Digital Subtraction Angiography (DSA) can be used for diagnosing the pathologies of vascular system including systemic vascular disease, coronary heart disease, arrhythmia, valvular disease and congenital heart disease. Previous studies have provided some image enhancement algorithms for DSA images. However, these studies are not suitable for automated processes in huge amounts of data. Furthermore, few algorithms solved the problems of image contrast corruption after artifact removal. In this paper, we propose a fully automatic method for cerebrovascular DSA sequence images artifact removal based on rigid registration and guided filter. The guided filtering method is applied to fuse the original DSA image and registered DSA image, the results of which preserve clear vessel boundary from the original DSA image and remove the artifacts by the registered procedure. The experimental evaluation with 40 DSA sequence images shows that the proposed method increases the contrast index by 24.1% for improving the quality of DSA images compared with other image enhancement methods, and can be implemented as a fully automatic procedure.
Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter
