Image splitting method based on level set relay

Abstract

The invention discloses a sequential level set-based method for automatically segmenting images, which comprises the steps: (1) inputting an image I to be segmented and automatically initializing a level set function in an image domain by using a morphological method; (2) evolving the level set function by using an iterative approach in a corresponding subdomain to obtain the level set function and a detected image border, and using the inside domain of the edge as a next subdomain; (3) converting an image edge detected in the subdomain into a binary image and storing the binary image; (4) judging whether the area of the next subdomain is zero, and executing a step (5) if the area of the next subdomain is zero, or returning to the step (1) if the area of the next subdomain is not zero; and (5) performing a 'combination' operation of the stored binary image, combining the image edges of all subdomains, and completing the image segmentation. The method has the advantages of detecting more image edges and automatically completing image segmentation, and can be used for segmenting natural images and medical images.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a level set (Level Set Method) image segmentation method, which can be used for the segmentation of medical images and natural images. Background technique [0002] Images are obtained by observing the objective world in different forms and means through various observation systems, and can directly or indirectly act on the human eye to produce visual entities. About 75% of the information that humans obtain from the outside world is obtained from images, which not only shows the huge amount of image information, but also shows that humans have a high utilization rate of image information. With the development of signal processing theory and computer science and technology, image engineering has also become a subject with rich content and rapid development. As the basis of image engineering, image segmentation is a key step from image processing to image analysis, and it i...

AI Technical Summary

Problems solved by technology

[0007] (1) It is necessary to manually initialize the evolution curve, that is, manually place a closed curve on the image to be segmented, so as to initialize the level set function, and this process cannot be completed automatically;

[0008] (2) Guide the evolution curve to stop on the edge of the image by defining an edge indicator function to achieve the purpose of segmenting the image, but this indicator function can only make the curve stay on the edge closest to the initial evolution curve, thus Image edges that are far away from the initial evolution curve are missed;

[0011] (2) Using the edge indicator function based on the gray gradient of the image, the movement direction of the zero level set curve is shown as contraction or expansion, so that the method can only detect the edge closest to the initial evolution curve

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