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A Probabilistic Framework for Point-Based Shape Modeling in Medical Image AnalysisUsing the GGM-SSM as a Prior for Segmentation

A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis: Using the... [Segmentation algorithms play a major role in medical image analysis. However, due to typical medical image characteristics as poor contrasts, grey value inhomogeneities, contour gaps, and noise the automatic segmentation of many anatomical structures remains a challenge. Low-level algorithms as region growing, thresholding or simple edge-detection are often bound to fail or require heavy user interaction to lead to acceptable segmentation results in 3D images. In order to overcome these problems, a very popular approach is to employ models which incorporate a priori knowledge about mean and variance of shape or grey levels of the structure of interest. These models serve to constrain the resulting segmentation contour to probable shapes as defined by the underlying training data set. The concept of shape priors in segmentation methods has been analysed in section 2.4.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Probabilistic Framework for Point-Based Shape Modeling in Medical Image AnalysisUsing the GGM-SSM as a Prior for Segmentation

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Publisher
Vieweg+Teubner Verlag
Copyright
© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011
ISBN
978-3-8348-1722-8
Pages
81 –113
DOI
10.1007/978-3-8348-8600-2_5
Publisher site
See Chapter on Publisher Site

Abstract

[Segmentation algorithms play a major role in medical image analysis. However, due to typical medical image characteristics as poor contrasts, grey value inhomogeneities, contour gaps, and noise the automatic segmentation of many anatomical structures remains a challenge. Low-level algorithms as region growing, thresholding or simple edge-detection are often bound to fail or require heavy user interaction to lead to acceptable segmentation results in 3D images. In order to overcome these problems, a very popular approach is to employ models which incorporate a priori knowledge about mean and variance of shape or grey levels of the structure of interest. These models serve to constrain the resulting segmentation contour to probable shapes as defined by the underlying training data set. The concept of shape priors in segmentation methods has been analysed in section 2.4.]

Published: Aug 16, 2011

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