Resting brain function connected region detecting method based on affine clustering

A connected area and detection method technology, applied in the field of resting brain functional connected area detection based on affine clustering, can solve problems such as limiting the detection of functional connected areas, and achieve the advantages of shortening execution time, accurate positioning detection, and enhancing detection intensity. Effect

Inactive Publication Date: 2013-07-24
SHANGHAI MARITIME UNIVERSITY
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Although the data processing and analysis methods in the prior art can complete the detection of functional areas to a certain extent, they all have many deficiencies and defects. The number of limitations; independent component analysis is completely subject to the independent assumption of strong functional region source signals, which limits the detection of functionally connected regions

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Resting brain function connected region detecting method based on affine clustering
  • Resting brain function connected region detecting method based on affine clustering
  • Resting brain function connected region detecting method based on affine clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be further elaborated below by describing a preferred specific embodiment in detail in conjunction with the accompanying drawings.

[0036] like figure 1 As shown, a method for detecting functionally connected regions of the resting brain based on affine clustering, the detection method includes the following steps;

[0037] Step 1, obtaining a sparse approximate data set of a single fMRI signal;

[0038] Step 1.1, perform three-layer one-dimensional wavelet packet decomposition on each time point data of fMRI data, and obtain the corresponding wavelet tree of each time point data, wherein the wavelet base used for wavelet packet decomposition is (Daubechied, abbreviated as db ) family of db2 wavelet bases;

[0039] Step 1.2. Use the distance measure norm based on the normed linear space to measure the sparsity of each wavelet tree node, so as to obtain the sparsity quality vector of each node, so that the sparsity quality vector satisfies t...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a resting brain function connected region detecting method based on affine clustering. The detecting method includes the following steps: firstly, a sparse approximate data set of a functional magnetic resonance signal is obtained; secondly, multiple sparse approximate data sets of the functional magnetic resonance signals are obtained and an average is worked out to form an average sparse approximate data set; thirdly, affine clustering analyzing is performed on the average sparse approximate data set and a clustering central point data set is formed; fourthly, source signal reconstruction is performed on the average sparse approximate data set and a component image and a corresponding time process of a clustering central point are generated; fifthly, a brain function connected region is positioned. Sparse approximation is performed on the functional magnetic resonance blended signals so that detection strength of the signals is enhanced, massive functional magnetic resonance data are effectively compressed, an executing time of the whole operation process is shortened, the brain function connected region is accurately positioned and detected.

Description

technical field [0001] The invention relates to a method for detecting functionally connected regions of resting brain, in particular to a method for detecting functionally connected regions of resting brain based on affine clustering. Background technique [0002] Functional magnetic resonance imaging is a new technology developed in the 1990s, and it has been widely used as the preferred method of brain functional imaging. This technology is mainly based on the sensitivity of blood flow and the principle of blood oxygen level-dependent contrast enhancement for imaging. It combines functional, imaging and anatomical information, and is an effective method for locating functional areas in living human brains. At the same time, it provides a strong technical support for the detection of functional connectivity areas of the human brain, the study of neurocognition, and the prevention and diagnosis of brain diseases. In order to use functional magnetic resonance imaging to stu...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055
Inventor 任天龙曾卫明王倪传
Owner SHANGHAI MARITIME UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap