Registration of three dimensional medical images
Image registration is a fundamental problem that can be found in a diverse range of fields within the research community. It is used in areas such as engineering, science, medicine, robotics, computer vision and image processing, which often require the process of developing a spatial mapping between sets of data. In the field of medical imaging, image registration is required to match images acquired from various imaging modalities. Recent advances in these imaging modalities, including MRI, CTI and PET, now allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also metabolic function.
The amount of time and effort dedicated to the research of medical image registration is a testimony to the importance and significance that this area holds in the medical field. This has consequently lead to the development of new and fascinating opportunities for areas involving diagnosis and therapy. This includes applications such as surgical planning, image guided surgery and surgery simulation. However, the creation of such opportunities would not have been possible without the enormous advances made in computing technology, which is required in order to facilitate efficient 3D image registration.
A common task within medical image registration is the fusing of the complimentary and synergistic information provided by the various imaging modalities. This process is known as multimodal registration. Another common task is in the registration of images of the same patient taken at different times and/or in different positions. This process is referred to as mono-modal registration and can be used to track any pathological evolution. Other applications include inter-patient registration and registration of a patient's scan with an anatomical atlas. The latter application is extremely useful for further applications such as the statistical analysis of populations and automatic segmentation.
In a quest to further understand some of the inherent advantages and disadvantages of image registration algorithms, a literature review was undertaken. A classification of registration algorithms was also presented along with the literature review. This classification scheme is based on certain characteristics that a registration algorithm may exhibit. The categories include the algorithm's dimensionality, nature of the registration algorithm, nature and domain of the transformation, user interaction, optimisation procedure, modalities involved, and the type of subject and objects involved in the registration process.
Traditional registration methods were based on either manual methods or the use of fiducial markers. These methods either produced a poor accuracy or a greater accuracy obtained at the expense of patient comfort. There has since been a global trend towards the development of retrospective registration methods that are non-invasive. The bulk of these developed techniques are based on intrinsic methods that only utilise the inherent information contained in a patient's image. Surface-based and intensity-based techniques are currently the most popular form of intrinsic methods, where the latter is slowly setting the standard for registration accuracy.
From the literature review, it was found that surface-based registration methods are currently used the most in clinical applications. This is due to the slight speed advantage that they have over intensity-based methods. However, one of the drawbacks of surface based methods is that they cannot handle cases when the surfaces being matched significantly differ from each other. To overcome such problems requires the use of non-rigid registration techniques. However, more research is required into these approaches as the complexity involved is still too high to effectively utilise them in real-time applications. This research aims to further develop non-invasive retrospective registration techniques that are more accurate, robust and fully automatic.
This report presents a thorough introduction into the field of medical image registration. It includes background on the various imaging modalities, a look at some relevant applications of registration, a classification of registration algorithms and a literature review on specific techniques. The report is then finished with a conclusion and a discussion on some future directions of registration.
Impact and interest:
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|Keywords:||Medical Imaging, Image Registration, 3D Medical Images|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2000 Queensland University of Technology|
|Deposited On:||16 Feb 2009 16:00|
|Last Modified:||09 Jun 2010 23:23|
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