Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 44, Pages: 1-4
Tushar H. Jaware1 *, K. B. Khanchandani1 and Anita Zurani2
1SSGMCE, Near Krishna Cottage, District Buldana, Shegaon - 444203, Maharashtra, India; [email protected], [email protected] 2SGBAU, Near Tapovan Gate, Amravati - 444602, Maharashtra, India; [email protected]
*Author for correspondence
Tushar H. Jaware
SSGMCE, Near Krishna Cottage, District Buldana, Shegaon - 444203, Maharashtra, India; [email protected]
Objectives: Every year millions of babies born preterm and it is a serious issue in developed countries. Preterm birth is worldwide problem of young children. Methods/Statistical analysis: Advances in Magnetic Resonance Imaging (MRI), comprehensive images of the newborn brain is visualized non-invasively. Here we are focusing on the early assessment of brain development in neonates and premature infants using multi stage segmentation and classification approach with quantitative analysis. To achieve higher segmentation and classification accuracy neural network classifier are used. It is also helpful in detection and classification of more number of brain tissues. Findings: The accurate segmentation and classification of brain tissues is very much useful in evaluating the brain development of newborn and premature infants. Therefore, newborn brain MRI forms a crucial part in analytical neuro-imaging; principally in the neonatal stage. Our method gives higher dice similarity index values and higher computational speed as compared to existing methods. Application: Resulting segmentations are used for volumetric analysis and quantification of cortical descriptions. This analysis plays vital role for neonatologists in early detection and diagnosis of neural impairments.
Keywords: Classification, MRI, Multi-kernel Support Vector Machine, Newborn, Preterm, Segmentation
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