We describe a new method to automatically discriminate between patients with
Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly
controls, based on multidimensional classification of hippocampal shape
features. This approach uses spherical harmonics (SPHARM) coefficients to model
the shape of the hippocampi, which are segmented from magnetic resonance images
(MRI) using a fully automatic method that we previously developed. SPHARM
coefficients are used as features in a classification procedure based on
support vector machines (SVM). The most relevant features for classification
are selected using a bagging strategy. We evaluate the accuracy of our method
in a group of 23 patients with AD (10 males, 13 females, age $\pm$
standard-deviation (SD) = 73 $\pm$ 6 years, mini-mental score (MMS) = 24.4
$\pm$ 2.8), 23 patients with amnestic MCI (10 males, 13 females, age $\pm$ SD =
74 $\pm$ 8 years, MMS = 27.3 $\pm$ 1.4) and 25 elderly healthy controls (13
males, 12 females, age $\pm$ SD = 64 $\pm$ 8 years), using leave-one-out
cross-validation. For AD vs controls, we obtain a correct classification rate
of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we
obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of
84%. This accuracy is superior to that of hippocampal volumetry and is
comparable to recently published SVM-based whole-brain classification methods,
which relied on a different strategy. This new method may become a useful tool
to assist in the diagnosis of Alzheimer's disease.

本文提出一种使用多维分类和支持向量机基于海马形状特征来自动区分老年控制组、痴呆症 (AD) 以及轻度认知障碍 (MCI) 患者的新方法，并与海马体积测量和其他 SVM 整脑分类方法进行比较。结果表明，该方法的分类性能优于海马体积测量，并可作为帮助诊断 AD 的有用工具。