Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Neuroimage. 2010 Feb 1;49(3):2318-27. doi: 10.1016/j.neuroimage.2009.10.032. Epub 2009 Oct 21.
This paper presents feature-based morphometry (FBM), a new fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1).
本文提出了基于特征的形态测量学（FBM），这是一种新的完全数据驱动的技术，用于在容积成像中发现与组相关的解剖结构模式。与大多数形态测量学方法假设受试者之间存在一对一的对应关系不同，FBM 明确旨在识别可能仅存在于受试者子集（由于疾病或解剖变异性）中的独特解剖模式。该图像被建模为通用、局部化图像特征的拼贴画，这些特征不一定存在于所有受试者中。尺度空间理论用于分析图像特征在潜在解剖结构的特征尺度上，而不是在任意尺度（如全局或体素级）上。概率模型根据其外观、几何形状以及与受试者组的关系来描述特征，并从一组受试者图像和组标签中自动学习。从学习中得出的特征对应于与组相关的解剖结构，这些结构可以潜在地用作疾病的图像生物标志物，也可以作为计算机辅助诊断的基础。特征与组之间的关系通过特定组内特征出现的可能性与其余人群的可能性进行量化，并且特征显著性通过假发现率进行量化。实验使用免费提供的 OASIS 数据库，在对正常（NC）和阿尔茨海默病（AD）脑图像的分析中，从临床角度验证了 FBM。FBM 以完全数据驱动的方式自动识别 NC 和 AD 受试者之间的已知结构差异，并且对于年龄在 60-80 岁、表现出轻度 AD（CDR=1）的受试者，实现了 0.80 的等错误分类率。
