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Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). (Sectra Imtec, Linköping, Sweden)ORCID iD: 0000-0003-0908-9470
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. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
2010 (English)In: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, 2329-2332 p.Conference paper, Published paper (Refereed)
Abstract [en]

Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2010. 2329-2332 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-59332DOI: 10.1109/ICPR.2010.570OAI: oai:DiVA.org:liu-59332DiVA: diva2:351197
Conference
20th International Conference on pattern Recognition (ICPR 2010), 23-26 August 2010, Istanbul, Turkey
Funder
Swedish Research Council, 2007-4786
Available from: 2012-06-27 Created: 2010-09-13 Last updated: 2013-09-12Bibliographically approved
In thesis
1. Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing
Open this publication in new window or tab >>Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration.

This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon. 

In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation.

The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 120 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1514
Keyword
Image registration, deformable models, scoliosis, visualization, volume rendering, adaptive regularization, GPGPU, CUDA
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-91116 (URN)978-91-7519-637-4 (ISBN)
Public defence
2013-05-31, Eken, Campus US, Linköping University, Linköping, 09:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2007-4786
Available from: 2013-05-08 Created: 2013-04-17 Last updated: 2014-10-08Bibliographically approved

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Forsberg, DanielAndersson, MatsKnutsson, Hans

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