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Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. (Sectra Imtec, Linköping, Sweden)ORCID iD: 0000-0003-0908-9470
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology. (Sectra Imtec, Linköping, Sweden)ORCID iD: 0000-0002-9368-0177
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). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, 592-603 p.Conference paper, Published paper (Refereed)
Abstract [en]

The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011. Vol. 6688/2011, 592-603 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-71679DOI: 10.1007/978-3-642-21227-7_55ISI: 000308543900055ISBN: 978-3-642-21226-0 (print)OAI: oai:DiVA.org:liu-71679DiVA: diva2:452696
Conference
17th Scandinavian Conference on Image Analysis, SCIA 2011, Ystad, Sweden, May 2011.
Funder
Swedish Research Council, 2007-4786
Available from: 2012-06-27 Created: 2011-10-31 Last updated: 2015-10-08Bibliographically 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, DanielLundström, ClaesAndersson, MatsKnutsson, Hans

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