Segmentation of the Right Ventricle Using Diffusion Maps and Markov Random Fields

  • Oliver Moolan-Feroze
  • Majid Mirmehdi
  • Mark Hamilton
  • Chiara Bucciarelli-Ducci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Accurate automated segmentation of the right ventricle is difficult due in part to the large shape variation found between patients. We explore the ability of manifold learning based shape models to represent the complexity of shape variation found within an RV dataset as compared to a typical PCA based model. This is empirically evaluated with the manifold model displaying a greater ability to represent complex shapes. Furthermore, we present a combined manifold shape model and Markov Random Field Segmentation framework. The novelty of this method is the iterative generation of targeted shape priors from the manifold using image information and a current estimate of the segmentation; a process that can be seen as a traversal across the manifold. We apply our method to the independently evaluated MICCAI 2012 RV Segmentation Challenge data set. Our method performs similarly or better than the state-of-the-art methods.


Right Ventricular Gaussian Mixture Model Markov Random Field Principal Component Analysis Model Signed Distance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Oliver Moolan-Feroze
    • 1
  • Majid Mirmehdi
    • 1
  • Mark Hamilton
    • 2
  • Chiara Bucciarelli-Ducci
    • 2
  1. 1.Visual Information LaboratoryUniversity of BristolUK
  2. 2.Bristol Heart InstituteBristolUK

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