Statistical appearance models based on probabilistic correspondences
Statistical appearance models based on probabilistic correspondences
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DOI：
10.1016/j.media.2017.02.004
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发表时间：
2017-04-01
影响因子：
10.9
通讯作者：
Handels, Heinz
中科院分区：
文献类型：
--
作者：
Krueger, Julia;Ehrhardt, Jan;Handels, Heinz
基于模型的图像分析是医学图像处理中不可缺少的一环。建立统计形状和外观模型的一个关键方面是确定训练数据集中的一对一对应。同时，识别这些对应关系是这些方法中最具挑战性的部分。在我们早期的工作中，我们为统计形状模型开发了一种替代方法，使用对应概率而不是精确的一对一对应(Hufnagel等人，2008年)。在这项工作中，提出了一种新的无一一对应的统计外观模型方法。使用稀疏图像表示法来构建同时结合点位置和外观信息的模型。使用导出的多维特征向量之间的概率对应来省略寻找地标和对应的大量预处理的需要，以及减少所生成的模型对地标位置的依赖。现在可以通过优化从最大后验概率(MAP)方法获得的单个全局准则来表示模型生成和模型拟合，该全局准则相对于直接影响图像内所考虑的对象的形状和外观的模型参数。该方法在一个简洁灵活的数学框架中描述了统计外观建模。该方法不仅不需要确定昂贵的对应关系，还考虑了建模过程中作为拓扑规则性的附加约束，并将该模型应用于手部X射线图像的分割和标志点识别。实验结果表明，该模型对未知测试图像的手部轮廓和指骨关节位置的检测是可行的。此外，我们在中风患者的脑数据上对该模型进行了评估，以显示所提出的模型处理部分受损数据的能力，并展示了使用对应概率来指示这些受损病理性区域的可能性。(C)2017爱思唯尔B.V.保留所有权利。
Model-based image analysis is indispensable in medical image processing. One key aspect of building statistical shape and appearance models is the determination of one-to-one correspondences in the training data set. At the same time, the identification of these correspondences is the most challenging part of such methods. In our earlier work, we developed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model (Hufnagel et al., 2008). In this work, a new approach for statistical appearance models without one-to-one correspondences is proposed. A sparse image representation is used to build a model that combines point position and appearance information at the same time. Probabilistic correspondences between the derived multi-dimensional feature vectors are used to omit the need for extensive preprocessing of finding landmarks and correspondences as well as to reduce the dependence of the generated model on the landmark positions. Model generation and model fitting can now be expressed by optimizing a single global criterion derived from a maximum a-posteriori (MAP) approach with respect to model parameters that directly affect both shape and appearance of the considered objects inside the images. The proposed approach describes statistical appearance modeling in a concise and flexible mathematical framework. Besides eliminating the demand for costly correspondence determination, the method allows for additional constraints as topological regularity in the modeling process.In the evaluation the model was applied for segmentation and landmark identification in hand Xray images. The results demonstrate the feasibility of the model to detect hand contours as well as the positions of the joints between finger bones for unseen test images. Further, we evaluated the model on brain data of stroke patients to show the ability of the proposed model to handle partially corrupted data and to demonstrate a possible employment of the correspondence probabilities to indicate these corrupted pathological areas. (C) 2017 Elsevier B.V. All rights reserved.