Loris Nanni1, Sheryl Brahnam2, Christian Salvatore3, Isabella Castiglioni4. 1. Department of Information Engineering, University of Padua, Via Gradenigo, 6/A, 35131 Padua, Italy. Electronic address: loris.nanni@unibo.it. 2. Department of Management and Computer Information Systems, Glass Hall, Room 387, Missouri State University, Springfield, MO 65804, USA. Electronic address: sbrahnam@missouristate.edu. 3. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.lli Cervi, 93, 20090 Segrate, Milano, Italy. Electronic address: christian.salvatore@ibfm.cnr.it. 4. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.lli Cervi, 93, 20090 Segrate, Milano, Italy. Electronic address: IsabellaCastiglioni@ibfm.cnr.it.
Abstract
BACKGROUND AND OBJECTIVE: Early and accurate diagnosis of Alzheimer's Disease (AD) is critical since early treatment effectively slows the progression of the disease thereby adding productive years to those afflicted by this disease. A major problem encountered in the classification of MRI for the automatic diagnosis of AD is the so-called curse-of-dimensionality, which is a consequence of the high dimensionality of MRI feature vectors and the low number of training patterns available in most MRI datasets relevant to AD. METHODS: A method for performing early diagnosis of AD is proposed that combines a set of SVMs trained on different texture descriptors (which reduce dimensionality) extracted from slices of Magnetic Resonance Image (MRI) with a set of SVMs trained on markers built from the voxels of MRIs. The dimension of the voxel-based features is reduced by using different feature selection algorithms, each of which trains a separate SVM. These two sets of SVMs are then combined by weighted-sum rule for a final decision. RESULTS: Experimental results show that 2D texture descriptors improve the performance of state-of-the-art voxel-based methods. The evaluation of our system on the four ADNI datasets demonstrates the efficacy of the proposed ensemble and demonstrates a contribution to the accurate prediction of AD. CONCLUSIONS: Ensembles of texture descriptors combine partially uncorrelated information with respect to standard approaches based on voxels, feature selection, and classification by SVM. In other words, the fusion of a system based on voxels and an ensemble of texture descriptors enhances the performance of voxel-based approaches.
BACKGROUND AND OBJECTIVE: Early and accurate diagnosis of Alzheimer's Disease (AD) is critical since early treatment effectively slows the progression of the disease thereby adding productive years to those afflicted by this disease. A major problem encountered in the classification of MRI for the automatic diagnosis of AD is the so-called curse-of-dimensionality, which is a consequence of the high dimensionality of MRI feature vectors and the low number of training patterns available in most MRI datasets relevant to AD. METHODS: A method for performing early diagnosis of AD is proposed that combines a set of SVMs trained on different texture descriptors (which reduce dimensionality) extracted from slices of Magnetic Resonance Image (MRI) with a set of SVMs trained on markers built from the voxels of MRIs. The dimension of the voxel-based features is reduced by using different feature selection algorithms, each of which trains a separate SVM. These two sets of SVMs are then combined by weighted-sum rule for a final decision. RESULTS: Experimental results show that 2D texture descriptors improve the performance of state-of-the-art voxel-based methods. The evaluation of our system on the four ADNI datasets demonstrates the efficacy of the proposed ensemble and demonstrates a contribution to the accurate prediction of AD. CONCLUSIONS: Ensembles of texture descriptors combine partially uncorrelated information with respect to standard approaches based on voxels, feature selection, and classification by SVM. In other words, the fusion of a system based on voxels and an ensemble of texture descriptors enhances the performance of voxel-based approaches.