Managing Biomedical Image Metadata for Search and Retrieval of Similar Images
Managing Biomedical Image Metadata for Search and Retrieval of Similar Images
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DOI：
10.1007/s10278-010-9328-z
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发表时间：
2011-08-01
影响因子：
4.4
通讯作者：
Beaulieu, Chris
中科院分区：
文献类型：
--
作者：
Korenblum, Daniel;Rubin, Daniel;Beaulieu, Chris
放射学图像通常与描述其内容的元数据（例如成像观察（“语义”元数据））断开连接，其通常在不直接链接到图像的文本报告中描述。我们开发了一个系统，生物医学图像元数据管理器（BIMM），以（1）解决管理生物医学图像元数据的问题，（2）促进检索相似的图像使用语义特征元数据。我们的方法允许放射科医生，研究人员和学生，以利用庞大的和不断增长的存储库的医学图像数据，明确链接图像到其相关的元数据在一个关系数据库，是全球范围内可通过Web应用程序。BIMM使用Web服务以基于标准的元数据文件的形式接收输入，并解析元数据并将其存储在关系数据库中，从而实现高效的数据查询和维护功能。在向BIMM查询图像时，作为元数据存储的2D感兴趣区域（ROI）被自动渲染到包括在搜索结果中的预览图像上。系统的“匹配观察”功能基于描述成像观察特征（IOC）的特定语义特征检索具有相似ROI的图像。我们证明了该系统，单独使用IOC，可以准确地检索图像与诊断匹配的查询图像，我们评估其性能的一组注释的肝脏病变图像。BIMM具有几个潜在的应用，例如，计算机辅助检测和诊断，基于内容的图像检索，自动化医疗分析协议，以及收集疾病流行率等人口统计数据。该系统为决策支持系统提供了一个框架，有可能提高其诊断准确性和选择适当的治疗方法。
Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.