Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features.
Daniel AndreasenJens M. EdmundVasileios ZografosBjoern H. MenzeKoen Van LeemputPublished in: Medical Imaging: Image Processing (2016)
Keyphrases
- magnetic resonance images
- computed tomography
- random forests
- medical images
- medical imaging
- ct scans
- ct images
- magnetic resonance imaging
- random forest
- image reconstruction
- mr images
- ensemble methods
- decision trees
- three dimensional
- ct data
- anatomical structures
- x ray
- image features
- computer vision
- brain tissue
- manual segmentation
- white matter
- feature vectors
- x ray images
- image registration
- feature set
- magnetic resonance
- machine learning algorithms
- logistic regression
- image intensity
- image processing
- feature space
- pattern recognition
- mri data
- prediction accuracy
- benchmark datasets