Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
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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 ± standard-deviation (SD) = 73 ± 6 years, mini-mental score (MMS) = 24.4 ± 2.8), 23 patients with amnestic MCI (10 males, 13 females, age ± SD = 74 ± 8 years, MMS = 27.3 ± 1.4) and 25 elderly healthy controls (13 males, 12 females, age ± SD = 64 ± 8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94 obtain a classification rate of 83 84 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.
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