摘要
18F-FDG PET影像学资料广泛用于协助诊断阿尔茨海默病(AD)。通过寻找低灌注/低代谢区,临床医生可以预测或证实对患者的诊断。基于全脑神经图像统计分析的现代计算机辅助诊断(CAD)系统比基于量化预定义的兴趣区域(ROIs)摄取的经典系统更准确。此外,这些新的系统允许确定新的预定义的兴趣区域(ROIs)和利用大量的包含影像数据的信息。AD的现代CAD系统的一个主要分支是基于多元技术,分析神经影像作为一个整体,不仅考虑像素的强度还考虑之间的关系。为了处理大量的数据的维数,许多特征提取的方法已成功地被应用。在这项工作中,我们提出了基于几个特征提取技术结合的计算机辅助诊断系统。首先,一些常用的基于方差分析(主成分分析),对数据的分解(非负矩阵分解法)和经典的量级(如Haralick特征)的特征提取方法同时被应用到原始数据。然后用2种不同的组合方法结合这些特征集:(一)使用单分类器和多重核学习方法;(二)使用整体的分类和通过多数表决选择最终结果。使用标记的神经影像学数据库以及交叉验证方案评价了所提出的方法。结论是使用所提出的计算机辅助诊断系统优于仅使用一个特征提取的技术。我们还公平地比较(使用相同的数据库)了所选择的特征提取方法。
关键词: 阿尔茨海默病、计算机辅助诊断系统,降维,机器学习,支持向量机,18F-FDG PET。
Current Alzheimer Research
Title:Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer’s Disease
Volume: 13 Issue: 7
Author(s): F. Segovia, J. M. Górriz, J. Ramírez, C. Phillips, for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
关键词: 阿尔茨海默病、计算机辅助诊断系统,降维,机器学习,支持向量机,18F-FDG PET。
摘要: Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.
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F. Segovia, J. M. Górriz, J. Ramírez, C. Phillips, for the Alzheimer’s Disease Neuroimaging Initiative , Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer’s Disease, Current Alzheimer Research 2016; 13 (7) . https://dx.doi.org/10.2174/1567205013666151116141906
DOI https://dx.doi.org/10.2174/1567205013666151116141906 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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