Zhang Jun, Gao Yue, Gao Yaozong, Munsell Brent C, Shen Dinggang
IEEE Trans Med Imaging. 2016 Dec;35(12):2524-2533. doi: 10.1109/TMI.2016.2582386. Epub 2016 Jun 20.
Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41 mm , and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster.
结构磁共振成像（MRI）是一种非常流行且有效的用于诊断阿尔茨海默病（AD）的技术。使用结构MRI数据的计算机辅助诊断方法的成功很大程度上取决于两个耗时的步骤：1）跨个体的非线性配准，以及2）脑组织分割。为了克服这一局限性，我们提出了一种基于地标点的特征提取方法，该方法不需要非线性配准和组织分割。在训练阶段，为了将AD患者与健康对照（HC）区分开来，首先基于局部形态特征进行组间比较，以识别具有显著组间差异的脑区。一般来说，所识别区域的中心成为能够区分AD患者与HC的地标位置（简称为AD地标点）。在测试阶段，使用学习到的AD地标点，基于形状约束回归森林算法的高效技术在测试图像中检测相应的地标点。为了提高检测精度，还识别出一组额外的显著且一致的地标点来指导AD地标点检测。基于所识别的AD地标点，提取形态特征以训练能够预测AD病情的支持向量机（SVM）分类器。在实验中，我们的方法依次在地标点检测和AD分类方面进行了评估。具体而言，所提出的地标点检测器的地标点检测误差（手动标注与自动检测）为2.41毫米，我们基于地标点的AD分类准确率为83.7%。最后，我们方法的AD分类性能与现有基于区域和基于体素的方法相当，甚至更好，同时所提出方法的速度快约50倍。
