Unsupervised image segmentation method based on Chan-Vse model

An image segmentation, unsupervised technology, applied in image analysis, image enhancement, graphic image conversion and other directions, can solve the problem of not being able to balance the relationship between pixel distance and gray level, time-consuming, and unable to meet a large amount of data segmentation, etc. To achieve the effect of reducing processing time, reducing the number, and accurate segmentation results
CN112419344AActive Publication Date: 2021-02-26TSINGHUA UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
TSINGHUA UNIV
Publication Date
2021-02-26

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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.
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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 ...

Claims

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