Digital subtraction angiography image segmentation based on multiscale Hessian matrix applied to medical diagnosis and clinical nursing of coronary stenting patients

Abstract

ObjectiveTo introduce an improved method of digital subtraction angiography image segmentation based on a multi-scale Hessian matrix, to accurately segment vascular images before and after coronary stent implantation, which will have strong applications in medical diagnostics and clinical nursing of patients who underwent coronary stents implantation. MethodsFirstly, a vessel edge enhancement algorithm is proposed, which enhances the gradient of the vessel edge, which not only makes the obtained vessel edge smoother, but also enhances the visual effect of the intersection of vessels; secondly, introduces noise filtering based on morphology, which can detect and remove linear noise similar to blood vessels; finally, in view of the problem that each image in the DSA blood vessel sequence can only show part of the blood vessels, which is not conducive to observing the overall state of the blood vessels, a blood vessel sequence image fusion is designed to display the blood vessels in each image on the same image. The state of blood vessels can be understood as a whole in one image. ResultsCompared with the segmentation method in the literature, the blood vessel overlap rate increased by 0.0089, and the mis-segmentation rate decreased by 0.0334. In the quantitative analysis, the improved blood vessel segmentation algorithm in this paper improves the overlapping rate and reduces the mis-segmentation rate. ConclusionFor the comparison of visual effects, the blood vessels segmented by the algorithm in this paper have higher clarity, which is helpful for doctors and nurses to comprehensively synthesize blood vessel clinical information. There is a more objective image basis in the preoperative diagnosis and postoperative rehabilitation nursing of coronary angiographic stent implantation.