Statismo - A framework for PCA based statistical models

M. Lüthi, R. Blanc, Thomas Albrecht, T. Gass, O. Goksel, P. Büchler, M. Kistler, Habib Bousleiman, M. Reyes, P. Cattin, T. Vetter
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引用次数: 66

Abstract

This paper describes the Statismo framework, which is a framework for PCA based statistical models.Statistical models are used to describe the variability of an object within a population, learned from a set of training samples. Originally developed to model shapes, statistical models are now increasingly used to model the variation in different kind of data, such as for example images, volumetric meshes or deformation fields. Statismo has been developed with the following main goals in mind: 1) To provide generic tools for learning different kinds of PCA based statistical models, such as shape, appearance or deformations models. 2) To make the exchange of such models easier among different research groups and to improve the reproducibility of the models. 3) To allow for easy integration of new methods for model building into the framework. To achieve the first goal, we have abstracted all the aspects that are specific to a given model and data representation, into a user defined class. This does not only make it possible to use Statismo to create different kinds of PCA models, but also allows Statismo to be used with any toolkit and data format. To facilitate data exchange, Statismo defines a storage format based on HDF5, which includes all the information necessary to use the model, as well as meta-data about the model creation, which helps to make model building reproducible. The last goal is achieved by providing a clear separation between data management, model building and model representation. In addition to the standard method for building PCA models, Statismo already includes two recently proposed algorithms for building conditional models, as well as convenience tools for facilitating cross-validation studies. Although Statismo has been designed to be independent of a particular toolkit, special efforts have been made to make it directly useful for VTK and ITK. Besides supporting model building for most data representations used by VTK and ITK, it also provides an ITK transform class, which allows for the integration of Statismo with the ITK registration framework. This leverages the efforts from the ITK project to readily access powerful methods for model fitting.
一个基于PCA的统计模型框架
本文介绍了Statismo框架,这是一个基于PCA的统计模型框架。统计模型用于描述从一组训练样本中学习到的总体中对象的可变性。统计模型最初是用来模拟形状的,现在越来越多地用于模拟不同类型数据的变化,例如图像、体积网格或变形场。Statismo的开发主要有以下几个目标:1)为学习不同类型的基于PCA的统计模型(如形状、外观或变形模型)提供通用工具。2)使不同研究组之间更容易交换模型,提高模型的可重复性。3)允许轻松地将模型构建的新方法集成到框架中。为了实现第一个目标,我们将特定于给定模型和数据表示的所有方面抽象到用户定义的类中。这不仅使使用Statismo创建不同类型的PCA模型成为可能,而且还允许将Statismo与任何工具包和数据格式一起使用。为了方便数据交换,Statismo定义了一种基于HDF5的存储格式,其中包括使用模型所需的所有信息,以及关于模型创建的元数据,这有助于使模型构建可重复。最后一个目标是通过在数据管理、模型构建和模型表示之间提供清晰的分离来实现的。除了构建PCA模型的标准方法外,Statismo还包括两种最近提出的用于构建条件模型的算法,以及用于促进交叉验证研究的便利工具。虽然Statismo被设计为独立于特定工具包,但已作出特别努力使其对VTK和ITK直接有用。除了支持VTK和ITK使用的大多数数据表示的模型构建之外,它还提供了一个ITK转换类,它允许将Statismo与ITK注册框架集成。这就利用了ITK项目的成果,方便地访问模型拟合的强大方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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