Functional activity maps based on significance measures and Independent Component Analysis.
Functional activity maps based on significance measures and Independent Component Analysis.
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DOI：
10.1016/j.cmpb.2013.03.015
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发表时间：
2013-07
影响因子：
6.1
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Martínez-Murcia FJ;Górriz JM;Ramírez J;Puntonet CG;Illán IA;Alzheimer's Disease Neuroimaging Initiative
功能成像的使用已被证明是非常有帮助的神经退行性疾病，如阿尔茨海默病（AD）的诊断过程。在许多情况下，对这些图像的分析是通过手动重新定向和目视判读来执行的。因此，需要新的统计技术来进行更定量的分析。在这项工作中，提出了一种新的统计近似的功能图像的分析，基于显着性措施和独立分量分析（伊卡）。在图像预处理之后，使用诸如Mann-Whitney-Wilcoxon U检验（MWW）和相对熵（RE）的显著性测量来提取允许更好地分离两个类的体素。在此特征选择步骤之后，体素向量通过伊卡建模，提取将用作分类器的输入的一些独立分量。在这项工作中使用朴素贝叶斯和支持向量机（SVM）分类器。该系统已被应用到两个不同的数据库。来自西班牙格拉纳达的“Virgen de las Nieves”医院的96名受试者的单光子发射计算机断层扫描（SPECT）数据库，以及来自阿尔茨海默病神经成像倡议（ADNI）的196名受试者的正电子发射断层扫描（PET）数据库。SPECT和PET数据库的准确度高达96.9%和91.3%的值实现了所提出的系统，这已经产生了许多好处，在最近的作品提出的方法。
The use of functional imaging has been proven very helpful for the process of diagnosis of neurodegenerative diseases, such as Alzheimer’s Disease (AD). In many cases, the analysis of these images is performed by manual reorientation and visual interpretation. Therefore, new statistical techniques to perform a more quantitative analysis are needed. In this work, a new statistical approximation to the analysis of functional images, based on significance measures and Independent Component Analysis (ICA) is presented. After the images preprocessing, voxels that allow better separation of the two classes are extracted, using significance measures such as the Mann-Whitney-Wilcoxon U-Test (MWW) and Relative Entropy (RE). After this feature selection step, the voxels vector is modelled by means of ICA, extracting a few independent components which will be used as an input to the classifier. Naive Bayes and Support Vector Machine (SVM) classifiers are used in this work. The proposed system has been applied to two different databases. A 96-subjects Single Photon Emission Computed Tomography (SPECT) database from the “Virgen de las Nieves” Hospital in Granada, Spain, and a 196-subjects Positron Emission Tomography (PET) database from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Values of accuracy up to 96.9% and 91.3% for SPECT and PET databases are achieved by the proposed system, which has yielded many benefits over methods proposed on recent works.
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