Image segmentation by shape particle filtering

Marleen de Bruijne; Mads Nielsen

doi:10.1109/ICPR.2004.1334630

Image segmentation by shape particle filtering

47 Citationer (Scopus)

Abstract

Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but cannot cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.

Originalsprog	Engelsk
Titel	Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Vol/bind	3
Forlag	IEEE Signal Processing Society
Publikationsdato	2004
Sider	722- 725
ISBN (Trykt)	0-7695-2128-2
ISBN (Elektronisk)	1051-4651
DOI	https://doi.org/10.1109/ICPR.2004.1334630
Status	Udgivet - 2004
Udgivet eksternt	Ja
Begivenhed	International Conference on Pattern Recognition (ICPR) - Cambridge, Storbritannien Varighed: 29 nov. 2010 → … Konferencens nummer: 17

Konference

Konference	International Conference on Pattern Recognition (ICPR)
Nummer	17
Land/Område	Storbritannien
By	Cambridge
Periode	29/11/2010 → …

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10.1109/ICPR.2004.1334630

http://ieeexplore.ieee.org/iel5/9258/29387/01334630.pdf?tp=&isnumber=&arnumber=1334630

Citationsformater

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title = "Image segmentation by shape particle filtering",

abstract = "Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but cannot cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.",

author = "{de Bruijne}, Marleen and Mads Nielsen",

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AU - Nielsen, Mads

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N2 - Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but cannot cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.

AB - Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but cannot cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.

U2 - 10.1109/ICPR.2004.1334630

DO - 10.1109/ICPR.2004.1334630

M3 - Article in proceedings

SN - 0-7695-2128-2

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BT - Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

PB - IEEE Signal Processing Society

T2 - International Conference on Pattern Recognition (ICPR)

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ER -

Image segmentation by shape particle filtering

Abstract

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