Thermal modelling and screening method for skin pathologies using active thermography

Strąkowska, M.; Strąkowski, R.; Strzelecki, Michał; Mey, G. De; Więcek, B.

doi:10.1016/j.bbe.2018.03.009

Cited by 20 publications

(35 citation statements)

References 16 publications

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“…In the heat transfer domain, the thermal system can be modelled by RC ladder networks. 1,2,11,[14][15][16][17][18][19] Identification of the system consists of solving an inverse heat conduction problem. Although an electrical network without inductances seems easier to be identified, in many cases, the inverse heat conduction problem is ill conditioned.…”

Section: Introductionmentioning

confidence: 99%

“…There is a software tool for dynamic system identification, which is written in the Matlab environment and uses in-built Transfer Function Estimation (TFEST). 16,17,21 Recently, based on TFEST, a comprehensive package for the inverse heat transfer problem for biomedical applications was presented. 18 In addition, optimization procedures are also suitable for a non-linear function fitting problem, that is, either gradient or gradientless methods, for example, the fminsearch or PatternSearch implemented in the Matlab library.…”

Section: Introductionmentioning

confidence: 99%

“…Such identification can be helpful for the screening and diagnosis of inflammation and cancerous lesions in skin tissues. 17,18…”

Section: Introductionmentioning

confidence: 99%

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Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Strąkowska

Chatzipanagiotou

Mey

et al. 2020

Circuit Theory & Apps

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Summary This paper deals with the identification of the thermal parameters of multilayer objects using the concept of thermal impedance. In order to perform such identification, temperature evolution in time is obtained by an infrared camera after power excitation is applied in the investigated structure. Infrared thermography offers the advantage of being a noncontact temperature detection and measurement method. In many practical cases, it is impossible to use contact temperature measurements. Typically, the power in the form of a step function is applied. In order to calculate the thermal impedance of an object, temperature and power are converted into the frequency domain using the Laplace transform for s = jω. Then, the poles of the thermal impedance are identified using vector fitting, which allows calculating the thermal impedance as a sum of partial fractions. This corresponds directly to the Foster network of a thermal object. In addition, the vector fitting method offers much better convergence in comparison with other methods using the polynomial rational approximation of thermal impedance. A considerable improvement of the numerical Laplace transform in high frequency range is proposed. In this approach, the variable s = jω is replaced by trues~=()α+jω, and then, the integration result is corrected by the Taylor series. It leads to a kind of filtering of the temperature signal.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Strąkowska

Chatzipanagiotou

Mey

et al. 2020

Circuit Theory & Apps

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“…To ease the comprehension of the information retrieved from thermal images and optimize the final diagnosis, some authors have explored the application of artificial intelligence (AI) classifiers. The implementation of this joint approach can be encountered in the detection of thyroid cancer, extreme fatigue, rheumatoid arthritis, hypertension and psoriasis, but, not to our knowledge, to melanoma and nevi classification. Nonetheless, it is consensus the positive contribution of AI in medical diagnosis, particularly in rural areas where lack of specialized healthcare professionals occurs …”

Section: Introductionmentioning

confidence: 99%

Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Magalhães

Vardasca

Rebelo

et al. 2019

Acad Dermatol Venereol

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Background The incidence rates of melanoma have risen to worrying levels over the last decade. Delayed diagnosis, due to faults on the detection stage, indicates the necessity of new aiding diagnosis techniques. Since metabolic activity is highly connected to neoplasia formation, a detection technique that focuses its results on vascular responses, as Infrared thermal (IRT), seems to be a viable option. Materials and methods Static and dynamic (cooling) thermal images of melanoma and melanocytic nevi lesions were collected and analysed to retrieve thermal parameters characteristic of this skin lesion types. The steady‐state and dynamic variables were tested separately with different machine learning classifiers to verify whether the distinction of melanoma and nevi lesions was achievable. Results The differentiation of both types of skin tumours was doable, achieving an accuracy of 84.2% and a sensitivity of 91.3% with the implementation of a learner based on support vector machines and an input vector composed by static variables. Conclusion The use of IRT for skin tumour classification is achievable, but some improvement is needed to raise the metrics of sensitivity and specificity. For future work, it is recommended the study of dynamic parameters for the classification of other types of skin neoplasia.

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“…González (2011) states that the results of metabolic heat production of breast tumors using infrared digital images (digital infrared imaging) has the potential to noninvasively estimate the malignancy of a tumor. More recently Strąkowska, Strąkowski, Strzelecki, Mey, and Więcek (2018) presented a new Active Thermography (AT) based method for detection of skin pathologies. The method was tested on a group of patients with psoriasis, however the authors explain that AT can be extended for screening other pathologies of the skin and the inner tissues.…”

Section: Introductionmentioning

confidence: 99%

Numerical two-dimensional thermal analysis of the human skin using the multigrid method

Ströher

Santiago

2019

Acta Sci. Technol.

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Heat can be used as an adjuvant treatment of many diseases and also as a powerful tool to help diagnose cancers, with the advantage to be a noninvasive exam. Some tumors may be best diagnosed by evaluating body temperature distribution, for instance, it is observed that local temperatures of the skin over a tumor are higher than the average skin temperature. Certainly, it is expected from medical diagnostics to be, early, fast and very precise. Especially if the health problem is a tumor, it is necessary to know the shape and the size of the cancer. Thermal images can provide further information about the tumor, generally, the thermal diagnostic is made comparing images of the region with a bioheat model. In this context, the present study shows interesting results about the multigrid method applied to solve the Pennes bioheat equation in two dimensions, using a non-stationary and steady state cases for the skin heath and with melanoma. The multigrid method presented itself as an extremely efficient and fast tool to solve the bioheat equation with refined grids that provide good spatial precision.

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Thermal modelling and screening method for skin pathologies using active thermography

Cited by 20 publications

References 16 publications

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Numerical two-dimensional thermal analysis of the human skin using the multigrid method

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