Automatic segmenting method for lesion tissue in lung CT image

Abstract

The invention provides an automatic segmenting method for lesion tissue in a lung CT image. The automatic segmenting method comprises the steps that the lung parenchyma CT image is searched for the minimum neighborhood gradient value of all points through a toboggan algorithm, the area of the initial growth point of the lung lesion is obtained according to the minimum neighborhood gradient value, and the initial growth seed point is determined according to the area of the initial growth point of the lung lesion; pixel grey level constraints and growth distance constraints are obtained according to the initial growth seed point, a lung lesion area is determined from the pixel grey level constraints and the growth distance constraints through an area growth method; boundaries of all layers of the lung lesion area are obtained according to the lug lesion area, boundary pixel points of adjacent layers in the lung lesion area are obtained in the central points of the boundaries of all the layers of the lung lesion area in the preset direction, the average distance difference value is obtained according to the boundary pixel points of the adjacent layers and the central point of the lung lesion area, and the pixel points exceeding the average distance difference value are horizontally slide to obtain segmentation images of the lung lesion tissue.

Description

technical field [0001] The invention relates to image segmentation technology, in particular to an automatic segmentation method for lung CT image lesion tissue. Background technique [0002] The automatic segmentation of lung CT image lesions is an important issue in the field of computer-aided diagnosis. It can automatically acquire lung lesions and shorten the waiting time for clinical diagnosis. It has important application value in the field of computer-aided diagnosis. [0003] Lung CT imaging lesions can be classified into isolated types, vascular adhesion types, and pleural adhesion types according to the location of the lesions; according to the types of lesions, they can be divided into ground-glass opacities, pulmonary nodules, and lung tumors. Among them, the ground-glass opacity type is the most difficult to segment in the lung lesions. Existing segmentation algorithms for lung lesions based on region growth all require manual acquisition of initial growth seed...

AI Technical Summary

Problems solved by technology

The existing segmentation algorithms for lung lesion tissue based on region growth all need to manually obtain the initial growth seed point, resulting in inaccuracy

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