Wavelet-based multiscale level-set curve evolution in noise reduction for MR imaging

Junmei Zhong, Bernard Dardzinski, Janaka Wansapura

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In magnetic resonance (MR) imaging, there is a tradeoff between the spatial resolution, temporal resolution and signal to noise ratio (SNR). MR images usually suffer from low SNR and low resolutions. In order to make it practical for MR imaging with higher resolutions as well as sufficient SNR, it is necessary to reduce noise efficiently while preserving important image features. In this paper, we propose to use the wavelet-based multiscale level-set curve evolution algorithm to reduce noise for MR imaging. Experimental results demonstrate that this denoising algorithm can significantly improve the SNR and contrast to noise ratio (CNR) for MR images while preserving edges with good visual quality. The denoising results indicate that in MR imaging applications, we can almost doubly improve the temporal resolution or improve the spatial resolution while achieving sufficient SNR, CNR, and satisfactory image quality.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2006
Subtitle of host publicationImage Processing
StatePublished - 2006
Externally publishedYes
EventMedical Imaging 2006: Image Processing - San Diego, CA, United States
Duration: Feb 13 2006Feb 16 2006

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6144 III
ISSN (Print)1605-7422


OtherMedical Imaging 2006: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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