Halos are usually used to enhance depth perception and display spatial relationships in illustrative visualization. In this paper, we present a simple and effective method to create volumetric halo illustration. In th...
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Halos are usually used to enhance depth perception and display spatial relationships in illustrative visualization. In this paper, we present a simple and effective method to create volumetric halo illustration. In the pre-processing stage, we generate a view-independent halo intensity volume, which contains all potential halos around the boundaries of features, based on the opacity volume on graphic hardware. During halo rendering, the halo intensity volume is used to extract halos only around the contours of structures for the current viewpoint. The performance of our approach is significantly faster than previous halo illustration methods, which perform both halo generation and rendering during volume rendering. Experimental results demonstrate volumetric halo effects and the efficiency of the proposed approach.
While there has been significant progress in the treatment of ischemic heart failure, it remains a significant health and economic problem worldwide. In this paper, we present the challenges of modelling ischemic hear...
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While there has been significant progress in the treatment of ischemic heart failure, it remains a significant health and economic problem worldwide. In this paper, we present the challenges of modelling ischemic heart failure and introduce a user-friendly software system that will be a sub-set of the Virtual Pathological Heart environment which is currently being developed under the FP7 VPH2 project. This will provide patient-specific computational modelling and simulation of the human heart to assist the cardiologist and the cardiac surgeon in defining the severity and extent of disease in patients with post-ischemic Left Ventricular Dysfunction. The proposed system will provide visualisation tools for surgical assessment and planning: the registration and display of necrotic and hypo-kinetic regions; simulated surgical restoration (cutting and patching); and finally post-operative functional prediction (volume, shape and mitral valve regurgitation).
Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this pap...
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Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering algorithms since it models the voxel-to-voxel relationship as opposed to voxel-to-cluster relationships. In the semi-supervised spectral clustering, two types of instance-level constraints: must-link and cannot-link as background prior knowledge are incorporated into spectral clustering, and the self-tuning parameter is applied to avoid the selection of the scaling parameter of spectral clustering. The semi-supervised spectral clustering is an effective tissue segmentation method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality segmentation results as it can obtain the global optimal solutions in the relaxed continuous domain by eigen-decomposition and combines the pairwise constraints information. Experimental results on simulated and real MRI data demonstrate its effectiveness.
One of the methods for 3D data model acquisition is real object digitisation followed by surface reconstruction. Many algorithms have been developed to do this, each with its advantages and disadvantages. We use for t...
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