Extracting branching object geometry via cores
Y. Fridman, Stephen M. Pizer · 2004
The extraction of branching objects and their geometry from 3D medical images is the goal of the research described here. The methods I develop and analyze are based on optimization of medial strength embodied in cores, a type of multiscale medial axis. The cores research described here focuses on object branches. The dissertation describes two sets of methods, one that computes ID cores of tubular objects and another that computes 2D cores of non-tubular, or slab-like, objects. Each of these sets of methods uses a core-following technique that optimizes a medialness measure over position, radius, and orientation. I combine methods for branch-finding, branch-reseeding, and core-termination with core-following to produce a tool that can extract complex branching objects from 3D images without the need for user interaction. I demonstrate the performance of the tubular core methods by extracting blood vessel trees from 3D head MR data, and I demonstrate the performance of the slab-like core methods by extracting kidneys from 3D abdominal CT data. I also integrate the two sets of methods with each other to extract kidneys and adjoining vasculature from CT data. Finally, I perform a thorough analysis of the effects of object geometry on cores using synthetic images. This analysis shows impressive resistance of the tubular core methods to image noise. It also shows that the slab-like core methods are not as robust but are still effective for extracting branching objects from medical images with relatively low noise.