Multi-object segmentation using shape particles

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

Standard

Multi-object segmentation using shape particles. / de Bruijne, Marleen; Nielsen, Mads.

Information Processing in Medical Imaging. <Forlag uden navn>, 2005. p. 762-773 (Lecture notes in computer science, Vol. 3565/2005).

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

Harvard

de Bruijne, M & Nielsen, M 2005, Multi-object segmentation using shape particles. in Information Processing in Medical Imaging. <Forlag uden navn>, Lecture notes in computer science, vol. 3565/2005, pp. 762-773, Information Processing in Medical Imaging (IPMI), Glenwood Springs, CO, United States, 29/11/2010. https://doi.org/10.1007/b137723

APA

de Bruijne, M., & Nielsen, M. (2005). Multi-object segmentation using shape particles. In Information Processing in Medical Imaging (pp. 762-773). <Forlag uden navn>. Lecture notes in computer science Vol. 3565/2005 https://doi.org/10.1007/b137723

Vancouver

de Bruijne M, Nielsen M. Multi-object segmentation using shape particles. In Information Processing in Medical Imaging. <Forlag uden navn>. 2005. p. 762-773. (Lecture notes in computer science, Vol. 3565/2005). https://doi.org/10.1007/b137723

Author

de Bruijne, Marleen ; Nielsen, Mads. / Multi-object segmentation using shape particles. Information Processing in Medical Imaging. <Forlag uden navn>, 2005. pp. 762-773 (Lecture notes in computer science, Vol. 3565/2005).

Bibtex

@inproceedings{cb0c40504c0811dd8d9f000ea68e967b,
title = "Multi-object segmentation using shape particles",
abstract = "Deformable template models, in which a shape model and its corresponding appearance model are deformed to optimally fit an object in the image, have proven successful in many medical image segmentation tasks. In some applications, the number of objects in an image is not known a priori. In that case not only the most clearly visible object must be extracted, but the full collection of objects present in the image.We propose a stochastic optimization algorithm that optimizes a distribution of shape particles so that the overall distribution explains as much of the image as possible. Possible spatial interrelationships between objects are modelled and used to steer the evolution of the particle set by generating new shape hypotheses that are consistent with the shapes currently observed.The method is evaluated on rib segmentation in chest X-rays.",
author = "{de Bruijne}, Marleen and Mads Nielsen",
year = "2005",
doi = "10.1007/b137723",
language = "English",
isbn = "978-3-540-26545-0",
series = "Lecture notes in computer science",
publisher = "<Forlag uden navn>",
pages = "762--773",
booktitle = "Information Processing in Medical Imaging",
note = "null ; Conference date: 29-11-2010",

}

RIS

TY - GEN

T1 - Multi-object segmentation using shape particles

AU - de Bruijne, Marleen

AU - Nielsen, Mads

N1 - Conference code: 19

PY - 2005

Y1 - 2005

N2 - Deformable template models, in which a shape model and its corresponding appearance model are deformed to optimally fit an object in the image, have proven successful in many medical image segmentation tasks. In some applications, the number of objects in an image is not known a priori. In that case not only the most clearly visible object must be extracted, but the full collection of objects present in the image.We propose a stochastic optimization algorithm that optimizes a distribution of shape particles so that the overall distribution explains as much of the image as possible. Possible spatial interrelationships between objects are modelled and used to steer the evolution of the particle set by generating new shape hypotheses that are consistent with the shapes currently observed.The method is evaluated on rib segmentation in chest X-rays.

AB - Deformable template models, in which a shape model and its corresponding appearance model are deformed to optimally fit an object in the image, have proven successful in many medical image segmentation tasks. In some applications, the number of objects in an image is not known a priori. In that case not only the most clearly visible object must be extracted, but the full collection of objects present in the image.We propose a stochastic optimization algorithm that optimizes a distribution of shape particles so that the overall distribution explains as much of the image as possible. Possible spatial interrelationships between objects are modelled and used to steer the evolution of the particle set by generating new shape hypotheses that are consistent with the shapes currently observed.The method is evaluated on rib segmentation in chest X-rays.

U2 - 10.1007/b137723

DO - 10.1007/b137723

M3 - Article in proceedings

SN - 978-3-540-26545-0

T3 - Lecture notes in computer science

SP - 762

EP - 773

BT - Information Processing in Medical Imaging

PB - <Forlag uden navn>

Y2 - 29 November 2010

ER -

ID: 4924884