Hull detection based on largest empty sector angle with application to analysis of realtime MR images
We present a novel view of the hull detection problem in two dimensions. Our proposed method is based on the principle of finding Pareto optimal boundaries and extends it to the general problem of finding a hull for a given set of points. We first compute the largest empty sector angle (LESA) score for each point. The desired hull can then be obtained as a super-level set of this score. We show how the proposed representation is related to a convex hull and demonstrate the flexibility it provides in choosing the geometry of the hull. As a target application we also present a head movement correction technique for real-time MR images of the dynamic vocal tract.