Total views : 283

Hybrid Method for Brain Tissue Classification in Magnetic Resonance Images of Children


  • School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, India
  • Department of Radiology, Sri Ramachandra University, Chennai - 600116, Tamil Nadu, India
  • Philips Innovation Centre, Bangalore - 560045, Karnataka, India


Background: Automatic brain tissue classification in children is tedious owing to motion artifacts, rapid brain maturation, etc. Considering this, we propose a hybrid approach for tissue classification. Methods: This approach utilizes atlas-free technique for brain extraction and labeling of Cerebrospinal Fluid and subsequent atlas-based method for classification of Gray and White Matter tissues. The results for brain, Gray Matter, White Matter and Cerebrospinal Fluid are validated quantitatively using Dice ratio. Findings: Mean Dice values of 0.9767 for brain, 0.8503 for Gray Matter, 0.7850 for White Matter and 0.8224 for Cerebrospinal Fluid are achieved. This signifies excellent agreement since Dice values for all tissues are much above the accepted similarity level of 0.7. Applications: The automatic tissue classification can be used to carry out volumetric tissue computations to understand brain maturation in childhood.


Child’s Brain, Hybrid Approach, Magnetic Resonance Imaging, Tissue Classification.

Full Text:

 |  (PDF views: 229)


  • Naik PPS, Gopal TV. Quantitative analysis and segmentation of metastasis brain images using hybrid mean shift clustering. Indian Journal of Science and Technology. 2015; 8(35):1–10.
  • Naveen A, Velmurugan T. Identification of calcification in MRI brain images by k-means algorithm. Indian Journal of Science and Technology. 2015; 8(29):1– 7.
  • Sasirekha N, Kashwan KR. Improved segmentation of MRI brain images by denoising and contrast enhancement. Indian Journal of Science and Technology. 2015; 8(22):1–7.
  • Murgasova MK. Segmentation of brain MRI during early childhood [PhD thesis]. Department of Computing, Imperial College London; 2008.
  • Aljabar P. Tracking longitudinal change using MR image data [PhD thesis]. Department of Computing, Imperial College London, University of London; 2007.
  • Li G, Nie J, Wang L, Shi F, Gilmore JH, Lin W, Shen D. Measuring longitudinally dynamic cortex development in infants by reconstruction of consistent cortical surfaces. IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro; San Francisco, CA, USA. 2013. p. 1380–3.
  • Wang L, Shi F, Yap PT, Gilmore JH, Lin W, Shen D. 4D multi-modality tissue segmentation of serial infant images. PLoS ONE. 2012; 7(9).
  • Wang L, Gao Y, Shi F, Li G, Gilmore JH, Lin W, Shen D. LINKS: Learning-based multi-source integration framework for segmentation of infant brain images. NeuroImage. 2015; 108:160–72.
  • Sanchez CE, Richards JE, Almli CR. Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. Dev Psychobiol. 2012; 54(1):77–91.
  • Shi F, Yap PT, Wu G, Jia H, Gilmore JH, Lin W, Shen D. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE. 2011; 6(4).
  • Altaye M, Holland SK, Wilke M, Gaser C. Infant brain probability templates for MRI segmentation and normalization. NeuroImage. 2008; 43(4):721–30.
  • Bhatia KK, Aljabar P, Boardman JP, Srinivasan L, Murgasova M, Counsell SJ, Rutherford MA, Hajnal J, Edwards AD, Rueckert D. Groupwise combined segmentation and registration for atlas construction. MICCAI 2007, Part I, Lect Notes Comput Sci. 2007; 4791:532–40.
  • Jelacic S, de Regt D, Weinberger E. Interactive digital MR atlas of the pediatric brain. Radio Graphics. 2006; 26(2):497–501.
  • Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Pat Anal Mach Intel. 1990; 12(7):629–39.
  • Myronenko A. Non-rigid image registration: Regularization, algorithms and applications [PhD thesis]. Department of Science and Engineering, School of Medicine, Oregon Health and Science University; 2010.
  • Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE Trans Med Imag. 1994; 13(4):716–24.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.