Density Invariant Detection of Osteoporosis Using Growing Neural Gas
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.
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