VASCULAR STRUCTURE SEGMENTATION AND BIFURCATION DETECTION

Abstract

Delineation and reconstruction of vascular structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vascular structure segmentation with fully automatic detection of bifurcation points. First, we perform a preselection of tubular objects and trace the vessels based on the eigenanalysis of the Hessian matrix. This provides us the estimated direction of vessels as well as the cross-sectional planes orthogonal to the vessels. Then, we apply AdaBoost learning method with specially designed filters on cross-sectional planes to automatically detect the bifurcation points of the vessels. Our method has over 97% success rate for detecting bifurcation points. We present very promising results of our method applied to the reconstruction of pulmonary vessels from clinical chest CT. Our method allows for fully automatic detection of bifurcation points as well as segmentation of vessels