Level set image segmentation method based on spacing moment

Abstract

The invention relates to a level set image segmentation method on the basis of a spatial moment in the technical field of image segmentation, which comprises the following steps: firstly, the extraction of the spatial moment is carried out on an original image and local information of the image is obtained. Then the local information of the image obtained is utilized to carry out power model building to each pixel of the original image. Finally, the general information of the image is utilized to build a general power model of all pixels on the basis of the power mode of each pixel; and the level set method is utilized to minimize the power model to carry out image segmentation. The method solves the defect of an LBF active contour model; during the segmentation process, the spatial moment is utilized to introduce the local information of the image; the detailed features of the image are fully utilized, thereby completely solving the problem of initialization dependence; images with uneven gray level are well applied; and the segmentation speed and the accuracy are greatly improved.

Description

technical field [0001] The invention relates to an image segmentation method in the technical field of image processing, in particular to a level set image segmentation method based on image space moments. Background technique [0002] Inhomogeneous gray images exist everywhere in practice, especially in medical images. Due to its uneven gray level, it brings great difficulties to image segmentation. Therefore, it is a tricky and important task to correctly segment the desired target from the uneven gray level image, which is important for medical images. Of particular interest, it is a key step for successful subsequent image understanding, description and analysis. At present, one of the more popular image segmentation research directions is the level set segmentation method based on the region-based geometric active contour model. Because it can change the shape very freely to converge to the contour of the target, it has achieved good applications in the field of image...

AI Technical Summary

Problems solved by technology

In this paper, the acquisition of local information of the image fundamentally uses the global information of the image indirectly, resulting in the correctness of the segmentation results largely depending on the position of the initialization curve, and the segmentation time is also long, which does not fundamentally Solve the problem of using the local information of the image itself to segment images with uneven gray levels

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