Skip to main content

Development of a Multimodal Image Registration and Fusion Technique for Visualising and Monitoring Chronic Skin Wounds

  • Conference paper
  • First Online:
Information Technology in Biomedicine (ITIB 2018)

Abstract

Chronic skin wounds from diabetes, atherosclerosis, and cancer form a large source of morbidity and medical complications. While individual imaging modalities (e.g. visual images, thermograms, ultrasound) can be useful for monitoring the healing process, their use is limited because of the difficulty acquiring and registering multiple images from different modalities. This paper presents a methodology for image registration using an alignment phantom for grayscale images and thermograms. The registration system achieves a Fiducial Registration Error of 0.61 mm and Mutual Information value of 0.774. Future studies will seek to add additional imaging modalities and improve registration for other areas of the body.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Didkowska, J., Zatonski, W., Wojciechowska, U., Didkowska.: Prediction of cancer incidence and mortality in Poland up to the year 2025. Centrum Onkologii. Instytut im. Marii Skłodowskiej-Curie (2009)

    Google Scholar 

  2. Greenwade, G.D.: Type 2 diabetes. part i. a modern epidemic. Przemysl Spozywczy 57(6), 38–40 (2013)

    Google Scholar 

  3. Beard, J.D.: Chronic lower limb ischemia. West. J. Med. 173(1), 60–63 (2000)

    Article  MathSciNet  Google Scholar 

  4. Czeleko, T., Sliwczynski, A., Nawrot, I., Karnafel, W.: The incidence of major, nontraumatic lower amputations in patients without diabetes mellitus in poland during 2009–2012, based on polish national health found data. Acta Angiologica 20(3), 124–131 (2014)

    Google Scholar 

  5. Defloor, T., Schoonhoven, L., Fletcher, J., Furtado, K., Heyman, H., Lubbers, M., Witherow, A., Bale, S., Bellingeri, A., Cherry, G., Clark, M., Colin, D., Dassen, T., Dealey, C., Gulacsi, L., Haalboom, J., Halfens, R., Hietanen, H., Lindholm, C., Moore, Z., Romanelli, M., Soriano, J.V.: Statement of the european pressure ulcer advisory panel-pressure ulcer classification: differentiation between pressure ulcers and moisture lesions. J. Wound Ostomy Continence Nurs. 32(5), 302–306 (2005)

    Article  Google Scholar 

  6. Ud-Din, S., Bayat, A.: Non-invasive objective devices for monitoring the inflammatory, proliferative and remodelling phases of cutaneous wound healing and skin scarring. Exp. Dermatol. 25(8), 579–585 (2016)

    Article  Google Scholar 

  7. Emilia, M., Chaves, A., da Silva, F.S., Pinheiro, V., Soares, C., Augusto, R., Ferreira, M., Sampaio, F., Gomes, L., de Andrade, R.M., Pinotti, M.: Evaluation of healing of pressure ulcers through thermography: a preliminary study. Res. Biomed. Eng. 31(1), 3–9 (2015)

    Article  Google Scholar 

  8. Ozturk, C., Nissannov, J., Dubin, S., Wy, S., Nichols, J., Mark, R.: Measurement of wound healing by image analysis. Biomed. Sci. Instrum. 31, 189–193 (1995)

    Google Scholar 

  9. Wild, T., Prinz, M., Fortner, N., Krois, W., Sahora, K., Stremitzer, S., Hoelzenbein, T.: Digital measurement and analysis of wounds based on colour segmentation. Eur. Surg. 40(1), 5–10 (2008)

    Article  Google Scholar 

  10. Mukherjee, R., Manohar, D.D., DAS, D.K., Achar, A., Mitra, A., Chakraborty, C.: Automated tissue classification framework for reproducible chronic wound assessment. Biomed. Res. Int. 2014, 1–9 (2014)

    Google Scholar 

  11. Nakagami, G., Sanada, H., Iizaka, S., Kadono, T., Higashino, T., Koyanagi, H., Haga, N.: Predicting delayed pressure ulcer healing using thermography: a prospective cohort study. J. Wound Care 19(11), 465–6, 468, 470 (2010)

    Article  Google Scholar 

  12. Nagase, T., Sanada, H., Takehara, K., Oe, M., Iizaka, S., Ohashi, Y., Oba, M., Kadowaki, T., Nakagami, G.: Variations of plantar thermographic patterns in normal controls and non-ulcer diabetic patients: Novel classification using angiosome concept. J. Plast. Reconstr. Aesthetic Surg. 64(7), 860–866 (2011)

    Article  Google Scholar 

  13. Huang, C.L., Wu, Y.W., Hwang, C.L., Jong, Y.S., Chao, C.L., Chen, W.J., Wu, Y.T., Yang, W.S.: The application of infrared thermography in evaluation of patients at high risk for lower extremity peripheral arterial disease. J. Vasc. Surg. 54(4), 1074–1080 (2011)

    Article  Google Scholar 

  14. Thatcher, J.E., Squiers, J.J., Kanick, S.C., King, D.R., Lu, Y., Wang, Y., Mohan, R., Sellke, E.W., DiMaio1, J.M.: Imaging techniques for clinical burn assessment with a focus on multispectral imaging. Adv. Wound Care (New Rochelle) 5(8), 360–378 (2016)

    Article  Google Scholar 

  15. Paul, D., Ghassemi, P., Ramella-Roman, J., Prindeze, N., Moffatt, L., Alkhalil, A., Shupp, J.: Noninvasive imaging technologies for cutaneous wound assessment: a review. Wound Repair Regen. 23(2), 149–162 (2015)

    Article  Google Scholar 

  16. Spinczyk, D., Karwan, A., Rudnicki, J., WrÃşblewski, T.: Stereoscopic liver surface reconstruction. Videosurgery and Other Miniinvasive Techniques 3, 181–187 (2012)

    Article  Google Scholar 

  17. Spinczyk, D., Karwan, A., Zylkowski, J., WrÃşblewski, T.: Experimental study in-vitro evaluation of stereoscopic liver surface reconstruction. Videosurgery and Other Miniinvasive Techniques 1, 80–85 (2013)

    Article  Google Scholar 

  18. Brewster, M.Q.: Thermal Radiative Transfer and Properties. Wiley, New Jersey (1992)

    Google Scholar 

  19. Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1106–1112, June 1997

    Google Scholar 

  20. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  21. Fuersattel, P., Dotenco, S., Placht, S., Balda, M., Maier, A., Riess, C.: Ocpad 8212: occluded checkerboard pattern detector. In: Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9, March 2016

    Google Scholar 

  22. Geiger, A., Moosmann, F., Car, Ã., Schuster, B.: Automatic camera and range sensor calibration using a single shot. In: Proceedings of 2012 IEEE International Conference on Robotics and Automation, pp. 3936–3943, May 2012

    Google Scholar 

  23. Bhujle, H.: Weighted-average fusion method for multiband images. In: Proceedings of 2016 International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, June 2016

    Google Scholar 

  24. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22(8), 986–1004 (2003)

    Article  Google Scholar 

  25. West, J., Fitzpatrick, J., Wang, M., Dawant, B., Maurer, C.J., Kessler, R., Maciunas, R., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., van den Elsen, P., Napel, S., Sumanaweera, T., Harkness, B., Hemler, P., Hill, D., Hawkes, D., Studholme, C., Maintz, J., Viergever, M., Malandain, G., Woods, R.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J. Comput. Assist. Tomogr. 21(4), 554–566 (1997)

    Article  Google Scholar 

  26. Labadie, R., Shah, R., Harris, S., Cetinkaya, E., Haynes, D., Fenlon, M., Juscyzk, A., Galloway, R., Fitzpatrick, J.: Submillimetric target-registration error using a novel, non-invasive fiducial system for imageguided otologic surgery. Comput. Aided Surg. 9(4), 145–153 (2004)

    Article  Google Scholar 

  27. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)

    Article  Google Scholar 

  28. Gong, M., Zhao, S., Jiao, L., Tian, D., Wang, S.: A novel coarse-to-fine scheme for automatic image registration based on sift and mutual information. IEEE Trans. Geosci. Remote Sens. 52(7), 4328–4338 (2014)

    Article  Google Scholar 

  29. Rivaz, H., Karimaghaloo, Z., Collins, D.L.: Self-similarity weighted mutual information: a new nonrigid image registration metric. Med. Image Anal. 18(2), 343–358 (2014)

    Article  Google Scholar 

  30. Karimi, A., Rahmati, S.M., Razaghi, R.: A combination of experimental measurement, constitutive damage model, and diffusion tensor imaging to characterize the mechanical properties of the human brain. Comput. Methods Biomech. Biomed. Eng. 20(12), 1350–1363 (2017)

    Article  Google Scholar 

  31. Zhuang, Y., Gao, K., Miu, X., Han, L., Gong, X.: Infrared and visual image registration based on mutual information with a combined particle swarm optimization-powell search algorithm. Optik-Int. J. Light Electron Opt. 127(1), 188–191 (2016)

    Article  Google Scholar 

  32. Darkner, S., Sporring, J.: Locally orderless registration. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1437–1450 (2013)

    Article  Google Scholar 

  33. Rivaz, H., Karimaghaloo, Z., Fonov, V., Collins, D.: Nonrigid registration of ultrasound and MRI using contextual conditioned mutual information. IEEE Trans. Med. Imaging 33(3), 708–25 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This research is supported by the Polish National Science Centre (NCBR) grant No.: UMO-2016/21/B/ST7/02236. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andre Woloshuk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Woloshuk, A. et al. (2019). Development of a Multimodal Image Registration and Fusion Technique for Visualising and Monitoring Chronic Skin Wounds. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_12

Download citation

Publish with us

Policies and ethics