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张颈椎和腰椎X光图像的数据库。这项建议的目的是为NHANES II创建图形检索机制，以便可以根据前路骨赘的严重程度和对齐不良来检索图像。提出了两种检索机制：图形化查询机制，其中使用示例图像来指示骨赘严重程度和排列不良；以及图形分类机制，其中使用专家分类对骨赘严重程度和排列不良进行分类，并使用类别进行检索。 对骨赘严重程度和排列不良的图形查询需要通过形状相似性进行检索。Shape属于高维Shape空间，在这样的空间中进行检索是一个悬而未决的问题。私家侦探已经为在这样的空间中建立了一个索引的数学框架。该框架的独特之处在于，它确保能够找到最适合高维空间中给定数据分布的索引树。这一框架的扩展及其在NHANES H中的应用是拟议研究的主要目标之一。 骨赘严重程度和排列不良的图形类别也可以从形状索引树中构建。其思想是通过索引树中的节点覆盖的联合来近似该类别。提出了一种错误率可控的类别生成机制，并将其应用于NHANES II。将使用NHANES H和合作调查员(他在解释脊柱X光方面有专业知识)提供的专家分类来培训图形类别。 提出了一种基于NHANES II图像的图形查询和分类验证方法。将在作为该领域专家的联合调查员的帮助下进行验证。

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.