CT image pulmonary parenchyma template weasand elimination method based on breadth-first search

Abstract

The invention provides a CT image pulmonary parenchyma template weasand elimination method based on breadth-first search. The method comprises the steps that (1) traversal starts from traversal pixels in a pulmonary parenchyma template; (2) whether the traversal is completed or not is determined, and if so, the step (8) is carried out, otherwise the step (3) is carried out; (3) a next traversal pixel is acquired; (4) whether the pixel is traversed is determined, and if so, the step (2) is carried out, otherwise the step (5) is carried out; (5) whether the pixel value is 1 is determined, and if so, the step (6) is carried out, otherwise the step (7) is carried out; (6) in a communication area with the breadth-first search pixel value of 1, the step (2) is carried out; (7) the point is marked as traversed, and then the step (2) is carried out; (8) whether the number of communication areas is three is determined, and if so, the step (9) is carried out, otherwise a final pulmonary parenchyma template is acquired; and (9) the pixel value of all pixels in the smallest communication area is set to 0 to acquire the final pulmonary parenchyma template.

Description

technical field [0001] The invention relates to a method for eliminating the trachea area of ​​a lung parenchyma template in a CT image of a lung based on breadth-first search. Background technique [0002] With the development and maturity of medical imaging technology, medical imaging plays an important role in the diagnosis of lung diseases. CT uses precisely collimated X-ray beams, γ-rays, ultrasound, etc., together with highly sensitive detectors, to perform cross-sectional scanning around a certain part of the human body one by one. It is already an important means of lung disease inspection. In computer-aided detection, it is necessary to perform feature extraction, analysis, and recognition on lung CT images. In addition to lung parenchyma, there are some other organs and tissues in ordinary lung CT images, as well as the background of CT images. The feature extraction of a CT image will affect the accuracy of recognition. Therefore, it is necessary to segment the l...

AI Technical Summary

Problems solved by technology

In computer-aided detection, it is necessary to perform feature extraction, analysis, and recognition on lung CT images. In addition to lung parenchyma, there are some other organs and tissues in ordinary lung CT images, as well as the background of CT images. The feature extraction of a CT image will affect the accuracy of recognition. Therefore, it is necessary to segment the lung parenchyma on the CT image of the lungs.

In the existing methods, the lung parenchyma segmentation template is often obtained by iterative threshold method, region growing method, etc., and the lung parenchyma image is obtained by masking the original lung CT image through the template, and the obtained lung parenchyma segmentation template often has The trachea region, so that the extracted lung parenchyma will include the trachea region. If the image is expanded, it may cause the connection between the lung parenchyma region and the trachea region, causing interference. regions, and computer-aided diagnosis will also have varying degrees of interference

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