Abstract
With Kohonen’s self-organizing map based brain MRI image segmentation, there are still some regions which are not partitioned accurately, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. In this paper, we propose a dynamically growing hierarchical self-organizing map integrated with a multi-scale feature vector to overcome the problem mentioned above, which uses the spatial relationships between image pixels and using multi-scale processing method to reduce the noise effect and the classification ambiguity. The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.
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Zhang, J., Dai, DQ. (2007). A Multi-scale Dynamically Growing Hierarchical Self-organizing Map for Brain MRI Image Segmentation. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_128
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DOI: https://doi.org/10.1007/978-3-540-72393-6_128
Publisher Name: Springer, Berlin, Heidelberg
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