title: Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.
abstract: OBJECTIVE
  Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested.
  
  
  METHODS
  Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations.
  
  
  RESULTS
  The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes.
  
  
  CONCLUSIONS
  The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.
authors.affiliation: Department of Mathematics, University of Bergen, Bergen, Norway, erik.hanson@math.uib.no.
authors.forename: Erik A
authors.initials: EA
authors.lastname: Hanson
authors.forename: Arvid
authors.initials: A
authors.lastname: Lundervold
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*