Maani Rouzbeh, Yang Yee Hong, Kalra Sanjay
Department of Computing Science, University of Alberta, Edmonton, Canada.
Departments of Medicine, Computing Science, and Biomedical Engineering, University of Alberta, Edmonton, Canada.
PLoS One. 2015 Mar 10;10(3):e0117759. doi: 10.1371/journal.pone.0117759. eCollection 2015.
This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.
本文提出了一种基于体素的新型脑图像纹理分析方法。纹理分析是一种用于分析体素强度及其相互关系的强大定量方法,但迄今为止仅限于分析感兴趣区域。所提出的方法提供了一个三维统计图谱,可逐体素比较纹理特征。该方法的有效性在人工生成的效应以及阿尔茨海默病(AD)的真实MRI数据上进行了检验。人工生成的效应包括添加到30名健康受试者的T1加权脑MRI上的高信号和低信号。AD数据集包括30名AD患者和30名年龄/性别匹配的健康对照受试者。所提出的方法以高精度检测到人工效应,并揭示了AD组和对照组之间的统计学显著差异。本文将纹理分析的应用从当前的感兴趣区域分析扩展到逐体素三维统计映射,并提供了一种无假设分析工具来研究神经疾病中的脑病理学。