Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI
- 1 January 2012
- journal article
- Published by Elsevier BV in Medical Image Analysis
- Vol. 16 (1), 177-188
- https://doi.org/10.1016/j.media.2011.07.001
Abstract
No abstract availableKeywords
This publication has 33 references indexed in Scilit:
- Conventional and Diffusion-Weighted Magnetic Resonance Imaging Findings in a Pediatric Patient with a Posterior Fossa Brain Tumor and PapilledemaPediatric Neurosurgery, 2009
- Classification of brain tumor type and grade using MRI texture and shape in a machine learning schemeMagnetic Resonance in Medicine, 2009
- Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random FieldComputerized Medical Imaging and Graphics, 2009
- A brain tumor segmentation framework based on outlier detection*1Medical Image Analysis, 2004
- Prognostic factors of CNS tumours in Neurofibromatosis 1 (NF1): A retrospective study of 104 patientsBrain, 2002
- Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantizationIEEE Transactions on Medical Imaging, 1999
- Nonlinear spatial normalization using basis functionsHuman Brain Mapping, 1998
- Quantitative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18Cerebral Cortex, 1996
- Resection of Intraventricular Tumors via a Computer-assisted Volumetric Stereotactic ApproachNeurosurgery, 1993
- Thirteen Ways to Look at the Correlation CoefficientThe American Statistician, 1988