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Automatic eye corners detection and tracking algorithm in sequence of thermal medical images

Strąkowska M., Strąkowski R.

Measurement Automation Monitoring

2015

Vol. 61, No. 6

199--202

The article presents automatic eye corners detection algorithms in thermal images. Its target application is to perform quick and unnoticed measurement of human body temperature. It is proved that the temperature of eyes’ corners is the most reliable and stable temperature considering infrared imaging. That measurements were done manually so far. Our approach is to do this automatically and create complete system for measurement of core human body temperature in crowded places where it is impossible to do this in another way (for example on the airport, railway station). Such system could prevent people for spreading off the epidemic. Two proposed algorithms are presented: first based on morphological operations and geometric features of human face, second based on the cross-correlation and idea of pattern tracking. The selection of appropriate ROI size for reliable temperature extraction was tested according to the distance to person under observation.

Surface temperature control using thermal image processing

Jaworski T., Kucharski J., Frączyk A., Urbanek P.

Image Processing & Communications

2013

Vol. 18, no. 2-3

23--31

In the paper the computerized system for the surface temperature control of the rotating cylinder has been presented. In the presented solution, an IR camera works as a source of information about the thermal state of cylinder surface for the rest of the system. The necessary image processing algorithm as a basis for a control algorithm has been developed. An algorithm for the automatic localization of the cold cylinder by an IR camera using a set of thermal markers and a fuzzy recognition algorithm was developed. It enables one to calibrate the system properly in various operating conditions.

Classification of breast thermal images using artificial neural networks

Jakubowska T., Wiecek B., Wysocki M., Drews-Peszynski C., Strzelecki M.

Journal of Medical Informatics & Technologies

2004

Vol. 7

MIP41--50

In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.