Clustering method for functional magnetic resonance images

Abstract

The invention relates to a clustering method for functional magnetic resonance images, belonging to biological information technologies. The method comprises the following steps of pre-processing an original fMRI image, clustering an affine in regions, acquiring a new image and determining a corresponding bias parameter, acquiring clustering results corresponding to all bias parameters, determining an optimal clustering result and acquiring a finally clustered fMRI image. Because the hierarchical clustering, the affine clustering and the self-adapting affine clustering are organically combined, and the images are comprehensively processed, the invention overcomes the defects that the conventional clustering methods are affected by the subjective factor of people, heavily dependent on the personal experience and cannot effectively carry out the clustering processing on the images with large data quantity, and effectively solves the problem of objectively selecting the optimal clustering result. Therefore, the invention has the characteristics of having strong ability for processing the images with large data quantity and effectively improving the objectivity in the fMRI image processing procedure as well as the accuracy of selecting the optimal clustering result.

Description

technical field [0001] The invention belongs to the field of pattern and image recognition in biological information technology, and in particular relates to a functional magnetic resonance imaging (functional magnetic resonance imaging, fMRI) image post-processing technology. Background technique [0002] At present, functional brain imaging technology has been widely used, and functional magnetic resonance imaging (fMRI) is developed on the basis of magnetic resonance imaging (MRI). Imaging techniques measure many physiological and biophysical parameters and are noninvasive means of detecting and imaging brain functional activity. The imaging methods mainly include two types: model-driven analysis method and data-driven analysis method. in: [0003] Model-driven analysis method: This method relies on the experimental mode (type) when processing fMRI images. Now, the statistical method proposed by Friston et al. based on the General Linear Model (GLM) is more widely used....

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Problems solved by technology

[0006] The purpose of the present invention is to research and design a clustering method of functional magnetic resonance images, to overcome the difficulty of hierarchical clustering in the selection of merging or splitting points in fMRI image processing, the large dependence on personal experience, and the adaptive affine Clustering is unable to process images with a large amount of data, so as to effectively improve the objectivity of fMRI image processing and the ability to process images with large amounts of data.

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