Clustering white matter fibers using support vector machines: a volumetric conformal mapping approach

White matter tractography is non-invasive method to study white matter microstructure within the brain and its connectivity across the different regions. Various neuro-degenerative diseases affect the white matter connectivity in the brain. In order to study the neurodegeneration and localize the affected fiber bundles, it is important to cluster the white matter fibers in an anatomically consistent manner. Clustering white matter fiber bundles in the brain is a challenging problem. The present approaches include region of interest (ROI) based clustering as well as template based clustering. A novel clustering technique using support vector machine framework is introduced. In this method, a conformal volumetric bijective mapping between the brain and the topologically equivalent sphere is established. The white matter fibers are then parameterized in this domain. Such a parameterization also introduces a spatial normalization without requiring any prior registration. We show that such a mapping is useful to learn statistical models of white matter fiber bundles and use it for clustering in a new subject.