Topology conserving mappings for learning motor tasks
AIP Conference Proceedings 151 on Neural Networks for Computing
Topology representing networks
Neural Networks
Self-Organizing Maps
Segmentation of 3D Brain Structures Using Level Sets and Dense Registration
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Automatic landmark extraction from image data using modified growing neural gas network
IEEE Transactions on Information Technology in Biomedicine
Video and image processing with self-organizing neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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
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 segmentation of lung nodules with growing neural gas and support vector machine
Computers in Biology and Medicine
Surface approximation using growing self-organizing nets and gradient information
Applied Bionics and Biomechanics
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MR Imaging techniques provide a non-invasive and accurate method for determining the ultra-structural features of human anatomy. In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. Our approach is based on an automated landmark extraction algorithm which automatically selects points along the contour of the ventricles from a series of 2D MRI brain images. Automated landmark extraction is accomplished through the use of the self-organising network the growing neural gas (GNG) which is able to topographically map the low dimension of the network to the high dimension of the manifold of the contour without requiring a priori knowledge of the structure of the input space. The GNG method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and an error metric is applied to quantify the performance of our algorithm compared to the other two.