VISUALIZATION: RUI: User-Directed Segmentation and Visualization of Volumetric Data Sets in a Collaborative, Remotely Hosted Environment

可视化：RUI：协作、远程托管环境中体积数据集的用户引导分割和可视化

• 批准号: 0222519
• 负责人: Steven Senger
• 金额: $ 18.01万
• 依托单位: University of Wisconsin-La Crosse
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0222519&HistoricalAwards=false
• 关键词: VISUALIZATION; RUI; User; Directed; Segmentation

By their nature, volumetric data sets, such as CT, MRI and the Visible Human cryosection images, are not subject to the same form of visual inspection and cognitive refinement that human experts apply when viewing and interpreting 2D images. The internal cycle of choices made by humans as they visually survey an image is disrupted andforced into an external process where users must adapt to the technical requirements of thevisualization system. This project proposes to continue research into an immersive visualizationsystem that is a first step toward creating a user interaction paradigm that more naturallyallows users to apply their interpretive expertise to the visualization and segmentation ofvolumetric data sets. In this environment, segmentation, the process of isolating discrete structures within a data volume, is performed in parallel with visualization. Users have continuous control of the visualization and segmentation algorithms and can freely exercise their expertinterpretive knowledge. The current implementation, called the Immersive Segmentation Environment, emphasizes the controlled application of high computational cost algorithms to local regions of the data volume. Using stereo graphics, the user is presented with the illusion that the data volume occupies physical space. The user interacts with the system using a 3D space tracked probe. Visualization and segmentation algorithms continuously operate on the data volume within a local neighborhood of the user.s position. This neighborhood corresponds to the user.s visual focus point. The algorithms initialize themselves based upon an analysis of the local neighborhood and heuristics. The system is implemented as a client/server application and consequently allows a user at a low-cost workstation to effectively use remote computational resources to visualize large volumetric data sets. The system implements two main classes of segmentation algorithms. Both determine initial algorithm parameters based upon a cluster analysis of the local neighborhood and are characterized by an iterative refinement approach. The proposed research will continue to investigate the capabilities of this unique user interaction model for visualizing, exploring and interpreting volumetric data sets. The research plan contained in this proposal is a natural outgrowth of prior work performed by the PI. This ongoing work has had a positive impact on the curriculum, student research projects and faculty research within the Computer Science department at the UW-La Crosse.

就其本质而言，体积数据集（例如 CT、MRI 和可见人体冰冻切片图像）不会受到人类专家在查看和解释 2D 图像时所应用的相同形式的视觉检查和认知细化。人类在视觉观察图像时做出的选择的内部循环被破坏并被迫进入外部过程，用户必须适应可视化系统的技术要求。该项目建议继续研究沉浸式可视化系统，这是创建用户交互范例的第一步，该范例可以更自然地允许用户将其解释专业知识应用于体积数据集的可视化和分割。 在这种环境中，分段（隔离数据体中离散结构的过程）与可视化并行执行。用户可以持续控制可视化和分割算法，并可以自由地运用他们的专业解释知识。 当前的实现称为沉浸式分段环境，强调高计算成本算法对数据量局部区域的受控应用。使用立体图形，向用户呈现数据卷占据物理空间的错觉。用户使用 3D 空间跟踪探头与系统交互。可视化和分割算法持续对用户位置的局部邻域内的数据量进行操作。该邻域对应于用户的视觉焦点。这些算法基于对局部邻域和启发式的分析来初始化自身。该系统被实现为客户端/服务器应用程序，因此允许用户在低成本工作站上有效地使用远程计算资源来可视化大容量数据集。该系统实现了两类主要的分割算法。两者都根据局部邻域的聚类分析来确定初始算法参数，并通过迭代细化方法来表征。 拟议的研究将继续研究这种独特的用户交互模型在可视化、探索和解释体积数据集方面的能力。本提案中包含的研究计划是 PI 先前工作的自然产物。这项正在进行的工作对威斯康星大学拉克罗斯分校计算机科学系的课程、学生研究项目和教师研究产生了积极影响。