Paper
25 February 2014 Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection
Author Affiliations +
Proceedings Volume 9019, Image Processing: Algorithms and Systems XII; 90190E (2014) https://doi.org/10.1117/12.2042575
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kevin Estupinan Roldan, Marco A. Ortega Piedrahita, and Hernan D. Benitez "Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection", Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190E (25 February 2014); https://doi.org/10.1117/12.2042575
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Thermography

Infrared imaging

Image registration

Infrared radiation

Scalable video coding

Image segmentation

Image acquisition

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