High Dimensional Indexing in Medical Image Databases

描述（由申请人提供）： NHANES II由国家医学图书馆维护，包含17，000个颈椎和腰椎X射线图像的数据库。本提案的目的是为NHANES II创建图形检索机制，以便根据前部骨赘严重程度和对线不良检索图像。提出了两种检索机制：一种图形查询机制，其中一个示例图像用于指示骨赘的严重程度和对线不良，和一个图形分类机制，其中骨赘的严重程度和对线不良使用专家分类和类别进行分类用于检索。 骨赘严重程度和对线不良的图形查询需要通过形状相似性进行检索。形状属于高维形状空间，在高维形状空间中进行检索是一个开放的问题。私家侦探已经开发了一个数学框架，用于在这种空间中进行索引。这个框架是独一无二的，因为它保证了索引树，最适合于给定的数据分布在高维spaces.The扩展的框架和NHANES H的应用程序将被发现是所提出的研究的主要目标之一。 骨赘严重程度和对线不良的图形分类也可以从形状索引树构建。其思想是通过索引树中节点覆盖的并集来近似类别。这种类别创建机制与可控错误率的建议和NHANES II的应用。将使用NHANES H和合作研究者（具有解释脊柱X线片的专业知识）提供的专家分类来训练图形分类。 验证图形查询和类别与NHANES II图像的建议。将在该领域专家合作研究者的帮助下进行验证。

DESCRIPTION (provided by applicant): NHANES II, which is maintained by the National Library of Medicine, contains a database of 17,000 cervical and lumbar spine x-ray images. The aim of this proposal is to create graphical retrieval mechanisms for NHANES II so that images may be retrieved on the basis of anterior osteophyte severity and malalignment. Two retrieval mechanisms are proposed: a graphical query mechanism, where an example image is used to indicate the osteophyte severity and malalignment, and a graphical category mechanism, where osteophyte severity and malalignment are categorized using expert classification and the categories are used for retrieval. Graphical queries for osteophyte severity and malalignment require retrieval by similarity of shape. Shape belongs to high-dimensional shape spaces and indexing for retrieval in such spaces is an open problem. The P.I. has developed a mathematical framework for indexing in such spaces. This framework is unique in that it guarantees that the indexing tree that is best adapted to the given data distribution in high dimensional spaces will be found. The extension of this framework and its application to NHANES H is one of the main goals of the proposed research. Graphical categories for osteophyte severity and malalignment can also be constructed from shape indexing trees. The idea is to approximate the category by a union of node covers in the indexing tree. Such a category creation mechanism with controllable error rates is proposed and its application to NHANES II is suggested. Expert classification available from NHANES H and from the co-investigator (who has expertise in interpreting spine x-rays) will be used to train the graphical category. Validation for graphical queries and categories with NHANES II images is proposed. Validation will be carried out with the help of the co-investigator who is an expert in this domain.