Blood vessel enhancement for DSA images based on adaptive multi-scale filtering

Digital subtraction angiography (DSA) plays a significant role in the diagnosis, treatment planning and assessment of diseases. However, because of the geometrical complexity and fine characteristics of blood vessel structures, accurate and robust detection of blood vessels still remains a problem. In this paper, a blood vessel enhancement algorithm is proposed. The main purpose of this work is to improve the visual quality of blood vessels in DSA images. The new blood vessel enhancement algorithm is based on the multi-scale space theory and Hessian matrix. Not only the eigenvalues of Hessian matrix but also the angles between eigenvectors are utilized for the blood vessel enhancement of DSA. The filter parameters and scale factors are decided adaptively. Eigenvalues of the Hessian matrix are also used for the noise elimination. Experimental results show that the proposed algorithm has a good performance in blood vessel enhancement of DSA images. The proposed algorithm filters image background and non-vascular structure effectively. The deformation of blood vessels occurred in the enhancement process is avoided and more small blood vessels are visible in DSA images.