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Non-rigid registration using morphons
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2005 (English)In: Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings / [ed] Heikki Kalviainen, Jussi Parkkinen and Arto Kaarna, Springer Berlin/Heidelberg, 2005, Vol. 3540, 501-510 p.Chapter in book (Refereed)
Abstract [en]

The Morphon, a non-rigid registration method is presented and applied to a number of registration applications. The algorithm takes a prototype image (or volume) and morphs it into a target image using an iterative, multi-resolution technique. The deformation process is done in three steps: displacement estimation, deformation field accumulation and deformation. The framework could be described in very general terms, but in this paper we focus on a specific implementation of the Morphon framework. The method can be employed in a wide range of registration tasks, which is shown in four very different registration examples, 2D photographs of hands and faces, 3D CT data of the hip region, and 3D MR brain images.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2005. Vol. 3540, 501-510 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 3540
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-48191DOI: 10.1007/11499145_51ISBN: 978-3-540-26320-3 (print)ISBN: e-978-3-540-31566-7 ISBN: 3-540-26320-9 (print)OAI: oai:DiVA.org:liu-48191DiVA: diva2:269087
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-11-26Bibliographically approved
In thesis
1. Automatic generation of patient specific models for hip surgery simulation
Open this publication in new window or tab >>Automatic generation of patient specific models for hip surgery simulation
2006 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Simulation of surgical procedures in computer based simulator systems is a relatively new area of interest in the field of medical technology. Many different systems are under development, but it is still far until this technique becomes routine in a clinical environment.

One limitation with these systems is that the computer generated models of the anatomy are ususally not patient specific. In general these systems contain one or a couple of generic models, reflecting the normal anatomy. In order to use these systems as tools for preoperative planning, it must be possible to generate computer models from real patient cases. This is relevant also when these systems are used for training and education, since the surgeon can practice on a variety of real cases, ranging from routine procedures to more complex, or more unusual, tasks.

To produce patient specific models a segmentation process is generally involved. A manual segmentation method, where a user delineates the interesting structure slice by slice in a patient dataset, is a very tedious process, and the result tends to vary between different users. The results would be more consistent, and the process would be more time and cost efficient, if a computer performed this task automatically, with a limited amount of user interaction.

This work focus on automatic segmentation of hip bones from computer tomography scans, in order to generate patient specific computer models for a hip fracture surgery simulator. The segmentation is carried out by matching a template dataset to a patient dataset using a method called the morphon method. Each point in the template is categorised as one of three tissue types, soft tissue (background), spongy bone (bone interior), or cortical bone (bone surface). When this dataset has been matched to the patient dataset the information in the template can be projected onto the patient dataset and used to find the different objects in this dataset.

The presented method can be used in other applications as well. One example demonstrated in this thesis is segmentation of a structure in the brain called the hippocampus.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2006. 49 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1243
Series
LiU-TEK-LIC, 24
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-34135 (URN)20909 (Local ID)91-85523-95-X (ISBN)20909 (Archive number)20909 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-11-26

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Wrangsjö, AndreasPettersson, JohannaKnutsson, Hans

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