Multi-kernel support vector machine and Levenberg-Marquardt classification approach for neonatal brain MR images
- 16 February 2017
Abstract
This paper focuses on the development of an accurate neonatal brain MRI segmentation algorithm and its clinical application to characterize normal brain growth and explore the neuro-anatomical correlates of cognitive impairments. The segmentation of MR images of the neonatal brain is a fundamental step in the study and assessment of infant brain development. The highest level of development techniques for adult brain MRI segmentation are not suitable for neonatal brain, because of substantial contrasts in structure and tissue properties between newborn and fully developed brains. Current newborn brain MRI segmentation techniques either depend on manual interaction or require the utilization of atlases or templates, which unavoidably presents a bias of the results towards the population that was utilized to derive the atlases. In this paper, we proposed an atlas-free approach for the segmentation of neonatal brain MRI, based on the neural network approach. The segmentation is primary stage to obtain quantitative analysis of regional brain tissues. These measurements allow characterization of the regional brain growth and inspect the correlations with clinical factors.Keywords
This publication has 17 references indexed in Scilit:
- Automatic segmentation of MR brain images of preterm infants using supervised classificationNeuroImage, 2015
- Perinatal cortical growth and childhood neurocognitive abilitiesNeurology, 2011
- Support vector machines combined with feature selection for breast cancer diagnosisExpert Systems with Applications, 2009
- Primary cortical folding in the human newborn: an early marker of later functional developmentBrain, 2008
- Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detectionEngineering Applications of Artificial Intelligence, 2006
- Automated defect recognition of C-SAM images in IC packaging using Support Vector MachinesThe International Journal of Advanced Manufacturing Technology, 2004
- Ensemble Feature RankingLecture Notes in Computer Science, 2004
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression DataJournal of the American Statistical Association, 2002
- Gene Assessment and Sample Classification for Gene Expression Data Using a Genetic Algorithm / k-nearest Neighbor MethodCombinatorial Chemistry & High Throughput Screening, 2001
- Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networksNature Medicine, 2001