Method for specification extraction of magnetic resonance imaging brain active region based on pattern recognition

Abstract

The present invention discloses an arithmetic of picking up magnetic resonance imaging cerebral active regions by sorting based on mode identification, which comprises the steps that cerebral active regions are extracted based on the multi-element mode distance between fine activity modes in partial cerebral regions for the pretreatment of an fMRI image; a partial consistent cerebral region is obtained by clustering; the combined activities of a plurality of tissues inside the partial consistent cerebral region are used for constructing the multi-element mode; the multi-element distance function is constructed by a mode sorting method to measure the separable characters of the partial cerebral region motion under different stimulation conditions, so as to judge whether the cerebral regionis activated or not. The present invention indicates the cerebral motions under different stimulation conditions by multi-element mode information formed by multiple tissues inside the partial cerebral region directly, the multi-element mode can reflect the partial cerebral motion state over all, and multi-element statistical distance can be effectively integrated with the information in the partial cerebral region to measure the difference between different cerebral activate states, so the multi-element mode and the multi-element statistical distance ensure that the arithmetic of the presentinvention can detect the fine cerebral action mode more completely than the traditional fMRI analyzing technology.

Description

technical field [0001] The invention belongs to the technical field of neuroimaging data analysis, in particular to a magnetic resonance imaging fMRI activation area extraction algorithm, and in particular to using a multi-pattern recognition method to perform an fMRI brain activation area. Background technique [0002] Functional Magnetic Resonance Imaging (fMRI) has been widely used in the study of human brain function due to its high spatial and temporal resolution and non-invasive characteristics. fMRI generally refers to fMRI imaging based on blood oxygen level-dependent (BOLD). Activity. With the rapid growth of experimental data in recent years, reasonable and effective fMRI data analysis techniques are becoming more and more important. The core problem of functional data analysis is to find brain regions whose activity can significantly distinguish between different experimental conditions (eg, stimulation conditions and baseline conditions) based on measured fMRI ...

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Problems solved by technology

Therefore, a large amount of fine information will be filtered out, and the high spatial resolution information provided by fMRI is still far from being utilized

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