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Supervised Classification of Thermal High-Resolution IR images for the diagnosis of Raynaud’s Phenomenon (G. Aretusi et al.).

Lavoro pubblicato sulla rivista scientifica, New Perspectives in Statistical Modeling and Data Analysis, Springer-Verlag, ISBN: 978-3-642-11362-8, 2011.

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 spatial framework to deal with the feature extraction problem. The classification of data from healthy subjects and from patients suffering for 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 images related to 44 subjects.

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L’articolo è stato pubblicato sulla rivista scientifica New Perspectives in Statistical Modeling and Data Analysis, Springer-Verlag, ISBN: 978-3-642-11362-8. 2011.