Feature Points Selection Algorithm of DSA Images Based on Adaptive Multi-scale Vascular Enhancement and Mean Shift

In the Digital Subtraction Angiography (DSA) image registration algorithm, the precision of the control points as well as their number and the distribution in image determines the accuracy of geometric correction and registration. Control points usually adopt the grid points; however, a more effective method is to extract control points adaptively according to the image feature. In this paper, a control point’s selection algorithm of DSA images is proposed based on adaptive multi-Scale vascular enhancement, error diffusion and means shift algorithms. This paper introduce error diffusion algorithm is to take advantage of the image intrinsic characteristics to adaptively select control points. In the edge and texture complex area, more control points (grid intensive) will be selected. On the contrary in the flat area less control points will be selected. Introduction of the mean shift is to reduce the number of control points, and achieve the optimal image registration. Experimental results show that the proposed algorithm can adaptively put the control points to blood vessels and other key image characteristics, and can optimize the number of control points according to practical needs, which will ensure the accuracy of DSA image registration.