Paper Info
Title
A Semi-automatic Method for Vascular Image Segmentation.
Abstract
Vascular diseases are major public heath problem around the world. Vessel segmentation has been widely concerned because it is a key step for diagnosis and surgical planning. Among past strategies, multi-scale line filters are very popular detectors. However, multi-scale integration results in undesirable diffusion when two vessels are closely located. To avoid this problem, we use gradient vector flow as vector field and introduce a vesselness measure to detect vessel which gives high and homogeneous output for line structure so that it is more suitable for segmentation over Frangi's vesselness measure. Level set method is applied to perform vessel segmentation. Our model is tested on real images. Experimental results demonstrate that our approach can successfully separate closely adjacent vessels and address the problems of low contrast and varying vessel width. It shows better performance than multi-scale approach. Furthermore, gradient vector flow makes the contour moving into boundary concavities.
Year
DOI
Venue
2011
10.1109/ICIG.2011.36
ICIG
Keywords
Field
DocType
vessel segmentation,multi-scale line filter,semi-automatic method,multi-scale integration result,vesselness measure,gradient vector flow,varying vessel width,adjacent vessel,vector field,vascular image,multi-scale approach,line structure,image segmentation,computer model,surgery,level set,fluid flow,biomedical imaging,computational modeling,edge detection
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Vector field,Computer science,Level set method,Level set,Image segmentation,Vector flow,Artificial intelligence,Real image
Conference
Citations 
PageRank 
References 
2
0.36
7
Authors
2
Name
Order
Citations
PageRank
Liping Chen16010.10
Shutao Li22594139.10