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Automatic segmentation of CT and MR volume data using non-rigid morphon registration
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).ORCID iD: 0000-0002-9091-4724
Department of Philosophy, Göteborg University, Sweden.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9267-2191
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Automatic segmentation of anatomical structures is often performed using model based non-rigid registration methods. The morphon algorithm is one such method. In this algorithm, two or three dimensional images are registered using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image, using local phase difference to estimate the displacement between the images. This method has been extended with normalised convolution, to guide the registration process using prior knowledge about the target image. By defining a certainty mask used in the normalised convolution one can either specify a region of interest in the target image, or, on the other hand, occlude regions in the image that should not affect the matching. Two different applications are presented, which each demonstrates the use of this method for automatic segmentation.

National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-102004OAI: oai:DiVA.org:liu-102004DiVA: diva2:667304
Available from: 2013-11-26 Created: 2013-11-26 Last updated: 2014-10-08
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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Pettersson, JohannaKnutsson, HansBorga, Magnus

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