A Method for Obtaining Terminal Nodes and Branch Nodes of Blood Vessel Segments

Abstract

The invention belongs to the field of medical image processing technology, and particularly relates to a method for obtaining terminal nodes and branch nodes of blood vessel segments. The method includes: analyzing the neighborhood relationship of skeleton points to obtain a skeleton relationship set; reducing pseudo-skeleton branches; and analyzing the skeleton branches. point, according to the connectivity relationship of these skeleton branch nodes, when the branch node has an adjacent relationship, a branch aggregation is formed; the skeleton relationship is reconstructed through the branch node and the connecting edge, and the above steps are repeated until the number of connecting neighborhoods of the branch node is greater than 3 or the branch The number of nodes is no longer reduced; the present invention better constructs the correlation between skeleton branch nodes and other skeleton points, providing a basis for measuring the length of blood vessel segments and the radius of blood vessels.

Description

[0001] technology neighborhood [0002] The invention belongs to the technical field of medical image processing, in particular to a method for acquiring terminal nodes and branch nodes of blood vessel segments. Background technique [0003] In recent years, with the rapid development and popularization of new imaging technologies and equipment such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), the world Massive research images are generated every day in various places, which makes the use of research images for organ, tissue and blood vessel analysis one of the current research hotspots. The acquisition of blood vessels has important guiding significance for the analysis of individual blood vessels. Through automatic detection of nodules in images, the correctly segmented blood vessel structure can be used to resolve the ambiguity of the lung tissue structure in the same area. [0004] In low-dose imaging, in addition ...

AI Technical Summary

Problems solved by technology

However, due to the multi-level branch structure of blood vessels, the vascular tree structure segmented by the usual segmentation methods will mostly lose the branches of many small blood vessels. In low-dose images, the contrast between blood vessels and other lung tissues is usually low; The influence of noise will cause the segmented blood vessels to have a large number of broken or lost vascular branches that should be connected

Therefore, such a segmentation method is difficult to obtain a complete vascular tree structure, which makes it lack of quantification ability and cannot provide parameter information of specific vascular length and corresponding diameter.

The fundamental reason is that there is a lack of basic means for processing and analyzing segmented blood vessels, and most methods only provide qualitative image display. For specific blood vessel cases, there is a lack of accurate skeleton analysis methods, and due to the discrete nature of the blood vessel skeleton itself, it is difficult to accurately Determine the exact direction of blood vessels, so it is difficult to get better analysis and segmentation results

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