Automatic Landmarking of 2D Medical Shapes Using the Growing Neural Gas Network

  • Anastassia Angelopoulou
  • Alexandra Psarrou
  • José García Rodríguez
  • Kenneth Revett
Conference paper

DOI: 10.1007/11569541_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)
Cite this paper as:
Angelopoulou A., Psarrou A., Rodríguez J.G., Revett K. (2005) Automatic Landmarking of 2D Medical Shapes Using the Growing Neural Gas Network. In: Liu Y., Jiang T., Zhang C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Anastassia Angelopoulou
    • 1
  • Alexandra Psarrou
    • 1
  • José García Rodríguez
    • 2
  • Kenneth Revett
    • 1
  1. 1.Harrow School of Computer ScienceUniversity of WestminsterHarrowUnited Kingdom
  2. 2.Departamento de Tecnología Informática y ComputaciónUniversidad de AlicanteAlicanteEspana

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