BACKGROUND: The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability. This article presents a classification method for AD based on kernel density estimation (KDE) of local features. METHODS: First, a large number of local features were extracted from stable image blobs to represent various anatomical patterns for potential effective biomarkers. Based on distinctive descriptors and locations, the local features were robustly clustered to identify correspondences of the same underlying patterns. Then, the KDE was used to estimate distribution parameters of the correspondences by weighting contributions according to their distances. Thus, biomarkers could be reliably quantified by reducing the effects of further away correspondences which were more likely noises from inter-subject variability. Finally, the Bayes classifier was applied on the distribution parameters for the classification of AD. RESULTS: Experiments were performed on different divisions of a publicly available database to investigate the accuracy and the effects of age and AD severity. Our method achieved an equal error classification rate of 0.85 for subject aged 60 - 80 years exhibiting mild AD and outperformed a recent local feature-based work regardless of both effects. CONCLUSIONS: We proposed a volumetric brain MRI classification method for neurodegenerative disease based on statistics of local features using KDE. The method may be potentially useful for the computer-aided diagnosis in clinical settings.
BACKGROUND: The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability. This article presents a classification method for AD based on kernel density estimation (KDE) of local features. METHODS: First, a large number of local features were extracted from stable image blobs to represent various anatomical patterns for potential effective biomarkers. Based on distinctive descriptors and locations, the local features were robustly clustered to identify correspondences of the same underlying patterns. Then, the KDE was used to estimate distribution parameters of the correspondences by weighting contributions according to their distances. Thus, biomarkers could be reliably quantified by reducing the effects of further away correspondences which were more likely noises from inter-subject variability. Finally, the Bayes classifier was applied on the distribution parameters for the classification of AD. RESULTS: Experiments were performed on different divisions of a publicly available database to investigate the accuracy and the effects of age and AD severity. Our method achieved an equal error classification rate of 0.85 for subject aged 60 - 80 years exhibiting mild AD and outperformed a recent local feature-based work regardless of both effects. CONCLUSIONS: We proposed a volumetric brain MRI classification method for neurodegenerative disease based on statistics of local features using KDE. The method may be potentially useful for the computer-aided diagnosis in clinical settings.