SPIRS: a Web-based image retrieval system for large biomedical databases.
SPIRS: a Web-based image retrieval system for large biomedical databases.
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DOI：
10.1016/j.ijmedinf.2008.09.006
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发表时间：
2009-04
影响因子：
4.9
通讯作者：
Thoma, George R
中科院分区：
文献类型：
--
作者：
Hsu, William;Antani, Sameer;Long, L Rodney;Neve, Leif;Thoma, George R
随着图像在疾病研究、教育和临床医学中的使用越来越多，对根据图像内容有效归档、查询和检索这些图像的方法的需求日益凸显。本文介绍了基于网络的检索系统 SPIRS（脊柱病理学和图像检索系统）的实现，该系统允许使用视觉和文本查询相结合的方式探索数字化脊柱 X 射线图像和国家健康调查数据的大型生物医学数据库。 SPIRS 是一个通用框架，由四个组件组成：客户端小程序、网关、索引和检索系统以及图像和相关文本数据的数据库。该原型系统使用第二次美国国家健康和营养检查调查 (NHANES II) 收集的文本和图像数据进行演示。用户通过提供椎骨轮廓草图或选择示例椎骨图像和一些相关文本参数来搜索图像数据。可以对图像/草图上的相关病理进行注释和加权以指示重要性。在开发过程中，我们探索了不同的算法来执行分段、索引和检索等功能。每个算法都经过单独测试，然后作为 SPIRS 的一部分实施。为了评估整个系统，我们首先测试了系统从给定查询形状的数据库中返回相似椎骨形状的能力。仅使用视觉查询（无文本）的初步评估表明，系统在数据库中查找表现出类似异常类型和严重程度的图像时，准确率高达 68%。事实证明，经过 3 次迭代后，相关性反馈机制可将准确性额外提高 22%。虽然我们主要在检索椎骨形状的背景下演示该系统，但我们的框架也适用于搜索 100,000 个子宫颈图像的集合，以研究宫颈癌的进展。 SPIRS 是自动化的、易于访问且可与其他补充信息检索系统集成。该系统支持用户通过提供视觉示例和文本关键字直观地查询大量成像数据，并且在研究、教育和患者护理领域具有有益的影响。
With the increasing use of images in disease research, education, and clinical medicine, the need for methods that effectively archive, query, and retrieve these images by their content is underscored. This paper describes the implementation of a Web-based retrieval system called SPIRS (Spine Pathology & Image Retrieval System), which permits exploration of a large biomedical database of digitized spine x-ray images and data from a national health survey using a combination of visual and textual queries. SPIRS is a generalizable framework that consists of four components: a client applet, a gateway, an indexing and retrieval system, and a database of images and associated text data. The prototype system is demonstrated using text and imaging data collected as part of the second U.S. National Health and Nutrition Examination Survey (NHANES II). Users search the image data by providing a sketch of the vertebral outline or selecting an example vertebral image and some relevant text parameters. Pertinent pathology on the image/sketch can be annotated and weighted to indicate importance. During the course of development, we explored different algorithms to perform functions such as segmentation, indexing, and retrieval. Each algorithm was tested individually and then implemented as part of SPIRS. To evaluate the overall system, we first tested the system’s ability to return similar vertebral shapes from the database given a query shape. Initial evaluations using visual queries only (no text) have shown that the system achieves up to 68% accuracy in finding images in the database that exhibit similar abnormality type and severity. Relevance feedback mechanisms have been shown to increase accuracy by an additional 22% after three iterations. While we primarily demonstrate this system in the context of retrieving vertebral shape, our framework has also been adapted to search a collection of 100,000 uterine cervix images to study the progression of cervical cancer. SPIRS is automated, easily accessible, and integratable with other complementary information retrieval systems. The system supports the ability for users to intuitively query large amounts of imaging data by providing visual examples and text keywords and has beneficial implications in the areas of research, education, and patient care.