Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease
Résumé
Using a single imaging modality to diagnose Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In this paper, we develop a novel method by using single modality (FDG-PET) but multi-level feature, which considers both region properties and connectivities between regions to classify AD or MCI from Normal Control (NC). First, three levels of features are extracted: statistical, connectivity and graph-based features. Then the connectivity features are decomposed into 3 different sets of features according to a proposed similarity-driven ranking method, which can not only reduce the feature dimension but also increase the classifier's diversity. Last, after feeding the 3 levels of features to different classifiers, a new classifier selection strategy, maximum Mean squared Error (mMsE), is developed to select a pair of classifiers with high diversity. In order to do the majority voting, a decision-making scheme, a nested cross validation technique is applied to choose another classifier according to the accuracy. Experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed method outperforms most FDG-PET-based classification algorithms, especially for classifying progressive MCI (pMCI) from stable MCI (sMCI).
Origine | Fichiers produits par l'(les) auteur(s) |
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