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Automatic segmentation of magnetic resonance images using a decision tree with spatial information
Wen Hung Chao,
You Yin Chen
*
, Sheng Huang Lin, Yen Yu I. Shih, Siny Tsang
*
此作品的通信作者
生物醫學工程學系
研究成果
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引文 斯高帕斯（Scopus）
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Keyphrases
Decision Tree
100%
Spatial Data
100%
Magnetic Resonance Imaging
100%
Automatic Segmentation
100%
Gray Level
75%
Accuracy Rate
37%
Phantom
37%
Noise Level
37%
Brain Magnetic Resonance Imaging
37%
Gray Matter
25%
Decision Tree Algorithm
25%
Detection Method
12%
Low Noise
12%
Brain Tissue
12%
White Matter
12%
Segmentation Method
12%
Noise Variation
12%
Polar Coordinates
12%
Coordinate System
12%
Wavelet Transform
12%
Binary Tree
12%
Boundary Detection
12%
Neuroimaging
12%
Two-coordinate
12%
Anatomical Structure
12%
Phantom Image
12%
Hausdorff Distance
12%
Automated Segmentation Methods
12%
Two-dimensional Wavelet Transform
12%
BrainWeb
12%
Automatic Decision
12%
Inter-observer Differences
12%
Computer Science
Spatial Information
100%
Automatic Segmentation
100%
Decision Tree
100%
Wavelet Transforms
25%
Average Accuracy
25%
Segmentation Method
25%
Decision Tree Algorithm
25%
Polar Coordinate
12%
Boundary Detection
12%
Binary Tree
12%
segmented image
12%
Hausdorff Distance
12%
Engineering
Spatial Information
100%
Grey Level
75%
Noise Level
37%
Obtained Image
25%
Based Segmentation Method
25%
Nodes
12%
Two Dimensional
12%
Brain Tissue
12%
Input Feature
12%
Polar Coordinate
12%
Boundary Detection
12%
Binary Tree
12%
Earth and Planetary Sciences
Magnetism
100%
Wavelet Analysis
25%
Low Noise
12%
Polar Coordinate
12%
Neuroscience
Magnetic Resonance Imaging
100%
Gray Matter
25%
Agricultural and Biological Sciences
Gray Matter
100%