123 research outputs found
Carried baggage detection and recognition in video surveillance with foreground segmentation
Security cameras installed in public spaces or in private organizations continuously
record video data with the aim of detecting and preventing crime. For that reason,
video content analysis applications, either for real time (i.e. analytic) or post-event
(i.e. forensic) analysis, have gained high interest in recent years. In this thesis,
the primary focus is on two key aspects of video analysis, reliable moving object
segmentation and carried object detection & identification.
A novel moving object segmentation scheme by background subtraction is presented
in this thesis. The scheme relies on background modelling which is based
on multi-directional gradient and phase congruency. As a post processing step,
the detected foreground contours are refined by classifying the edge segments as
either belonging to the foreground or background. Further contour completion
technique by anisotropic diffusion is first introduced in this area. The proposed
method targets cast shadow removal, gradual illumination change invariance, and
closed contour extraction.
A state of the art carried object detection method is employed as a benchmark
algorithm. This method includes silhouette analysis by comparing human temporal
templates with unencumbered human models. The implementation aspects of
the algorithm are improved by automatically estimating the viewing direction of
the pedestrian and are extended by a carried luggage identification module. As
the temporal template is a frequency template and the information that it provides
is not sufficient, a colour temporal template is introduced. The standard
steps followed by the state of the art algorithm are approached from a different
extended (by colour information) perspective, resulting in more accurate carried
object segmentation.
The experiments conducted in this research show that the proposed closed
foreground segmentation technique attains all the aforementioned goals. The incremental
improvements applied to the state of the art carried object detection
algorithm revealed the full potential of the scheme. The experiments demonstrate
the ability of the proposed carried object detection algorithm to supersede the
state of the art method
Curvilinear Structure Enhancement in Biomedical Images
Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing.
Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis.
In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts.
First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images.
Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images.
The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions.
Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D
ENHANCEMENT ANALYSIS OF IMMUNE FLUORESCENT CELL IMAGES
There are different patterns of immune fluorescence cells, which serve in determining different autoimmune disease. Hence, clearly identifying the features of the figures in the image will assist in automating the classification of these patterns. This project aims to enhance the quality of the Hep2-cell images obtained from Indirect Immune Fluorescence (IIF) Test. The enhancement of the quality in this project will be focused on enhancing the contrast, reducing the noise, and sharpening the edges of images. This enhancement will have a real serious impact on the stages coming after, which are patterns recognition and automatic classification. Creating an automatic battern classification system will improve the diagnostic process of the autoimmune disease instead of handling it manually. Consequently, many disadvantages of the manual interpretation can be overcome, such as level of expertise, time consuming and prone to mistakes. This research analyzed the performance of three enhancement approaches namely wavelet transform filter, diffusion filter, and wavelet transform filter combined with diffusion filter. The combination of wavelet transform filter with diffusion filter produced better result. However, the diffusion filter produced best result among all the three enhancement approach of the indirect immune fluorescence images. The recommendation for the future work is to explore an automatic determination of noise variance in the image when wavelet transform filter is being applied
Mathematical Morphology for Quantification in Biological & Medical Image Analysis
Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology.
Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery.
Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios.
I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown.
This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis
Adaptive processing of thin structures to augment segmentation of dual-channel structural MRI of the human brain
This thesis presents a method for the segmentation of dual-channel structural magnetic
resonance imaging (MRI) volumes of the human brain into four tissue classes. The
state-of-the-art FSL FAST segmentation software (Zhang et al., 2001) is in widespread
clinical use, and so it is considered a benchmark. A significant proportion of FAST’s
errors has been shown to be localised to cortical sulci and blood vessels; this issue has
driven the developments in this thesis, rather than any particular clinical demand.
The original theme lies in preserving and even restoring these thin structures,
poorly resolved in typical clinical MRI. Bright plate-shaped sulci and dark tubular
vessels are best contrasted from the other tissues using the T2- and PD-weighted data,
respectively. A contrasting tube detector algorithm (based on Frangi et al., 1998) was
adapted to detect both structures, with smoothing (based on Westin and Knutsson,
2006) of an intermediate tensor representation to ensure smoothness and fuller coverage
of the maps.
The segmentation strategy required the MRI volumes to be upscaled to an artificial
high resolution where a small partial volume label set would be valid and the segmentation
process would be simplified. A resolution enhancement process (based on Salvado
et al., 2006) was significantly modified to smooth homogeneous regions and sharpen
their boundaries in dual-channel data. In addition, it was able to preserve the mapped
thin structures’ intensities or restore them to pure tissue values. Finally, the segmentation
phase employed a relaxation-based labelling optimisation process (based on Li
et al., 1997) to improve accuracy, rather than more efficient greedy methods which are
typically used. The thin structure location prior maps and the resolution-enhanced data
also helped improve the labelling accuracy, particularly around sulci and vessels.
Testing was performed on the aged LBC1936 clinical dataset and on younger brain
volumes acquired at the SHEFC Brain Imaging Centre (Western General Hospital,
Edinburgh, UK), as well as the BrainWeb phantom. Overall, the proposed methods
rivalled and often improved segmentation accuracy compared to FAST, where the
ground truth was produced by a radiologist using software designed for this project.
The performance in pathological and atrophied brain volumes, and the differences with
the original segmentation algorithm on which it was based (van Leemput et al., 2003),
were also examined. Among the suggestions for future development include a soft labelling
consensus formation framework to mitigate rater bias in the ground truth, and
contour-based models of the brain parenchyma to provide additional structural constraints
Left-invariant Stochastic Evolution Equations on SE(2) and its Applications to Contour Enhancement and Contour Completion via Invertible Orientation Scores
We provide the explicit solutions of linear, left-invariant,
(convection)-diffusion equations and the corresponding resolvent equations on
the 2D-Euclidean motion group SE(2). These diffusion equations are forward
Kolmogorov equations for stochastic processes for contour enhancement and
completion. The solutions are group-convolutions with the corresponding Green's
function, which we derive in explicit form. We mainly focus on the Kolmogorov
equations for contour enhancement processes which, in contrast to the
Kolmogorov equations for contour completion, do not include convection. The
Green's functions of these left-invariant partial differential equations
coincide with the heat-kernels on SE(2), which we explicitly derive. Then we
compute completion distributions on SE(2) which are the product of a forward
and a backward resolvent evolved from resp. source and sink distribution on
SE(2). On the one hand, the modes of Mumford's direction process for contour
completion coincide with elastica curves minimizing , related to zero-crossings of 2 left-invariant derivatives of the
completion distribution. On the other hand, the completion measure for the
contour enhancement concentrates on geodesics minimizing . This motivates a comparison between geodesics and elastica,
which are quite similar. However, we derive more practical analytic solutions
for the geodesics. The theory is motivated by medical image analysis
applications where enhancement of elongated structures in noisy images is
required. We use left-invariant (non)-linear evolution processes for automated
contour enhancement on invertible orientation scores, obtained from an image by
means of a special type of unitary wavelet transform
ENHANCEMENT ANALYSIS OF IMMUNE FLUORESCENT CELL IMAGES
There are different patterns of immune fluorescence cells, which serve in determining different autoimmune disease. Hence, clearly identifying the features of the figures in the image will assist in automating the classification of these patterns. This project aims to enhance the quality of the Hep2-cell images obtained from Indirect Immune Fluorescence (IIF) Test. The enhancement of the quality in this project will be focused on enhancing the contrast, reducing the noise, and sharpening the edges of images. This enhancement will have a real serious impact on the stages coming after, which are patterns recognition and automatic classification. Creating an automatic battern classification system will improve the diagnostic process of the autoimmune disease instead of handling it manually. Consequently, many disadvantages of the manual interpretation can be overcome, such as level of expertise, time consuming and prone to mistakes. This research analyzed the performance of three enhancement approaches namely wavelet transform filter, diffusion filter, and wavelet transform filter combined with diffusion filter. The combination of wavelet transform filter with diffusion filter produced better result. However, the diffusion filter produced best result among all the three enhancement approach of the indirect immune fluorescence images. The recommendation for the future work is to explore an automatic determination of noise variance in the image when wavelet transform filter is being applied
Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis
Multiple sclerosis (MS) is a chronic and severe disease of the central nervous system characterized by complex pathology including inflammatory demyelination and neurodegeneration. MS impacts >2.8 million people worldwide, with most starting with a relapsing-remitting form (RRMS) in young adulthood, and many of them worsening to a secondary-progressive course (SPMS) despite treatment. So, there is a clear need for improved disease characterization. MRI is an ideal tool for non-invasive assessment of MS pathology, but there is still no established measure of disease activity and functional consequences. This project aims to overcome the challenge by developing novel imaging measures based on brain diffusion MRI and phase congruency texture analysis of conventional MRI. Through advanced modeling and analysis of clinically feasible brain MRI, this thesis investigates whether and how the derived measures differentiate MS pathology types and disease severity and predict functional outcomes in MS. The overall process has led to important technical innovations in several aspects. These include: innovative modeling of simple diffusion acquisitions to generate high angular resolution diffusion imaging (HARDI) measures; new optimization and harmonization techniques for diffusion MRI; innovative neural network models to create new diffusion data for comprehensive HARDI modeling; and novel methods and a graphic user interface for optimizing phase congruency analyses. Assisted by different machine learning methods, collective findings show that advanced measures from both diffusion MRI and phase congruency are highly sensitive to subtle differences in MS pathology, which differentiate disease severity between RRMS and SPMS through multi-dimensional analyses including chronic active lesions, and predict functional outcomes especially in physical and neurocognitive domains. These results are clinically translational and the new measures and techniques can help improve the evaluation and management of both MS and similar diseases
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