DICOM数据的语义查询及优化

医学信息领域用DICOM类型的数据存储由各类检查设备产生的医学图像信息。DICOM标准的优点是标准化和语义化,它使各类医学图像设备和医学图像处理系统之间有了统一的数据交换模式。一个DICOM图像包含丰富的语义信息,包括患者相关、检查相关和图像相关的信息,但目前各类系统对其应用得还不够,尤其是数据挖掘方面,大多系统是通过构建关系数据库来存储和描述图像相关的信息。针对DICOM图像本身所携带的语义信息进行的挖掘还不够多,这违背了当初创建DICOM标准的初衷。造成这个应用现状的主要原因是国内系统厂商只利用了DICOM标准信息交换的功能,却对其语义的理解有欠缺。为了解决上述问题,对基于DICOM语义信息的数据检索模型、检索方法及检索优化方法进行了研究。根据目前国内业界的应用偏好,对DICOM标准的语义模型进行了扩展,在扩展模型下应用了文本模糊和数据模糊查询方法,最后提出了DICOM语义查询智能Agent的概念。

Semantic retrieval and optimization of DICOM data

In medical science information domain, researchers use the DICOM data to store medical graphic files which are created by radio examination equipment. The advantages of the DICOM standard are standardization and semantization, which provide a unified data exchange mode amongst all types of medical image equipment and medical image processing systems. A DICOM image contains rich semantic information, including patient related, examination related and image related information. However, at present, most systems make little use of its semantic information, especially in data mining. The systems store and describe image related information by constructing a relational database. This is contrary to the original intention of the organization that created the DICOM standards. The main cause of the current situation is that the Chinese system manufacturers only make use of the information exchange function of the DICOM standards, but they lack semantic understanding. In order to solve the above problems, we study information retrieval models, retrieval methods and retrieval optimization methods based on DICOM semantic information. The semantic model of DICOM standards is extended according to the preferences of current Chinese users. The text-fuzzy-query method and the data-fuzzy-query method are both used. Finally, the concept of intelligent agent for DICOM semantic retrieval is presented.