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

就其性质而言，体积数据集（如CT、MRI和可视人体冷冻切片图像）不受人类专家在查看和解释2D图像时应用的相同形式的视觉检查和认知细化的影响。人类在视觉上对图像进行调查时所做的选择的内部循环被打乱，并被迫进入一个外部过程，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，更自然地允许用户将其解释性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，分割，隔离数据量内的离散结构的过程，与可视化并行执行。用户可以连续控制可视化和分割算法，并可以自由运用他们的专业解释知识。 目前的实现，称为沉浸式分割环境，强调高计算成本算法的控制应用程序的局部区域的数据量。使用立体图形，向用户呈现数据量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。可视化和分割算法在用户位置的局部邻域内连续地对数据体进行操作。该邻域对应于用户的视觉焦点。该算法基于对局部邻域和几何学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，因此允许用户在一个低成本的工作站，以有效地使用远程计算资源来可视化大型体积数据集。该系统实现了两个主要类别的分割算法。这两个确定初始算法参数的基础上的聚类分析的局部邻域，其特征在于通过迭代细化方法。 拟议的研究将继续调查这种独特的用户交互模型的可视化，探索和解释体积数据集的能力。本提案中包含的研究计划是PI先前工作的自然结果。这项正在进行的工作对UW-La Crosse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的影响。

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.