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Curve Propagation, Level Set Methods and Grouping

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Handbook of Biomedical Imaging

Abstract

Image segmentation and object extraction are among the most well addressed topics in computational vision. In this chapter we present a comprehensive tutorial of level sets towards a flexible frame partition paradigm that could integrate edge-drive, regional-based and prior knowledge to object extraction. The central idea behind such an approach is to perform image partition through the propagation planar curves/surfaces. To this end, an objective function that aims to account for the expected visual properties of the object, impose certain smoothness constraints and encode prior knowledge on the geometric form of the object to be recovered is presented. Promising experimental results demonstrate the potential of such a method.

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Authors and Affiliations

  1. Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale Paris, Paris, France

    N. Paragios

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  1. N. Paragios
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Correspondence to N. Paragios .

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Editors and Affiliations

  1. Department of Applied Mathematics, École Centrale de Paris / INRIA Saclay, Île De France, Chatenay-Malabry, France

    Nikos Paragios

  2. Department of Biomedical Engineering, Diagnostic Radiology and Electrical Engineering, Yale University, New Haven, Connecticut, USA

    James Duncan

  3. INRIA Sophia Antipolis - Méditerranée, Sophia Antipolis Cedex, France

    Nicholas Ayache

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Paragios, N. (2015). Curve Propagation, Level Set Methods and Grouping. In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_3

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  • DOI: https://doi.org/10.1007/978-0-387-09749-7_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09748-0

  • Online ISBN: 978-0-387-09749-7

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