CT image noise reduction using rotational-invariant feature in Stockwell transform

Jian Su, Zhoubo Li, Lifeng Yu, Joshua Warner, Daniel Blezek, Bradley Erickson

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


Iterative reconstruction and other noise reduction methods have been employed in CT to improve image quality and to reduce radiation dose. The non-local means (NLM) filter emerges as a popular choice for image-based noise reduction in CT. However, the original NLM method cannot incorporate similar structures if they are in a rotational format, resulting in ineffective denoising in some locations of the image and non-uniform noise reduction across the image. We have developed a novel rotational-invariant image texture feature derived from the multiresolutional Stockwell-transform (ST), and applied it to CT image noise reduction so that similar structures can be identified and fully utilized even when they are in different orientations. We performed a computer simulation study in CT to demonstrate better efficiency in terms of utilizing redundant information in the image and more uniform noise reduction achieved by ST than by NLM.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
ISBN (Print)9780819498274
StatePublished - 2014
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: Feb 16 2014Feb 18 2014

Publication series

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


OtherMedical Imaging 2014: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • CT dose reduction
  • Image denoising
  • Image feature
  • Multiresolutional analysis
  • Non-local means filtering
  • Rotational invariant
  • Stockwell transform

ASJC Scopus subject areas

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


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