Titlebook: Biomedical Image Registration; 5th International Wo Beno?t M. Dawant,Gary E. Christensen,Daniel Ruecke Conference proceedings 2012 Springer

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978-3-642-31339-4Springer-Verlag Berlin Heidelberg 2012

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Contemporary History in Contextobserved a failure rate of approximately 4% for direct affine registrations of knee MRI without manual initialisation. Despite this, the problem of robust affine registration has not received much attention in recent years. With the increase in large medical image datasets, manual intervention is no

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Introduction to Biological Macromolecules,e time-series through an initial image and an initial momentum. Geodesic regression requires the definition of a squared residual (squared distance) between the regression geodesic and the measurement images. In principle, this squared distance should also be defined through a geodesic connecting an

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https://doi.org/10.1007/978-3-662-57996-1 assistance. Error in specific instances of a non-rigid registration process, however, is often not determined. In this paper, we propose a method to determine the magnitude and spatial location of error in non-rigid registration. The method is independent of the registration method and similarity m

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Sarah Stauffer,Aaron Gardner,Philip Wismerspatiotemporal B-splines [3], we present a diffeomorphic B-spline-based image registration algorithm combining and extending these techniques. The advancements of the proposed framework over previous work include a preconditioned gradient descent algorithm and potential weighting of the metric gradi

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https://doi.org/10.1007/978-3-662-58744-7arlier work has only been for mono-modal image registration. In this work, it is shown how polynomial expansion and mutual information can be linked to achieve multi-modal image registration. The proposed method is evaluated using MRI data and shown to have a satisfactory accuracy while not increasi

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https://doi.org/10.1007/978-3-662-58744-7e made based on the registered solution. This is especially important in non-rigid registration where the higher degrees of freedom may provide unwarranted confidence in the results, through over-fitting. The Bayesian approach, which defines uncertainty as the posterior distribution on deformations,

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arthroscopy

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https://doi.org/10.1007/978-3-7643-8350-3and can have an adverse effect in group separation studies. Most image registration algorithms are formulated in an asymmetric fashion and the solution is biased towards the transformation direction. The popular free-form deformation algorithm has been shown to be a robust and accurate method for me