Building dynamic population graph for accurate correspondence detection
Building dynamic population graph for accurate correspondence detection
复制标题
构建动态群体图以进行准确的对应检测
DOI：
10.1016/j.media.2015.10.001
复制
发表时间：
2015-12-01
影响因子：
10.9
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
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作者：
Du, Shaoyi;Guo, Yanrong;Shen, Dinggang
在医学成像研究中，在数据集中发现个体受试者之间的内在解剖差异的趋势越来越明显，例如用于骨骼骨龄估计的手部图像。成对匹配通常用于检测每个个体受试者与具有手动放置的地标的预选模型图像之间的对应关系。然而，个体受试者之间的大的解剖变异性可以容易地损害这种成对匹配步骤。在本文中，我们提出了一个新的框架，同时检测人口的个体受试者之间的对应关系，通过传播所有手动放置的地标从一个小的模型图像集通过动态构建的图像图。具体来说，我们首先根据成对形状相似性在模型和个体主题之间建立图链接（称为向前步骤）。接下来，我们检测对应的直接链接到任何模型图像，这是通过一个新的多模型对应检测方法的基础上，我们最近发表的稀疏点匹配方法的个人主题。为了纠正这些不准确的对应关系，我们进一步应用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（称为向后步骤）。之后，将检测到对应关系的所有被摄体图像包括到模型图像集合中，并且重复上述图扩展和误差校正的两个步骤，直到建立所有被摄体图像的准确对应关系。对真实的手部X射线图像的评价表明，我们提出的方法使用动态图构造方法可以实现更高的准确性和鲁棒性，当与最先进的成对对应检测方法以及类似的方法，但使用静态人口图相比。(C)2015 Elsevier B.V.版权所有。
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.