Thalamus function partitioning method based on subspace feature learning

A subspace feature and functional partitioning technology, applied in the field of digital images, can solve the problems of high dimension of connection feature data and the influence of noise.

A subspace feature and functional partitioning technology, applied in the field of digital images, can solve the problems of high dimension of connection feature data and the influence of noise.

CN110599461AActive Publication Date: 2019-12-20SOUTHEAST UNIV

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  • Thalamus function partitioning method based on subspace feature learning
  • Thalamus function partitioning method based on subspace feature learning
  • Thalamus function partitioning method based on subspace feature learning

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Embodiment Construction

[0065] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0066] A kind of thalamic functional partitioning method based on subspace feature learning of the present invention, such as figure 1 As shown, first use diffusion tensor imaging to perform fiber tracking to obtain the internal structural connection information of the living brain; secondly, for complex nonlinear thalamocortical structural connection features, the present invention uses the hidden subspace mapping (latent representation) of deep subspace network learning features ); Finally, spatial constraints are placed on the voxel features to reduce the impact of noise, better reflect the spatial topology, enrich the extraction of spatial information, and facilitate the effective clustering of thalamic voxels. details as follows:

[0067] Step 1, preprocessing the raw data of the diffusion tensor image, extracting image ...

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Abstract

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 reduced, and the topological structure of voxel space can be better reflected, and extraction of space information is enriched, and thalamus function partitions can be efficiently obtained.

Description

technical field [0001] The invention belongs to the field of digital images, in particular to a thalamus function partition method based on subspace feature learning. Background technique [0002] In recent years, medical imaging techniques have provided new avenues for the non-invasive construction of thalamic functional partitions. Diffusion tensor imaging is based on measuring the diffusion properties of water molecules in brain tissue to obtain tissue information, and can reconstruct fiber bundles to characterize the structural connections between different regions. Diffusion refers to the random and irregular movement of molecules, which can be divided into isotropy and anisotropy based on the degree of diffusion of molecules in a specific direction in space. Isotropy means that the chances of molecules moving in all directions are equal. For example, in pure water, the dispersion of water molecules is isotropic and the diffusion of water molecules in brain gray matter...

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Application Information

Patent Timeline
20 Dec 2019
Publication
CN110599461A
IPC
G06T7/00; G06K9/62; G06K9/46
CPC
G06T7/0012; G06T2207/10012; G06T2207/20081; G06T2207/30016; G06V10/40; G06F18/23; G06F18/214
Inventors
孔佑勇; 高和仁