Geometric Flows for Segmenting Vasculature in MRI: Theory and Validation

Abstract. Often in neurosurgical planning a dual spin echo acquisition is performed that yields proton density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. The development of vessel segmentation algorithms for PD images is of general interest since this type of acquisition is widespread and is entirely noninvasive. Whereas vessels are signaled by black blood contrast in such images, extracting them is a challenge because other anatomical structures also yield similar contrasts at their boundaries. In this paper, we present a novel multi-scale geometric flow for segmenting vasculature from standard MRI which can also be applied to the easier cases of angiography data. We first apply Frangi's vesselness measure [3] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which is orthogonal to vessel boundaries so that the flux maximizing geometric flow algorithm of [14] ca...

• Type: Conference
• Year: 2004
• Where: MICCAI
• Authors: Maxime Descoteaux, D. Louis Collins, Kaleem Siddiqi
• Keywords: Angiography Data, Medical Imaging, MICCAI 2004, PD Images, PD Volume