Selecting Scales by Multiple Kernel Learning for Shape Diffusion Analysis

Brain morphological abnormalities can typically be detected by advanced geometrical shape analysis techniques. Recently, shape diffusion methods have proved to be very effective in providing useful descriptions for brain classification purposes. In particular, they allow the analysis of such shapes at multiple scales, but the selection of the correct range of scales remains an open issue heavily affecting the performance of methods, and it needs to be estimated adaptively for different classes of shapes. In this paper, we focus on the diffusion scale selection in order to define a robust shape descriptor for brain classification. To this end, geometric features are extracted for each scale and the best feature combination is selected by employing multiple kernel learning (MKL). In the presented experiments, we compare the shape of Thalamic regions in order to discriminate between normal subjects and schizophrenic patients. We demonstrate that MKL allows to obtain classifiers which are more accurate with respect to other competing algorithms for schizophrenia detection. Moreover, using the weights computed by the MKL algorithm, we can select at which scale the features are more effective for schizophrenia classification.