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dc.contributor.authorSchäfer, Sebastianen_US
dc.contributor.authorToennies, Klausen_US
dc.contributor.editorMichael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preimen_US
dc.date.accessioned2013-11-08T10:35:35Z
dc.date.available2013-11-08T10:35:35Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905673-95-1en_US
dc.identifier.urihttp://dx.doi.org/10.2312/PE/VMV/VMV12/151-158en_US
dc.description.abstractUltrasound 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.3 [Image Processing and Computer Vision]en_US
dc.subjectEnhancementen_US
dc.subjectRegistrationen_US
dc.titleRegistration of Temporal Ultrasonic Image Sequences Using Markov Random Fieldsen_US
dc.description.seriesinformationVision, Modeling and Visualizationen_US


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  • VMV12
    ISBN 978-3-905673-95-1

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