A Knowledge-Based Multi-Media Distributed Database System For Radiology

基于知识的放射学多媒体分布式数据库系统

• 批准号: 9116849
• 负责人: Wesley Chu
• 金额: $ 44.89万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-15 至 1994-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9116849&HistoricalAwards=false
• 关键词: Knowledge; Based; Multi; Media; Distributed

This research deals with the use of domain and application knowledge to merge and manage scientific multi-media data from multiple sources. The primary goal of this research is to develop methodologies to integrate knowledge with bio-medical image databases and to provide approximate, summary and conceptual query answering via generalization, specialization, and association operations. Further, logical indexing techniques for retrieving images by semantic contents and the requirements of the query language constructs for supporting such cooperativequery answering will be developed. Such features are important in aiding scientists to extract new scientific knowledge from the raw data. These concepts will be validated in a testbed linked with the bio-medical image databases. The joint research between the investigators from the Computer Science Department and Medical School will assure the prototype system and experiments used are of direct interest to biological and medicalapplications. The new methodology enables access to the vast storehouse of images by content and features rather than by artificial keys, such as a person's ID. Such capabilities enhance the image databases by characterizing the objects functions or dysfunctions and behavior which will lead to improved image analysis and diagnosis techniques. The outcomes of this research should be extensible to other image and multi- media database applications and result in an experimental knowledge-based image database system that will be made available to the research community.

本研究涉及使用领域和应用知识来合并和管理来自多个来源的科学多媒体数据。本研究的主要目标是发展将知识与生物医学图像数据库整合的方法，并通过泛化、专门化和关联操作提供近似、摘要和概念性的查询答案。此外，还将开发用于根据语义内容检索图像的逻辑索引技术，以及支持这种协作查询应答的查询语言结构的要求。这些特征对于帮助科学家从原始数据中提取新的科学知识非常重要。这些概念将在与生物医学图像数据库相连的试验台进行验证。来自计算机科学系和医学院的研究人员的联合研究将确保所使用的原型系统和实验对生物学和医学应用具有直接的兴趣。新的方法可以通过内容和特征来访问庞大的图像库，而不是通过人为的钥匙，比如一个人的ID。这种能力通过描述物体的功能或功能障碍和行为来增强图像数据库，这将导致改进的图像分析和诊断技术。这项研究的结果应该扩展到其他图像和多媒体数据库应用，并产生一个实验性的基于知识的图像数据库系统，将提供给研究界。