Archives of Neuroscience

Published by: Kowsar
Uncorrected Proof scheduled for 6 (Special Issue)

Computerized Detection of Normal and Abnormal Tissues from Brain MRI

Sudipta Roy 1 and Samir Kumar Bandyopadhyay 2 , *
Authors Information
1 Department of Computer Science and Engineering, Institute of Computer Technology (UVPCE), Ganpat University, Ahmedabad, India
2 Department of Computer Science and Engineering, University of Calcutta, Kolkata, India
Article information
  • Archives of Neuroscience: In Press (In Press); e84581
  • Published Online: February 2, 2019
  • Article Type: Research Article
  • Received: September 24, 2018
  • Revised: October 25, 2018
  • Accepted: November 14, 2018
  • DOI: 10.5812/ans.84581

To Cite: Roy S, Bandyopadhyay S K. Computerized Detection of Normal and Abnormal Tissues from Brain MRI, Arch Neurosci. Online ahead of Print ; In Press(In Press):e84581. doi: 10.5812/ans.84581.

Copyright © 2019, Archives of Neuroscience. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Literature Review
3. Methods
4. Results
5. Discussion
  • 1. Yi Z, Criminisi A, Shotton J, Blake A. Discriminative, semantic segmentation of brain tissue in MR images. International conference on medical image computing and computer-assisted intervention. Berlin: Heidelberg; 2009. p. 558-65.
  • 2. Huang A, Abugharbieh R, Tam R, Alzheimer's Disease Neuroimaging I. A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI. IEEE Trans Biomed Eng. 2009;56(7):1838-48. doi: 10.1109/TBME.2009.2017509. [PubMed: 19336280]. [PubMed Central: PMC3068615].
  • 3. Li C, Huang R, Ding Z, Gatenby C, Metaxas D, Gore J. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. Med Image Comput Comput Assist Interv. 2008;11(Pt 2):1083-91. [PubMed: 18982712]. [PubMed Central: PMC2782702].
  • 4. Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process. 2011;20(7):2007-16. doi: 10.1109/TIP.2011.2146190. [PubMed: 21518662].
  • 5. Hunderi AH, Karunakaran N. Segmentation of medical image data using level set methods [dissertation]. Department of Computer and Information Science, Norwegian University of Science and Technology; 2013.
  • 6. Verma N, Muralidhar GS, Bovik AC, Cowperthwaite MC, Markey MK. Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2821-4. doi: 10.1109/IEMBS.2011.6090780. [PubMed: 22254928].
  • 7. Pohl KM, Bouix S, Kikinis R, Grimson WEL. Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework. Proc IEEE Int Symp Biomed Imaging. 2004;2004:81-4. doi: 10.1109/ISBI.2004.1398479. [PubMed: 28593029]. [PubMed Central: PMC5459362].
  • 8. Javeed Hussain S, Venkatesh C, Asif hussain S, Chetana L, Gireesha V. Segmentation of normal and pathological tissues in MRI brain images using dual classifier. International Conference on Advancements in Information Technology Singapore. 2011.
  • 9. Zhao HK, Chan T, Merriman B, Osher S. A variational level set approach to multiphase motion. J Comput Phys. 1996;127(1):179-95. doi: 10.1006/jcph.1996.0167.
  • 10. Madhukumar S, Santhiyakumari N. Evaluation of K-means and fuzzy C-means segmentation on MR images of brain. Egypt J Radiol Nucl Med. 2015;46(2):475-9. doi: 10.1016/j.ejrnm.2015.02.008.
  • 11. Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys. 1988;79(1):12-49. doi: 10.1016/0021-9991(88)90002-2.
  • 12. Paragios N, Deriche R. Coupled geodesic active regions for image segmentation: A level set approach. European Conference in Computer Vision, Lecture Notes in Computer Science. Springer; 2003. p. 224-40.
  • 13. Dickie DA, Shenkin SD, Anblagan D, Lee J, Blesa Cabez M, Rodriguez D, et al. Whole brain magnetic resonance image atlases: A systematic review of existing atlases and caveats for use in population imaging. Front Neuroinform. 2017;11:1. doi: 10.3389/fninf.2017.00001. [PubMed: 28154532]. [PubMed Central: PMC5244468].
  • 14. Sutton D. The whole brain atlas. BMJ. 1999;319(7223):1507. [PubMed: 10582957]. [PubMed Central: PMC1117228].

Featured Image:

Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:



Create Citiation Alert
via Google Reader

Readers' Comments