Representative Patch-based Active Appearance Models Generated from Small Training Populations
Representative Patch-based Active Appearance Models Generated from Small Training Populations
复制标题
由小规模训练群体生成的代表性基于补丁的主动外观模型
DOI：
10.1007/978-3-319-66182-7_18
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Ehrhardt
中科院分区：
文献类型：
--
作者：
M. Wilms;H. Handels;J. Ehrhardt
主动外观模型和约束局部模型是医学图像分析中基于模型的图像分割的经典方法。aam由形状和外观的全局统计模型组成，已知难以拟合，并且在训练数据有限的情况下往往泛化能力不足。CLMs仅对局部斑块进行外观建模，放松或完全去除全局外观约束。因此，它们更容易优化，但在某些情况下，它们缺乏aam的鲁棒性。在本文中，我们提出了一个基于补丁的主动外观建模框架，该框架巧妙地结合了aam和clm的优点。我们的模型提供全局形状和外观约束，我们利用计算机视觉的最新方法进步，在模型拟合期间有效地联合优化形状和外观参数。此外，这些全局模型泛化能力不足的问题，可以通过整合和扩展最近的一种方法来解决，这种方法可以从小的训练群体中学习有代表性的统计形状模型。我们通过公开的胸部x线片和心脏MRI数据来评估我们的方法。结果表明，即使在训练样本较少的情况下，所提出的框架在具有挑战性的多目标分割问题上也能取得具有竞争力的分割精度。
Active Appearance Models (AAMs) and Constrained Local Models (CLMs) are classical approaches for model-based image segmentation in medical image analysis. AAMs consist of global statistical models of shape and appearance, are known to be hard to fit, and often suffer from insufficient generalization capabilities in case of limited training data. CLMs model appearance only for local patches and relax or completely remove the global appearance constraint. They are, therefore, much easier to optimize but in certain cases they lack the robustness of AAMs. In this paper, we present a framework for patch-based active appearance modeling, which elegantly combines strengths of AAMs and CLMs. Our models provide global shape and appearance constraints and we make use of recent methodological advances from computer vision for efficient joint optimization of shape and appearance parameters during model fitting. Furthermore, the insufficient generalization abilities of those global models are tackled by incorporating and extending a recent approach for learning representative statistical shape models from small training populations. We evaluate our approach on publicly available chest radiographs and cardiac MRI data. The results show that the proposed framework leads to competitive results in terms of segmentation accuracy for challenging multi-object segmentation problems even when only few training samples are available.
影响因子：
10.9
作者：
M. Wilms;H. Handels;J. Ehrhardt
通讯作者：
M. Wilms;H. Handels;J. Ehrhardt
DOI：
10.1023/b:visi.0000029666.37597.d3
发表时间：
2004-11-01
影响因子：
19.5
作者：
Matthews, I;Baker, S
通讯作者：
Baker, S