Individualized brain covariant network construction method based on three-dimensional textural features

Abstract

The invention relates to an individualized brain covariant network construction method based on three-dimensional textural features, which comprises the following steps of: 1) segmenting a brain structure image into brain tissue component concentration graphs by using tissue segmentation, and registering the brain tissue component concentration graphs to a standard space template to obtain a standardized brain structure image; 2) extracting three-dimensional texture features corresponding to the standardized brain structure image at a voxel level through at least two gray feature extraction modes, and obtaining a spatial distribution diagram of each texture feature; and 3) defining a brain region map as a network node, extracting the texture feature of each brain region of the individual subject from the grayscale matrix texture feature data set, calculating the Pearson's correlation of the texture feature vectors of any two brain regions, and constructing a covariant matrix of the texture features between the brain regions. According to the method, brain image data of an individual subject can be utilized, the similarity of brain region texture feature vectors serves as measurement of a brain network edge, and then a brain covariant network of the individual subject is constructed.

Description

technical field [0001] The present invention designs an individualized brain covariation network construction method based on three-dimensional texture features. Background technique [0002] With the rapid development of computer science and medical imaging technology, new imaging techniques and brain image analysis methods emerge in an endless stream. Among them, magnetic resonance imaging (magnetic resonance imaging, MRI) can reflect the physiological state of the structure and function of the internal tissue (such as the cerebral cortex) of the human body in a non-invasive way, and the complex network analysis based on graph theory can be used to evaluate the cortical structure of the brain region. Covariation relations provide an efficient method. Therefore, the combination of the above two methods has gradually become an important tool in medical research and clinical diagnosis and treatment. [0003] The traditional brain structure covariation network is mainly meas...

AI Technical Summary

Problems solved by technology

However, the above research extracts a limited number of texture features and cannot fully describe the information of the image, which has certain limitations.

Images