Migratory active contour model based stomach CT (computerized tomography) sequence image segmentation method

Abstract

The invention discloses a migratory active contour model based stomach CT (computerized tomography) sequence image segmentation method, which mainly overcomes the shortcomings that the CT sequence image segmentation speed is slow, and an edge leakage phenomenon is easy to occur in the prior art. The method is implemented through the steps of firstly, manually drawing a line so as to make the initial contour of a target area to be segmented of a first image, and carrying out segmentation by using an area-edge combined active contour model so as to obtain a target contour of the current image; then, repeatedly migrating the target contour of the image subjected to segmentation to the adjacent next image, and taking the target contour of the image as the initial contour of the adjacent next image, and then carrying out segmentation by using a GLCV model until images in the whole sequence are completely segmented. Compared with the traditional active contour model, the method disclosed by the invention has the advantages of rapid speed and good effect and the like, and can be applied to the segmentation of stomach CT sequence images; and by using the method, possibly occurring target areas of gastric lymph nodes can be segmented better.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the processing of medical images, in particular to the segmentation of gastric CT sequence images. Background technique [0002] With the rapid development of medical imaging technology, a large number of high-resolution images have emerged, such as magnetic resonance imaging MRI, computed tomography CT, magnetoencephalography MEG, three-dimensional ultrasound imaging, solution positron emission tomography PET, single photon emission computed tomography SPECT, diffusion weighted imaging DWI, functional magnetic resonance FMRI, etc., these imaging techniques have their own characteristics, and they can provide people with various anatomical and functional information at different temporal and spatial resolutions. However, relying solely on the information provided by these devices is far from meeting people's requirements, and the image must be further analyzed and interpre...

AI Technical Summary

Problems solved by technology

However, if the target to be segmented is the lymph node area 5 cm around the gastric wall in the CT sequence of the stomach, the segmentation effect of this method is poor.

Because the shape of these target areas is irregular, the change is relatively large, and there are noises, blood vessels and other interference points in the target area, and the existing active contour model method has less consideration for the local consistency of the target area and the complex topological changes of its edges, so the segmentation This kind of target area requires many iterations, resulting in slow segmentation and easy to cause edge leakage

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