QC-SPHRAM：基于准共形球谐函数的海马表面几何畸变用于阿尔茨海默病的早期检测
QC-SPHRAM: Quasi-conformal Spherical Harmonics Based Geometric Distortions on Hippocampal Surfaces for Early Detection of the Alzheimer's Disease
摘要：
我们提出了一个疾病分类模型，称为QC-SPHARM，用于早期检测阿尔茨海默病（AD）。所提出的QC-SPHARM可以区分正常对照（NC）受试者和AD患者，以及遗忘型轻度认知障碍（aMCI）患者之间有很大的可能性进展到AD和那些谁不。使用基于球谐函数（SPHARM）的配准，将从ADNI数据分割的海马表面单独配准到使用SPHARM从NC受试者构建的模板表面。从模板表面到每个主题的变形的局部几何失真被量化的保形性失真和曲率失真。将测量结果与球谐系数和来自模板的对象的总体积变化相结合。然后，一个基于t检验的特征选择方法，结合装袋策略被应用到提取那些具有高区分能力的两个类的局部区域。因此，可以在支持向量机（SVM）设置下使用数据来构建疾病诊断机器。使用110 NC科目和110 AD患者从ADNI数据库中，该算法实现了85：2%的测试准确率对80个随机样本作为测试科目，与表面几何形状的分类机中的结合。使用20名在两年期间进展为AD的aMCI患者和另外20名在接下来的两年中保持非AD的aMCI患者，该算法使用10名随机挑选的受试者作为测试数据实现了81：2%的准确性。我们提出的方法是6%-15%，优于其他分类模型，没有纳入表面几何。结果表明，使用局部几何畸变作为早期AD诊断的判别标准的优势。
英文摘要：
We propose a disease classification model, called the QC-SPHARM, for the early detection of the Alzheimer's Disease (AD). The proposed QC-SPHARM can distinguish between normal control (NC) subjects and AD patients, as well as between amnestic mild cognitive impairment (aMCI) patients having high possibility progressing into AD and those who do not. Using the spherical harmonics (SPHARM) based registration, hippocampal surfaces segmented from the ADNI data are individually registered to a template surface constructed from the NC subjects using SPHARM. Local geometric distortions of the deformation from the template surface to each subject are quantified in terms of conformality distortions and curvatures distortions. The measurements are combined with the spherical harmonics coefficients and the total volume change of the subject from the template. Afterwards, a t-test based feature selection method incorporating the bagging strategy is applied to extract those local regions having high discriminating power of the two classes. The disease diagnosis machine can therefore be built using the data under the Support Vector Machine (SVM) setting. Using 110 NC subjects and 110 AD patients from the ADNI database, the proposed algorithm achieves 85:2% testing accuracy on 80 random samples as testing subjects, with the incorporation of surface geometry in the classification machine. Using 20 aMCI patients who has advanced to AD during a two-year period and another 20 aMCI patients who remain non-AD for the next two years, the algorithm achieves 81:2% accuracy using 10 randomly picked subjects as testing data. Our proposed method is 6%-15% better than other classification models without the incorporation of surface geometry. The results demonstrate the advantages of using local geometric distortions as the discriminating criterion for early AD diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
