Alzheimer's disease auxiliary diagnosis device and method based on brain network multi-frequency fusion image core

Abstract

The invention provides an auxiliary diagnosis device and a method for Alzheimer's disease of a brain network multi-frequency fusion map core, which relates to the technical field of computer-aided diagnosis. The device comprises an image preprocessing module, an image frequency dividing module, a graph kernel generation module, a graph kernel fusion module and an auxiliary diagnosis module. The image dividing module matches the fMRI image with the AAL template and divides the fMRI image. The image kernel generation module constructs a multi-frequency brain network for the divided image and forms a matrix. The graph kernel fusion module fuses all the graph kernels into one graph kernel. The auxiliary diagnostic module combines the fused graph kernel with the kernel limit learning machine torealize the diagnosis of Alzheimer's disease. The invention also provides a method for diagnosing by using the device. The brain network multi-frequency fusion image core auxiliary diagnosis device and method for Alzheimer's disease provided by the invention can sufficiently express the difference of brain activity information under multi-frequency bands, so that the signal information of the functional nuclear magnetic resonance image can be fully exerted.

Description

technical field [0001] The present invention relates to the technical field of computer-aided diagnosis, in particular to a device and method for assisting the diagnosis of Alzheimer's disease with brain network multi-frequency fusion map kernel. Background technique [0002] In recent years, neuroimaging technology, especially functional imaging, has made rapid progress, which provides corresponding techniques for studying the functions of various brain regions in patients with Alzheimer's disease. As one of the mature functional imaging detection technologies, functional magnetic resonance imaging has the advantage of non-invasive monitoring of brain function and activity with high spatial and temporal resolution. Measuring correlations between various brain regions using a blood oxygen level-dependent approach has proven to be a powerful tool for studying the functional organization of the brain. In the resting state functional Magnetic Resonance Imaging (fMRI), it can b...

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Problems solved by technology

[0003] In fMRI data, signals in different frequency bands contain brain activity information in different frequency bands, while the brain networks in existing studies are constructed based on time-domain signals, ignoring the difference information in each frequency band in the frequency domain. The obtained graph kernel also cannot more accurately describe the frequency domain information of the brain

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