Automatic Segmentation of Brain MR Images of Neonates and Premature Infants using KNN Classifier


  • Tushar H Jaware Research Scholar, SSGMCE, Shegaon
  • Dr K B Khanchandani Dean R &D, SSGMCE, Shegaon
  • Ms Anita Zurani Research Scholar, SSGMCE, Shegaon


KNN; Newborn; Premature; Segmentation, MRI


This paper focuses on the development of an accurate neonatal brain MRI segmentation algorithm and its clinical
application to characterize normal brain development and investigate the neuro-anatomical correlates of cognitive impairments.
Neonatal brain segmentation is challenging due to the large anatomical variability as a result of the rapid brain development in the
neonatal period. 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 adult brains. Existing newborn
brain MRI segmentation approaches 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 KNN classifier. The segmentation of the brain in
Magnetic Resonance Imaging (MRI) is a prerequisite to obtain quantitative measurements of regional brain structures. These
measurements allow characterization of the regional brain development and the investigation of correlations with clinical factors.



How to Cite

Tushar H Jaware, Dr K B Khanchandani, & Ms Anita Zurani. (2016). Automatic Segmentation of Brain MR Images of Neonates and Premature Infants using KNN Classifier. International Journal of Advance Research in Engineering, Science & Technology, 3(13), -. Retrieved from