13 research outputs found

    Curvilinear Structure Enhancement in Biomedical Images

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    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

    Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

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    A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

    Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees.

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    A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, bifurcations - has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically-aware framework, by comparing the proposed method against popular vascular segmentation tool-kits on clinical angiographies. VTrails potentials are discussed towards integrating group-wise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery

    Digital Straight Segment Filter for Geometric Description

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    International audienceIn this paper, an algorithmic scheme is proposed to estimate different local characteristics of image structures using discrete geometry tools. The arithmetic properties of Digital Straight Lines and their link with the Farey sequences allow the introduction of a new directional filter. In an incremental process, it provides local geometric information at each point in an image, such as the length, orientation and thickness of the longest Digital Straight Segment passing through that point. Experiments on binary and grayscale images are proposed and show the interest of this tool. Comparisons to a well-known morphological filter for grayscale images are also presented

    Automatic hepatic vessels segmentation using RORPO vessel enhancement filter and 3D V-Net with variant Dice loss function

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    The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.Web of Science131art. no. 54

    Automatic forest road extraction from LiDAR data of mountainous areas

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    International audienceIn this paper, a framework is proposed to extract forest roads from LiDAR (Light Detection and Ranging) data in mountainous areas. For that purpose, an efficient and simple solution based on discrete geometry and mathematical morphology tools is proposed. The framework is composed of two steps: (i) detecting road candidates in DTM (Digital Terrain Model) views using a mathematical morphology filter and a fast blurred segment detector in order to select a set of road seeds; (ii) extracting road sections from the obtained seeds using only the raw LiDAR points to cope with DTM approximations. For the second step, a previous tool for fast extraction of linear structures directly from ground points was adapted to automatically process each seed. It first performs a recognition of the road structure under the seed. In case of success, the structure is tracked and extended as far as possible on each side of the segment before post-processing validation and cleaning. Experiments on real data over a wide mountain area (about 78 km^2) have been conducted to validate the proposed method

    Analysis and processing of dynamic and structural magnetic resonance imaging signals for studying small vessel disease

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    Cerebral small vessel disease (CSVD) describes multiple and dynamic pathological processes disrupting the optimum functioning of perforating arterioles, capillaries and venules, increasing the risk of stroke and dementia. Although the pathogenesis of this disease is still elusive, the breakdown of the blood-brain barrier (BBB), which would hinder brain waste clearance, is thought to play a pivotal factor in it. Nonetheless, the microscopic origin and nature of these abnormalities and the lack of a ground truth make the study of CSVD in vivo in humans via magnetic resonance imaging (MRI) challenging and signal processing schemes likely to be sub-optimal. In this doctoral thesis, we proposed signal analysis and processing techniques to improve the quantification and characterisation of subtle and clinically relevant neuroimaging features of CSVD. We applied our proposals to analyses of structural and dynamic-contrast enhanced MRI (sMRI and DCE-MRI) to better characterise CSVD. DCE-MRI is commonly used to investigate cerebrovascular dysfunction, but the extremely subtle nature of the signal in CSVD makes it unclear whether signal changes are caused by microscopic yet critical BBB abnormalities. Moreover, ethical and safety considerations in vivo and the lack of validation frameworks hinder optimising imaging protocols and processing schemes. To cope with these issues, we thus proposed an open-source computational human brain model for mimicking the four-dimensional DCE-MRI acquisition process. With it, we quantified the substantial impact of spatiotemporal considerations on permeability mapping, detected sources of errors that had been overlooked in the past, and provided evidence of the harmful effect of post-processing or lack thereof on DCE-MRI assessments. Perivascular spaces (PVS) in the brain, which are involved in brain waste clearance, can become visible in sMRI scans of patients with neuroimaging features of CSVD, but their automatic quantification is challenging due to the size of PVS, the incidence and presence of imaging artefacts, and the lack of a ground truth. We first proposed a computational model of sMRI to study and compare current PVS segmentation techniques and identify major areas of improvement. We confirmed that optimal segmentation requires tuning depending on image quality and that motion artefacts are particularly detrimental to PVS quantification. We then proposed a processing strategy that distinguished high-quality from motion-corrupted images and processed them accordingly. We demonstrated such an approximation leads to estimates that correlate better with clinical visual scores and agree more with full manual counts. After optimisation using our proposals, we also found PVS measurements were associated with BBB permeability, in accordance with the link between brain waste clearance and endothelial dysfunction. This work provides means for understanding the effect of image acquisition and processing on the assessment of subtle markers of brain health to maximise confidence of studies of endothelial dysfunction and brain waste clearance via MRI. It also constitutes a cornerstone on which future optimisation and development can be based upon

    Variational methods and its applications to computer vision

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    Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces
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