Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease
Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease
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DOI：
10.1016/j.patrec.2010.03.004
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发表时间：
2010-08-01
影响因子：
5.1
通讯作者：
Puntonet, C. G.
中科院分区：
文献类型：
--
作者：
Alvarez Illan, I.;Gorriz, J. M.;Puntonet, C. G.
寻找敏感和适当的技术来早期发现阿尔茨海默病（AD）是开发早期治疗的根本意义。单光子发射计算机断层扫描（SPECT）图像是非侵入性的观察工具，以协助诊断，通常通过无监督的统计测试处理，或视觉评估。在这项工作中，我们提出了一个基于监督学习方法的计算机辅助诊断系统，探索了两种不同的新方法。本研究使用独立分量分析（ICA）从图像数据库中提取相关特征并降低特征空间维数，利用得到的数据构建支持向量机。该方法误差估计低于9%，能够检测AD灌注模式，并以无监督的方式对新受试者进行分类。(C) 2010 Elsevier B.V.版权所有
Finding sensitive and appropriate technologies for early detection of the Alzheimer's disease (AD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are non-invasive observation tools to assist the diagnosis, commonly processed through unsupervised statistical tests, or assessed visually. In this work, we present a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches. Independent Component Analysis (ICA) was used within this work to extract the relevant features from the image database and reduce the feature space dimensionality, to build a SVM with the resulting data. The proposed approach led to an error estimation below the 9%, and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner. (C) 2010 Elsevier B.V. All rights reserved.