierent noise distribution. The developed algorithm was tested on simulated datafrom the Brainweb database and compared with the well-known Non Local Mean lteringmethod for MRI. The results indicated better detailed structural preserving forthe NIDe approach on the magnitude data while the signal to noise ratio is compatible.The algorithm shows an important advantageous which is less computational complexitythan the NLM method. On the other hand, the Unbiased NLM technique is combinedwith the proposed technique, it can yield the same structural similarity while the signalto noise ratio is improved.

Place, publisher, year, edition, pages
2012. , 64 p.
Keyword [en]
Magnetic Resonance Imaging, Noise Invalidation Denoising, Wavelet Transform Function
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-85215ISRN: LiTH-IMT/MASTER-EX--12/019--SEOAI: oai:DiVA.org:liu-85215DiVA: diva2:566868
External cooperation
Ryerson University
Subject / course
Master's Program Biomedical Engineering
Presentation
2012-09-18, Falken, Department of Biomedical Engineering, Linkoping University, Linkoping, 14:00 (English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-11-12 Created: 2012-11-09 Last updated: 2012-11-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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