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Języki publikacji
Abstrakty
This paper presents a novel screening approach of human skin pathologies using Active IR Thermography. The inputs of the proposed algorithm are the values of the physical parameters of the skin. Parameters are estimated based on dynamic thermographic measurements of human skin and the developed thermal model of the tissue. The calculations were based on the inverse thermal modelling. Classification was done using Support Vector Machine, Linear Discriminant Analysis and k-Nearest Neighbours classifiers. As an example, one presented the results of screening for psoriasis.
Wydawca
Czasopismo
Rocznik
Tom
Strony
602--610
Opis fizyczny
Bibliogr. 29 poz., rys., tab., wykr.
Twórcy
autor
- Institute of Electronics, Lodz University of Technology, Wólczańska Str., 211/215, 90-924 Łódź, Poland
autor
- Institute of Electronics, Lodz University of Technology, Łódź, Poland
autor
- Institute of Electronics, Lodz University of Technology, Łódź, Poland
autor
- Electronics and Information Systems Dept., Ghent University, Ghent, Belgium
autor
- Institute of Electronics, Lodz University of Technology, Łódź, Poland
Bibliografia
- [1] Buzug TM, Schumann S, Pfaffmann L, Reinhold U, Ruhlmann J. Functional infrared imaging for skin-cancer screening.. Proceedings of the 28th IEEE EMBS Annual International Conference; 2006.
- [2] Ring EFJ, Ammer K. The technique of infrared imaging in medicine. Thermol Int 2000;10(1).
- [3] Ring EFJ, Jung A, Żuber J. Infrared imaging: a casebook in clinical medicine. Bristol: IOP Publishing Ltd.; 2015, ISBN: 9780750311441.
- [4] Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: a review. Infrared Phys Technol 2012;55(1):221–35.
- [5] Vainer B. Applications of infrared thermography to medicine. In: Meola C, editor. Infrared thermography recent advances and future trends. Bentham e-Books; 2012. p. 61–84.
- [6] Strąkowska M, Kaszuba A, Więcek B, Strzelecki M, De Mey G. System and software for thermal images screening in medicine: application to psoriasis. Quant InfraRed Thermogr J 2015;12(2):127–36.
- [7] Nowakowski A, Kaczmarek M. Active dynamic thermography – problems of implementation in medical diagnostics. Quant InfraRed Thermogr J 2011;8(1):89–106.
- [8] Kaczmarek M, Nowakowski A. Active IR-thermal imaging in medicine. J Nondestruct Eval 2016;35(January (1)):19.
- [9] Kaczmarek M. A new diagnostic IR-thermal imaging method for evaluation of cardiosurgery procedures. Biocybern Biomed Eng 2016;36:344–54.
- [10] Jakubowska T, Wiecek B, Wysocki M, Drews-Peszynski C, Strzelecki M. Classification of breast thermal images using artificial neural networks. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1; 2004. pp. 1155–8.
- [11] Kandlikar SG, Perez-Raya I, Raghupathi PA, Gonzalez-Hernandez J-L, Dabydeen D, Medeiros L, et al. Infrared imaging technology for breast cancer detection: current status, protocols and new directions. Int J Heat Mass Transf 2017;108:2303–20.
- [12] Nowakowski A. Quantitative active dynamic thermal Irimaging and thermal tomography in medical diagnostics. In: Diakides M, Bronzino JD, Petereson DR, editors. Medical infrared imaging, principles and practices. Boca Raton: CRC Press, Taylor & Francis Group; 2013. ISBN: 9781439872499.
- [13] Strąkowska M, Strzelecki M, Kaszuba A. Novel methodology of medical screening using IR thermography. Signal Processing Algorithms, Architectures, Arrangements and Applications, SPA; 2014. 9/2014.
- [14] Strąkowska M, Strąkowski R, Strzelecki M. Thermal-time constant imaging in cold-stress screening. Signal Processing Algorithms, Architectures, Arrangements and Applications, SPA; 2015. 9/2015.
- [15] Di Carlo A. Thermography and the possibilities for its applications in clinical and experimental dermatology. Clin Dermatol 1995;13(4):329–36.
- [16] Bonmarin M, Le Gal F-A. Lock-in thermal imaging for the early-stage detection of cutaneous melanoma: a feasibility study. Comput Biol Med 2014;47:36–43.
- [17] Bonmarin M, Le Gal F-A. A lock-in thermal imaging setup for dermatological applications. Skin Res Technol 2015;21:284–90.
- [18] Herman C. The role of dynamic infrared imaging in melanoma diagnosis. Expert Rev Dermatol 2013;8(2):177–84.
- [19] Pirtini Çetingül M, Herman C. Quantification of the thermal signature of a melanoma lesion. Int J Thermal Sci 2011;50:421–31.
- [20] Çetingül MP, Herman C. A heat transfer model of skin tissue for the detection of lesions: sensitivity analysis. Phys Med Biol 2010;55:5933–51.
- [21] Jasiński M. Modelling of 1D bioheat transfer with perfusion coefficient dependent of tissue necrosis. Prace Naukowe Instytutu Matematyki i Informatyki Politechniki Czestochowskiej 2008;7(1):57–62.
- [22] Khanafer K, Vafai K. Advances in numerical heat transfer: synthesis of mathematical models representing bioheat transport. CRC Press; 2009.
- [23] Ng EYK, Tan HM, Ooi EH. Boundary element method with bioheat equation for skin burn injury. Burns 2009;35 (7):987–97.
- [24] Pennes H. Analysis of tissue and arterial blood temperatures in the resting human forearm. J Appl Physiol 1948;1(2):93–122.
- [25] Strakowska M, De Mey G, Wiecek B, Strzelecki M. A three layer model for the thermal impedance of the skin: modeling and experimental measurements. J Mech Med Biol 2015;15(3).
- [26] Zolfaghari A, Maerefat M. Bioheat transfer, developments in heat transfer. InTech; 2011.
- [27] Wiecek B, De Mey G, Stąkowska M, Chatziathanasiou V, Gmyrek Z, Strzelecki M, et al. Various applications of complex thermal impedance for transient and ac heat transfer analysis. Meas Autom Monit 2015;61 (6):210–4.
- [28] Audet Jr C, Dennis JE. Analysis of generalized pattern searches. SIAM J Optim 2003;13(3):889–903.
- [29] Fawcett T. An introduction to roc analysis. Pattern Recogn 2006;27:861–74.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1cfcae68-4c4d-4b04-b818-5faf72de7eac