A Kernel-Based Representation to Support 3D MRI Unsupervised Clustering

A new kernel-based image representation is proposed on this paper aiming to support clustering tasks on 3D magnetic resonances images. The approach establishes an effective way to encode inter-slice similarities, so that the main shape information is kept on a lower dimensional space. Additionally, a spectral clustering technique is employed to estimate a compact embedding space where natural groups are easily detectable. Proposed approach outperforms the conventional voxel-wise sum of squared differences on clustering the gender category. Additionally, a pair of eigenvectors describing accurately the subject age is found.