LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease

• MRI image segmentation using SOM and CONN linkage.
• Effective feature reduction method based on FDR and LVQ.
• Hybrid LVQ-SVM tool for MRI classification.
• Method validation through cross-validation and statistical significance testing.
• NORMAL/AD classification results using MRI up to 92% accuracy and 95% sensitivity.

This paper presents a novel computer-aided diagnosis (CAD) tool for the diagnosis of the Alzheimer’s disease (AD) using structural Magnetic Resonance Images (MRIs). The proposed method uses information learnt from the tissue distribution of Gray Matter (GM) and White Matter (WM) in the brain, which is previously obtained by an unsupervised segmentation method. The tissue distribution of control (normal) and AD images is modelled by means of Learning Vector Quantization (LVQ) algorithm, generating a set of representative prototypes of each class. The devised method projects new images onto the model vectors space for further classification using Support Vector Machine (SVM). The tool proposed here yields classification results over 90% (accuracy) for controls (normal) and Alzheimer’s disease (AD) patients and sensitivity up to 95% to AD. Moreover, statistical significance tests have been also performed in order to validate the proposed approach.

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