Angelopoulou, Anastassia and Psarrou, Alexandra and Garcia Rodriguez, Jose and Revett, Kenneth (2005) Automatic landmarking of 2D medical shapes using the growing neural gas network. In: Liu, Yanxi and Jiang, Tianzi and Zhang, Changshui, (eds.) Computer Vision for Biomedical Image Applications: First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings. Lecture notes in computer science (3765). Springer, Berlin, Germany, pp. 210-219. ISBN 3540294112
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Official URL: http://dx.doi.org/10.1007/11569541_22
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.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||19 Jun 2006|
|Last Modified:||14 Oct 2009 12:41|
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