An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality
(2013) In IEEE Transactions on Medical Imaging 32(2). p.178-188- Abstract
- We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to... (More)
- We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to lung segmentation in full-body X-ray CT. We evaluate our approach on a publicly available benchmark with competitive results. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3459310
- author
- Ulén, Johannes LU ; Strandmark, Petter LU and Kahl, Fredrik LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cardiac segmentation, discrete optimization, image segmentation, lung segmentation
- in
- IEEE Transactions on Medical Imaging
- volume
- 32
- issue
- 2
- pages
- 178 - 188
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000314367100004
- scopus:84873282421
- pmid:22987510
- ISSN
- 1558-254X
- DOI
- 10.1109/TMI.2012.2218117
- language
- English
- LU publication?
- yes
- id
- 76b55772-9953-40c8-97ad-2a61f85d7972 (old id 3459310)
- alternative location
- http://www.maths.lth.se/vision/publdb/reports/pdf/ulen-strandmark-etal-itmi-12.pdf
- http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298014
- date added to LUP
- 2016-04-01 11:07:25
- date last changed
- 2022-01-26 05:38:50
@article{76b55772-9953-40c8-97ad-2a61f85d7972,
abstract = {{We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to lung segmentation in full-body X-ray CT. We evaluate our approach on a publicly available benchmark with competitive results.}},
author = {{Ulén, Johannes and Strandmark, Petter and Kahl, Fredrik}},
issn = {{1558-254X}},
keywords = {{Cardiac segmentation; discrete optimization; image segmentation; lung segmentation}},
language = {{eng}},
number = {{2}},
pages = {{178--188}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Medical Imaging}},
title = {{An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality}},
url = {{http://dx.doi.org/10.1109/TMI.2012.2218117}},
doi = {{10.1109/TMI.2012.2218117}},
volume = {{32}},
year = {{2013}},
}