Thalamus function partitioning method based on subspace feature learning

The invention discloses a thalamus function partitioning method based on subspace feature learning. The thalamus function partitioning method comprises the following steps: firstly, carrying out fibertracking by using diffusion tensor imaging to obtain internal structure connection information of th...

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Bibliographic Details
Main Authors REN ZHOUFU, KONG YOUYONG, SHU HUAZHONG, GAO HEREN, ZHOU WEIPING
Format Patent
LanguageChinese
English
Published 20.12.2019
Subjects
CALCULATING
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:The invention discloses a thalamus function partitioning method based on subspace feature learning. The thalamus function partitioning method comprises the following steps: firstly, carrying out fibertracking by using diffusion tensor imaging to obtain internal structure connection information of the brain of a living body, and extracting complex nonlinear thalamus cortex features by using fine cortex partitions to form structure connection features; then, using the deep subspace network and the hidden subspace mapping of the added self-expression feature learning features to extract low-dimensional subspace characteristics; and finally, performing spatial constraint on voxel features to reduce the influence of noise, better reflecting a spatial topological structure, enriching the extraction of spatial information, constructing an affinity matrix, and obtaining functional partitions by using a normalized segmentation method. According to the thalamus function partitioning method, theinfluence of noise can be
Bibliography:Application Number: CN201910772126