Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Binary digital image processing: a discrete approach
Binary digital image processing: a discrete approach
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Pre-Processing of CT Brain Images for Content-Based Image Retrieval
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Brain Tissue Characterization via Non-supervised One-Dimensional Kohonen Networks
CONIELECOMP '09 Proceedings of the 2009 International Conference on Electrical, Communications, and Computers
Non-supervised classification of 2d color images using kohonen networks and a novel metric
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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An approach for nonsupervised segmentation of Computed Tomography (CT) brain slices which is based on the use of Vector Quantization Networks (VQNs) is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC) which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen's original algorithm and also under our similarity metric. Some experiments are established in order tomeasure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.