Object representation with self-organising networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Learning 2D hand shapes using the topology preservation model GNG
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Automatically building 2D statistical shapes using the topology preservation model GNG
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Automatic landmarking of 2d medical shapes using the growing neural gas network
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG (MGNG) corresponding dominant points of contours extracted from two corresponding images are found. These contours are borders of segmented anatomical regions from brain images. The presented method is compared to: 1) the node splitting-merging Kohonen model and 2) the Teh-Chin algorithm (a well-known approach for dominant points extraction of ordered curves). It is shown that the proposed algorithm has lower distortion error, ability of extracting landmarks from two corresponding curves simultaneously, and also generates the best match according to five medical experts.