Content-Based Image Retrieval for Medical Databases

获取和存储诊断图像的过程日益电脑化，需要更复杂的图像检索方法，以便最佳地利用图像改善保健服务、医生教育和研究。目前正在开发一种基于内容的图像检索系统，该系统采用人在环的方法，因为完全自动化的方法对于放射图像是不可行的，因为放射图像中临床有用的信息可能包含图像高度定位但难以分割的区域的灰度和纹理偏差。在这种方法中，医生手动描绘图像中的病理承载区域。然后，CBIR系统分析这些区域的属性，并使用图形表示，增强病理承载区域与解剖地标的空间关系，将图像编入数据库，并检索具有已知诊断的典型相似图像。本项目专注于各种肺部疾病患者的高分辨率计算机断层扫描（HRCT）图像领域。关于科学目标，该项目涉及：(1)检索的计算复杂性，并通过使用适当的启发式方法降低其复杂性；(2)选择相似度标准，用于比较查询图像和索引图像的病理承载区域；(3)设计一个医生友好的软件架构，允许扩展到其他疾病类别。

Increasing computerization of the process of acquiring and storing diagnostic images requires more sophisticated approaches to image retrieval in order to optimally use the images for improved health-care delivery, physician education and research. A content-based image retrieval system is being developed that takes a human-in-the-loop approach, because completely automated approaches are not feasible for radiological images in which the clinically useful information may consist of gray level and texture deviations in highly localized but difficult-to-segment regions of an image. In this approach, a physician manually delineates the pathology-bearing regions in an image. The CBIR system then analyzes these regions for their properties and uses a graph representation, augmented with the spatial relationships of the pathology bearing regions to anatomical landmarks, to index the images into a database and retrieve similar images typically with known diagnoses. This project focuses on the domain of high resolution computed tomography (HRCT) images in patients with a variety of lung diseases. Regarding scientific goals, the project addresses: (1) the computational complexity of retrieval and its mitigation by the use of appropriate heuristics; (2) selection of similarity criteria to use for comparing the pathology-bearing regions of the query and indexed images; and (3) the design of a physician-friendly software architecture that allows for extension to other disease categories.