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...
Saved in:
Main Authors | , , , , |
---|---|
Format | Patent |
Language | Chinese English |
Published |
20.12.2019
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |