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Automated Segmentation of Thermal Images
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Automated Segmentation of Thermal Images for Peripheral Vascular Disease Monitoring

Lead Investigator: Mr Jean Gauci
Supervisor: Dr Owen Falzon, Centre for Biomedical Cybernetics
Co-supervisor: Prof. Ing. Kenneth P. Camilleri, Centre for Biomedical Cybernetics and Dept. of Systems and Control Engineering, Faculty of Engineering 


Diabetes is a significant health problem worldwide with its prevalence having been on the increase for at least the last three decades and all estimates showing that this trend will continue. Diabetic patients are at a higher risk for developing peripheral arterial disease (PAD). PAD is a disease in which plaque build-up in the arteries restricts blood flow to the peripheries which may lead to complications such as ulcerations and amputations in the limbs. 

Any object which has a temperature above 0 Kelvin emits infrared radiation which is electromagnetic radiation with a wavelength between 2 and 14 µm. Thermal imaging is an imaging technique which gathers this infrared radiation and with the knowledge of some properties of the object being imaged and its’ surroundings can construct an image of the surface temperature of the object being imaged. 


This work presents a system for the monitoring of peripheral arterial disease in the lower limbs of diabetic patients using thermal imaging. Thermal data was collected from three different population samples, which include both healthy and diabetic participants. The thermal data consists of images of the volar aspect of the hands, anterior aspect of the shins and dorsal aspect of the foot acquired using a pre-defined acquisition protocol. A set of algorithms were developed with the aim of automatically extracting temperature data from 44 anatomical regions of interest across the three body regions. Analysis of this data may identify relevant patterns of interest which may be used to identify between different sub-groups in the thermal image database collected for this work. Results have shown that the regions of interest are extracted with a high accuracy from the participants in our database. The system also provides standardised and repeatable results, and does so in less time than a manual extraction process would take. This shows that a clinical tool which monitors PAD in diabetic patients based on thermal imaging is possible. 




J. Gauci, O. Falzon, K.P. Camilleri, C. Formosa, A. Gatt, C. Ellul, S. Mizzi, A. Mizzi, K. Cassar, N. Chockalingam, “Automated Segmentation and Temperature Extraction from Thermal Images of Human Hands, Shins and Feet” in IFMBE Proceedings, XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, vol. 57, pp. 275 - 280, Springer International Publishing, 2016.

Other Links 

THINK magazine: Attacking the silent epidemic of diabetes [Link] 

Engineering Today Issue 53, Pages 14-21: An Automated Procedure for Temperature Extraction from Medical Thermal Images [Link]

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