Zhang Jun, Liu Mingxia, An Le, Gao Yaozong, Shen Dinggang
Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA.
Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC, USA.
Med Comput Vis Bayesian Graph Models Biomed Imaging (2016). 2016 Oct;10081:35-45. doi: 10.1007/978-3-319-61188-4_4. Epub 2017 Jul 1.
In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.
在本文中，我们提出了一种基于地标点的特征提取方法，用于利用纵向结构磁共振图像进行阿尔茨海默病（AD）诊断。该方法在应用阶段无需进行非线性配准或组织分割，并且对纵向扫描之间的不一致具有鲁棒性。具体而言，（1）首先从全脑自动发现具有判别性的地标点，对于测试图像可使用快速地标点检测方法高效地对其进行定位；（2）然后基于那些检测到的地标点提取高级统计空间特征和上下文纵向特征。利用这些空间和纵向特征，分别采用线性支持向量机（SVM）将AD患者与健康对照（HC）区分开来，以及将轻度认知障碍（MCI）患者与HC区分开来。实验结果证明了其具有竞争力的分类准确率以及良好的计算效率。
