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A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis

  • Authors
  • Heike Hufnagel

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Heike Hufnagel
    Pages 1-5
  3. Heike Hufnagel
    Pages 57-80
  4. Heike Hufnagel
    Pages 81-113
  5. Heike Hufnagel
    Pages 115-120
  6. Back Matter
    Pages 121-147

About this book

Introduction

In medical image analysis, major areas such as radiotherapy, surgery planning, and quantitative diagnostics benefit from shape modeling to facilitate solutions to analysis, segmentation and reconstruction problems.
Heike Hufnagel proposes a mathematically sound statistical shape model using correspondence probabilities instead of 1-to-1 correspondences. The explicit probabilistic model is employed as shape prior in an implicit level set segmentation. Due to the particular attributes of the new model, the challenging integration of explicit and implicit representations can be done in an elegant mathematical formulation, thus combining the advantages of both explicit model and implicit segmentation. Evaluations are performed to depict the characteristics and strengths of the new model and segmentation method.

The dissertation has received the Fokusfinder award 2011 by the Innovationsstiftung Schleswig-Holstein (ISH), the Basler AG and Philips Medical Systems.

Keywords

Medical Image Model-based Segmentation Probabilistic Correspondences Segmentierung Shape Modeling

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-8348-8600-2
  • Copyright Information Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011
  • Publisher Name Vieweg+Teubner Verlag
  • eBook Packages Engineering
  • Print ISBN 978-3-8348-1722-8
  • Online ISBN 978-3-8348-8600-2
  • Buy this book on publisher's site
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