Level Set Image Segmentation Method and System Based on Region Information and Edge Information

Abstract

The invention discloses a level set image segmentation method and a system thereof based on regional information and edge information. The level set image segmentation method comprises the steps of: constructing an energy functional and initializing a level set function according to the regional information and edge information of an image, so as to obtain an initial level set function; adopting a gradient descent flow method to obtain a level set function evolution equation; and carrying out level set function evolution processing at a coarse segmentation phase and a smooth phase based on the energy functional, and carrying out rough segmentation and accurate positioning of the image to obtain the segmented image. The system comprises an initialization module, a level set function evolution module, a color variance calculation module and a segmented result output module. The level set image segmentation method and the system thereof reduce the number of iterations of the level set function, and improve the segmentation and positioning ability of the level set on a color image.

Description

technical field [0001] The present invention relates to a technology in the field of image processing, in particular to a level set image segmentation method and system based on region information and edge information. Background technique [0002] The level set method was proposed by Osher and Sethian, and its basic idea is to use the zero level set of high-dimensional functions to represent the contour. Due to its free topology, it has been widely used in image segmentation in recent years. There are two main types of level set energy models, region-based models and edge-based models. Region-based models rely on region detection operators to drive the evolution of level set functions, such as the C‐V model proposed by Chan and Vese, which uses color variance as a region detection operator, while adding contour length and area constraints, but has no edges Detection items, the positioning of edges is not precise enough during the evolution process. [0003] The edge dete...

AI Technical Summary

Problems solved by technology

However, this technology lacks the self-regulation term of the level set function, and singularities are prone to appear during the evolution process, and the edge information is not fully utilized. The initialization of the level set function is not flexible enough, resulting in limited application range, and it is not easy to extend to other types of images. segmentation

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