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Titlebook: Biomedical Image Registration; Third International Josien P. W. Pluim,Boštjan Likar,Frans A. Gerritse Conference proceedings 2006 Springer

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The Use of Labour by Sector and Locatione in a user-friendly fashion..We propose an adaptive regularization approach where the rigidity constraints are informed by the registration application at hand and whose support is controlled by the geometry of the images to be registered..We investigate the behavior of this technique and discuss its sensitivity to the rigidity parameter.
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https://doi.org/10.1007/978-1-349-07204-0left and right ventricles and it has been evaluated against manual segmentation. The volume agreement between the model and the manual segmentation exceeds 90% and the distance between the model and the manually generated endocardial and epicardial surface is 1.65. on average.
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https://doi.org/10.1007/978-1-349-18790-4cans and large deformations. The experiments show that the stochastic approximation approach is superior in terms of speed and robustness. However, more accurate solutions are obtained with the first technique.
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A Comparison of Acceleration Techniques for Nonrigid Medical Image Registration,cans and large deformations. The experiments show that the stochastic approximation approach is superior in terms of speed and robustness. However, more accurate solutions are obtained with the first technique.
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Pavika Lal,Igor V. Lakhno,Garima Guptaorithm that includes spatial adaptation of the transformation’s stiffness. The approach has been applied to whole body CT images of mice and to CT images of the human upper torso. We demonstrate that the approach we propose can successfully register image volumes even when these volumes are very dif
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https://doi.org/10.1007/978-1-349-04678-2t that our novel framework provides a general and efficient way of fusing local rigid or affine deformations into a global invertible transformation without introducing artifacts, independently of the way local deformations are first estimated.
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