324 research outputs found

    Compression of 4D medical image and spatial segmentation using deformable models

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    Ph.DDOCTOR OF PHILOSOPH

    3D vasculature segmentation using localized hybrid level-set method

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    Background: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. Methods: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Results: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. Conclusions: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does

    Robust semi-automated path extraction for visualising stenosis of the coronary arteries

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    Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

    Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo

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    Many diseases involve either the formation of new blood vessels (e.g., tumor angiogenesis) or the damage of existing ones (e.g., diabetic retinopathy) at the microcirculation level. Optical-resolution photoacoustic microscopy (OR-PAM), capable of imaging microvessels in 3D in vivo down to individual capillaries using endogenous contrast, has the potential to reveal microvascular information critical to the diagnosis and staging of microcirculation-related diseases. In this study, we have developed a dedicated microvascular quantification (MQ) algorithm for OR-PAM to automatically quantify multiple microvascular morphological parameters in parallel, including the vessel diameter distribution, the microvessel density, the vascular tortuosity, and the fractal dimension. The algorithm has been tested on in vivo OR-PAM images of a healthy mouse, demonstrating high accuracy for microvascular segmentation and quantification. The developed MQ algorithm for OR-PAM may greatly facilitate quantitative imaging of tumor angiogenesis and many other microcirculation related diseases in vivo

    Brain vasculature segmentation from magnetic resonance angiographic image

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    Master'sMASTER OF ENGINEERIN

    Accurate geometry reconstruction of vascular structures using implicit splines

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    3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

    Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo

    Get PDF
    Many diseases involve either the formation of new blood vessels (e.g., tumor angiogenesis) or the damage of existing ones (e.g., diabetic retinopathy) at the microcirculation level. Optical-resolution photoacoustic microscopy (OR-PAM), capable of imaging microvessels in 3D in vivo down to individual capillaries using endogenous contrast, has the potential to reveal microvascular information critical to the diagnosis and staging of microcirculation-related diseases. In this study, we have developed a dedicated microvascular quantification (MQ) algorithm for OR-PAM to automatically quantify multiple microvascular morphological parameters in parallel, including the vessel diameter distribution, the microvessel density, the vascular tortuosity, and the fractal dimension. The algorithm has been tested on in vivo OR-PAM images of a healthy mouse, demonstrating high accuracy for microvascular segmentation and quantification. The developed MQ algorithm for OR-PAM may greatly facilitate quantitative imaging of tumor angiogenesis and many other microcirculation related diseases in vivo
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