Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images

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.