Blood vessel center line automatic extraction method based on parallel structure detection and clustering

Abstract

The invention provides a blood vessel center line automatic extraction method based on parallel structure detection and clustering. The blood vessel center line automatic extraction method solves the problems that in a contrastographic picture, the grey level distribution is not even, and due to a large amount of background noise, the vascular structure is hard to extract. The method comprises the steps of utilizing three-order differential operators for calculating the direction of an image border by arranging the position of the detection border of a gradient response operator; combining a curve model on the basis of straight lines, circular curves and Euler spiral lines to build an intrinsic curve bundle of image border points, adopting the intrinsic curve bundle to conduct description of the geometrical features on the image border, and extracting all contours of blood vessels according to the geometrical consistency of the image border; utilizing the parallel features of the borders of the blood vessels for completely detecting two parallel borders of the blood vessels, searching for the positions with the same distance as the two parallel borders, connecting the centers of all the blood vessels, and finally obtaining the center lines of the blood vessels. The method is high in precision, strong in adaptability and suitable for the fields of cardiovascular disease computer-assisted diagnosis and treatment.

Description

technical field [0001] The invention relates to a method for automatically extracting blood vessel centerlines based on parallel structure detection and clustering, and belongs to the field of computer-aided diagnosis and treatment of cardiovascular diseases. Background technique [0002] With the improvement of human living standards, cardiovascular and cerebrovascular diseases have become the number one cause of death affecting human health. The average prevalence of coronary heart disease in my country is 6.49%, and the morbidity and mortality have shown a rapid upward trend in recent years. Therefore, quantitative diagnosis and risk assessment of cardiovascular and cerebrovascular diseases in the early stage of disease can effectively prevent the deterioration of the disease, thereby prolonging human life expectancy and improving human quality of life. [0003] In current clinical treatment, coronary artery structure analysis is based on two-dimensional X-ray contrast i...

AI Technical Summary

Problems solved by technology

[0006] 1. The traditional vascular centerline extraction algorithm requires a lot of manual interaction. Setting the initial conditions of the algorithm is not only time-consuming, but also susceptible to human interference and cannot be reproduced

[0007] 2. In the process of blood vessel extraction, due to the high background complexity and low contrast of coronary angiography images, and the lack of a large amount of information in two-dimensional images, the automatic segmentation of blood vessels is facing great challenges

[0008] 3. The traditional vascular centerline extraction algorithm will eliminate the vascular boundary with weak local image feature information, and cannot extract the complete boundary of the blood vessel well

Traditional blood vessel extraction methods not only require a large number of human intervention operations, but also involve a large amount of algorithm calculations

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