Abstract
Kohonen’s self-organizing feature map (SOFM) is a two-layer feed-forward competitive learning network, and has been used as a competitive learning clustering algorithm in brain MRI image segmentation. However, most brain MRI images always present overlapping gray-scale intensities for different tissues. In this paper, fuzzy methods are integrated with Kohonen’s competitive algorithm to overcome this problem (we will name the algorithm F_KCL). The F_KCL algorithm fuses the competitive learning with fuzzy c-means (FCM) cluster characteristic and can improve the segment result effectively. Moreover, in order to enhancing the robustness to noise and outliers, a kernel induced method is exploited in our study to measure the distance between the input vector and the weights (KF_KCL). The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.
This work is supported by science foundation for young teachers of Northeast Normal University, No. 20061002, China.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pham, D.L, Xu, C. Y, Prince, J. L: A Survey of Current Methods in Medical image Segmentation. [Technical report version, JHU/ECE 99-01, Johns Hopkins University], Ann. Rev. Biomed. Eng. 2 (2000) 315–37
Wells, W. M., Grimson, W. E. L., Kikinis, R., Arrdrige, S. R.: Adaptive Segmentation of MRI Data. IEEE Trans Med Imaging 15 (1996) 429–42
Gerig, G, Martin, J., Kikinis, R, Kubler, D, Shenton, M., Jolesz, F. A: Unsupervised Tissue Type Segmentation of 3D Dual-echo MR Head Data. Image Vision Compute, 10 (1992) 349–60
Alan, W. C. L., Yan, H.: An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation. IEEE Transaction on Medical Imaging 22(9) (2003) 1063–1075
Philips, W. E., Velthuizen, R. P., Phuphanich, S, Hall, L. O, Clarke, L. P, Silbiger, M. L: Application of Fuzzy C-means Segmentation Technique for Differentiation in MR Images of a Hemorrhagic Glioblastoma Multiforme. Mag Reson Imaging 13 (1995) 277–90
Pham, D. L, Prince, J. L.: Adaptive Fuzzy Segmentation of Magnetic Resonance Images. IEEE Trans Med Imaging 18 (1999) 737–752
Bezdek, J: Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press (1981)
Wu, K. L., Yang, M. S.: Alternative C-means Clustering Algorithms. Pattern Recognition 35 (2002) 2267–2278
Hall, L. O, Bensaid, A.M, Clarke L. P, Velthuizen, R. P, Silbiger, M. S, Bezdek, J. C: A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain. IEEE Trans Neural Networks, 3 (1992) 672–682
Ozkan, M, Dawant, B. M, Maciunas, R. J: Neural-network-based Segmentation of Multimodal Medical Images: A Comparative and Prospective Study. IEEE Trans Medical Imaging, 12 (1993) 534–544
Reddick, W. E, Glass, J. O, Cook, E. N, Elkin, T. D, Deaton, R: Automated Segmentation and Classi-fication of Multispectral Magnetic Resonance Images of Brain using Artificial Neural Networks. IEEE Trans Med Imaging, 16 (1997) 911–918
Reddick, W. E, Mulhern, R. K, Elkin, T. D, Glass, J. O, Merchant, T. E, Langston, J. W: A Hybrid Neural Network Analysis of Subtle Brain Volume Differences in Children Surviving Brain Tumors. Mag Reson Imaging, 16 (1998) 413–421
Chuang, K. H, Chiu, M. J, Lin, C. C, Chen, J. H: Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy c-means. IEEE Trans Medical Imaging, 18 (1999) 1117–1128
Glass, J. O, Reddick, W. E, Goloubeva, O., Yo, V., Steen, R. G: Hybrid Artificial Neural NetWork Segmentation of Precise and Accurate Inversion Recovery (PAIR) Images From Normal Human Brain. Mag Reson Imaging 18 (2000) 1245–1253
Kohonen, T.: Self-Organizing Maps. New York: Springer-Verlag (1995)
Kwan, R. S., Evans, A., Pike, G: MRI Simulation-based Evaluation of Image-processing and Classification Methods. IEEE Trans. Med. Imaging 18(11) (1999) 1085–1097. Available: http://www.bic.mni.mcgill.ca/brainweb
Kennedy, D. N, Filipek, P. A, Caviness, V. S: Anatomic Segmentation and Volumetric Calculations in Nuclear Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 8 (1989) 1–7. Available: http://www.cma.mgh.harvard.edu/ibsr/
Zijdenbos, A., Dawant, B.: Brain Segmentation and White Matter Lesion Detection in MR Images. Crit. Rev. Biomed. Eng. 22(5–6) (1994) 401–465
Karen, C. R. Lin, M. S., Yang, H. C., Liu, J.F., Lirng, Wang, P. N.: Generalized Kohonen’s Competitive Learning Algorithm for Ophthalmological MR Image Segmentation. Magnetic Resonance Imaging 21 (2003) 863–870
Chen, S. G., Zhang, D. Q.: Robust Image Segmentation using FCM with Spatial Constraints Based on New Kernel Induced Distance Measure. IEEE Transaction on SMC-Part B 34 (2004) 1907–1916
Zhang, D. Q., Chen, S. C.: A Novel Kernelized Fuzzy C-means Algorithm with Application in Medical Image Segmentation. Artificial Intelligence in Medicine 32 (2004) 37–50
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kong, J., Wang, J., Lu, Y., Zhang, J., Zhang, J. (2006). A Fuzzy Kohonen’s Competitive Learning Algorithm for 3D MRI Image Segmentation. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-37258-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
eBook Packages: EngineeringEngineering (R0)