Title
Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images
Abstract
Vessel segmentation is the base of three dimensional reconstruction on digital subtraction angiography (DSA) images. In this paper we propose two simple but efficient methods of vessel segmentation for DSA images. The original DSA image is divided into several appropriate subimages according to a prior knowledge of the diameter of vessels. We introduce the vessels existence measure to determine whether each subimage contains vessels and then choose an optimal threshold, respectively, for every subimage previously determined to contain vessels. Finally, an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments are implemented on cerebral and hepatic DSA images. The results demonstrate that our proposed methods yield better binary results than global thresholding methods and some other local thresholding methods do.
Year
DOI
Venue
2007
10.1016/j.imavis.2006.07.026
Image Vision Comput.
Keywords
Field
DocType
digital subtraction angiography,adaptive threshold,knowledge-based adaptive,dsa image,global thresholding method,the busyness,vessel segmentation,original dsa image,thresholded subimages,local thresholding method,vessels existence measure,digital subtraction angiography image,original image,hepatic dsa image,appropriate subimages,knowledge base,adaptive thresholding
Vessel segmentation,Digital subtraction angiography,Computer vision,Pattern recognition,Segmentation,Artificial intelligence,Thresholding,Mathematics,Binary number
Journal
Volume
Issue
ISSN
25
8
Image and Vision Computing
Citations 
PageRank 
References 
14
0.81
6
Authors
4
Name
Order
Citations
PageRank
Nong Sang147572.22
Heng Li232533.39
Weixue Peng3171.30
Tianxu Zhang420623.18