3D tubular structure extraction using kernel-based superellipsoid model with Gaussian process regression

Abstract

To analyze the tubular structure correctly and obtain a record of the centerlines has become significantly more challenging and infers countless applications in a large amount of fields. Hence, a robust and automated technique for extracting the centerlines of the tubular structure is required. To address complicated 3D tubular objects, a novel kernel-based modeling approach with regard to minimizing tracking energy is presented in this paper. The 3D tubular structure can be demonstrated as a kernel-based superellipsoid model with non-uniform weights. To improve the performance, Gaussian process is also introduced to update the parameters of the kernel-based model, especially for the complicated structure with cross sections, varying radii, and complicated branches. At last, the extensive experimental results on 3D tubular data demonstrate that our proposed method deals effectively with complicated tubular structure.