Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Overview and fundamentals of medical image segmentation
Handbook of medical imaging
Segmentation with neural networks
Handbook of medical imaging
Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Hierarchical SOMs: Segmentation of Cell-Migration Images
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Anisotropic mean shift based fuzzy c-means segmentation of skin lesions
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Computer Science - Research and Development
Segmentation of medical images by using wavelet transform and incremental self-organizing map
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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In this paper, we present a fully automated image segmentation method based on an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate similarity transformation, which is based on a self-organized deformation of the underlying multidimensional probability distributions. We apply this algorithm to the real-world problem of fully automated voxel-based multispectral image segmentation, employing magnetic resonance data sets of the human brain. In contrast to previous segmentation approaches, the knowledge obtained within the segmentation procedure of a single prototypical reference data set can be re-utilized for the segmentation of new, 'similar' data employing a strategy of incremental adaptive learning based on the DM algorithm. Thus, we obtain a fully automatic segmentation method that does neither require manual contour tracing of training regions, visual classification of voxel clusters, nor any other kind of human intervention. Our application demonstrates that flexible learning by a strategy of self-organized incremental model adaptation can contribute to increase the efficiency and practicability of biomedical image processing systems.