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Space-time texture analysis in thermal infrared imaging for classification of Raynaud’s Phenomenon

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Abstract

This paper proposes a supervised classification approach for the differential diagnosis of Raynaud’s Phenomenon on the basis of functional infrared imaging (IR) data. The segmentation and registration of IR images are briefly discussed and two texture analysis techniques are introduced in a spatio-temporal framework to deal with the feature extraction problem. The classification of data from healthy subjects and from patients suffering from primary and secondary Raynaud’s Phenomenon is performed by using Stepwise Linear Discriminant Analysis (LDA) on a large number of features extracted from the images. The results of the proposed methodology are shown and discussed for a temporal sequence of images related to 44 subjects.

Key words

  • Raynaud’s Phenomenon
  • classification
  • functional infrared imaging
  • texture analysis
  • Gaussian Markov Random Fields

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Aretusi, G., Fontanella, L., Ippoliti, L., Merla, A. (2010). Space-time texture analysis in thermal infrared imaging for classification of Raynaud’s Phenomenon. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_1

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