Application of infrared thermography in computer aided diagnosis

Abstract

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

Keywords: Classifier; Computer aided diagnosis; Feature evaluation; Feature extraction; Performance evaluation; Thermography.

Fig. 1

Blockdiagram of a CAD system based on thermograms.

Fig. 2

Header structure of the result tables.

Fig. 3

Examples of temperature profiles using fever thermal imager with temperature reading (Reproduced with permission of SPRING Singapore. Copyright remains with SS 582: Part 2: 2013 – ‘Specification for thermal imagers for human temperature scanning’). The picture captions report the mean body temperature, measured by an aural thermometer.

Fig. 4

Typical breast themograms. (The figure is reproduced from International Journal of Thermal Sciences 48 (2009) 849–859 from Elsevier Masson SAS. All rights reserved.)

Fig. 5

IR images of the human eye.