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Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models

  • Mustafa Gökhan Uzunbaş
  • Chao Chen
  • Shaoting Zhang
  • Kilian M. Pohl
  • Kang Li
  • Dimitris Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models’ evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.

Keywords

Graphical Model Right Ventricle Markov Random Fields Right Atrium Deformable Model 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mustafa Gökhan Uzunbaş
    • 1
  • Chao Chen
    • 1
  • Shaoting Zhang
    • 1
  • Kilian M. Pohl
    • 2
  • Kang Li
    • 3
  • Dimitris Metaxas
    • 1
  1. 1.CBIMRutgers UniversityPiscatawayUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA
  3. 3.Dept. of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA

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