ITR: Semantic Biological Image Management & Analysis (SBIMA)

本研究提出开发一个语义图像数据库，可以在三个子领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，需要鉴定细胞分裂不同阶段的细胞核；(3)神经生物学，人类或动物脑组织中细胞类型和神经病理对象的定量。本研究推进并整合了迭代图像对象分类和对象关系知识库两种基本信息技术。前者用于与领域专家交互以定义对象和概念的模型，后者用于以声明的方式基于这些模型检索图像信息。基于此，本研究旨在开发一套完整的关键概念，以支持神经生物学和癌症研究中的语义生物图像检索。虽然任意图像的语义检索通常是一个非常困难的问题，但生物图像的特定领域可能在内容上受到足够的限制，人们可能希望实现语义检索。该研究的成功将证明这一原则，并导致许多其他专用语义图像系统的发展。最后，对对象关系数据库、决策支持系统和数据挖掘系统提出了新的研究要求。

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.