Adaptive smoothing of MR images by fitting planes

Prakash Adiseshan, Tracy L. Faber, Roderick W McColl, Ronald M Peshock

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


We present a solution method for adaptively smoothing magnetic resonance (MR) images while preserving discontinuities. We assume that the spatial behavior of MR data can be captured by a first order polynomial defined at every pixel. The formulation itself is similar to Leclerc's work on piecewise-smooth image segmentation, but we use the graduated non- convexity (GNC) algorithm as an optimizing tool for obtaining the solution. This requires initial values for polynomial coefficients of order greater than zero. These values are obtained by using ideas similar to that found in robust statistics. This initial step is also useful in determining the variance of the noise present in the input image. The variance is related to an important parameter α required by the GNC algorithm. Firstly, this replaces the heuristic nature of α with a quantity that can be estimated. Secondly, it is useful especially in situations where the variance of the noise is not uniform across the image. We present results on synthetic and MR images. Though the results of this paper are given using first order polynomials, the formulation can handle higher order polynomials.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Number of pages10
ISBN (Print)0819408042
StatePublished - Jan 1 1992
EventMedical Imaging VI: Image Processing - Newport Beach, CA, USA
Duration: Feb 24 1992Feb 27 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherMedical Imaging VI: Image Processing
CityNewport Beach, CA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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