Coping with Noise in Ultrasound Images: A review

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dc.contributor.author Zwiggelaar, Reyer
dc.contributor.author Roscoe, Jonathan Francis
dc.contributor.author Dee, Hannah
dc.date.accessioned 2012-06-13T12:09:14Z
dc.date.available 2012-06-13T12:09:14Z
dc.date.issued 2012
dc.identifier.citation Zwiggelaar , R , Roscoe , J F & Dee , H 2012 , ' Coping with Noise in Ultrasound Images: A review ' . en
dc.identifier.other PURE: 176368
dc.identifier.other dspace: 2160/7844
dc.identifier.uri http://hdl.handle.net/2160/7844
dc.description Roscoe, Dee, Zwiggelaar, Coping with Noise in Ultrasound Images: A review, MIUA 2012 en
dc.description.abstract Ultrasound is notorious for having significant noise with a low signal-to-noise ratio. This inhibits the performance of segmentation and causes difficulty for clinical evaluation, thus noise reduction is paramount to achieving adequate segmentation in ultrasound images. Consequently, the modeling and handling of noise is a significant area of research. In this review paper we introduce the typical characteristics of noise in B-mode ultrasound and analyse th performance of multiple state-of-the-art methodologies for dealing with such noise. A similar paper was written by by Coupé when they introduced OBNLM; though we provide an independent review and generalised description of the problem area. We also discuss the issue of typical image quality assessment methods and consider the impact speckle noise could have on ultrasound image analysis. Three state-of-the-art denoising algorithms (SRAD, SBF, and OBNLM) are evaluated using three different image quality assessment methods (SSIM, MSE and USDSAI) in comparison with traditional filters such as Lee's. We worked with simulated phantom images, as well as prostate ultrasound images to assess these methods. SRAD and OBNLM seem to be the most effective algorithms and in our discussion we contemplate ways in which they might be further expanded. en
dc.language.iso eng
dc.title Coping with Noise in Ultrasound Images: A review en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Non peer reviewed en


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