A 3D Laplacian-driven parametric deformable model

Tian Shen, Xiaolei Huang, Hongsheng Li, Edward Kim, Shaoting Zhang, Junzhou Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

3D parametric deformable models have been used to extract volumetric object boundaries and they generate smooth boundary surfaces as results. However, in some segmentation cases, such as cerebral cortex with complex folds and creases, and human lung with high curvature boundary, parametric deformable models often suffer from over-smoothing or decreased mesh quality during model deformation. To address this problem, we propose a 3D Laplacian-driven parametric deformable model with a new internal force. Derived from a Mesh Laplacian, the internal force exerted on each control vertex can be decomposed into two orthogonal vectors based on the vertex's tangential plane. We then introduce a weighting function to control the contributions of the two vectors based on the model mesh's geometry. Deforming the new model is solving a linear system, so the new model can converge very efficiently. To validate the model's performance, we tested our method on various segmentation cases and compared our model with Finite Element and Level Set deformable models.

Original languageEnglish (US)
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages279-286
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
CountrySpain
CityBarcelona
Period11/6/1111/13/11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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