Collaborative Research: Interactive Level-Set Modeling for Visualization of Biological Volume Datasets

随着科学数据变得越来越大、越来越复杂，有效地呈现数据的问题与另一个可能更困难的问题结合在一起——如何从大量的原始信息中提取出可呈现的数据。对于大型模拟结果和高分辨率仪器的数据来说，这个问题同样困难。因此，科学可视化领域与更传统的数据分析研究密切相关，包括图像处理、模式识别、人工智能和计算机视觉。然而，与这些字段相反，可视化显式地将用户包括在过滤、提取和呈现有意义数据的过程中。这个项目的目标是使水平集建模（一种有用但计算代价昂贵的可视化技术）在生物数据集的三维可视化中具有交互性。从技术上讲，该项目有三个组成部分。首先，设计并实现了一个交互式水平集曲面模型计算与显示的软硬件系统。该系统将使用现成的PC硬件和显卡以及新的算法和软件来实现其交互性目标。第二个组件将创建人机界面，允许用户与水平集模型进行交互。这需要将用户输入映射到模型中控制表面运动和变形的数学描述上。最后，第三个组成部分将是该项目中研究人员对大型生物数据集可视化技术的应用。这是犹他大学和加州理工学院的合作项目。

As scientific data becomes larger and more complex, the problem of presenting data effectively is joined by another, potentially more difficult one - how to extract presentable data from the flood of raw information. This problem is equally difficult for results from large simulations and data from high-resolution instruments. Thus, the field of scientific visualization becomes intimately tied to more traditional studies of data analysis, including image processing, pattern recognition, artificial intelligence, and computer vision. However, in contrast to those fields, visualization explicitly includes the user in the process of filtering, extracting, and rendering meaningful data. The goal of this project is to make level-set modeling - a useful but computationally expensive visualization technique - interactive for use in 3-D visualization of biological data sets.Technically, the project has three components. The first is the design and implementation of a hardware and software system for interactive level-set surface model computation and display. This system will use off-the-shelf PC hardware and graphics boards and new algorithms and software to reach its interactivity goals. The second component will create the human-computer interface that allows users to interact with the level-set models. This will require mapping user input onto the mathematical descriptions controlling surface motion and deformation in the models. Finally, the third component will be the application of the techniques to visualize large biological data sets by researchers in this project.This is a collaborative project between the University of Utah and the California Institute of Technology .