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Multiregional Segmentation Modeling in Medical Ultrasonography: Extraction, Modeling and Quantification of Skin Layers and Hypertrophic Scars

  • Iveta Bryjova
  • Jan Kubicek
  • Kristyna Molnarova
  • Lukas Peter
  • Marek Penhaker
  • Kamil Kuca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

In the clinical practice of the burns treatment, an autonomous modeling of the burns morphological structure is important for a correct diagnosis. Unfortunately, the geometrical parameters of burns and skin layers are subjectively estimated. This approach leads to the inaccurate assessment depending on the experience of an individual physician. In our research, we propose the analysis of multiregional segmentation method which is able to differentiate individual skin layers in the ultrasound image records. The segmentation method is represented by the mathematical model of skin layers while other structures are suppressed. Skin layers are consequently approximated by their skeleton with target of the layers distance measurement. The main applicable output of our research is the clinical SW SkinessMeter 1.0.0 serving for an autonomous modeling and quantification of the skin layers.

Keywords

Multiregional segmentation Burns Skin layers Ultrasound Hypertrophic scars 

Notes

Acknowledgment

The work and the contributions were supported by the project SV4506631/2101 ‘Biomedicínské inženýrské systémy XII’. This study was supported by the research project The Czech Science Foundation (GACR) No. 17-03037S, Investment evaluation of medical device development.

References

  1. 1.
    Klosová, H., Štětinský, J., Bryjová, I., Hledík, S., Klein, L.: Objective evaluation of the effect of autologous platelet concentrate on post-operative scarring in deep burns. Burns 39(6), 1263–1276 (2013). doi: 10.1016/j.burns.2013.01.020. [cit. 2017-04-29]. ISSN 03054179CrossRefGoogle Scholar
  2. 2.
    Štětinský, J., Klosová, H., Kolářová, H., Šalounová, D., Bryjová, I., Hledík, S.: The time factor in the LDI (laser doppler imaging) diagnosis of burns. Lasers Surg. Med. 47(2), 196–202 (2015). doi: 10.1002/lsm.22291. [cit. 2017-04-29]. ISSN 01968092CrossRefGoogle Scholar
  3. 3.
    Hambleton, J., Shakespeare, P.G., Pratt, B.J.: The progress of hypertrophic scars monitored by ultrasound measurements of thickness. Burns 18(4), 301–307 (1992). doi: 10.1016/0305-4179(92)90151-J. [cit. 2017-04-29]. ISSN 03054179CrossRefGoogle Scholar
  4. 4.
    Katz, S.M., Frank, D.H., Leopold, G.R., Wachtel, T.L.: Objective measurement of hypertrophic burn scar: a preliminary study of tonometry and ultrasonography. Ann. Plastic Surg. 14(2), 121–127 (1985)CrossRefGoogle Scholar
  5. 5.
    Fong, S.S.L., Hung, L.K., Cheng, J.C.Y.: The cutometer and ultrasonography in the assessment of postburn hypertrophic scar—a preliminary study. Burns 23, S12–S18 (1997). doi: 10.1016/S0305-4179(97)90095-4. [cit. 2017-04-29]. ISSN 03054179CrossRefGoogle Scholar
  6. 6.
    Kubicek, J., Penhaker, M., Pavelova, K., Selamat, A., Hudak, R., Majernik, J.: Segmentation of MRI data to extract the blood vessels based on fuzzy thresholding. Stud. Comput. Intell. 598, 43–52 (2015)Google Scholar
  7. 7.
    Kubicek, J., Valosek, J., Penhaker, M., Bryjova, I., Grepl, J.: Extraction of blood vessels using multilevel thresholding with color coding. In: Sulaiman, H.A., Othman, M.A., Othman, M.F.I., Rahim, Y.A., Pee, N.C. (eds.) Advanced Computer and Communication Engineering Technology. LNEE, vol. 362, pp. 397–406. Springer, Cham (2016). doi: 10.1007/978-3-319-24584-3_33CrossRefGoogle Scholar
  8. 8.
    Kubicek, J., Valosek, J., Penhaker, M., Bryjova, I.: Extraction of chondromalacia knee cartilage using multi slice thresholding method. In: Vinh, P.C., Alagar, V. (eds.) ICCASA 2015. LNICSSITE, vol. 165, pp. 395–403. Springer, Cham (2016). doi: 10.1007/978-3-319-29236-6_37CrossRefGoogle Scholar
  9. 9.
    Sciolla, B., Cowell, L., Dambry, T., Guibert, B., Delachartre, P.: Segmentation of skin tumors in high-frequency 3-D ultrasound images. Ultrasound Med. Biol. 43(1), 227–238 (2017)CrossRefGoogle Scholar
  10. 10.
    Csabai, D., Szalai, K., Gyöngy, M.: Automated classification of common skin lesions using bioinspired features. In: IEEE International Ultrasonics Symposium, IUS, art. no. 7728752, November 2016Google Scholar
  11. 11.
    Ewertsen, C., Carlsen, J.F., Christiansen, I.R., Jensen, J.A., Nielsen, M.B.: Evaluation of healthy muscle tissue by strain and shear wave elastography - dependency on depth and ROI position in relation to underlying bone. Ultrasonics 71, 127–133 (2016)CrossRefGoogle Scholar
  12. 12.
    Sciolla, B., Delachartre, P., Cowell, L., Dambry, T., Guibert, B.: Multigrid level-set segmentation of high-frequency 3D ultrasound images using the Hellinger distance. In: 9th International Symposium on Image and Signal Processing and Analysis, ISPA 2015, art. no. 7306052, pp. 165–169 (2015)Google Scholar
  13. 13.
    Sciolla, B., Ceccato, P., Cowell, L., Dambry, T., Guibert, B., Delachartre, P.: Segmentation of inhomogeneous skin tissues in high-frequency 3D ultrasound images, the advantage of non-parametric log-likelihood methods. Phys. Proc. 70, 1177–1180 (2015)CrossRefGoogle Scholar
  14. 14.
    Wawrzyniak, Z.M., Szyszka, M.: Layer measurement in high frequency ultra-sonography images for skin. In: Proceedings of SPIE - The International Society for Optical Engineering, 9662, art. no. 96621 (2015)Google Scholar
  15. 15.
    Gao, Y., Tannenbaum, A., Chen, H., Torres, M., Yoshida, E., Yang, X., Wang, Y., Curran, W., Liu, T.: Automated skin segmentation in ultrasonic evaluation of skin toxicity in breast cancer radiotherapy. Ultrasound Med. Biol. 39(11), 2166–2175 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Iveta Bryjova
    • 1
  • Jan Kubicek
    • 1
  • Kristyna Molnarova
    • 1
  • Lukas Peter
    • 1
  • Marek Penhaker
    • 1
  • Kamil Kuca
    • 2
  1. 1.VSB–Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KrálovéCzech Republic

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