Feature selection in order to extract multiple sclerosis lesions automatically in 3D brain magnetic resonance images using combination of support vector machine and genetic algorithm

Journal of Medical Signals and Sensors

Volumn 2, Issue 4, 2012, Pages 211-218

  Khotanlou, Hassan ^a   Afrasiabi, Mahlagha ^a  

^a BU ALI SINA UNIVERSITY   (Iran)

Author keywords

Classification; features selection; genetic algorithm; medical images; multiple sclerosis lesions; support vector machine

Indexed keywords

EID: 84988733543     PISSN: None     EISSN: 22287477     Source Type: Journal    
DOI: 10.4103/2228-7477.110333     Document Type: Article

References (19)

1. Guttmann CR, Kikinis R, Anderson MC, Jakab M, Warfield SK, Killiany RJ, et al. Quantitative follow-up of patients with multiple sclerosis using MRI: Reproducibility. J Magn Reson Imaging 1999;9:509-18.
2. Khayati R, Vafadust M, Towhidkhah F, Nabavi SM. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 2008;32:124-33.
3. Anbeek P, Vincken KL, van Osch MJ, Bisschops RH, van der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 2004;21:1037-44.
4. Lorenzo D, Prima S, Morrissey S, Barillot C. A robust expectation-maximization algorithm for multiple sclerosis lesion segmentation. In: Proceeding of MICCAI Workshop; 2008. p. 277.
5. Prastawa M, Guido G. Automatic MS lesion segmentation by outlier detection and information theoretic region partitioning. In: Proceeding of A Grand Challenge II Workshop at Medical Image Computing and Computer Assisted Intervention (MICCAI); 2008.
6. Ardizzone E, Pirrone R, Gambino O, Peri D. Two channels fuzzy c-means detection of multiple sclerosis lesions in multispectral MR images. In: IEEE Proceeding of ICIP2002; 2002. p. 345-8.
7. Admasu F, Al-Zubi S, Toennies K, Bodammer N, Hinrichs H. Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks. In: IEEE Proceedings of ICIP2003; 2003. p. 1081-4.
8. Admiraal-Behloul F, van den Heuvel DM, Olofsen H, van Osch MJ, van der Grond J, van Buchem MA, et al. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. Neuroimage 2005;28:607-17.
9. Khotanlou H, Afrasiabi M. Segmentation of multiple sclerosis lesions in brain MR images using spatially constrained possibilistic fuzzy C-means classification. J Med Sign Sens 2011;1:149-55.
10. Vapnik VN. Methods of Pattern Recognition. The Nature of Statistical Learning Theory. New York: Springer; 1999.
11. Cristianini N, Shawe-Taylor J. Support Vector Machines. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge: Cambridge University Press; 2000. p. 93.
12. Burges CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2:121-67.
13. Van Gestel T, Suykens JA, Baesens B, Viaene S, Vanthienen J, Dedene G, et al. Benchmarking least squares support vector machine classifiers. Mach Learn 2004;54:5-32.
14. Cui D, Curry D. Predictions in marketing using the support vector machine. Mark Sci 2005;24:595-615.
15. Suykens JA, Vandewalle J. Least square support vector machine classifiers. Neural Process Lett 1999;9:293-300.
16. Coley DA. Writing a Genetic Algorithm. An Introduction to Genetic Algorithms for Scientists and Engineers. Singapore: World Scientific; 1999.
17. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23:S208-19.
18. Polman CH, Reingold SC, Edan G, Fillippi M, Hartung HP, Kappos L. Diagnostic criteria for MS 2005 revisions to the MC Donald criteria. Ann Neurol 2005;58:840-6.
19. Khotanlou H, Atif J, Colliot O, Bloch I. 3D brain tumor segmentation using fuzzy classification and deformable models. Lect Notes Artif Intell 2005;3849:3128.