Surface-bounded growth modeling applied to human mandibles

Per Rønsholt Andresen; F. L. Brookstein; Knut Conradsen; Bjarne Kjær Ersbøll; J. Marsh; S. Kreiborg

doi:10.1109/42.896780

Surface-bounded growth modeling applied to human mandibles

Per Rønsholt Andresen, F. L. Brookstein, Knut Conradsen, Bjarne Kjær Ersbøll, J. Marsh, S. Kreiborg

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Abstract

From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semilandmarks are mapped into Procrustes space. Principal component analysis extracts a one-dimensional subspace, which is used to construct a linear growth model. The worst case mean modeling error in a cross validation study is 3.7 mm.

Original language	English
Journal	I E E E Transactions on Medical Imaging
Volume	19
Issue number	11
Pages (from-to)	1053-1063
ISSN	0278-0062
DOIs	https://doi.org/10.1109/42.896780
Publication status	Published - 2000

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Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Surface-bounded growth modeling applied to human mandibles",

abstract = "From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semilandmarks are mapped into Procrustes space. Principal component analysis extracts a one-dimensional subspace, which is used to construct a linear growth model. The worst case mean modeling error in a cross validation study is 3.7 mm.",

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AU - Brookstein, F. L.

AU - Conradsen, Knut

AU - Ersbøll, Bjarne Kjær

AU - Marsh, J.

AU - Kreiborg, S.

N1 - Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 2000

Y1 - 2000

N2 - From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semilandmarks are mapped into Procrustes space. Principal component analysis extracts a one-dimensional subspace, which is used to construct a linear growth model. The worst case mean modeling error in a cross validation study is 3.7 mm.

AB - From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semilandmarks are mapped into Procrustes space. Principal component analysis extracts a one-dimensional subspace, which is used to construct a linear growth model. The worst case mean modeling error in a cross validation study is 3.7 mm.

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JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

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Surface-bounded growth modeling applied to human mandibles

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