Biomedical subjects

Han-Wei Shen

Publications and source records attributed to Han-Wei Shen.

4 recordsLinked to original sources

PubMedDate not supplied

Dynamic view selection for time-varying volumes.

Animation is an effective way to show how time-varying phenomena evolve over time. A key issue of generating a good animation is to select ideal views through which the user can perceive the maximum amount of information from the time-varying dataset. In this paper, we first propose an improved view selection method for static data. The method measures the quality of a static view by analyzing the opacity, color and curvature distributions of the corresponding volume rendering images from the given view. Our view selection metric prefers an even opacity distribution with a larger projection area, a larger area of salient features' colors with an even distribution among the salient features, and more perceived curvatures. We use this static view selection method and a dynamic programming approach to select time-varying views. The time-varying view selection maximizes the information perceived from the time-varying dataset based on the constraints that the time-varying view should show smooth changes of direction and near-constant speed. We also introduce a method that allows the user to generate a smooth transition between any two views in a given time step, with the perceived information maximized as well. By combining the static and dynamic view selection methods, the users are able to generate a time-varying view that shows the maximum amount of information from a time-varying data set.

Journal Article↗

PubMedDate not supplied

LOD map--A visual interface for navigating multiresolution volume visualization.

In multiresolution volume visualization, a visual representation of level-of-detail (LOD) quality is important for us to examine, compare, and validate different LOD selection algorithms. While traditional methods rely on ultimate images for quality measurement, we introduce the LOD map--an alternative representation of LOD quality and a visual interface for navigating multiresolution data exploration. Our measure for LOD quality is based on the formulation of entropy from information theory. The measure takes into account the distortion and contribution of multiresolution data blocks. A LOD map is generated through the mapping of key LOD ingredients to a treemap representation. The ordered treemap layout is used for relative stable update of the LOD map when the view or LOD changes. This visual interface not only indicates the quality of LODs in an intuitive way, but also provides immediate suggestions for possible LOD improvement through visually-striking features. It also allows us to compare different views and perform rendering budget control. A set of interactive techniques is proposed to make the LOD adjustment a simple and easy task. We demonstrate the effectiveness and efficiency of our approach on large scientific and medical data sets.

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PubMedDate not supplied

Multi-variate, time-varying, and comparative visualization with contextual cues.

Time-varying, multi-variate, and comparative data sets are not easily visualized due to the amount of data that is presented to the user at once. By combining several volumes together with different operators into one visualized volume, the user is able to compare values from different data sets in space over time, run, or field without having to mentally switch between different renderings of individual data sets. In this paper, we propose using a volume shader where the user is given the ability to easily select and operate on many data volumes to create comparison relationships. The user specifies an expression with set and numerical operations and her data to see relationships between data fields. Furthermore, we render the contextual information of the volume shader by converting it to a volume tree. We visualize the different levels and nodes of the volume tree so that the user can see the results of suboperations. This gives the user a deeper understanding of the final visualization, by seeing how the parts of the whole are operationally constructed.

Journal Article↗

PubMedDate not supplied

Interactive level-of-detail selection using image-based quality metric for large volume visualization.

For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as to adjust to transfer function changes. In this paper, we introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to runtime transfer function changes and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm.

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