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The shape language in application to the diagnosis of cervical vertebrae pathology.

Marzena BieleckaRafał ObuchowiczMariusz Korkosz
Published in: PloS one (2018)
In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes-the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour.
Keyphrases
  • deep learning
  • convolutional neural network
  • machine learning
  • optical coherence tomography
  • neural network
  • solid state