Blood vessel image segmentation method based on centerline extraction and nuclear magnetic resonance imaging system

Abstract

The invention belongs to the technical field of medical image processing and discloses a blood vessel image segmentation method based on centerline extraction and a nuclear magnetic resonance imagingsystem. The method comprises steps of: preprocessing cerebral blood vessel data based on the vesselness filtering of a Hessian matrix; extracting a blood vessel centerline by using a topology refinement method; extracting the features of training samples and test samples by using centerline points as positive samples and non-blood vessel points as negative sample points; by using the feature of the training samples and a corresponding tag training SVM model, using the features of the test samples as the inputs of a trained SVM model and using an output tag as the segmentation method of the blood vessel. The blood vessel image segmentation method reduces workload, improves computational efficiency, requires no manual target calibration or background, fully automatically segments the blood vessel, and greatly improves segmentation efficiency. The blood vessel image segmentation method segments cerebral blood vessels accurately and rapidly without human intervention and can achieve a truepositive rate and a true negative rate as high as 0.85.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a blood vessel image segmentation method based on central line extraction and a nuclear magnetic resonance imaging system. Background technique [0002] Vessel segmentation is one of the most important medical image processing techniques, which is crucial to the diagnosis and treatment of cardiovascular and cerebrovascular diseases and other related diseases. Accurate segmentation is the primary issue of image analysis and recognition, and it is also a factor that restricts the development and application of other related technologies, such as blood vessel matching, 3D reconstruction, and motion estimation. Due to the influence of imaging noise, complex vascular structure, and other factors, medical images usually have low contrast and blurred boundaries between different tissues, while fine structures like blood vessels are susceptible to noise and n...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the current blood vessel segmentation method is sensitive to noise, and the segmentation accuracy is low; manually specifying the label is time-consuming and laborious, which affects the calculation efficiency

Images