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Proceedings Paper

Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection
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Paper Abstract

Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

Paper Details

Date Published: 25 February 2014
PDF: 15 pages
Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190E (25 February 2014); doi: 10.1117/12.2042575
Show Author Affiliations
Kevin Estupinan Roldan, Pontificia Univ. Javeriana (Colombia)
Marco A. Ortega Piedrahita, Pontificia Univ. Javeriana (Colombia)
Hernan D. Benitez, Pontificia Univ. Javeriana (Colombia)

Published in SPIE Proceedings Vol. 9019:
Image Processing: Algorithms and Systems XII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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