Skin cancer characterization with active infrared thermography and finite element modelling
Active infrared thermography (IRT) offers a non-invasive route to detect and characterize cutaneous lesions by measuring transient surface temperature responses following controlled cooling. This thesis presents an end-to-end investigation combining physics-based simulation, thermogram creation, algorithmic feature extraction, machine learning and prototype instrumentation to assess the feasibility of lesion localization and parameter estimation (diameter, depth, shape) from reheating sequences.
A five-layer parametric finite-element skin model based on Pennes’ bioheat equation was developed to produce large, labelled synthetic datasets. Data pipelines converted the generated 3D thermal data into clinically comparable 2D thermograms. Three network architectures were evaluated — a CNN regressor (diameter/depth), a U-Net segmentation network (localization/area) and a 3D-CNN for spatiotemporal reheating analysis. Experimental validation used skin-mimicking phantoms to characterize the cooling method and the HypIRskin prototype; a multi-camera imaging platform was developed as a scalable pathway for clinical acquisition.
Limitations include reliance on synthetic data, limited clinical samples and sensitivity to cooling protocol, calibration and registration. These results indicate that active IRT preserves measurable surface signatures that reflect underlying lesion geometry, and that physics-based simulation can reliably generate labeled training data when clinical examples are limited. Successful translation will require a large, carefully annotated clinical dataset to enable robust validation.
