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3D Flux Maximizing Flows

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2134))

Abstract

A number of geometric active contour and surface models have been proposed for shape segmentation in the literature. The essential idea is to evolve a curve (in 2D) or a surface (in 3D) so that it clings to the features of interest in an intensity image. Several of these models have been derived, using a variational formulation, as gradient flows which minimize or maximize a particular energy functional. However, in practice these models often fail on images of low contrast or narrow structures. To address this problem we have recently proposed the idea of maximizing the rate of increase of flux of an auxiliary vector field through a curve. This has lead to an interpretation as a 2D gradient flow, which is essentially parameter free. In this paper we extend the analysis to 3D and prove that the form of the gradient flow does not change. We illustrate its potential with level-set based segmentations of blood vessels in a large 3D computed rotational angiography (CRA) data set.

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Author information

Authors and Affiliations

  1. School of Computer Science & Center for Intelligent Machines, McGill University, 3480 University Street, Montréal, QC H3A 2A7, Canada

    Kaleem Siddiqi & Alexander Vasilevskiy

Authors
  1. Kaleem Siddiqi
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  2. Alexander Vasilevskiy
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Editor information

Editors and Affiliations

  1. Instituto de Telecomunicacoes and Instituto Superior Tecnico, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  2. INRIA, 2004 Route des Lucioles, 06902 Sophia-Antipolis cedex, France

    Josiane Zerubia

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Anil K. Jain

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© 2001 Springer-Verlag Berlin Heidelberg

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Siddiqi, K., Vasilevskiy, A. (2001). 3D Flux Maximizing Flows. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_42

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  • DOI: https://doi.org/10.1007/3-540-44745-8_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42523-6

  • Online ISBN: 978-3-540-44745-0

  • eBook Packages: Springer Book Archive

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