Tree-Encoded Conditional Random Fields for Image Synthesis

  • Amod Jog
  • Aaron Carass
  • Dzung L. Pham
  • Jerry L. Prince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)


Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF). We model the conditional distribution as a Gaussian by defining quadratic association and interaction potentials encoded in leaves of a regression tree. The parameters of these quadratic potentials are learned by maximizing the pseudo-likelihood of the training data. Final synthesis is done by inference on this model. We applied this method to synthesize \(T_2\)-weighted images from \(T_1\)-weighted images, showing improved synthesis quality as compared to current image synthesis approaches. We also synthesized Fluid Attenuated Inversion Recovery (FLAIR) images, showing similar segmentations to those obtained from real FLAIRs. Additionally, we generated super-resolution FLAIRs showing improved segmentation.


Magnetic resonance Image synthesis Conditional random field 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Amod Jog
    • 1
  • Aaron Carass
    • 1
  • Dzung L. Pham
    • 2
  • Jerry L. Prince
    • 1
  1. 1.Image Analysis and Communications LaboratoryThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.Henry M. Jackson Foundation for the Advancement of Military MedicineBethesdaUSA

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