Rigid and non-rigid image registration and its association with mutual information: a review
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
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
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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