VMV12

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https://diglib.eg.org/handle/10.2312/1016

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Browsing VMV12 by Subject "Enhancement"

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    Cyclic Numerical Time Integration in Variational Non-Rigid Image Registration based on Quadratic Regularisation
    (The Eurographics Association, 2012) Mang, Andreas; Schuetz, Tina Anne; Becker, Stefan; Toma, Alina; Buzug, Thorsten M.; Michael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preim
    In the present work, a novel computational framework for variational non-rigid image registration is discussed. The fundamental aim is to provide an alternative to approximate approaches based on successive convolution, which have gained great popularity in recent years, due to their linear complexity and ease of implementation. An optimise-then-discretise framework is considered. The corresponding Euler-Lagrange equations (ELEs), which arise from calculus of variation, constitute a necessary condition for a minimiser of the variational optimisation problem. The conventional, semi-implicit (SI) time integration for the solution of the ELEs is replaced by an explicit approach rendering the implementation straightforward. Since explicit methods are subject to a restrictive stability requirement on the maximal admissible time step size, they are in general inefficient and prone to get stuck in local minima. As a remedy, we take advantage of methods based on cyclic explicit numerical time integration. With this the strong stability requirement on each individual time step can be replaced by a relaxed stability requirement. This in turn results in an unconditionally stable method, which is as efficient as SI approaches. As a basis of comparison, SI methods are considered. Generalisability is demonstrated within a generic variational framework based on quadratic regularisation. Qualitative and quantitative analysis of numerical experiments based on synthetic test data demonstrates accuracy and efficiency.
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    Registration of Temporal Ultrasonic Image Sequences Using Markov Random Fields
    (The Eurographics Association, 2012) Schäfer, Sebastian; Toennies, Klaus; Michael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preim
    Ultrasound perfusion imaging is a rapid and inexpensive technique which enables observation of a dynamic process with high temporal resolution. The image acquisition is disturbed by various motion influences due to the acquisition procedure and patient motion. To extract valid information about perfusion for quantification and diagnostic purposes this influence must be compensated. In this work an approach to account for non-linear motion using a markov random field (MRF) based optimization scheme for registration is presented. Optimal transformation parameters are found all at once in a single optimization framework. Spatial and temporal constraints ensure continuity of a displacement field which is used for image transformation. Simulated datasets with known transformation fields are used to evaluate the presented method and demonstrate the potential of the system. Experiments with patient datasets show that superior results could be achieved compared to a pairwise image registration approach. Furthermore, it is shown that the method is suited to include prior knowledge about the data as the MRF system is able to model dependencies between the parameters of the optimization process.