Content-Based Image Retrieval for Medical Databases

获取和存储诊断图像的过程日益计算机化，需要更复杂的图像检索方法，以便最佳地利用图像来改善保健服务、医生教育和研究。一个基于内容的图像检索系统正在开发，需要一个人在循环的方法，因为完全自动化的方法是不可行的放射图像中的临床有用的信息可能包括灰度和纹理偏差在高度本地化，但难以分割的图像区域。在该方法中，医师手动描绘图像中的病理承载区域。CBIR系统然后分析这些区域的属性，并使用用病理承载区域与解剖标志的空间关系增强的图形表示，将图像索引到数据库中，并检索通常具有已知诊断的类似图像。该项目的重点是各种肺部疾病患者的高分辨率计算机断层扫描（HRCT）图像。关于科学目标，该项目解决了：（1）检索的计算复杂性及其通过使用适当的分类学的缓解;（2）选择相似性标准，用于比较查询和索引图像的病理承载区域;以及（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.