Deformation texture-based features for classification in Alzheimer's disease

Neurological pathologies are often reflected in brain magnetic resonance images as abnormal global or local anatomical changes. These variations can be computed using non-rigid registration and summarized using Jacobian determinant maps of the resulting deformation field, which characterise local volume changes. We propose a new approach which exploits the information contained in Jacobian determinant maps of the whole brain in Alzheimer’s disease (AD) classification by means of texture analysis. Textural features were derived from whole-brain Jacobian determinant maps based on 3D Grey Level Co-occurrence Matrix. The large number of features obtained depicts anatomical variations at different resolution, allowing retaining both global and local information. Principle component analysis was applied for feature reduction such that 95% of the data variance was retained. Classification was performed using a linear support vector machine. We evaluated our approach using a bootstrapping procedure in which 92 subjects were randomly split into separate training and testing sets. For comparison purposes, we implemented two dissimilarity-based classification approaches, one based on pairwise registration and the other based on registration to a single template. Our new approach significantly outperformed the other approaches. The results of this study showed that pairwise registration did not bring added value compared to registration to a single template and textural features were more informative than dissimilarity-based features. This study demonstrates the potential of texture analysis on whole brain Jacobian determinant map for diagnosis of AD subjects.