
Gauss-Newton Method for Segmentation assisted Deformable Registration
Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3492 |
Published in The MIDAS Journal - Image-Guided Adaptive Radiation Therapy (IGART).
Submitted by Miro Jurisic on 10-20-2014.
In this work, we try to develop a fast converging method for
segmentation assisted deformable registration. The segmentation step
consists of a piece-wise constant Mumford-Shah energy model while reg-
istration is driven by the sum of squared distances of both initial images
and segmented mask with a diffusion regularization. In order to solve
this energy minimization problem, a second order Gauss-Newton opti-
mization method is used. For the numerical experiments we used CT
data sets from the EMPIRE10 challenge. In this preliminary study, we
show high accuracy of our algorithm.
segmentation assisted deformable registration. The segmentation step
consists of a piece-wise constant Mumford-Shah energy model while reg-
istration is driven by the sum of squared distances of both initial images
and segmented mask with a diffusion regularization. In order to solve
this energy minimization problem, a second order Gauss-Newton opti-
mization method is used. For the numerical experiments we used CT
data sets from the EMPIRE10 challenge. In this preliminary study, we
show high accuracy of our algorithm.
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Categories: | Deformable registration, Segmentation |
Keywords: | energy based segmentation, deformable registration |
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