Vascular Image Segmentation Method Based on Centerline Extraction, 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, a nuclear magnetic resonance imaging system, and preprocessing of brain blood vessel data by vesselness filtering based on Hessian matrix; topology refinement method for blood vessel centerline Extraction; take the centerline point as a positive sample and the non-vascular point as a negative sample to extract the features of the training sample and the test sample; use the features of the training sample and the corresponding label to train the SVM model, and use the feature of the test sample as the input of the trained SVM model , the output label is the segmentation result of blood vessels. The invention reduces the workload and improves the calculation efficiency; it does not need to manually calibrate the target and the background, completes automatic blood vessel segmentation, and greatly improves the segmentation efficiency. The invention realizes the segmentation of cerebral blood vessels, which is accurate, fast and does not require human intervention; the true positive rate and true negative rate can reach 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

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