Optimal embedding for shape indexing in medical image databases.
Optimal embedding for shape indexing in medical image databases.
复制标题
医学图像数据库中形状索引的最佳嵌入。
DOI：
10.1016/j.media.2010.01.001
复制
发表时间：
2010
影响因子：
10.9
通讯作者：
Antani,Sameer
中科院分区：
文献类型：
--
作者：
Qian,Xiaoning;Tagare,HemantD;Fulbright,RobertK;Long,Rodney;Antani,Sameer
本文讨论了医学图像数据库中的形状索引问题。器官的形状通常指示疾病，使得形状相似性查询在医学图像数据库中很重要。在数学上，具有地标的形状属于形状空间，该形状空间是具有良好定义的度量的弯曲流形。形状索引的挑战是在这种弯曲的空间中索引数据。一种自然的索引方案是使用度量树，但度量树容易效率低下。本文提出了一种更有效的替代方案。我们表明，它是可能的，以最佳方式嵌入有限集的形状空间到一个欧氏空间。嵌入后，经典的基于坐标的树可以用于有效的形状检索。本文提出的嵌入是最优的，在这个意义上，它最少扭曲的部分Procrustes形状距离。建议的索引技术是用来检索图像的椎体形状从NHANES II数据库的颈椎和腰椎X射线图像保存在国家医学图书馆。椎体形状与骨赘的存在密切相关，形状相似性检索被提出作为骨赘存在和严重程度检索的工具。本文中的实验结果评估（1）形状相似性作为骨赘的代理的有用性，（2）新索引方案的计算和磁盘访问效率，（3）嵌入索引与不嵌入索引的相对性能，以及（4）使用建议的嵌入索引的计算成本与替代嵌入的成本。实验结果清楚地表明形状索引的相关性和使用所提出的嵌入的优势。
This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative. We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance. The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity. Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding.