Directional mean shift and its application for topology classification of local 3D structures

In this study, we introduce a new directional nonparametric clustering algorithm for 3D medical structure topology classification. This paper proposes directional mean shift (DMS) which extends the well known mean shift-based clustering, for handling directional statistics, toward analyzing directional/circular-domain data with phase-wraparound boundary conditions. Our overall approach transforms the 3D topology classification problem into a clustering analysis of a 2D image, following the work by Bahlmann et al. in the context of computer-aided diagnosis (CAD). The proposed DMS replaces the expectation-maximization (EM) algorithm for Gaussian mixture model (GMM) fitting used in the previous method addressing the shortcomings of the Bahlmann´s method. Results from our experiments demonstrate the effectiveness of DMS in contrast to the original EM-based approach in solving the clustering problem with a 2D image unwrapped from a 3D spherical data, leading to better accuracy in the topology classification task.