A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Clustering Method Using Hierarchical Self-Organizing Maps
Journal of VLSI Signal Processing Systems
Visualising an Image Collection?
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2005 Special issue: IJCNN 2005
A computerized cellular imaging system for high content analysis in Monastrol suppressor screens
Journal of Biomedical Informatics
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Annotation and retrieval of cell images
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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The application of hierarchical self organizing maps (HSOM) to the segmentation of cell migration images, obtained during high-content screening in molecular medicine, is described. The segmentation is critical to our larger project for developing methods for the automatic annotation of cell migration images. The HSOM appears to perform better than the conventional computer-vision methods of histogram thresholding, edge detection, and the newer techniques involving single-layer SOMs. However, the HSOM techniques have to be complemented by region-based techniques to improve the quality of the segmented images.