SHAPE-BASED RETRIEVAL IN MEDICAL IMAGE DATABASES

描述（改编自摘要）： 医学图像数据库中的查询极大地促进了检索， 使用视觉和图形示例。 在这样的查询中，用户指向 示例图像中感兴趣的特征，并且数据库预期 检索具有类似特征的其他图像。 该建议旨在通过形状相似性来创建检索能力 在超声心动图图像数据库中。 该提案有三个目标。 的 第一个目标是创建鲁棒的形状特征。 以前的许多工作， 形状分析使用对特定图像不变的形状特征 转变 不幸的是，这些特征对噪声非常敏感 并且实际上没有用。 该提案的主要目的是寻求 替代的和更鲁棒的形状特征。 秘书处的初步工作 研究人员和其他人认为，存在非不变的特征， 满足这些要求，该提案旨在调查这些在更多的 详细 第二个目标是创造新的结构和算法， 索引这些特征。 传统的索引技术，如索引 树，适用于索引不变特征。 该提案旨在 基本上扩展这些技术用于索引非不变形状 功能. 这种推广是基于区间理论的应用 有值算术到索引。 最后，该提案旨在确认 超声心动图中形状相似性检索的机制。 一是 新形状特征的鲁棒性将与旧形状特征的鲁棒性进行比较。 通过向数据添加噪声来塑造特征。 第二，效率 利用新的索引机制的检索将通过模拟和 通过评估超声心动图图像数据库的性能。 最后，形状相似性检索的效用将通过以下方式进行评估： 将数据库的性能与心脏专家的性能进行比较， 放射科医生

DESCRIPTION (adapted from the Abstract): Retrieval in Medical Image Databases is greatly facilitated by queries which use visual and graphical examples. In such queries, the user points to features of interest in an example image and the database is expected to retrieve other images which have similar features. This proposal seeks to create the capacity for retrieval by shape similarity in an echocardiographic image database. The proposal has three aims. The first aim is to create robust shape features. Much of the previous work on shape analysis uses shape features which are invariant to certain image transformations. Unfortunately these features are very susceptible to noise and are not practically useful. A primary aim of this proposal is to seek alternate and more robust shape features. Preliminary work by the investigators and others suggests that there are non-invariant features which satisfy these demands, and the proposal seeks to investigate these in more detail. The second aim is to create novel structures and algorithms for indexing these features. Classical indexing techniques, such as indexing trees, are applicable for indexing invariant features. The proposal seeks to extend these techniques substantially for indexing non-invariant shape features. The extension is based on the application of the theory of interval valued arithmetic to indexing. Finally, the proposal seeks to validate the mechanism of retrieval by shape similarity in echocardiography. First, the robustness of the new shape features will be compared to the robustness of old shape features by adding noise to the data. Second, the efficiency of retrieval with the new indexing mechanisms will be measured by simulations and by evaluating the performance for the echocardiographic image database. Lastly, the utility of shape similarity retrieval will be evaluated by measuring the performance of the database to that of expert cardiac radiologists.