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Automated 3D segmentation using deformable models and fuzzy affinity

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

We have developed an algorithm for segmenting objects with closed, non-intersecting boundaries, such as the heart and the lungs, that is independent of the imaging modality used (e.g., MRI, CT, echocardiography). Our method is automatic and requires as initialization a single pixel/voxel within the boundaries of the object. Existing segmentation techniques either require much more information during initialization, such as an approximation to the object's boundary, or are not robust to the types of noisy data encountered in the medical domain. By integrating region-based and physics-based modeling techniques we have devised a hybrid design that overcomes these limitations. In our experiments we demonstrate across imaging modalities, that this integration automates and significantly improves the object boundary detection results. This paper focuses on the application of our method to 3D datasets.

Keywords

  • Object Boundary
  • Deformable Model
  • Active Contour Model
  • Model Node
  • Fuzzy Connectedness

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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© 1997 Springer-Verlag Berlin Heidelberg

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Jones, T.N., Metaxas, D.N. (1997). Automated 3D segmentation using deformable models and fuzzy affinity. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_9

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  • DOI: https://doi.org/10.1007/3-540-63046-5_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63046-3

  • Online ISBN: 978-3-540-69070-2

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