Three-dimensional vessel segmentation using a novel combinatory filter framework

Blood vessel segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood vessel segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microvessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged vessels eventually lead to poor segmentation results. In this paper, we present a novel method for 3D vessel segmentation that is also suitable for segmenting microvessels, incorporating the advantages of a line filter and a Hessian-based vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.