Automatic segmentation method for fuzzy spectral clustering brain tumor images based on super pixel

Abstract

The invention comprises invention discloses an automatic segmentation method for fuzzy spectral clustering brain tumor images based on super pixel, comprising the following steps of : firstly, performing super pixel segmentation on a FLAIR mode image of magnetic resonance imaging containing brain tumors, and extracting gray histogram features of super pixel blocks as input of an algorithm, calculating a fuzzy similarity matrix of images through the input features; then performing clustering through NJW spectral clustering algorithm to obtain a final segmentation result. According to the automatic segmentation method for fuzzy spectral clustering brain tumor images based on super pixel, fuzzy theory is used to optimize similarity measurement mode of spectral clustering, fuzzy weight parameters are introduced in Gaussian distance measurement method of spectral clustering, and a fuzzy similarity measurement mode based on super pixel features is defined. The invention is an automatic imagesegmentation method, human intervention is not needed, and time complexity of spectral clustering algorithm is greatly reduced and segmentation accuracy can be improved by utilizing fuzzy spectral clustering algorithm based on super pixel.

Description

technical field [0001] The invention relates to the fields of image processing, pattern recognition and machine learning, in particular to a brain tumor image segmentation method based on fuzzy clustering. Background technique [0002] Brain tumor is a disease with a high incidence rate in modern times. This disease will oppress the normal brain tissue of the human body, which has a relatively large impact on the health of the patient and may even lead to death. At present, the incidence of brain tumors accounts for 1.5% of all tumors in the whole body. In recent years, with the wide application of imaging technologies such as magnetic resonance imaging and CT, it provides a basis for reliable diagnosis of doctors. Because this type of technology is relatively simple, fast and effective, and has high accuracy, the detection rate of brain tumors has been greatly improved. At present, the treatment plan for brain tumors is basically surgery, supplemented by radiotherapy and c...

AI Technical Summary

Problems solved by technology

Among them, manual segmentation requires doctors to have rich clinical experience, and the disadvantages are obvious. For example, the segmentation process is very time-consuming, and the segmentation results of different experts are different, even if the same expert has different results in different time periods; semi-automatic segmentation is often The complete segmentation process can be realized by means of "human-computer interaction" such as initialization operation or manual correction of segmentation results. At present, this semi-automatic segmentation method is widely used in clinical applications; automatic segmentation usually combines human intelligence and prior knowledge, using software Computing and other technologies realize intelligent segmentation without human intervention. However, due to the diversity of brain tumors in different patients and the characteristics of easy confusion with normal tissues in most cases, the accuracy of automatic segmentation has always been a challenge.

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