Abstract Morphological networks constructed with structural magnetic resonance imaging (sMRI) images have been widely investigated by exploring interregional alterations of different brain regions of interest (ROI) in the spatial domain for Alzheimer’s disease (AD) classification. However, few attentions are attracted to construct a subband-based individual network with the sMRI image in the frequency domain. In order to verify the feasibility of constructing individual networks with subbands and extract features from the subband-based individual network for AD classification, in this study, we propose a novel method to capture correlations of the abnormal energy distribution patterns related to AD by constructing nonsubsampled contourlet subband-based individual networks (NCSINs) in the frequency domain. Specifically, a 2-dimensional representation of the preprocessed sMRI image is firstly reshaped by downsampling and reconstruction steps. Then, the nonsubsampled contourlet transform is performed on the 2-dimensional representation to obtain directional subbands, and each directional subband at one scale is described by a column energy feature vector (CV) regarded as a node of the NCSIN. Subsequently, edge between any two nodes is weighted with connection strength (CS). Finally, the concatenation of node and edge features of the NCSINs at different scales is used as a network feature of the sMRI image for AD classification. Meanwhile, the support vector machine (SVM) classifier with a radial basis function (RBF) kernel is applied for categorizing 680 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that it is feasible to construct the subband-based individual network in the frequency domain and also show that our NCSIN method outperforms five other state-of-the-art approaches.

中文翻译：

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使用从基于非下采样 Contourlet 子带的个体网络中提取的特征对阿尔茨海默病进行分类

摘要 通过探索阿尔茨海默病 (AD) 分类空间域中不同大脑感兴趣区域 (ROI) 的区域间变化，已经广泛研究了使用结构磁共振成像 (sMRI) 图像构建的形态学网络。然而，很少有人关注用频域中的 sMRI 图像构建基于子带的个体网络。为了验证构建具有子带的个体网络并从基于子带的个体网络中提取特征用于 AD 分类的可行性，在本研究中，我们提出了一种通过构建非下采样来捕获与 AD 相关的异常能量分布模式的相关性的新方法。频域中基于轮廓波子带的个体网络 (NCSIN)。具体来说，预处理的 sMRI 图像的二维表示首先通过下采样和重建步骤进行重塑。然后，对二维表示进行非下采样Contourlet变换，得到方向子带，每个方向子带在一个尺度上用列能量特征向量（CV）描述，作为NCSIN的一个节点。随后，任意两个节点之间的边用连接强度（CS）加权。最后，将不同尺度的 NCSIN 节点和边缘特征的串联用作 sMRI 图像的网络特征，用于 AD 分类。同时，应用具有径向基函数 (RBF) 核的支持向量机 (SVM) 分类器对来自阿尔茨海默病神经影像学倡议 (ADNI) 数据库的 680 名受试者进行分类。