Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

Rasmus Larsen; Klaus Baggesen Hilger; Mark Christoph Wrobel

Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

Rasmus Larsen, Klaus Baggesen Hilger, Mark Christoph Wrobel

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furthermore, we study metrics based on repated annotations of a training set. We define a way of assessing the correlation between landmarks contrary to landmark coordinates. Finally, we apply the proposed methods to a 2D data set consisting of outlines of lungs and a 3D/(4D) data set consisting of sets of mandible surfaces. In the latter case the end goal is to construct a model for growth prediction and simulation.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan
Publisher	Springer
Publication date	2002
Publication status	Published - 2002

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title = "Statistical 2D and 3D shape analysis using Non-Euclidean Metrics",

abstract = "We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furthermore, we study metrics based on repated annotations of a training set. We define a way of assessing the correlation between landmarks contrary to landmark coordinates. Finally, we apply the proposed methods to a 2D data set consisting of outlines of lungs and a 3D/(4D) data set consisting of sets of mandible surfaces. In the latter case the end goal is to construct a model for growth prediction and simulation.",

keywords = "maximum noise fractions, shape analysis, growth modelling, maximum autocorrelation factors",

author = "Rasmus Larsen and Hilger, {Klaus Baggesen} and Wrobel, {Mark Christoph}",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan",

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Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. / Larsen, Rasmus; Hilger, Klaus Baggesen; Wrobel, Mark Christoph.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan. Springer, 2002.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

AU - Larsen, Rasmus

AU - Hilger, Klaus Baggesen

AU - Wrobel, Mark Christoph

PY - 2002

Y1 - 2002

N2 - We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furthermore, we study metrics based on repated annotations of a training set. We define a way of assessing the correlation between landmarks contrary to landmark coordinates. Finally, we apply the proposed methods to a 2D data set consisting of outlines of lungs and a 3D/(4D) data set consisting of sets of mandible surfaces. In the latter case the end goal is to construct a model for growth prediction and simulation.

AB - We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furthermore, we study metrics based on repated annotations of a training set. We define a way of assessing the correlation between landmarks contrary to landmark coordinates. Finally, we apply the proposed methods to a 2D data set consisting of outlines of lungs and a 3D/(4D) data set consisting of sets of mandible surfaces. In the latter case the end goal is to construct a model for growth prediction and simulation.

KW - maximum noise fractions

KW - shape analysis

KW - growth modelling

KW - maximum autocorrelation factors

M3 - Article in proceedings

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan

PB - Springer

ER -

Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

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