CFP last date
20 May 2024
Reseach Article

Accuracy based Comparison of Three Brain Extraction Algorithms

by Gayatri Mirajkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 18
Year of Publication: 2012
Authors: Gayatri Mirajkar
10.5120/7731-1232

Gayatri Mirajkar . Accuracy based Comparison of Three Brain Extraction Algorithms. International Journal of Computer Applications. 49, 18 ( July 2012), 45-57. DOI=10.5120/7731-1232

@article{ 10.5120/7731-1232,
author = { Gayatri Mirajkar },
title = { Accuracy based Comparison of Three Brain Extraction Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 18 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number18/7731-1232/ },
doi = { 10.5120/7731-1232 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:36.713671+05:30
%A Gayatri Mirajkar
%T Accuracy based Comparison of Three Brain Extraction Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 18
%P 45-57
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skull stripping is an important image processing step in many neuroimaging studies. In this paper, a comparison of three brain extraction algorithms is done, namely Brain Surface Extractor (BSE), skull stripping algorithm using Geodesic Active Contour (GAC), and skull stripping using active contours without edges. The comparison is done with respect to accuracy of the three algorithms. The results provided by the three algorithms are compared against the processed results available in the OASIS dataset. A comparison of the three algorithms shows that BSE provides the best results with respect to the percentage of non-brain matter contained in the final segmented output. The algorithm using GAC produces a conservative result containing some amount of non-brain matter that can be removed using morphological operator. The algorithm using active contours without edges produces segmentation results containing some amount of brain matter removed from the result. This is mainly due to the sensitivity of the active contour to intensity values in the sulci present in the brain magnetic resonance image.

References
  1. H. H. Zhuang, D. J. Valentino, and A. W. Toga, "Skull-stripping magnetic resonance images using a model-based level set," NeuroImage, vol. 32, pp. 79 – 82, 2006.
  2. C. DeCarli, J. Maisog, D. G. M. Murphy, D. Teichberg, S. I. Rapoport, and B. Horwitz, "Method for quantification of brain ventricular and subarachnoid CSF volumes from MR images," J. Comput. Assist. Tomogr. , vol. 16, no. 2, pp. 274 – 284, 1992
  3. C. Lee, S. Huh, T. A. Keller, and M. Unser, "Unsupervised connectivity-based thresholding segmentation of midsaggital brain MR images," Comput. Biol. Med. , vol. 28, pp. 309 – 338, 1998
  4. S. Huh, T. A. Keller, K. H. Sohn, and C. Lee, "Automated cerebrum segmentation from 3-D saggital brain MR images," Comput. Biol. Med. , vol. 32, pp. 311 – 328, 2002
  5. D. W. Shuttuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenburg, and R. M. Leahy, "Magnetic resonance image tissue classification using a partial volume model," NeuroImage, vol. 13, pp. 856 – 876, 2001
  6. G. B. Aboutanos, J. Nikanne, N. Watkins, and B. M. Dawant, "Model creation and deformation for the automatic segmentation of the brain in MR images," IEEE Trans. Biomed. Engg. , vol. 46, no. 11, pp. 1346 – 1356, 1999
  7. X. Zeng, L. H. Staib, R. T. Schultz, and J. S. Duncan, "Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation," IEEE Trans. Med. Imag. , vol. 18, no. 10, pp. 927 – 937, 1999
  8. J. S. Suri, "Two-dimensional fast magnetic resonance brain segmentation," IEEE Trans. Med. Biol. , pp. 84 – 95, 2001
  9. C. Baillard, P. Hellier, and C. Barillot, "Segmentation of brain 3D MR images using level sets and dense registration," Med. Image Anal. , vol. 5, pp. 185 – 194, 2001
  10. S. M. Smith, "Fast robust automated brain extraction," Hum. Brain Mapp. , vol. 17, pp. 143 – 155, 2002
  11. H. Rifai, I. Bloch, S. Hutchinson, J. Wiart, and L. Garnero, "Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account," Med. Image Anal. , vol. 4, pp. 219 – 233, 2000
  12. S. Shah and A. Ross, "Iris segmentation using geodesic active contours," IEEE Trans. Inf. Foren. & Sec. , vol. 4, no. 4, pp. 824 – 836, 2009
  13. J. A. Sethian, "A review of recent numerical algorithms for hypersurfaces moving with curvature dependent speed," J. Different. Geometry, vol. 31, pp. 131 – 161, 1989
  14. R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," IEEE Trans. Pattern Anal. , Mach. Intell. , vol. 17, no. 2, pp. 125 – 133, 1995
  15. T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Trans. Image Process. , vol. 10, no. 2, pp. 266 – 277, 2001
  16. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 12, no. 7, pp. 629 – 639, 1990
  17. M. Sussman, P. Smereka, and S. Osher, "A level set approach for computing solutions to incompressible two-phase flow," J. Comput. Phys. , vol. 114, pp. 146 – 159, 1994
  18. S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi Formulation," J. Comput. Phys. , vol. 79, pp. 12 – 49, 1988
Index Terms

Computer Science
Information Sciences

Keywords

Magnetic resonance imaging skull stripping active contours geodesic active contours level sets