Estimation of growth features and thermophysical properties of melanoma within 3-D human skin using genetic algorithm and simulated annealing

Numerical modelling of skin tumour tissue with temperature-dependent properties for dynamic thermography

“…They used a 3D model similar to Ç etingül and Herman [14] and showed that the amplitude and phase image obtained from the periodical thermal stimulation can be of great importance for early detection, especially when the penetration depth of the lesion is less than 0.1mm. We can conclude that numerical models described in the work of Ç etingül and Herman [10,14] or Bhowmik and Repaka [34] are the closest to describe bioheat transfer in skin tumour realistically. However, none of them took into account local thermoregulation of the skin tissue [35][36][37], which mostly affects the blood perfusion rate.…”

“…The numerical model of skin tumour treats skin tissue as non-homogeneous, composed of several different layers as can be seen in Figure 1, representing the computational domain. Its basis has been taken from [10,11,14,34]. Ç etingül and Herman [10] concluded that the lesion shape does not affect the surface temperature response and that only the average thickness and diameter or volume is important.…”

“…Ç etingül and Herman [10] concluded that the lesion shape does not affect the surface temperature response and that only the average thickness and diameter or volume is important. Therefore, we decided to model the lesion geometry as a cylinder, with representative diameter and thickness, placed in the centre of our computational domain, as has been done by Bhowmik and Repaka [34]. However, the computational domain or surrounding skin is not modelled as a square [10,34], but as a cylinder because of the applied boundary conditions.…”

“…They also concluded that other model parameters do not greatly affect the temperature distribution and that the variation in temperature difference is small. A similar numerical model has also been used by Bhowmik et al [32][33][34], who solved inverse bioheat problem of estimating the diameter, thickness, blood perfusion rate and metabolic heat generation of the skin tumour under steady-state conditions (static thermography). They included thermally significant vessels to the skin model under the lesion [32], which affects the lesion identification in its early stage.…”

Inverse method for quantitative characterisation of breast tumours from surface temperature data

“…Kleinman and Roemer [28] also used the transient analysis on a one-dimensional model to estimate the temperature distribution and blood perfusion rate beneath the surface based on surface temperature data. Furthermore, transient analysis was also used by Bhowmik and Repaka [29] to estimate the geometric and thermophysical properties, but for melanoma. Although this looks similar to our work there are major dissimilarities, among other things we have given an explanation for the need of transient analysis, have used initial guesses far away from the exact parameters, used a different optimisation algorithm and have smaller computational time.…”