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Segmentation of medical image objects using deformable shape loci

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

Abstract

Robust segmentation of normal anatomical objects in medical images requires (1) methods for creating object models that adequately capture object shape and expected shape variation across a population, and (2) methods for combining such shape models with unclassified image data to extract modeled objects. Described in this paper is such an approach to model-based image segmentation, called deformable shape loci (DSL), that has been successfully applied to 2D MR slices of the brain ventricle and CT slices of abdominal organs. The method combines a model and image data by warping the model to optimize an objective function measuring both the conformation of the warped model to the image data and the preservation of local neighbor relationships in the model. Methods for forming the model and for optimizing the objective function are described.

Keywords

  • Training Image
  • Markov Random Field
  • Object Shape
  • Medial Site
  • Model Template

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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Fritsch, D., Pizer, S., Yu, L., Johnson, V., Chaney, E. (1997). Segmentation of medical image objects using deformable shape loci. 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_10

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  • DOI: https://doi.org/10.1007/3-540-63046-5_10

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  • Print ISBN: 978-3-540-63046-3

  • Online ISBN: 978-3-540-69070-2

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