International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed, and Fully Refereed Scientific Research Journal
An Automated Skull-Stripping Method by Windowing The Histogram
An Automated Skull-Stripping Method by Windowing The Histogram
S. Sarkar1 , A. Mandal2 , K. Sarkar3

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-3 , Page no. 45-49, Mar-2017

Online published on Mar 31, 2017

Copyright © S. Sarkar, A. Mandal, K. Sarkar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
View this paper at   Google Scholar | DPI Digital Library
  XML View PDF Download  
Citation

IEEE Style Citation: S. Sarkar, A. Mandal, K. Sarkar, “An Automated Skull-Stripping Method by Windowing The Histogram”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.45-49, 2017.

MLA Style Citation: S. Sarkar, A. Mandal, K. Sarkar "An Automated Skull-Stripping Method by Windowing The Histogram." International Journal of Computer Sciences and Engineering 5.3 (2017): 45-49.

APA Style Citation: S. Sarkar, A. Mandal, K. Sarkar, (2017). An Automated Skull-Stripping Method by Windowing The Histogram. International Journal of Computer Sciences and Engineering, 5(3), 45-49.
Downloads (107)     Full view (117)
           
Abstract :
An automated method for segmentation of Magnetic Resonance (MR) head images into brain and non-brain has been proposed. It combines the strategy used in intensity and morphological skull-stripping methods. The method is very fast and requires no preprocessing of MR images. It is testified on T1-Weighted MR image modality and produces accurate output.
Key-Words / Index Term :
MRI, Skull-Stripping , ROI, T1-Weighted, Windowing
References :
[1] S.M. Smith, “Fast robust automated brain extraction, Hum. Brain. Mapp.” Vol.17, pp. 143–155, 2002.
[2] D. Shattuck, S. Sandor-Leahy, K. Schaper, D. Rottenberg, and R. Leahy, “Magnetic resonance image tissue classification using a partial volume model,”NeuroImage, vol.13(5), pp. 856–876, 2001
[3] A. Mikheev, G. Nevsky, S. Govindan, R. Grossman, and H. Rusinek, “Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm,”Journal of Magnetic Resonance Imaging, vol.27(6), pp. 1235–1241, 2008.
[4] Rusinek, “Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm,”Journal of Magnetic Resonance Imaging, vol. 27(6), pp. 1235–1241, 2008.
[5] S. Sadananthan, W. Zheng, M. Chee, and V. Zagorodnov, “Skull stripping using graph cuts,”NeuroImage, vol. 49(1), pp. 225–239, 2010.
[6] P. Kalavathi , V.B.Surya Prasath,”Methods on Skull Stripping of MRI Head Scan Images—aReview”, J Digit Imaging Vol.29, pp.365 –379, 2016.
[7] M.E. Brummer, R.M. Mersereau, R.L. Eisner, R.R.J. Lewine, V. Caeslles, R. Kimmel , G. Sapiro : “Automatic detection of brain contours in MRI datasets.” IEEE Trans Image Process vol.12(2), pp.153–166, 1993.
[8] B.D. Ward “Automatic segmentation of intracranial region. Technical Report”, 1999.
[9] R.W. Cox,” AFNI: software for analysis and visualization of func-tional magnetic resonance Neuroimages.” Comput Biomed Res Vol.29(3), pp.162 –173, 1996.
[10] N. Otsu, “A Threshold Selection Method from Gray-Level Histogram”, IEEE Transaction on System,Man and Cybernetics, Vol. SMC-9(1), pp.62-66, 1979.
[11] M. Sonka, V. Hlavac and R. Boyle, “Image Processing, Analysis and Machine Vision”, Thomson Learning Inc., SecondEdition,2007.