Abstract
In the existing segmentation algorithms, most of them take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural information between pixels provides even more important information of the image. In order to effectively exploit both the gray value and the spatial information of pixels, this paper proposes an image segmentation method based on Vector Quantization (VQ) technique. In the method, the image to be segmented is divided into small sub-blocks with each sub-block constituting a feature vector. Further, the vectors are classified through vector quantization. In addition, the self-organizing map (SOM) neural network is proposed for realizing the VQ algorithm adaptively. Simulation experiments and comparison studies have been conducted with applications to medical image processing in the paper, and the results validate the effectiveness of the proposed method.
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
Zhang, Y.J.: Image Segmentation, pp. 6–58. Science Press, Beijing (2001)
Gao, X.L., Wang, Z.L., Liu, J.W.: Algorithm for Image Segmentation using Statistical Models based on Intensity Features. Acta Optica Sinica 31(1), 1–6 (2011)
Zhao, J., Shao, F.Q., Zhang, X.D.: Vector-valued Images Segmentation based on Improved Variational GAC Model. Control and Decision 26(6), 909–915 (2011)
Wu, Y., Xiao, P., Wang, C.M.: Segmentation Algorithm for SAR Images based on the Persistence and Clustering in the Contourlet Domain. Acta Optica Sinica 30(7), 1977–1983 (2010)
Veksler, O.: Image Segmentation by Nested Cuts. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 339–344. IEEE Press (2000)
Wang, S., Lu, H.H., Yang, F.: Superpixel Tracking. In: Proc. of IEEE International Conference on Computer Vision, pp. 1323–1330. IEEE Press (2011)
Luo, S.Q., Zhou, G.H.: Medical Image Processing and Analysis, pp. 67–68. Science Press, Beijing (2003)
Zhang, Q., Lu, Z.T., Chen, C.: Spinal MRI Segmentation based on Local Neighborhood Information and Gaussian Weighted Chi-square Distance. Chinese Journal of Biomedical Engineering 30, 358–362 (2011)
Luo, Z.Z., Shen, H.X.: Hermite Interpolation-based Wavelet Transform Modulus Maxima Reconstruction Algorithm’s Application to EMG De-noising. Journal of Electronics & Information Technology 31(4), 857–860 (2009)
Liu, B., Huang, L.J.: Multi-scale Fusion of Well Logging Data Based on Wavelet Modulus Maximum. Journal of China Coal Society 35(4), 645–649 (2010)
Linde, Y., Buzo, A., Gray, R.: An Algorithm for Vector Quantizer Design. IEEE Trans. on Communications 18(l), 84–95 (1980)
Costa, J.A.F., de Andrade Netto, M.L.: Automatic Data Classification by a Hierarchy of Self-organizing Maps. In: Proc. IEEE International Conference on System, Man and Cybernetics, vol. 5, pp. 419–424 (1999)
Kohonen, T.: The self-organizing maps. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Gustafson, D., Kessel, W.: Fuzzy Clustering with a Fuzzy Covariance matrix. In: Decision and Control including the 17th Symposium on Adaptive Processes, vol. 17(1), pp. 761–766 (1978)
Babuska, R., Verbruggen, H.: Constructing Fuzzy Models by Product Space Clustering. In: Fuzzy Model Identification, pp. 53–90 (1997)
Stelios, K., Vassilios, C.A.: Robust Fuzzy Local Information C-means Clustering Algorithm. IEEE Transactions on Image Processing 19(5), 1328–1337 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
De, A., Guo, C. (2013). A Vector Quantization Approach for Image Segmentation Based on SOM Neural Network. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_73
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)