Image sag repairing method based on double-Graham scanning method

Abstract

The invention relates to an image sag repairing method based on a double-Graham scanning method. The method comprises the following steps that: utilizing a boundary tracing method for a lung mask subjected to preliminary segmentation to solve the boundary point set P of a left lung and a right lung; utilizing the Graham scanning method to obtain the salient point set Q of the left lung and the right lung; solving the lung sag point to be repaired; removing the solved lung sag point to be repaired from the boundary point set P, wherein the obtained residual point set is the edge point set of the pulmonary parenchyma; detecting whether the segmented pulmonary parenchyma contains breakage or not, and if the segmented pulmonary parenchyma contains breakage, carrying out breakage connection; and finally, obtaining an integral segmented pulmonary parenchyma segmentation result. After sag repair and breakage connection are carried out, a final integral pulmonary parenchyma segmentation resultis obtained. The sag on the outer wall of the lung can be repaired, and the method performs a good repairing and smoothening function on adjacent sags including a heart, a mediastinum and the like between two pieces of pulmonary parenchyma and can provide a correct and reliable basis for subsequent pathological study.

Description

technical field [0001] The invention relates to the technical field of CT image processing, in particular to an image depression repair method based on a double Graham scanning method, aiming at the pleural-type pulmonary nodules, blood vessels, main bronchus, etc. The segmented lung fractures were repaired. Background technique [0002] The accuracy of lung parenchyma segmentation results is of great significance in practical clinical applications. However, due to the irregularity of the location, size, and shape of pulmonary nodules, the diversity of lung lesions, and the obvious differences in the anatomical structure of the human chest, various segmentation methods cannot be uniformly applied to all lung CT images. Moreover, due to the existence of pulmonary nodules connected to the pleura, the lung edge depression is often generated in the lung parenchyma segmentation, and at the same time, valuable information is lost. Similarly, when segmenting the blood vessels at ...

AI Technical Summary

Problems solved by technology

The presence of these depressions or breaks renders the segmented lung parenchyma incomplete

If the tumor area and pulmonary blood vessels are not included in the lung parenchyma, it will not be possible to correctly extract or identify tumors, blood vessels, trachea, etc., which will have a great impact on the subsequent detection of pulmonary nodules and analysis of lung lesions

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