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Amygdaloid nucleus spectral clustering segmentation method based on resting state function connection

An amygdala and resting state technology, applied in the field of automatic segmentation of amygdala sub-brain regions, can solve problems such as unfavorable promotion, long time-consuming, and low efficiency

Active Publication Date: 2016-10-12
XI AN JIAOTONG UNIV
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Problems solved by technology

On the one hand, such a segmentation method requires researchers to have a high medical anatomical background; on the other hand, it is inefficient and time-consuming, which is not conducive to promotion.

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  • Amygdaloid nucleus spectral clustering segmentation method based on resting state function connection
  • Amygdaloid nucleus spectral clustering segmentation method based on resting state function connection
  • Amygdaloid nucleus spectral clustering segmentation method based on resting state function connection

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Embodiment Construction

[0032] The present invention is described in detail below in conjunction with accompanying drawing.

[0033] The principle of the method for amygdala brain region segmentation based on the resting state functional connectivity of the present invention is as follows: figure 1 shown.

[0034] (1) First, preprocess the original resting-state magnetic resonance data collected. Due to the influence of various noises in the magnetic resonance scanning process, there are differences in the scale and position of the individual itself, so it is very necessary to analyze the data Do some preprocessing on the data before. In the data acquisition of the whole experiment, the main sources of noise information include: (1) physical head movement; (2) difference in scanning time between layers in the image; (3) inhomogeneity of the external magnetic field. Brain function image preprocessing is to use the brain function image and standard templates to perform affine registration transformat...

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Abstract

The invention discloses an amygdaloid nucleus spectral clustering segmentation method based on resting state function connection, and the method is used for carrying out the automatic high-efficiency segmentation of an encephalic region based on a spectral clustering algorithm according to the similarity of internal voxel functions of an amygdaloid nucleus. The method comprises the steps: firstly carrying out the preprocessing of resting state magnetic resonance data; secondly carrying out the extraction of the encephalic region of the amygdaloid nucleus; thirdly carrying out the connection calculation of internal voxel whole-brain functions of the amygdaloid nucleus; and finally carrying out the spectral clustering segmentation of a function connection matrix. The automatic segmentation algorithm proposed by the invention and an amygdaloid nucleus clinic dissection result are enabled to be greatly consistent with each other, and the stability and noise interference resistance are enabled to be more satisfying. Compared with a conventional manual segmentation method, the method is simpler and more convenient and efficient, and is high in repeatability.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an amygdala spectral clustering and segmentation method based on resting-state functional connectivity, in particular to using a spectral clustering algorithm to perform amygdala sub-brain segmentation based on the similarity of the whole-brain connection pattern of each voxel of the amygdala. A method for automatic segmentation of regions. Background technique [0002] fMRI is one of the main non-invasive methods to study brain activity and brain function, with millimeter-level spatial resolution. The proposal and development of the BOLD-fMRI method has made a breakthrough in the study of brain cognitive function, and it has become an important tool for neuroscience to explore the neural mechanism of the human brain. fMRI—generally refers to magnetic resonance imaging based on blood oxygen level-dependent (BOLD), which responds to changes in magnetic resonance signals caused by ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T2207/30016G06T2207/10088G06F18/22
Inventor 林盘窦顺阳王刚王雪丽
Owner XI AN JIAOTONG UNIV
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