Local Mean Multiphase Segmentation with HMMF Models

  • Jacob Daniel Kirstejn HansenEmail author
  • François Lauze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)


This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods’ capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.


Tikhonov Regularisation Bias Field Kernel Parameter Descent Step Classical Segmentation 
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.



J. Hansen and F. Lauze acknowledge funding from the Innovation Fund Denmark and Mærsk Oil and Gas A/S, for the P\(^3\) Project. We thank Henning Osholm Sørensen for giving us access to the experimental tomography data.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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