Skip to main navigation
Skip to search
Skip to main content
Korea University Pure Home
Home
Profiles
Research units
Equipment
Research output
Press/Media
Search by expertise, name or affiliation
Non-local atlas-guided multi-channel forest learning for human brain labeling
Guangkai Ma
, Yaozong Gao
, Guorong Wu
, Ligang Wu
, Dinggang Shen
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
1
Citation (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Non-local atlas-guided multi-channel forest learning for human brain labeling'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Multi-channel
100%
Human Brain
100%
Label Fusion
100%
Brain Labeling
100%
Context Features
66%
Learning-based
66%
Fusion Method
66%
Random Forest
66%
Target Image
66%
Brain MR Image
33%
State-of-the-art Techniques
33%
Computer Vision
33%
Complex Scenes
33%
Anatomical Structure
33%
Target Labels
33%
Multi-atlas
33%
Brain Research
33%
Inter-subject Variation
33%
Appearance Information
33%
Appearance Features
33%
Nonlinear Relationship
33%
Hybrid Features
33%
High-Level Context
33%
Computer Science
Random Decision Forest
100%
Existing Label
50%
Brain Research
50%
Computer Vision
50%