1,721,067 research outputs found
Registration of three dimensional medical images
No full textImage 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.\ud
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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.\ud
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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.\ud
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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.\ud
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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.\ud
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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.\ud
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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.\u
Rigid and non-rigid image registration and its association with mutual information: a review
No full textImage registration is a fundamental problem that can be found in a diverse range of fields within the\ud
research community. It is used in areas such as engineering, science, medicine, robotics, computer vision\ud
and image processing, which often require the process of developing a spatial mapping between sets of\ud
data. Registration plays a crucial role in the medical imaging field where continual advances in imaging\ud
modalities, including MRI, CTI and PET, allow the generation of 3D images that explicitly outline\ud
detailed in vivo information of not only human anatomy, but also human function.\ud
A common task within the medical imaging \ud
field is the fusing of the complimentary and synergistic\ud
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\ud
and/or in different positions. This process is referred to as mono-modal registration and can be used to\ud
track any pathological evolution. Other applications include inter-patient registration and patient-atlas\ud
matching. The \ud
first two applications are generally solved with rigid registrations, i.e. only rotations and\ud
translations are used in the transformation. However the last two examples are generally performed with\ud
a non-rigid registration. This allows one image to be deformed to match another in order to account for\ud
the non-linear local anatomic variations that exist between the images.\ud
Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced\ud
a prolific expansion in a number of image registration applications. Stemming from information theory,\ud
this measure generally outperforms most other intensity-based measures in multimodal applications as it\ud
only assumes a statistical dependence between images. Introduced in the computer vision field in 1995\ud
the basic concept behind its approach is to \ud
nd a transformation, which when applied to an image, will\ud
maximise the MI between two images.\ud
The power and versatility of this measure has been demonstrated many times in the literature and\ud
consequently, is now being routinely used in clinical applications. However, despite the success and\ud
popularity of its use, it has been shown that there are cases when maximising the MI measure will lead\ud
to incorrect spatial alignments. This may be due to the presence of local or spurious global extrema\ud
which may be a result of several factors including interpolation artifacts, small image overlap, or the\ud
absence of adequate spatial correlation in the images. As a result, ongoing research into improving the\ud
robustness of this measure is still continuing. This includes the investigation of hierarchical approaches,\ud
normalisation of MI, multi-variate MI, incorporation of spatial information, along with many other\ud
optimisation, algorithmic, and implementation issues.\ud
MI has also recently found use in the non-rigid domain as often there exists a need to compute a non-rigid\ud
multimodal registration. A prominent example is in the registration of pre-operative and intra-operative\ud
images. This allows the display of pre-operative anatomical and pathological tissue discrimination in\ud
the interventional \ud
field. There have been numerous methods proposed for incorporating the MI measure\ud
into a non-rigid registration. The most obvious distinction is whether the MI is calculated in a global or\ud
local manner. There are also many ways of computing the smoothness of the deformation field. Most\ud
methods however, ensure smoothness of the deformation \ud
field by altering of the vector \ud
eld and/or by\ud
regularisation terms to constrain local deformations.\ud
This report presents a thorough introduction into the \ud
eld of medical image registration and its association with MI. This includes a general overview of all registration techniques, a more in depth look\ud
at the original MI measure and its extensions proposed in the rigid domain, an overview of non-rigid\ud
registration techniques, and \ud
nally a look at the use of MI in the non-rigid domain. On the whole, MI\ud
has proved to be a very successful measure and will no doubt be a significant aspect in image registration\ud
for years to come
Hybrid non-rigid image registration using mutual information and the viscous fluid algorithm\ud
No full textRecently there has emerged a need to compute multimodal\ud
non-rigid registrations in a lot of clinical applications. To\ud
date, the viscous fluid algorithm is perhaps the most adept\ud
method at recovering large local mis-registrations that exist\ud
between two images. However, this model can only be\ud
used on images from the same modality as it assumes similar\ud
intensity values between images. This paper presents\ud
a solution to this problem by proposing a hybrid non-rigid\ud
registration using the viscous fluid algorithmand mutual information.\ud
The mutual information is incorporated via the\ud
use of a block matching procedure to generate a sparse deformation\ud
field which drives the viscous fluid algorithm.\ud
Although successful, the hybrid approach suffers from interpolation\ud
artifacts which prevent sub-voxel deformations\ud
and limit the accuracy of the algorithm
Fast exact nearest neighbour matching in high dimensions using d-D Sort
Full text linkData structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently
A visual attention approach to personal identification
No full textThis paper describes the use of visual attention characteristics,\ud
monitored by gaze tracking during presentation of\ud
a known visual scene to a viewer, as a biometric for distinguishing\ud
between individual viewers. The positions and\ud
sequences of gaze locations during viewing may be determined\ud
by overt (conscious) or covert (sub-conscious) viewing\ud
behaviour. Methods to quantify the spatial and temporal\ud
patterns established by the viewer for a particular image\ud
are proposed, and distance measures between these are established.\ud
Experimental results suggest that both types of\ud
gaze behaviours can provide simple and effective biometrics\ud
for this application
The use of mutual information for rigid medical image registration : a review
No full textMutual information (MI) is a popular entropy-based similarity measure used in the medical imaging field for multimodal registration. 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. This measure was introduced in the computer vision field by two separate and independent groups in 1995, using two slightly different formulations. This paper provides an introduction to this measure and its use in rigid medical image registration. A look at the extensions proposed to the original measure will also be provided. These were developed to improve the robustness of the measure and to avoid certain cases when maximising MI does not lead to the correct spatial alignment
Labelled silhouettes for human pose estimation
No full textThis paper proposes a new method of using foreground silhouette images for human pose estimation. Labels are introduced to the silhouette images, providing an extra layer of information that can be used in the model fitting process. The pixels in the silhouettes are labelled according to the corresponding body part in the model of the current fit, with the labels propagated into the silhouette of the next frame to be used in the fitting for the next frame. Both single and multi-view implementations are detailed, with results showing performance improvements over only using standard unlabelled silhouettes
Scene invariant multi camera crowd counting
No full textAutomated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap.\ud
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This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion.\ud
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Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%.\ud
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Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system
Automatic solder joint defect classification using the Log-Gabor filter
No full textThis paper proposes the validity of a Gabor filter bank for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance measure based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry
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