ITR: Semantic Biological Image Management & Analysis (SBIMA)

ITR：语义生物图像管理

• 批准号: 0312721
• 负责人: Phillip Sheu
• 金额: $ 30万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312721&HistoricalAwards=false
• 关键词: ITR; Semantic; Biological; Image; Management

This research proposes to develop a semantic image database that could serve investigations in cellular pathology within three sub domains: (1) Cancer, the storage/detection of tumor/cellular images; (2) Cellular biology, requiring the identification of nuclei in different stages of cellular division; and (3) Neurobiology, quantification of cell types and neuropathological objects in human or animal brain tissue. The proposed research advances and integrates two basic information technologies: iterative image object classification and object relational knowledge base. The former is employed to interact with domain experts to define models of object and concept, and the latter is employed to retrieve image information based on such models in a declarative fashion. Based on such, the research is aimed to develop a complete set of key concepts to support semantic biological image retrieval in neurobiology and cancer research.Although semantic retrieval of arbitrary images is, in general, a very difficult problem, specific domains of biological images may be sufficiently constrained in contents that one might hope to achieve semantic retrieval. The success of the proposed research will be a demonstration of this principle and lead to the development of many other special purpose semantic image systems. Finally, the proposed research will introduce new requirements to the areas of object relational database, decision support system and data mining system.

本研究建议开发一个语义图像数据库，可以在三个子域内服务于细胞病理学的研究：（1）癌症，肿瘤/细胞图像的存储/检测;（2）细胞生物学，需要识别细胞分裂不同阶段的细胞核;（3）神经生物学，量化人类或动物脑组织中的细胞类型和神经病理学对象。提出的研究进展，并集成了两个基本的信息技术：迭代图像对象分类和对象关系知识库。前者用于与领域专家交互，定义对象和概念的模型，后者用于以声明式方式基于这些模型检索图像信息。在此基础上，本研究的目的是发展一套完整的关键概念，以支持语义生物图像检索在神经生物学和癌症的研究。虽然语义检索的任意图像，在一般情况下，是一个非常困难的问题，特定领域的生物图像可能会受到足够的限制，在内容，人们可能希望实现语义检索。 这项研究的成功将证明这一原则，并导致许多其他特殊用途的语义图像系统的发展。最后，提出的研究将提出新的需求，对象关系数据库，决策支持系统和数据挖掘系统的领域。