Skip to main content
SpringerLink
Log in

Modeling Thermal Infrared Imaging Data for Differential Diagnosis

  • Chapter
  • First Online:
Application of Infrared to Biomedical Sciences

Part of the book series: Series in BioEngineering ((SERBIOENG))

  • 1309 Accesses

Abstract

Nowadays, thermal infrared imaging (IRI) is thought to be a fascinating and promising complementary imaging tool regarding typical gold-standard medical imaging for differential diagnosis. This chapter presents the commonly used approaches for modeling thermal infrared data for differential diagnosis purposes. Two main modeling approaches were proposed, i.e., (i) qualitative modeling approach based on using statistical and machine learning techniques, (ii) quantitative modeling approach based on performing mathematical/analytical modeling of the thermoregulatory processes by using three main approaches: (i) empirically using automatic control theory, (ii) non-empirically using bioheat equations and (iii) semi-empirically using both bioheat equations and automatic control theory. Also, three main modeling approaches based on control system theory were presented, i.e., (i-a) time domain analysis of the thermoregulatory system’s characteristics through a direct estimation of the closed loop dynamic response parameters of a prototype second-order system, (i-b) a direct identification of thermoregulatory system as a second-order system plus delay time (SOPDT) from a closed-loop step response, and (i-c) a state-space representation of the thermoregulatory system as a first-order differential equation from the experimental IR temperature curves. Moreover, this chapter summarizes the advantages and disadvantages of each modeling approach highlighting its assumptions and approximations. By implementing the proposed modeling approaches, thermal infrared imaging has been demonstrated to be able to (i) identify significant averaged and asymmetric temperature parameters that could be used for disease classification, (ii) provide a direct functional IR indicators of the thermoregulatory malfunctions/alternations indirectly assess the severity of functional perturbation of the autonomic sympathetic and parasympathetic physiological activations in the presence of a disease, (iii) compute physiological information, such as localized blood flow, cardiac pulse, and breath rate, and (iv) identify skin’s thermal parameters, location of heat source (particularly the vessels), depth of heat source used for defining the location and geometrical shape of the affected-area, mostly required for tumor detection, and (v) provide a clear description of the underlying alterations in the main thermoregulatory functions as for example, environmental heat exchange process, vasoconstriction and/or vasodilation, and sweating actions. The authors consider this chapter as a good material that provides a great insight about the utility of thermal infrared imaging for medical diagnostic purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Szentkuti, A., Kavanagh, H.S., Grazio, S.: Infrared thermography and image analysis for biomedical use. Periodicum Biologorum 113(4), 385–392 (2011)

    Google Scholar 

  2. Calin, M.A., Mologhianu, G., Savastru, R., Calin, M.R., Brailescu, C.M.: A review of the effectiveness of thermal infrared imaging in the diagnosis and monitoring of knee diseases. Infrared Phys. Technol. 69, 19–25 (2015)

    Article  Google Scholar 

  3. Anbar, M., D’Arcy, S.: Localized regulatory frequencies of human skin temperature derived from the analysis of series of infrared images. In: Proceedings of the Fourth Annual IEEE Symposium on Computer-Based Medical Systems, pp. 184–191 (1991)

    Google Scholar 

  4. Hildebrandt, C., Raschner, C., Ammer, K.: An overview of recent application of medical infrared thermography in sports medicine in Austria. Sensors (Basel) 10(5), 4700–4715 (2010)

    Google Scholar 

  5. Ring, E.F., Ammer, K.: Infrared thermal imaging in medicine. Physiol. Meas. 33, 33–46 (2012)

    Article  Google Scholar 

  6. Merla, A., Di Donato, L., Luzio, S.D., Romani, G.L.: Quantifying the relevance and stage of disease with the Tau image technique: a complementary diagnostic imaging technique based on infrared functional imaging. IEEE Eng. Med. Biol. Mag. 21, 6 (2002)

    Article  Google Scholar 

  7. Ring, E.F., Ammer, K.: The Technique of Infrared Imaging in Medicine, Thermology International, vol. 10(1), pp. 7–14

    Google Scholar 

  8. Merla, A.: Functional infrared imaging: new approaches and applications of thermal imaging to medicine and neuro-psychology. In: 6th International Infrared Conference InfraR&D, Hannover Fair, Hannover, Germany

    Google Scholar 

  9. Izhar, L.I., Petrou, M.: Thermal Imaging in Medicine, Advances in Imaging and Electron Physics, vol. 171, ISSN 1076-5670. doi:10.1016/B978-0-12-394297-5.00002-7

  10. Lahiri, B.B., Bagavathiappan, S., Jayakumar, T., Philip, J.: Medical applications of infrared thermography. Infrared Phys. Technol. 55, 221–235 (2012)

    Article  Google Scholar 

  11. Montoro, J.C., Anbar, M.: New modes of data handling in computerized thermography. In: Harris, G., Walker, C. (eds.) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 10, pp. 845–847. New Orleans (1988)

    Google Scholar 

  12. Iven, G., Chekh, V., Luan, S., Mueen, A., Soliz, P., Xu, W., Burge, M.: Non-contact sensation screening of diabetic foot using low cost infrared sensors. In: IEEE 27th International Symposium on Computer-Based Medical Systems, pp. 479–480 (2014)

    Google Scholar 

  13. Ismail, E., Orlando, G., Corradini, M.L., Amerio, P., Romani, G.L., Merla, A.: Differential diagnosis of Raynaud’s phenomenon based on modeling of finger thermoregulation. Physiol. Meas. 35, 703–716 (2014)

    Article  Google Scholar 

  14. Ismail, E., Orlando, G., Pompa, P., Gabrielli, D., Di Donato, L., Cardone, D., Merla, A.: Time-domain analysis of scrotal thermoregulatory impairment in varicocele. Front. Physiol. 5, 342 (2014)

    Article  Google Scholar 

  15. Ismail, E., Capo, A., Amerio, P., Merla, A.: Functional-thermoregulatory model for the differential diagnosis of psoriatic arthritis. BioMed. Eng. Online 13, 162 (2014)

    Article  Google Scholar 

  16. Capo, A., Ismail, E., Cardone, D., Celletti, E., Auriemma, M., Sabatini, E., Merla, A., Amerio, P.: Joint functional impairment and thermal alterations in patients with psoriatic arthritis: a thermal imaging study. Microvasc. Res. 102, 86–91 (2015)

    Article  Google Scholar 

  17. Capo, A., Merla, A., Mattei, P., Auriemma, M., Panarese, F., Celletti, E., Abate, M., Romani, G.L., Amerio, P.: Assessment of psoriatic arthritis by means of functional infrared imaging: a pilot study. Clin. Drug. Invest. (2013)

    Google Scholar 

  18. Diakides, N.A., Diakides, M., Lupo, J.C., Paul, J.L., Balcerak, R.: Advances in medical infrared imaging. In: Diakides, N.A., Bronzino, J.D. (eds.) Medical Infrared Imaging (Chapter 1). CRC Press, Boca Raton, FL (2008)

    Google Scholar 

  19. Mariotti, A., Grossi, G., Amerio, P., Orlando, G., Mattei, P.A., Tulli, A., Romani, G.L., Merla, A.: Finger thermoregulatory model assessing functional impairment in Raynaud’s phenomenon. Ann. Biomed. Eng. 37, 2631–2639 (2009)

    Article  Google Scholar 

  20. Mariotti, A., Orlando, G., Corradini, M.L., Pompa, P., Iezzi, R., Cotroneo, A.R., Romani, G.L., Merla, A.: Scrotal thermoregulatory model and assessment of the impairment of scrotal temperature control in varicocele. Ann. Biomed. Eng. 39, 664–673 (2011)

    Article  Google Scholar 

  21. Cardone, D., Pinti, P., Merla, A.: Thermal infrared imaging-based computational psychophysiology for psychometrics. In: Computational Psychophysiology for Psychometrics, Computational and Mathematical Methods in Medicine, pp. 8 pages (2015)

    Google Scholar 

  22. Dowdall, J., Pavlidis, I., Tsiamyrtzis, P.: Coalitional tracking. Comput. Vis. Image Underst. 106, 15 (2007)

    Article  Google Scholar 

  23. Herry, C.L., Frize, M.: Digital processing techniques for the assessment of pain with infrared thermal imaging. In: Proceedings of the 24th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1157–1158 (2002)

    Google Scholar 

  24. Kennedy, D., Lee, T., Seely, D.: A comparative review of thermography as a breast screening technique. Integr. Cancer Ther. 8(1), 9–16 (2009)

    Article  Google Scholar 

  25. Ammer, K.: The sensitivity of infrared imaging for diagnosing Raynaud’s phenomenon and for thoracic outlet syndrome is depended on the method of temperature extraction from thermal images. In: 9th International Conference on Quantitative InfraRed Thermography, Krakow, Poland (2008)

    Google Scholar 

  26. Hildebrandt, C., Raschner, C., Ammer, K.: An overview of recent application of medical infrared thermography in sports medicine in Austria. Sensors 10, 4700–4715 (2010). doi:10.3390/s100504700

    Article  Google Scholar 

  27. Oliver Faust, O., Acharya, U.R., Ng, Y., Hong, T.J., Yu, W.: Application of infrared thermography in computer aided diagnosis. Infrared Phys. Technol. 66, 160–175 (2014)

    Article  Google Scholar 

  28. Merla, A., Di Donato, L., Di Luzio, S., Farina, G., Pisarri, S., Proietti, M., Salsano, F., Romani, G.L.: Infrared functional imaging applied to Raynaud’s phenomenon. IEEE Eng. Med. Biol. Mag. 6, 73–79 (2002)

    Article  Google Scholar 

  29. Merla, A., Di Ledda, A., Di Donato, L., Di Luzio, S., Romani, G.L.: Use of infrared functional imaging to detect impaired thermoregulatory control in men with asymptomatic varicocele. Fertil. Steril. 18, 199–200 (2002)

    Article  Google Scholar 

  30. Foerster, J., Kuerth, A., Niederstrasser, E., Krautwald, E., Pauli, R., Paulat, R., Eweleit, M., Riemekasten, G., Worm, M.: A cold-response index for the assessment of Raynaud’s phenomenon. J. Dermatol. Sci. 45, 8 (2007)

    Article  Google Scholar 

  31. Hahn, M., Hahn, C., Jünger, M., Steins, A., Zuder, D., Klyscz, T., Büchtemann, A., Rassner, G., Blazek, V.: Local cold exposure test with a new arterial photoplethysmography sensor in healthy controls and patients with secondary Raynaud’s phenomenon. Microvasc. Res. 57, 12 (1999)

    Article  Google Scholar 

  32. Gat, Y., Bachar, G., Zukerman, N., Belenky, Z.A., Gornish, M.: Physical examination may miss the diagnosis of bilateral varicocele: a comparative study of 4 diagnostic modalities. Urol 172(4), 1414–1417 (2004)

    Article  Google Scholar 

  33. Merla, A., Ledda, A., Di Donato, L., Di Luzio, S., Romani, G.L.: Assessment of the effects of varicocelectomy on the thermoregulatory control of the scrotum. Fertil. Steril. 81, 471–472 (2004)

    Article  Google Scholar 

  34. Pavlidis, I., Levine, J., Baukol, P.: Thermal image analysis for anxiety detection. In: Proceedings in International Conference on Image Processing, vol. 2, pp. 315–318. Thessaloniki, Greece (2001)

    Google Scholar 

  35. Pavlidis, I., Levine, J.: Thermal image analysis for polygraph testing. IEEE Eng. Med. Biol. Mag. 21(6), 56–64 (2002)

    Article  Google Scholar 

  36. Pavlidis, I.: Continuous physiological monitoring. In: Proceedings of the 25th Annual International Conference of the IEEE EMBS, pp. 1084–1087. Cancun, Mexico (2003)

    Google Scholar 

  37. Johnson, J.M., scand, J.: Exercise in a hot environment: the skin circulation. Med. Sci. Sports 20, 29–39 (2010)

    Google Scholar 

  38. Johnson, J.M., Minson, C.T., Kellogg, D.L.: Cutaneous vasodilator and vasoconstrictor mechanisms in temperature regulation. Compr. Physiol. 4, 33–89 (2014)

    Article  Google Scholar 

  39. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edition. Wiley

    Google Scholar 

  40. Rollins, D., Hulting, S.: System identification of the human thermoregulatory system using continuous-time block-oriented predictive modelling. Chem. Eng. Sci. 61, 1516–1527 (2006)

    Article  Google Scholar 

  41. Nocedal, J.: Wright, Numerical Optimization, 2nd edition, pp. 248–250. Wiley (2006)

    Google Scholar 

  42. Tortura, G.J., Grabowski, S.R.: Principles of Anatomy and Physiology. Wiley, New York (2003)

    Google Scholar 

  43. Agurto, C., Chek, V., Edwards, A., Jarry, Z., Barriga, S., Simon, J., Soliz, P.: A thermoregulation model to detect diabetic peripheral neuropathy. In: 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 13–16 (2016)

    Google Scholar 

  44. Heller, H.C., Dennis, A.G.: Arteriovenous anastomoses’ function and Raynaud’s phenomenon. Angiology 1, 9 (2012)

    Google Scholar 

  45. Golnaraghi, F., Kuo, B.C.: Automatic Control Systems in Italic, 9th edn. Wiley, Hoboken (2010)

    Google Scholar 

  46. Rollins, D., Di Bhabdar, N., Hulting, S.: System identification of the human thermoregulatory system using continuous-time block-oriented predictive modelling. Chem. Eng. Sci. 61, 12 (2006)

    Article  Google Scholar 

  47. Yurkevich, V.D.: Predictive PID Control of Non-minimum Phase Systems. Kenny Uren and George van School, Intech (2011)

    Google Scholar 

  48. Ljung, L.: System Identification: Theory for the User, p. 809. Prentice-Hall, New Jersey (1999)

    Google Scholar 

  49. Seborg, D.E., Edgar, T.F., Mellichamp, D.A., Doyle, F.J.: Process Dynamics and Control, pp. 58–77, 102–123. Delhi Press, Delhi (2000)

    Google Scholar 

  50. Waterhouse, J.: Homeostatic control mechanism. Anaesth. Intensive Care 5, 236–240 (2004)

    Article  Google Scholar 

  51. Friedland, B.: Control Systems Design: State Space Methods. Dover, New York (2003)

    Google Scholar 

  52. Incropera, F.P., DeWitt, D.P., Bergman, T.L., Lavine, A.S.: Introduction to Heat Transfer, 5th edition. Wiley (2006)

    Google Scholar 

  53. Chekh, V., Soliz, P., Barriga, S., McGrew, E., Kanagy, N., Luan, S.: Novel model of thermoregulation based on control theory used to evaluate peripheral microvascular function. Exp. Biol. (2013)

    Google Scholar 

  54. Jiang, L., Zhan, W., Loew, M.H.: Modelling thermography of tumorous human breast: from forward problem to inverse problem solving. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 205–208. Rotterdam, The Netherlands (2010)

    Google Scholar 

  55. Cetingul, M.P., Herman, C.: A heat transfer model of skin tissue for the detection of lesion: sensitivity analysis. Phys. Med. Biol. 55(19), 5933–5951 (2010)

    Article  Google Scholar 

  56. Pavlidis, I., Dowdall, J., Sun, N., Puri, C., Fei, J., Garbey, M.: Interacting with human physiology. Comput. Vis. Image Underst. 108, 150–170 (2007)

    Article  Google Scholar 

  57. Agnelli, J.P., Cristina, A.B., Turner, V.: Tumor location and parameters estimation by thermography. Retrieved from http://www.famaf.unc.edu.ar/abarrea/Imagenes/tumordetection-25-03-09.pdf (2010). Accessed June 2010

  58. Bagavathiappan, S., Saravanan, T., Philip, J., Jayakumar, T., Raj, B., Karunanithi, R., Jagadeesan, K.: Investigation of peripheral vascular disorders using thermal imaging. Br. J. Diab. Vasc. Dis. 8(2), 102–104 (2008)

    Article  Google Scholar 

  59. Deng, Z.-S., Liu, J.: Mathematical modelling of temperature mapping over skin surface and its implementation in thermal disease diagnostics. Comput. Biol. Med. 34(6), 495–521 (2004)

    Article  Google Scholar 

  60. Mital, M., Scott, E.P.: Thermal detection of embedded tumors using infrared imaging. J. Biomed. Eng. 129(1), 33–39 (2007)

    Google Scholar 

  61. WilsonGarbey, nSB, Spence, V.A.: A tissue heat transfer model for relating dynamic skin temperature changes to physiological parameters. Phys. Med. Biol. 33(8), 895–912 (1988)

    Article  Google Scholar 

  62. Pennes, H.H.: Analysis of tissue and arterial blood temperature in resting human forearms. J. Appl. Physiol. 2, 93–122 (1948)

    Google Scholar 

  63. Garbey, M., Merla, A., Pavlidis, I.: Estimation of blood flow speed and vessel location from thermal video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), vol. 1, pp. 356–363 (2004)

    Google Scholar 

  64. Jiang, L., Zhan, W., Loew, M.H.: Toward understanding the complex mechanisms behind breast thermography: an overview for comprehensive numerical study. In: Proceedings of SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 7965 (2011)

    Google Scholar 

  65. Ren, Z.P., Liu, J., Wang, C.C., Jiang, P.X.: Boundary element method bem for solving normal or inverse bio heat transfer problem of biological bodies with complex shape. J. Therm. Sci. 4, 117–124 (1995)

    Google Scholar 

  66. González, F.J.: Non-invasive estimation of the metabolic heat production of breast tumors using digital infrared imaging. Quant. InfraRed Thermogr. J. 8, 2 (2011). doi:10.3166/qirt.8.139-148

    Google Scholar 

  67. Miccio, J., Parikh, S., Marinaro, X., Prasad, A., McClain, S., Singer, A.J., Clark, R.A.: Forward looking infrared imaging predicts ultimate burn depth in a porcine vertical injury progression model. Burns 42(2), 397–404 (2016). doi:10.1016/j.burns.2015.07.006

    Article  Google Scholar 

  68. Fujimasa, I., Chinzei, T., Saito, I.: Converting far infrared image information to other physiological data. IEEE Eng. Med. Biol. Mag. 19(3), 71–76 (2000)

    Article  Google Scholar 

  69. Kakuta, N., Yokoyama, S., Suzuki, T., Saito, T., Mabuchi, K.: Evaluation of infrared images by using a human thermal model. In: 2001 Proceedings of the 23rd Annual EMBS International Conference, 25–28 Oct, Istanbul, Turkey (2001)

    Google Scholar 

  70. Kakuta, N., Yokoyama, S., Mabuchi, K.: Human thermal models for evaluating infrared images: comparing infrared images under various thermal environmental conditions through normalization of skin surface temperature. IEEE Eng. Med. Biol. Mag. 21(6), 65–72 (2002)

    Article  Google Scholar 

  71. Werner, J.: Thermoregulatory models: recent research, current applications and future development. Scand. J. Work Environ. Health 15(1), 34–46 (1989)

    MathSciNet  Google Scholar 

  72. Stolwijk, J.A., Hardy, J.D.: Temperature regulation in man—a theoretical study. Pflügers Archiv 291, 129–162 (1966)

    Article  Google Scholar 

  73. Foda, E., Almesri, I., Awbi, H.B., Sirén, K.: Models of human thermoregulation and the prediction of local and overall thermal sensations. Build. Environ. 46, 2023–2032 (2011)

    Article  Google Scholar 

  74. El-Samahy, E., Mahfouf, M., Linkens, D.A.: A closed-loop hybrid physiological model relating to subjects under physical stress. Artif. Intell. Med. 38, 257–274 (2006)

    Article  Google Scholar 

  75. Konz, S., Hwang, C., Dhiman, B., Duncan, J., Masud, A.: An experimental validation of mathematical simulation of human thermoregulation. Comput. Biol. Med. 7, 71–82 (1977)

    Article  Google Scholar 

  76. Fiala, D., Lomas, K.J., Stohrer, M.: Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. Int. J. Biometeorol. 45, 143–159 (2001)

    Article  Google Scholar 

  77. Ng, E.Y.K., Sudharsan, N.M.: An improved three-dimensional direct numerical modelling and thermal analysis of a female breast with tumour. Proc. Inst. Mech. Eng. 215 (2001)

    Google Scholar 

  78. Zolfaghari, A., Maerefat, M.: A new simplified thermoregulatory bioheat model for evaluating thermal response of the human body to transient environments. Build. Environ. 45, 2068–2076 (2010)

    Article  Google Scholar 

  79. Zavisek, M.: Breast cancer diagnostics using infrared camera. Retrieved from http://www.feec.vutbr.cz/EEICT/2003/msbornik/01-Electronics/03-PhD/08-scallop.pdf (2011a). Accessed Jan 2011

  80. Zavisek, M.: Quantitative thermography in breast cancer detection—a survey of current research. Retrieved in January 2011b from http://www.feec.vutbr.cz/EEICT/2004/sbornik/03-Doktorskeprojekty/01-Elektronika/40-michal.pdf (2011b). Accessed 12 Feb 2012

  81. Jayakumar, S.B., Saravanan, T., Philip, J., Tammana, Raj, Karunanithi, B., Panicker, R., Korath, T.M.P., Jagadeesan, K.: Investigation of peripheral vascular disorders using thermal imaging. J. Diab. Vasc. Dis. 8(2), 102–104 (2008)

    Article  Google Scholar 

  82. Kennedy, D., Lee, T., Seely, D.: A comparative review of thermography as a breast screening technique. Integr. Cancer Ther. 8, 9–16 (2009)

    Article  Google Scholar 

  83. Acharya, U.R., Ng, E.Y.K, Tan, J.H., Sree, S.V.: Thermography-based breast cancer detection using texture features and support vector machine. J. Med. Syst., 1–8 (2010). doi:10.1007/s10916-010-9611-z

  84. Quek, C., Irawan, W., Ng, E.Y.K.: A novel brain-inspired neural cognitive approach to SARS thermal image analysis. Expert Syst. Appl. 37(4), 3040–3054 (2010)

    Article  Google Scholar 

  85. Marzec, M., Koprowski, R., Wrobel, Z.: Automatic temperature measurement on thermograms for headache diagnosis. Meas. Autom. Control 55(11), 923–926 (2009)

    Google Scholar 

  86. Spalding, S.J., Kwoh, K., Boudreau, R., Enama, J., Lunich, J., Huber, D., Denes, L., Hirsch, R.: Three-dimensional and thermal surface imaging produces reliable measures of joint shape and temperature: a potential tool for quantifying arthritis. Arthritis Res. Ther. 10 (2008). doi:10.1186/ar2360

  87. Armstrong, D.G., Lavery, L.A., Wunderlich, R.P., Boulton, A.J.M.: Skin temperatures as a one-time screening tool do not predict future diabetic foot complications. J. Am. Podiatr. Med. Assoc. 93(6), 443–447 (2003)

    Article  Google Scholar 

  88. Koay, J., Herry, C., Frize, M.: Analysis of breast thermography with an artificial neural network. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, pp. 1159–1162. San Francisco, California (2004)

    Google Scholar 

  89. Qi, H., Kuruganti, P.T., Snyder, W.E.: Detecting breast cancer from thermal infrared images by asymmetry analysis. In: Diakides, N.A., Bronzino, J.D. (eds.) Medical Infrared Imaging (Chapter 11). CRC Press, Boca Raton, FL (2008)

    Google Scholar 

  90. Tarnawski, W., Schaefer, G., Nakashima, T., Miroslaw, L.: Applications of fuzzy rulebased systems in medical image understanding. In: Pal, S.K., Peters, J.F. (eds.) Rough, Fuzzy Image Analysis: Foundations and Methodologies (Chapter 6). CRC Press, Boca Raton, FL (2010)

    Google Scholar 

  91. Wiecek, B., Strzelecki, M., Jakubowska, T., Wysocki, M., Drews-Peszynski, C.: Advanced thermal image processing. In: Diakides, N.A., Bronzino, J.D. (eds.) Medicalinfrared Imaging (Chapter 12). CRC Press, Boca Raton, FL (2008)

    Google Scholar 

  92. Merla, A., Romani, G.L.: Functional infrared imaging in medicine: quantitative, diagnostic approach. In: Proceedings of the 28th Annual International Conference of the IEEE, EMBS, vol. 1, pp. 224–227. New York City, USA (2006)

    Google Scholar 

  93. Grossi, G., Mariotti, A., Di Donato, L., Amerio, P., Tulli, A., Romani, G.L., Merla, A.: Functional infrared imaging of paroxysmal ischemic events in patients with Raynaud’s phenomenon. Int. J. Immunopathol. Pharmacol. 23(2), 627–632 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt

    Enas Ismail

  2. Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio”, Via dei Vestini, 31, 66013, Chieti, Italy

    Arcangelo Merla

  3. ITAB-Institute of Advanced Biomedical Technologies, Via dei Vestini, 32, 66013, Chieti, Italy

    Arcangelo Merla

Authors
  1. Enas Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arcangelo Merla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enas Ismail .

Editor information

Editors and Affiliations

  1. College of Engineering, Nanyang Technological University, Singapore, Singapore

    Eddie YK Ng

  2. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Mahnaz Etehadtavakol

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Ismail, E., Merla, A. (2017). Modeling Thermal Infrared Imaging Data for Differential Diagnosis. In: Ng, E., Etehadtavakol, M. (eds) Application of Infrared to Biomedical Sciences. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-3147-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3147-2_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3146-5

  • Online ISBN: 978-981-10-3147-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation