Paper Info
Title
Fast vascular skeleton extraction algorithm
Abstract
First step in fully automatic high-speed vascular tree segmentation.Vascular skeleton is extracted directly from CT volume without preprocessing.Transferring the problem to a lower dimensional space improves the speed.Automatically adapts to CT value variation between patients.Automatically detects abnormal situations (calcification, implants). Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels nodes that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.
Year
DOI
Venue
2016
10.1016/j.patrec.2015.06.024
Pattern Recognition Letters
Keywords
Field
DocType
Skeleton extraction,Centerline tree,Vascular tree,Blood vessels,CT angiography
Voxel,Computer vision,Curve fitting,Pattern recognition,Computed tomography angiography,Segmentation,Data acquisition,Artificial intelligence,Skeleton (computer programming),Gaussian function,Angiography,Mathematics
Journal
Volume
Issue
ISSN
76
C
0167-8655
Citations 
PageRank 
References 
0
0.34
13
Authors
5
Name
Order
Citations
PageRank
kristina lidayova100.34
Hans Frimmel2285.14
Chunliang Wang319917.56
ewert bengtsson413525.36
Orjan Smedby5212.80