Active Volume Models with Probabilistic Object Boundary Prediction Module

  • Tian Shen
  • Yaoyao Zhu
  • Xiaolei Huang
  • Junzhou Huang
  • Dimitris Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

We propose a novel Active Volume Model (AVM) which deforms in a free-form manner to minimize energy. Unlike Snakes and level-set active contours which only consider curves or surfaces, the AVM is a deforming object model that has both boundary and an interior area. When applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. The probabilistic object prediction module relies on the Bayesian Decision Rule to separate foreground (i.e. object represented by the model) and background. Optimization of the model is a natural extension of the Snakes model so that region information becomes part of the external forces. The AVM thus has the efficiency of Snakes while having adaptive region-based constraints. Segmentation results, validation, and comparison with GVF Snakes and level set methods are presented for experiments on noisy 2D/3D medical images.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tian Shen
    • 1
  • Yaoyao Zhu
    • 1
  • Xiaolei Huang
    • 1
  • Junzhou Huang
    • 2
  • Dimitris Metaxas
    • 2
  • Leon Axel
    • 3
  1. 1.Department of Computer Science and EngineeringLehigh UniversityBethlehem 
  2. 2.Computational Biomedicine Imaging and Modeling CenterRutgers University
  3. 3.Department of RadiologyNew York University School of MedicineNew York 

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