Unsupervised image segmentation method based on Chan-Vse model

Abstract

The invention provides an unsupervised image segmentation method based on a Chan-Vese model, and belongs to the technical field of computer application and the field of precision medical treatment. According to the method, firstly, an original image is cut, super-pixel segmentation is carried out on a cut image, then, an undirected graph is established according to a super-pixel segmentation result, an energy item of a ChanVese model is expressed by the weight of an edge, and by using a Markov chain, the relationship between the distance between super-pixels and the gray scale can be considered simultaneously in the edge assignment process. And finally, a corresponding segmentation result can be obtained by repeatedly using the maximum flow segmentation and updating the weight of the edge.According to the method, the image can be automatically and accurately segmented in the medical field lacking annotation data, so that accurate information of a segmented object is provided for subsequent medical research. According to the method, the required target can be accurately segmented in a shorter time and with less manual intervention.

Description

technical field [0001] The invention belongs to the field of computer application technology and the field of image segmentation, in particular to an unsupervised image segmentation method based on a Chan-Vese model. Background technique [0002] Image segmentation is a very important subtask in the field of computational vision, and is widely used in areas such as autonomous driving, face recognition, and image recognition. Nowadays, with the proposal and rapid development of precision medicine, how to quickly and accurately obtain the segmentation results of medical targets such as tumors is very important. Accurate medical segmentation results play a vital role in precision medicine, and it is usually applied in pre-operative planning, intra-operative guidance, and post-operative evaluation. Generally speaking, an accurate segmentation should divide the image into multiple regions, where each region has a uniform color (or texture), and the boundaries between each other ...

AI Technical Summary

Problems solved by technology

However, none of these methods can well balance the relationship between the distance between pixels and the gray level, and are not suitable for the segmentation of targets with uneven intensity. In addition, they use gradient descent flow to iteratively solve, which takes a long time and cannot satisfy a large amount of data. the division

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