Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier
Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier
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基于结构MRI图像小波变换能量特征和神经网络分类器的阿尔茨海默病识别
DOI：
10.1016/j.artmed.2020.101940
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发表时间：
2020-08-01
影响因子：
7.5
通讯作者：
Chen, Luonan
中科院分区：
文献类型：
--
作者：
Feng, Jinwang;Zhang, Shao-Wu;Chen, Luonan
阿尔茨海默病（Alzheimer's disease， AD）目前是临床医生难以识别的疾病，特别是在其前驱阶段，轻度认知障碍（mild cognitive impairment， MCI），因为该阶段没有明显的临床症状，对日常生活的影响也很小。此外，结构磁共振成像（sMRI）图像反映的MCI患者与老年健康对照（HC）脑萎缩能量分布差异很小且很微妙，难以通过空间分析捕捉到。在本研究中，我们提出了一种新的方法（AD-WTEF），即通过提取sMRI图像的小波变换能量特征（WTEF）来识别HC受试者中的AD和MCI患者。AD-WTEF首先对预处理后的sMRI图像的每次扫描进行小波变换，得到其在不同变换层次上大小相同的方向子带。然后，在解剖自动标记图谱（AAL）的基础上，AD-WTEF构建新的脑掩膜，在相同的方向和转换水平上分割子带到不同的能量感兴趣区域（EROIs）。第三，通过对EROI中的系数进行平均，得到能量特征，然后将不同EROI的能量特征连接起来，形成能量特征向量，用于描述同一方向和变换层次上的子带。结果，这些能量特征向量被进一步串联成sMRI图像的WTEF。最后，选择最近邻（NN）分类器进行AD识别。与其他7种最先进的方法相比，我们的AD- wtef可以有效地利用sMRI图像的细微能量分布差异来识别AD患者。此外，实验结果表明，我们的AD- wtef还可以发现与AD相关的重要脑roi。
Alzheimer's disease (AD) is now difficult to be identified for clinicians, especially, at its prodromal stage, mild cognitive impairment (MCI), because of no obvious clinical symptom and few impacts on daily life at this phase. In addition, energy distribution differences of brain atrophies reflected in structural magnetic resonance imaging (sMRI) images between MCI patients and older healthy controls (HC) are minimal and subtle, which are difficult to be captured by the spatial analysis. In this study, we propose a novel method (namely AD-WTEF) to identify AD and MCI patients from HC subjects by extracting the wavelet transformation energy feature (WTEF) of the sMRI image. AD-WTEF firstly transforms each scan of the preprocessed sMRI image by wavelet to obtain its directional subbands with the same size at different transformation levels. And then, based on the anatomical automatic labeling (AAL) atlas, AD-WTEF constructs a new brain mask to segment the subbands at the same direction and transformation level into different energy regions of interest (EROIs). Thirdly, by averaging coefficients in an EROI, AD-WTEF gets an energy feature, following that energy features of different EROIs are connected to form an energy feature vector for describing the subbands at the same direction and transformation level. As a result, these energy feature vectors are further concatenated to be a WTEF of the sMRI image. Finally, the nearest neighbor (NN) classifier is selected and used for AD identification. Compared with other seven stateof-the-art methods, our AD-WTEF can effectively identify AD patients using the subtle energy distribution differences of sMRI images. Furthermore, experimental results indicate that our AD-WTEF can also find important brain ROIs related to AD.