Three-dimensional lung vessel image segmentation method based on geometric deformation model

Abstract

The invention provides a three-dimensional lung vessel image segmentation method based on a geometric deformation model. The method comprises the following steps: (1) determining vessel segmentation computing regions according to the physiological structure characteristics of a human body, wherein region selection completely covers targets to be segmented and the shape characteristics of the regions are stable, thereby avoiding computing a global region and improving segmentation speed; (2) computing the mean value of the vessel regions and positioning internal and external homogeneous regions of the targets; (3) computing vessel edge energy and evolving a curved surface along second derivatives in an image gradient direction so that the curved surface is accurately converged to a target edge; (4) correspondingly establishing a three-dimensional vessel segmentation curved surface evolution model and effectively combining the mean value and edge energy of the internal and external regions of the lung vessels; and (5) adopting optimized level set evolution for obtaining solution according to the established deformation model and impliedly solving a curved surface motion according to the level set function curved surface evolution. A large quantity of lung CT image experiments proof that the method provided by the invention has the advantages of rapid and accurate lung vessel segmentation and strong robustness.

Description

technical field [0001] The invention belongs to the intersection field of digital image processing and medical imaging technology, and particularly relates to a three-dimensional pulmonary blood vessel image segmentation method based on a geometric deformation model. Background technique [0002] Because CT equipment can provide high-definition images and provide high contrast for various human tissues in the images, it is usually used in the diagnosis of lung diseases. Pulmonary blood vessels are one of the most important tissues and organs with the most complex topological structure in the human body. The precise segmentation of pulmonary vessels is the key to the detection of lung cancer lesions. This is because the pulmonary vessels are mostly distributed near the lung cancer lesions in CT images, and the gray values ​​of the two are similar, which is an important interference in the detection of lung cancer lesions. In addition, pulmonary vessel segmentation is also of...

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Problems solved by technology

[0007] In view of the problems that the speed and accuracy of the pulmonary vessel segmentation method used in the prior art cannot meet the application requirements, the te

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