The week's pick
Reseach Article
A Comparative Study of MRI Image Segmentation based on Fast Kernel Clustering Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 6 |
| Year of Publication: 2015 |
| Authors: Smita Haribhau Zol, R. R. Deshmukh |
10.5120/19321-0890
|
Smita Haribhau Zol, R. R. Deshmukh . A Comparative Study of MRI Image Segmentation based on Fast Kernel Clustering Analysis. International Journal of Computer Applications. 110, 6 ( January 2015), 26-29. DOI=10.5120/19321-0890
Abstract
Kernel-based clustering provides a better analysis tool for pattern classification, which implicitly maps input samples to a highdimensional space for improving pattern separability. For this implicit space map, the kernel trick is believed to elegantly tackle the problem of "curse of dimensionality", which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy, which traditional kernelized algorithms cannot effectively deal with. In this paper, we have analyzed the merits and deficiencies of KFCM-I/KFCM-II, and KFMC-III and pointed out the connections of these three algorithms.
References
- Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489–509
- Girolami M. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks, 2002, 13(3): 780–784
- Zhang D Q, Chen S C, Zhou Z H. Learning the kernel parameters in kernel minimum distance classifier. Pattern Recognition, 2006, 39(1): 133–135
- Liang Liao, Yanning Zhang MRI image segmentation based on fast kernel clustering Analysis, Front. Electr. Electron. Eng. China 2011, 6(2): 363–373 In:Higher Education Press and Springer-Verlag Berlin Heidelberg 2011
- Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Transactions on Information Theory, 2006, 52(12): 5406–5425
- Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4):1289–1306
- Szilagyi L, Benyo Z, Szilagyi S M, Adam H S. MR brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2003, 1: 724–726
- Cai W L, Chen S C, Zhang D Q. Fast and robust fuzzy cmeans clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007, 40(3): 825–838
- M¨uller K R, Mika S, R¨atsch G, Tsuda K. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181–202
- Zhang D Q, Chen S C. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 2004, 32(1): 37–40
- Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on System, Man and Cybernetics, Part B, 2004, 34(4): 1907–1916
- Bi L P, Huang H, Zheng Z Y, Song H T. New heuristic for determination Gaussian kernel's parameter. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics. 2005, 7: 4299 –4304
- Wang W J, Xu Z B, Lu W Z. Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing, 2003, 3(4): 643–663
- Ahmed M, Yamany S, Mohamed N, Farag A A, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 2002, 21(3): 193–199
- Aubert-Broche B, Evans A C, Collins L. A new improved version of the realistic digital brain phantom. Neuroimage, 2006, 32(1): 138–145
- Collins D L, Zijdenbos A P, Kollokian V, Sled J G, Kabani N J, Holmes C J, Evans A C. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging, 1998, 17(3): 463–468
- Cocosco C A, Kollokian V, Kwan R K S, Evans A C. BrainWeb: online interface to a 3-D MRI simulated brain.
Index Terms
Keywords