Han Qiong, Pizer Stephen M, Merck Derek, Joshi Sarang, Jeong Ja-Yeon
Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC 27599, USA.
Inf Process Med Imaging. 2005;19:701-12. doi: 10.1007/11505730_58.
Multi-figure m-reps allow us to represent and analyze a complex anatomical object by its parts, by relations among its parts, and by the object itself as a whole entity. This representation also enables us to gather either global or hierarchical statistics from a population of such objects. We propose a framework to train the statistics of multi-figure anatomical objects from real patient data. This training requires fitting multi-figure m-reps to binary characteristic images of training objects. To evaluate the fitting approach, we propose a Monte Carlo method sampling the trained statistics. It shows that our methods generate geometrically proper models that are close to the set of Monte Carlo generated target models and thus can be expected to yield similar statistics to that used for the Monte Carlo generation.
多图形m-表示法使我们能够通过复杂解剖对象的各个部分、各部分之间的关系以及该对象作为一个整体实体来对其进行表示和分析。这种表示法还使我们能够从这类对象的总体中收集全局或分层统计数据。我们提出了一个框架，用于从真实患者数据中训练多图形解剖对象的统计数据。这种训练需要将多图形m-表示法拟合到训练对象的二元特征图像上。为了评估这种拟合方法，我们提出了一种对训练统计数据进行采样的蒙特卡罗方法。结果表明，我们的方法生成的几何形状合适的模型与蒙特卡罗生成的目标模型集相近，因此可以预期会产生与用于蒙特卡罗生成的统计数据相似的统计结果。
