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Segmentation of medical images using three-dimensional active shape models

Josephson, Klas LU ; Ericsson, Anders LU and Karlsson, Johan LU (2005) 14th Scandinavian Conference on Image Analysis In Image Analysis (Lecture Notes in Computer Science) 3540. p.719-728
Abstract
In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result... (More)
In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Image Analysis (Lecture Notes in Computer Science)
editor
Kalviainen, Heikki; Parkkinen, Jussi; Kaarna, Arto; ; and
volume
3540
pages
719 - 728
publisher
Springer
conference name
14th Scandinavian Conference on Image Analysis
external identifiers
  • wos:000230372500073
  • scopus:26444495560
ISSN
1611-3349
0302-9743
ISBN
978-3-540-26320-3
DOI
10.1007/11499145_73
language
English
LU publication?
yes
id
c97fa77a-6789-48d2-9833-71e2b93bb953 (old id 231566)
date added to LUP
2008-09-03 14:31:20
date last changed
2017-03-05 03:27:42
@inproceedings{c97fa77a-6789-48d2-9833-71e2b93bb953,
  abstract     = {In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess.},
  author       = {Josephson, Klas and Ericsson, Anders and Karlsson, Johan},
  booktitle    = {Image Analysis (Lecture Notes in Computer Science)},
  editor       = {Kalviainen, Heikki and Parkkinen, Jussi and Kaarna, Arto},
  isbn         = {978-3-540-26320-3},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {719--728},
  publisher    = {Springer},
  title        = {Segmentation of medical images using three-dimensional active shape models},
  url          = {http://dx.doi.org/10.1007/11499145_73},
  volume       = {3540},
  year         = {2005},
}