Rigid and non-rigid image registration and its association with mutual information: a review
Fookes, Clinton B. & Bennamoun, Mohammed (2002) Rigid and non-rigid image registration and its association with mutual information: a review. Research Concentration in Computer Vision and Automation. Queensland University of Technology.
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. Registration plays a crucial role in the medical imaging field where continual advances in imaging modalities, including MRI, CTI and PET, allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also human function. A common task within the medical imaging field 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 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 patient-atlas matching. The first two applications are generally solved with rigid registrations, i.e. only rotations and translations are used in the transformation. However the last two examples are generally performed with a non-rigid registration. This allows one image to be deformed to match another in order to account for the non-linear local anatomic variations that exist between the images. Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced a prolific expansion in a number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it only assumes a statistical dependence between images. Introduced in the computer vision field in 1995 the basic concept behind its approach is to nd a transformation, which when applied to an image, will maximise the MI between two images. The power and versatility of this measure has been demonstrated many times in the literature and consequently, is now being routinely used in clinical applications. However, despite the success and popularity of its use, it has been shown that there are cases when maximising the MI measure will lead to incorrect spatial alignments. This may be due to the presence of local or spurious global extrema which may be a result of several factors including interpolation artifacts, small image overlap, or the absence of adequate spatial correlation in the images. As a result, ongoing research into improving the robustness of this measure is still continuing. This includes the investigation of hierarchical approaches, normalisation of MI, multi-variate MI, incorporation of spatial information, along with many other optimisation, algorithmic, and implementation issues. MI has also recently found use in the non-rigid domain as often there exists a need to compute a non-rigid multimodal registration. A prominent example is in the registration of pre-operative and intra-operative images. This allows the display of pre-operative anatomical and pathological tissue discrimination in the interventional field. There have been numerous methods proposed for incorporating the MI measure into a non-rigid registration. The most obvious distinction is whether the MI is calculated in a global or local manner. There are also many ways of computing the smoothness of the deformation field. Most methods however, ensure smoothness of the deformation field by altering of the vector eld and/or by regularisation terms to constrain local deformations. This report presents a thorough introduction into the eld of medical image registration and its association with MI. This includes a general overview of all registration techniques, a more in depth look at the original MI measure and its extensions proposed in the rigid domain, an overview of non-rigid registration techniques, and nally a look at the use of MI in the non-rigid domain. On the whole, MI has proved to be a very successful measure and will no doubt be a significant aspect in image registration for years to come.
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|Keywords:||Image Registration, Mutual Information, Review|
|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 2002 Queensland University of Technology|
|Deposited On:||17 Feb 2009 16:16|
|Last Modified:||09 Jun 2010 23:23|
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