Identifying the most suitable histogram normalization technique for machine learning based segmentation of multispectral brain MRI data.
Andrea KobleÁgnes GyorfiSzabolcs CsaholcziBéla SurányiLehel Dénes-FazakasLevente KovácsLászló SzilágyiPublished in: AFRICON (2021)
Keyphrases
- multispectral
- mri data
- mri scans
- fully automatic
- machine learning
- brain tissue
- mr images
- magnetic resonance imaging
- magnetic resonance images
- medical imaging
- partial volume
- brain mr images
- image data
- remote sensing
- mri images
- intensity distribution
- multispectral images
- accurate segmentation
- remote sensing images
- image analysis
- white matter
- brain tumors
- dce mri
- dt mri
- brain structures
- model based segmentation
- medical images
- mr imaging
- synthetic data
- hyperspectral
- magnetic resonance
- brain mri
- corpus callosum
- left ventricular
- human brain
- semi automatic
- brain imaging
- image segmentation
- contrast enhanced
- cortical thickness
- shape prior
- brain images
- region growing
- gray level
- segmentation algorithm
- level set
- diffusion tensor images
- graph cuts
- multiscale
- image processing
- computer vision