Dual-source CT coronary artery automatic extraction method

Abstract

The invention relates to a dual-source CT coronary artery automatic extraction method. The method comprises the following steps of S1, calculating eigenvalues lambda 1, lambda 2 and lambda 3 of a Hessian matrix for each pixel point in dual-source CT data, and building a new blood vessel model equation; effectively enhancing a blood vessel structure by utilizing the new blood vessel model; enablinga blood vessel region to be relatively bright in grayscale, and enabling a background region to be relatively dark; performing Gaussian modeling on the blood vessel region and the background region by using a statistical model; S2, by utilizing a Gaussian mixture model, classifying enhanced data into two types, namely, the background region and the blood vessel region; and S3, in the blood vesselregion, restraining a neighborhood relation through the growth of a ball model, growing coronary arteries, and detecting and removing pseudo-branch blood vessels in the grown coronary arteries by utilizing hierarchical clustering-based branch detection.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a dual-source CT coronary artery automatic extraction method. Background technique [0002] Coronary artery segmentation is the first step in image analysis for cardiovascular disease. CT angiography is one of the most common examination methods at present. The amount of CT angiography data is large, the individual differences of patients are large, and the cardiovascular structure is complex. At present, the coronary artery segmentation method requires manual interaction, and the problem of pipeline branch error is prone to occur during pipeline reconstruction, which directly affects the doctor's analysis of blood vessels. Therefore, most doctors are still using two-dimensional images such as Multi-planner reformation (MPR), Curved planner reformation (CPR) and maximum intensity projection (MIP) to observe blood vessels. The traditional method analyzes blood vessels to...

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Problems solved by technology

[0018] The present invention aims at the problems of difficult branch detection and manual operation in the existing heart coronary artery segmentation method, and simultaneously considers the gray scale characteristics, morphological characteristics and spatial relationship of blood vessels, and combines the statistical classification method to improve the multi-scale filtering and spherical model, Realize automatic three-dimensional coronary artery segmentation without doctor's interactive point selection

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