A semi-automatic sequential image segmentation method and system

Abstract

The invention discloses a semi-automatic medical sequence image segmentation method and system. The segmentation method includes the steps of: selecting an initial slice image containing a target to be segmented in the medical sequence image; generating the initial slice image according to user instructions. Target area: using the target area as the initial input image of the adjacent slice image, using the level set algorithm to iteratively segment the subsequent slice image until the subsequent slice image does not include the target to be segmented, and then obtain the segmentation result of the target area to be segmented . Thereby, the present invention can quickly and accurately segment the target region to be segmented in the medical sequence images.

Description

technical field [0001] The invention relates to the field of medical image segmentation, in particular to a semi-automatic sequential image segmentation method and system. Background technique [0002] In the medical field, 3D medical image segmentation is used to segment the region of interest or lesion in the 3D medical image, to observe and analyze the shape, characteristics and other pathological conditions of the region of interest or lesion, and to conduct 3D medical Image reconstruction and fusion, etc. Generally speaking, most medical image segmentation methods are based on CT (Computed Tomography, computerized tomography) sequence images or MRI (Magnetic Resonance Imaging, magnetic resonance imaging) sequence images. The current medical image segmentation methods mainly include : [0003] The basic feature of the threshold-based medical image segmentation method is to determine one or a series of image gray thresholds through artificial self-adaptive methods, and ...

AI Technical Summary

Problems solved by technology

Its disadvantages are: it is difficult to determine the precise threshold value; when the gray levels of multiple regions are roughly the same, the characteristics of different regions cannot be accurately distinguished

Its disadvantages are: it can only segment a single region or multi-regions without connectivity; in the case of uneven grayscale and large differences in the image, it will cause inaccurate image segmentation and extraction

Its disadvantages are: the energy characteristics are easy to fall into local minimization, and when the shape of the region of interest in the medical sequence image is separated or merged, it cannot be accurately tracked, segmented and extracted

[0007] However, the current medical image segmentation methods all include the inaccurate results of automatic segmentation algorithms; the shortcomings of manual segmentation speed is too slow

[0008] In summary, the existing medical image segmentation methods obviously have inconvenience and defects in actual use, so it is necessary to improve them

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