Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization.
- 2011-10-19
Published in:
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. - 2011
Algorithms
Artificial Intelligence
Brain Neoplasms
Diagnostic Imaging
Glioma
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Markov Chains
Models, Statistical
Pattern Recognition, Automated
Reproducibility of Results
English
Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.
- Language
-
- English
- Open access status
- bronze
- Identifiers
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- DOI 10.1007/978-3-642-23626-6_44
- PMID 22003719
- Persistent URL
- https://sonar.ch/global/documents/1341
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