Improving mobile MR applications using a cloud-based image segmentation approach with synthetic training data.
Marcel KlomannMichael EnglertKai WeberPaul GrimmYvonne JungPublished in: Web3D (2018)
Keyphrases
- image segmentation
- training data
- brain image segmentation
- data sets
- cloud computing
- magnetic resonance
- image registration
- mobile phone
- test data
- training set
- mobile devices
- decision trees
- deformable models
- supervised learning
- region growing
- graph cuts
- segmentation algorithm
- markov random field
- image analysis
- mobile users
- mobile computing
- mobile networks
- image segmentation algorithm
- mobile learning
- learning algorithm
- active contours
- image data
- computer vision
- probabilistic relaxation
- unsupervised image segmentation
- training instances
- loosely coupled
- boundary detection
- multiscale
- level set method
- gray level
- web browser
- medical images
- computing environments
- level set
- big data
- medical imaging
- context aware
- training samples
- mobile applications
- training process
- classification accuracy
- segmented images
- noisy data
- image processing
- learned from training data
- class labels