Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification.
Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification.
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DOI：
10.1109/jtehm.2018.2796600
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发表时间：
2018
影响因子：
3.4
通讯作者：
Guedj E
中科院分区：
文献类型：
--
作者：
Garali I;Adel M;Bourennane S;Guedj E
正电子发射断层扫描（PET）是一种分子医学成像模式，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可以帮助定量评估阿尔茨海默病（AD）等脑部疾病。本文提出了一种新的方法，排名的有效性脑体积的兴趣（VOI），以分离健康对照从AD脑PET图像。首先使用图谱将脑图像映射到解剖VOI。然后提取基于直方图的特征，并用于根据曲线下面积（AUC）参数选择和排列VOI，这产生了VOI在受试者组之间分离的能力的层次结构。然后将排名靠前的VOI输入到支持向量机分类器中。所开发的方法进行评估的本地数据库图像和已知的选择功能的方法进行比较。结果表明，在两组分离的情况下，使用AUC优于分类结果。计算机辅助诊断阿尔茨海默病PET图像区域排序和特征选择的一般方案。
Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer’s disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation. General scheme of region ranking and feature selection for computer-aided diagnosis of Alzheimer's Disease on PET images.