MRI image segmentation based on fast kernel clustering analysis

Abstract

Kernel-based clustering is supposed to provide a better analysis tool for pattern classification, which implicitly maps input samples to a high-dimensional 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 propose a novel kernel clustering algorithm, called KFCM-III, for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance. Moreover, a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm. The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging (MRI) images. For this task, an image intensity inhomogeneity correction is employed during image segmentation process. With a scheme called preclassification, the proposed intensity correction scheme could further speed up image segmentation. The experimental results on public image data show the superiorities of KFCM-III.

Keywords

kernel-based clustering / dimensionality reduction / speeding-up scheme / magnetic resonance imaging (MRI) image segmentation / intensity inhomogeneity correction

References

[1] Zhang D Q. Kernel-based associative memories, clustering algorithms and their applications. Dissertation for the Doctoral Degree. Nanjing: Nanjing University of Aeronautics and Astronautics, 2004 (in Chinese)

[2] Girolami M. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks, 2002, 13(3): 780-784

[3] Müller K R, Mika S, Rätsch G, Tsuda K. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181-202

[4] 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

[5] 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

[6] Schölkopf B, Mika S, Burges C J C, Knirsch P, Müller K, Rätsch G, Smola A J. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10(5): 1000-1017

[7] 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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

[13] Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306

[14] 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

[15] 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

[16] Aubert-Broche B, Evans A C, Collins L. A new improved version of the realistic digital brain phantom. Neuroimage, 2006, 32(1): 138-145

[17] 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

[18] Cocosco C A, Kollokian V, Kwan R K S, Evans A C. BrainWeb: online interface to a 3-D MRI simulated brain database. NeuroImage, 1997, 5(4): S425

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg