Author(s):
E. Udayakumar, K. Yogeshwaran, C. Ramesh
Email(s):
udayakumar.sujith@gmail.com
DOI:
10.5958/0974-360X.2019.00499.2 
Address:
E. Udayakumar1, K. Yogeshwaran2, C. Ramesh3
Assistant Professor, Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India.
*Corresponding Author
Published In:
Volume - 12,
Issue - 6,
Year - 2019
ABSTRACT:
The growth of brain can be assessed by using MRI. The MRI images of the neonatal brain have a much lower Contrast-to-Noise Ratio (CNR), frequently have lower signal-to-noise ratio due to the small size of the neonatal brain and vary enormously in terms of brain shape and appearance as a result of rapid brain development during this period. In addition, the partial volume effect present due to the inverted signal intensity in White Matter (WM) presents an obstacle for tissue classi?cation. The Manual segmentation of the abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. This system provides a framework for accurate intensity based segmentation of newborn brain using region growing algorithm.
Cite this article:
E. Udayakumar, K. Yogeshwaran, C. Ramesh. An Efficient Tissue Segmentation of Neonatal Brain Magnetic Resonance Imaging. Research J. Pharm. and Tech. 2019; 12(6):2963-2966. doi: 10.5958/0974-360X.2019.00499.2
Cite(Electronic):
E. Udayakumar, K. Yogeshwaran, C. Ramesh. An Efficient Tissue Segmentation of Neonatal Brain Magnetic Resonance Imaging. Research J. Pharm. and Tech. 2019; 12(6):2963-2966. doi: 10.5958/0974-360X.2019.00499.2 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2019-12-6-62