A Multi-scale Dynamically Growing Hierarchical Self-organizing Map for Brain MRI Image Segmentation

Authors:
Jingdan Zhang;Dao-Qing Dai
Affiliations:
Center for Computer Vision and Department of Mathematics, Sun Yat-Sen (Zhongshan) University, Guangzhou, 510275, China;Center for Computer Vision and Department of Mathematics, Sun Yat-Sen (Zhongshan) University, Guangzhou, 510275, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 4
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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.