3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1-weighted brain MRI

Early and antemortem diagnosis of Alzheimer's disease (AD) may help in the development of appropriate treatment and in slowing down the disease progression. In this work, a three-phase computer aided approach is suggested for classification of AD patients and controls using T1-weighted MRI. In the first phase, smoothed modulated gray matter (GM) probability maps are obtained from T1-weighted MRIs. In the second phase, 3D discrete wavelet transform is applied on GM of five brain regions, which are well-documented regions affected in AD, to construct features. In the third phase, a minimal set of relevant and nonredundant features are obtained using Fisher's discriminant ratio and minimum redundancy maximum relevance feature selection methods. To check the efficacy of the proposed approach, experiments were carried out on three datasets derived from the publicly available OASIS database, using three commonly used classifiers. The performance of the proposed approach was evaluated using three performance measures namely sensitivity, specificity and classification accuracy. Further, the proposed approach was compared with the existing state-of-the-art techniques in terms of three performance measures, ROC curves, scoring and computation time. Irrespective of the datasets and the classifiers, the proposed method outperformed the existing methods. In addition, the statistical test also demonstrated that the proposed method is significantly better in comparison to the other existing methods. The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls.