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Building dynamic population graph for accurate correspondence detection

机译：建立动态人口图以进行准确的对应检测

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In medical imaging studies, there is an increasing trend for discovering the intrinsic anatomical difference across individual subjects in a dataset, such as hand images for skeletal bone age estimation. Pair-wise matching is often used to detect correspondences between each individual subject and a pre-selected model image with manually-placed landmarks. However, the large anatomical variability across individual subjects can easily compromise such pair-wise matching step. In this paper, we present a new framework to simultaneously detect correspondences among a population of individual subjects, by propagating all manually-placed landmarks from a small set of model images through a dynamically constructed image graph. Specifically, we first establish graph links between models and individual subjects according to pair-wise shape similarity (called as forward step). Next, we detect correspondences for the individual subjects with direct links to any of model images, which is achieved by a new multi-model correspondence detection approach based on our recently-published sparse point matching method. To correct those inaccurate correspondences, we further apply an error detection mechanism to automatically detect wrong correspondences and then update the image graph accordingly (called as backward step). After that, all subject images with detected correspondences are included into the set of model images, and the above two steps of graph expansion and error correction are repeated until accurate correspondences for all subject images are established. Evaluations on real hand Xray images demonstrate that our proposed method using a dynamic graph construction approach can achieve much higher accuracy and robustness, when compared with the state-of-the-art pair-wise correspondence detection methods as well as a similar method but using static population graph. (C) 2015 Elsevier B.V. All rights reserved.
机译：在医学成像研究中，发现数据集中各个受试者（例如用于骨骼骨龄估计的手部图像）之间固有的解剖学差异的趋势越来越大。逐对匹配通常用于检测每个个体对象与带有手动放置的地标的预选模型图像之间的对应关系。然而，个体个体之间较大的解剖变异性很容易损害这种成对匹配步骤。在本文中，我们提出了一个新框架，可通过动态构建的图像图从一小组模型图像中传播所有手动放置的地标，从而同时检测各个主题之间的对应关系。具体来说，我们首先根据成对的形状相似性在模型和个体之间建立图形链接（称为前进步骤）。接下来，我们通过直接链接到任何模型图像来检测各个主题的对应关系，这是通过基于我们最近发布的稀疏点匹配方法的新的多模型对应关系检测方法实现的。为了纠正那些不正确的对应关系，我们进一步应用了错误检测机制来自动检测错误的对应关系，然后相应地更新图像图（称为后退步骤）。之后，将具有检测到的对应关系的所有被摄对象图像包括在模型图像集中，并且重复上述图扩展和纠错的两个步骤，直到针对所有被摄对象图像建立了准确的对应关系为止。对真实手部X射线图像的评估表明，与最新的成对对应检测方法以及类似方法相比，我们提出的使用动态图构造方法的方法可以实现更高的准确性和鲁棒性静态人口图。 （C）2015 Elsevier B.V.保留所有权利。

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