A framework for spacial-variant thresholding of digital subtraction angiography images

Vessel segmentation is the base of 3d reconstruction of Digital Subtraction Angiograph (DSA) images. This paper proposes a framework of adaptive local thresholding based on a verification-based approach for vessel segmentation of DSA images. The original DSA image is firstly divided into overlapping subimages according to a priori knowledge of the diameter of vessels. We implement a hypothesis test to determine whether each subimage contains vessels and then choose an optimal threshold respectively for every subimage previously determined to contain vessels, with a secondary verification process to exclude the condition that the subregion only containing the background but misclassified as one containing vessels by the hypothesis test. Finally an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments demonstrate superior performance over global thresholding and some adaptive local thresholding methods.