Abstract
Self-organising maps (SOMs) have been applied in many different areas of science. In a typical application, large numbers of objects (thousands or more) are mapped to a two-dimensional grid of units in such a way that very similar objects end up in the same unit, and that neighbouring units are more similar than far-away units. The similarities of the individual units can be used in visualisation of the data by choosing appropriate colour schemes. Examples from image segmentation will show the usefulness of this approach. Often, additional information is available, e.g., class information, or measurements of a different nature. To take this extra information into account, we have extended the basic principle of SOMs to accommodate extra layers, one for each data modality. The closest unit is then given by a weighted sum of per-layer distances. The result is an overall better mapping, incorporating all available information. This is implemented in an R package “kohonen”.
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.
Similar content being viewed by others
References
Fraley, C., Raftery, A. E., & Wehrens, R. (2005). Incremental model-based clustering for large datasets with small clusters. Journal of Computational and Graphical Statistics, 14(3), 1–18.
Hoekman, D. H., & Vissers, M. A. M. (2003). A new polarimetric classification approach evaluated for agricultural crops. IEEE Transactions on Geoscience and Remote Sensing, 41, 2881–2889.
Kohonen, T. (2001). Self-organizing maps. In Springer Series in Information Sciences (No. 30, 3 rd ed.). Berlin: Springer.
Li, Y., & Chi, Z. (2005). MR brain image segmentation based on self-organizing map network. International Journal of Information Technology, 11(8), 45–53.
Melssen, W. J., Wehrens, R., & Buydens, L. M. C. (2006). Supervised Kohonen networks for classification problems. Chemometrics and Intelligent Laboratory Systems, 83, 99–113.
R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.
Tasdemir, K., & Merényi, E. (2006, April). Data topology visualization for the self-organizing map. In Proceedings of 14th European Symposium on Artificial Neural Networks, ESANN2006 (pp. 125–130). Bruges, Belgium.
Thanh, T. N., Wehrens, R., Hoekman, D. H., & Buydens, L. M. C. (2005). Initialization of Markov Random Field clustering of large polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43, 1912–1919.
Ultsch, A. (1993). Self-organizing neural networks for visualization and classification. In O. Opitz, B. Lausen, & R. Klar (Eds.), Information and classification – Concepts, methods and applications (pp. 307–313). Berlin: Springer.
Villmann, T., & Merényi, E. (2001). Extensions and modifications of the kohonen-som and applications in remote sensing image analysis. In U. Seiffert, & L. C. Jain (Eds.), Self-organizing maps: Recent advances and applications (pp. 121–145). Berlin: Springer.
Wehrens, R., & Buydens, L. M. C. (2007). Self- and super-organising maps in R: The kohonen package. Journal of Statistical Software, 21(5), 9.
Wehrens, R., Buydens, L. M. C., Fraley, C., & Raftery, A. E. (2004). Model-based clustering for image segmentations and large datasets via sampling. Journal of Classification, 21, 231–253.
Willighagen, E. L., Wehrens, R., Melssen, W., de Gelder, R., & Buydens, L. M. C. (2007). Supervised self-organising maps in crystal structure prediction. Crystal Growth and Design, 7, 1738–1745.
Acknowledgements
Thanh N. Tran (now Schering-Plough) and Dirk Hoekman (Wageningen University) are acknowledged for the SAR data and valuable discussions. The MRI data are obtained in the EC-funded INTERPRET project, and were recorded at the Radiology department of the Radboud University Medical Center (prof. Heerschap).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wehrens, R. (2009). Self-Organising Maps for Image Segmentation. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-01044-6_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01043-9
Online ISBN: 978-3-642-01044-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)