Multi-object segmentation using shape particles
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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
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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