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Multimodal volume data commonly found in medical imaging applications present both opportunities and challenges to segmentation and visualization tasks. This paper presents a user directed volume segmentation system. Through a spreadsheets interface, the user can interactively examine and refine segmentation results obtained from automatic clustering. In addition, the user can isolate or highlight a feature of interest in a volume based on different modalities, and see the corresponding segmented results. Our system is easy to use since the preliminary segmentation results are organized and presented to the user in a relation-aware fashion based on the spatial relations between the segmented regions. We demonstrate this system using two multimodal datasets. [ABSTRACT FROM AUTHOR]

Hyperlinks among webpages are very important information and are widely used for webpage clustering and webpage ranking. With the explosive growth in the number of webpages available online, the exploration of hyperlinks among webpages becomes a very challenging problem. Information visualization provides an effective way of visualizing hyperlinks and can help users gain insights into the relationships of webpages. In this paper, we present some novel computer graphics techniques to visualize the hyperlinks among webpages. We propose a visual encoding scheme for five dimensional hyperlinks data and two constrained 3D layout techniques for incoming and outgoing links of a single webpage. To reveal the hierarchical structure of webpages as well as the hyperlinks information, we extend the treemap representation. Our representations are visually appealing and can effectively reveal linkage patterns among webpages. Experimental results and a user study demonstrate the effectiveness of our system. Our system can facilitate E-learning and help students understand the complex structures and hidden patterns in network datasets. [ABSTRACT FROM AUTHOR]

In this paper, we present VoxelBars as an informative interface for volume visualization. VoxelBars arrange voxels into a 2D space and visually encode multiple attributes of voxels into one display. VoxelBars allow users to easily find out clusters of interesting voxels, set opacities and colors of a specific group of voxels, and achieve various sophisticated visualization tasks at voxel level. We provide results on real volume data to demonstrate the advantages of the VoxelBars over scatterplots and traditional transfer function specification methods. Some novel visualization techniques including visibility-aware transfer function design and selective clipping based on VoxelBars are also introduced. [ABSTRACT FROM AUTHOR]

Node-link diagrams are widely used in information visualization to show relationships among data. However, when the size of data becomes very large, node-link diagrams will become cluttered and visually confusing for users. In this paper, we propose a novel controllable edge clustering method based on Delaunay triangulation to reduce visual clutter for node-link diagrams. Our method uses curves instead of straight lines to represent links and these curves can be grouped together according to their relative positions and directions. We further introduce progressive edge clustering to achieve continuous level-of-details for large networks. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel viewpoint selection framework for angiographic volume data. We propose several view descriptors based on typical concerns of clinicians for the view evaluation. Compared with conventional approaches, our method can deliver a more representative global optimal view by sampling at a much higher rate in the view space. Instead of performing analysis on sample views individually, we construct a solution space to estimate the quality of the views. Descriptor values are propagated to the solution space where an efficient searching process can be performed. The best viewpoint can be found by analyzing the accumulated descriptor values in the solution space based on different visualization goals. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users' preference. The framework relies on three techniques, i.e., user voting, genetic algorithm, and image similarity. In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework. [ABSTRACT FROM AUTHOR]

Direct volume rendering (DVR) is an effective way to visualize 3D vascular images for diagnosis of different vascular pathologies and planning of surgical treatments. Angiograms are typically noisy, fuzzy, and contain thin vessel structures. Therefore, some kinds of enhancements are usually needed before direct volume rendering can start. However, without visualizing the 3D structures in angiograms, users may find it difficult to select appropriate parameters and assess the effectiveness of the enhancement results. In addition, traditional enhancement techniques cannot easily separate the vessel voxels from other contextual structures with the same or very similar intensity. In this paper, we propose a framework to integrate enhancement and direct volume rendering into one visualization pipeline using multi-dimensional transfer function tailored for visualizing the curvilinear and line structures in angiograms. Furthermore, we present a feature preserving interpolation method to render very thin vessels which are usually missed using traditional approaches. To ease the difficulty in vessel selection, a MIP-guided method is suggested to assist the process. [ABSTRACT FROM AUTHOR]

In this paper, we present some novel animation techniques to help users understand complex structures contained in volumetric data from the medical imaging and scientific simulation fields. Because of the occlusion of 3D objects, these complex structures and their 3D relationships usually cannot be revealed using one image. By our animation techniques, the focus regions, the context regions, and their relationships can be better visualized at the different stages of an animation. We propose an image-centric method which employs layered-depth images (LDIs) to get correct depth cues, and a data-centric method which exploits a novel transfer function fusing technique to guarantee the smooth transition between frames. The experimental results on real volume data demonstrate the advantages of our techniques over traditional image blending and transfer function interpolation methods. [ABSTRACT FROM AUTHOR]