Skip to main content
SpringerLink
Log in

Evaluation of Dynamic Thermograms Using Semiautomatic Segmentation Software: Applied to the Diagnosis of Thyroid Cancer

  • Conference paper
  • First Online:
XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 83))

Included in the following conference series:

Abstract

Infrared thermography is an imaging technique applied in studies to aid in the diagnosis of several types of cancer, including thyroid cancer. Tumors generate changes (anatomical and vascular) that are shown in thermal images. This paper shows the creation of semiautomatic segmentation software to assist in the diagnosis of tumors in dynamic thermograms. For the beginning of the analysis, the software allows the creation of a project using the text files exported by the camera and interpreting them as a temperature matrix. Then, it allows to change the scale of the displayed image and choose the color palette used in the temperature conversion for color. The FloodFill algorithm delimits the regions of interest (area with tumor and healthy) when the user positions the cursor (seed expansion) on the tumor, and defines the threshold of difference. The extraction of data in the segmented region provides: the temperatures (maximum, minimum and average); the number of pixels involved in the segmentation area; allows the export of the data in a .cvs file (compatible with other programs, such as excel); and provides a graph with the thermal difference between the two areas analyzed (healthy and with tumor). The application of the software has been demonstrated in two thyroid tumors, one malignant and one benign. In the malignant one, it is possible to observe higher temperatures in the tumors compared to the healthy regions during the rewarming period after cold stress, different thermal behavior between benign and malignant tumors, as well as a larger number of pixels in the segmented area of the malignant tumor, when compared to benign. It is expected that the information extracted from each tumor (and surrounding healthy areas) can be useful for the clinical diagnosis in the correct indication of biopsies considering that the thermographic image contains information that goes beyond a simple temperature measurement.

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 509.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hennessy O, Potter S (2020) Use of infrared thermography for the assessment of free flap perforators in autologous breast reconstruction: a systematic review. JPRAS Open 23:60–70

    Article  Google Scholar 

  2. Alves M, Gabarra M (2016) Comparison of power Doppler and thermography for the selection of thyroid nodules in which fine-needle aspiration biopsy is indicated. Radiol Bras 49:311–315

    Article  Google Scholar 

  3. El Hadi H, Frascati A, Granzotto M, Silvestrin V, Ferlini E, Vettor R et al (2016) Infrared thermography for indirect assessment of activation of brown adipose tissue in lean and obese male subjects. Physiol Meas 37(12):N118–N128

    Article  Google Scholar 

  4. Magas V, Abreu de Souza M, Borba Neves E, Nohama P (2019) Evaluation of thermal imaging for the diagnosis of repetitive strain injuries of the wrist and hand joints. Res Biomed Eng RBE J 35(1):57–64

    Article  Google Scholar 

  5. INCA (2017) Estimativa 2018: a incidência de Câncer no Brasil. Vigilância, Rio de Janeiro: MS p. 128

    Google Scholar 

  6. Seib C, Sosa J (2019) Evolving Understanding of the Epidemiology of Thyroid Cancer. Endocrinol Metab Clin North Am 48(1):23–35

    Article  Google Scholar 

  7. Hou X, Chen G, Zhao Y (2019) Retrospective analysis of clinical pathology status of minor differentiated thyroid cancer. Zhonghuawaikezazhi 57(5):373–376

    Google Scholar 

  8. Kaliszewski K (2019) Does every classical type of well-differentiated thyroid cancer have excellent prognosis? A case series and literature review. Cancer Manag Res 11:2441–2448

    Article  Google Scholar 

  9. Hemashankara B, Srinivasa K (2016) Study of incidence in between benign and malignant tumors of solitary thyroid nodule, pp 5288–5293

    Google Scholar 

  10. Girardi F, Silva L, Flores C (2019) A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features. Braz J Otorhinolaryngol 85:24–31

    Article  Google Scholar 

  11. Song S, Kim H, Ahn S (2019) Role of immunohistochemistry in fine needle aspiration and core needle biopsy of thyroid nodules. Clin Exp Otorhinolaryngol 12(2):224–230

    Article  Google Scholar 

  12. Aweda M, Adeyomoye A, Abe G (2012) Thermographic analysis of thyroid diseases at the Lagos university teaching hospital, Nigeria. Pelag Res Lib 3(4):2027–2203

    Google Scholar 

  13. Erdem C, Koray K, Sevgi A, Nuray Bayar M, Cemal C (2018) Digital infrared thermal imaging analysis of thyroid nodules. Curr Med Imaging 14(5):807–811

    Article  Google Scholar 

  14. Gavriloaia G, Gavriloaia M, Novac M (2011) Bioacoustics response of small benign or malignant nodules. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference, pp 7695–7698

    Google Scholar 

  15. Gonzalez J, Damiao C, Conci A (2017) An infrared thermal images database and a new technique for thyroid nodules analysis. Stud Health Technol Inform 245:384–387

    Google Scholar 

  16. Bahramian F, Mojra A (2020) Thyroid cancer estimation using infrared thermography data. Infrared Phys Technol 104:103126

    Article  Google Scholar 

  17. Moran M, Conci A, González J, Araujo A, Fiirst W, Damião C et al (2018) Identification of thyroid nodules in infrared images by convolutional neural networks. 2018 International Joint Conference on Neural Networks (IJCNN) pp 1–7

    Google Scholar 

  18. Thiessen F, Tondu T, Cloostermans B, Dirkx L, Auman D, Cox S et al (2019) Dynamic infrared thermography (DIRT) in DIEP-flap breast reconstruction: a review of the literature. Eur J Obstet Gynecol Reprod Biol 242:47–55

    Article  Google Scholar 

  19. MATLAB, Jet Colormap Array, at: https://www.mathworks.com/help/matlab/ref/jet.html

  20. Burtsev S, Kuzmin P (2003) An efficient flood-filling algorithm. Laboratory for Computational Methods, Department of Mechanics and Mathematics, Moscow State University, 119 899

    Google Scholar 

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001; and to UTFPR for the Scientific Initiation Scholarship.

Conflict of Interest The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Undergraduate in Computer Engineering, Scientific Initiation, UniversidadeTecnológica Federal do Paraná, Curitiba, Brazil

    H. Salles

  2. Graduate Program in Biomedical Engineering, UniversidadeTecnológica Federal do Paraná, Curitiba, Brazil

    F. Ganacim & L. Ulbricht

  3. Graduate Program in Electrical Engineering and Industrial Informatics, UniversidadeTecnológica Federal do Paraná, Curitiba, Brazil

    V. Magas, H. R. Gamba & L. Ulbricht

Authors
  1. H. Salles
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Magas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Ganacim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. R. Gamba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Ulbricht
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. Salles .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  2. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Eliete Maria de Oliveira Caldeira

  3. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Anselmo Frizera-Neto

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salles, H., Magas, V., Ganacim, F., Gamba, H.R., Ulbricht, L. (2022). Evaluation of Dynamic Thermograms Using Semiautomatic Segmentation Software: Applied to the Diagnosis of Thyroid Cancer. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_357

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-70601-2_357

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70600-5

  • Online ISBN: 978-3-030-70601-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation