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Registration of Thermographic Video
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Registration of Thermographic Video for Dynamic Temperature Analysis in Humans

Lead Investigator: Ms Christina Bonett
Supervisor: Dr Owen Falzon, Centre for Biomedical Cybernetics
Co-supervisor: Dr Kenneth Scerri, Dept. of Systems and Control Engineering, Faculty of Engineering

Abstract

The use of infrared thermography in medical applications has increased in popularity in recent years. It facilitates the detection and examination of skin thermal signatures, under both normal and abnormal conditions. Thermography has been employed in numerous biomedical fields, including breast cancer detection, cutaneous temperature monitoring during exercise and the analysis of normative temperature patterns.

Thermal imaging may be dynamic or static in nature. Using static thermography, the steady state conditions and spatial distributions of the thermal patterns within a target are analysed at a particular instant, usually following an acclimatisation period. In contrast, via dynamic thermography, both spatial and temporal variations are considered, making the acquired data more informative. However, issues including involuntary target movement and the dynamic temperature changes undergone by the target need to be considered.

Video registration was opted for in this work. Four steps constitute the registration process. The Speeded-Up Robust Features (SURF) detector was utilised in the feature detection stage. Matching features between images were then found based on the sum of squared differences (SSD) error, following which an affine geometric transformation was computed to adequately map the images in consideration. Bilinear interpolation was then utilised to calculate pixel values in non-integer coordinates. Two video registration methods were proposed in this work to address the primary issues associated with dynamic thermography. Data was gathered from nine participants for the testing of these methods. Following implementation, their performance was assessed both qualitatively and quantitatively, and a two-sample t-test was applied to verify that the difference between the mean errors per method was statistically significant.

Figure (a) 

Original_target 

Figure (b)

Results 

Figures: (a) The original thermographic video using the plantar surface of the foot as the target region. (b) The final registered thermographic video using the plantar surface of the foot as the target region, after implementing  the developed registration method  (Method 2). Frame 1 (top left) corresponds to the initial reference frame, while the succeeding frames correspond the registered frames at 25, 33, 41, 50 and 58 minutes respectively (100 frame interval).

Dynamic temperature analysis was also carried out on the extracted temperature data in both the time and frequency domains, where cyclic patterns having different frequencies and magnitudes were observed across all participants. Such behaviour has not been documented in literature thus far, which implies that the biological significance of these patterns is yet to be determined. 

Other Links 

Research at the Dept. of Systems and Control Engineering [Link] 

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Notices
Funding Award for BrainApp

CBC awarded research funding under the FUSION Technology Development Programme (TDP) 2017

New peer-reviewed journal article

CBC publishes in Biomedical Physics & Engineering Express

CBC attends EMBC 2017

Three Papers Accepted for Presentation at the Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)

Research at UoM: Brain Computer Interface

Communicating using brain signals

 
 
Last Updated: 5 July 2017

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