Abstract
We present a method for osteoporosis detection using graph representations obtained running a Growing Neural Gas machine learning algorithm on X–ray bone images. The GNG induced graph, being dependent on density, represents well the features which may be in part responsible for the illness. The graph connects well dense bone regions, making it possible to subdivide the whole image into regions. It is interesting to note, that these regions in bones, whose extraction might make it easier to detect the illness, correspond to some graph theoretic notions. In the paper, some invariants based on these graph theoretic notions, are proposed and if used with a machine classification method, e.g. a neural network, will make it possible to help recognize images of bones of ill persons. This graph theoretic approach is novel in this area. It helps to separate solution from the actual physical properties. The paper gives the proposed indices definitions and shows a classification based on them as input attributes.
This work was in part supported by the NCN grant N N519 654 480.
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References
Dlotko, P.: Private Communications (2012)
Efron, B.: Bootstrap methods: another look at the jackknife. Annals of Statistics 7, 1–26 (1979)
Fiser, D., Feigl, J., Kulich, M.: Growing Neural Gas Efficiently. Neurocomputing (2012) (in press)
Fritzke, B.: Kohonen Feature Maps and Growing Cell Structures a Performance Comparison. Advances in Neural Information Processing Systems 5, 115–122 (1993)
Fritzke, B.: A Growing Neural Gas network learns topologies. Advances in Neural Information Processing Systems 7, 625–632 (1995)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: International Conference on Artificial Neural Networks, Lausanne, pp. 613–618 (1997)
Garrahan, N.J., Mellish, R.W.E., Compston, J.E.: A new method for the two-dimensional analysis of bone structure in human iliac crest biopsies. J. Microsc. 142, 341–349 (1986)
Hahn, M., Vogel, M., Pompesius–Kempa, M., Delling, G.: Trabecular bone pattern factor – a new parameter for simple quantification of bone micro architecture. Bone 13, 327–330 (1992)
R: A language and environment for statistical computing. R Foundation for Statistical Computing (2005)
Saha, P.K., Wehrli, F.W.: A robust method for measuring trabecular bone orientation anisotropy at in vivo resolution using tensor scale. Pattern Recognition 37, 1935–1944 (2004)
Tabor, Z., Rokita, E.: Quantifying deterioration of bone tissue from grey-level images. Md. Eng. Phys. 29, 497–504 (2007)
Tabor, Z.: Detecting surfaces of minimal cut – a graph theoretical approach. Inżynieria Materiałowa 4, 439–442 (2008)
World Health Organization, Assessment of fracture risk and its application to screening for post-menopausal osteoporosis, Technical Report Series 843 (1994)
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© 2013 Springer International Publishing Switzerland
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Podolak, I.T., Jastrzębski, S.K. (2013). Density Invariant Detection of Osteoporosis Using Growing Neural Gas. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_62
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DOI: https://doi.org/10.1007/978-3-319-00969-8_62
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