Shape-Driven 3D Segmentation Using Spherical Wavelets

  • Delphine Nain
  • Steven Haker
  • Aaron Bobick
  • Allen Tannenbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.


Image Force Surface Evolution Shape Representation Statistical Shape Modelling Prior Probability Distribution 
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 2006

Authors and Affiliations

  • Delphine Nain
    • 1
  • Steven Haker
    • 2
  • Aaron Bobick
    • 1
  • Allen Tannenbaum
    • 3
  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of RadiologyBrigham and Women’s HospitalBostonUSA
  3. 3.Electrical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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