利用图像处理技术对正电子发射断层扫描脑图像进行阿尔茨海默病计算机辅助诊断

M. Adel, Imene Garali, Xiaoxi Pan, C. Fossati, T. Gaidon, J. Wojak, S. Bourennane, E. Guedj
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引用次数: 5

摘要

正电子发射断层扫描(PET)是一种分子医学成像方式,通常用于神经退行性疾病的诊断。基于医学图像分析的计算机辅助诊断(CAD)可以帮助对阿尔茨海默病(AD)等脑部疾病进行定量评估。本章介绍了脑感兴趣体积(VOI)在从AD脑PET图像中分离健康或正常对照(HC或NC)方面的有效性。首先使用地图集将大脑图像映射到解剖学上的voi。然后在这些voi上计算不同的特征,包括统计、图形或基于连接性的特征。然后将排名靠前的声音输入到支持向量机(SVM)分类器中。在本地数据库图像和阿尔茨海默病神经成像倡议(ADNI)公共数据库上对所开发的方法进行了评估,然后与已知的选择特征方法进行了比较。这些新方法在两组分离的情况下优于分类结果。
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Alzheimer’s Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images Using Image Processing Techniques
Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative disease diagnosis. Computer-aided diagnosis (CAD), based on medical image analysis, could help with the quantitative evaluation of brain diseases such as Alzheimer ’ s disease (AD). Ranking the effectiveness of brain volume of interest (VOI) to separate healthy or normal control (HC or NC) from AD brain PET images is presented in this book chapter. Brain images are first mapped into anatomical VOIs using an atlas. Different features including statistical, graph, or connectivity-based features are then computed on these VOIs. Top-ranked VOIs are then input into a support vector machine (SVM) classifier. The developed methods are evaluated on a local database image as well as on Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) public database and then compared to known selection feature methods. These new approaches outperformed classification results in the case of a two-group separation.
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