Wound Healing Assessment Using Digital Photography: A Review
Digital photography as a non-invasive, simple, objective, reproducible, and practical imaging modality has been investigated for the wound healing assessment over the last three decades, and now has been widely used in clinical daily routine. Advances in the field of image analysis and computational intelligence techniques along with the improvements in digital camera instrumentation, expand the applications of standardized digital photography in diagnostic dermatology such as evaluation of tumours, erythema, and ulcers. A series of digital images taken at regular intervals carries the most informative wound healing indexes, color and dimension, that may help clinicians to evaluate the effectiveness of a particular treatment regimen, to relieve patient discomfort, to globally assess the healing kinetics, and to quantitatively compare different therapies; however, the extent of underlying tissue damage cannot be fully detected. This paper is an introductory review of the important investigations proposed by researchers in the context of clinical wound assessment. The principles of wound assessment using digital photography were shortly described, followed by review of the related literature in four main domains: wound tissue segmentation, automated wound area measurement, wound three dimensional (3D) analysis and volumetric measurement, and monitoring and evaluation of wound tissue changes during healing.
(1) Shaw, J. and P.M. Bell, Wound measurement in diabetic foot ulceration. 2011: INTECH Open Access Publisher.
(2) Dyson, M., et al., Wound healing assessment using 20 MHz ultrasound and photography. Skin Research and Technology, 2003. 9(2): p. 116-121.
(3) Treuillet, S., B. Albouy, and Y. Lucas, Three-dimensional assessment of skin wounds using a standard digital camera. IEEE Transactions on Medical Imaging, 2009. 28(5): p. 752-762.
(4) Plassmann, P. and T. Jones, MAVIS: a non-invasive instrument to measure area and volume of wounds. Medical Engineering & Physics, 1998. 20(5): p. 332-338.
(5) Galushka, M., et al. Case-based tissue classification for monitoring leg ulcer healing. in 18th IEEE Symposium on Computer-Based Medical Systems, 2005. 2005. IEEE.
(6) Humbert, P., S. Meaune, and T. Gharbi, Wound healing assessment. Phlebolymphology, 2004. 47: p. 312-319.
(7) Plassmann, P. and B. Belem, Early Detection of Wound Inflammation by Color Analysis, in Computational Intelligence in Medical Imaging: Techniques and Applications. 2009, CRC Press. p. 89-110.
(8) Belem, B., Non-invasive wound assessment by image analysis. 2004, University of Glamorgan.
(9) Hoppe, A., et al. Computer assisted assessment of wound
appearance using digital imaging. in Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference
of the IEEE. 2001. IEEE.
(10) Kolesnik, M. and A. Fexa, Multi-dimensional color histograms for segmentation of wounds in images, in Image Analysis and Recognition. 2005, Springer. p. 1014-1022.
(11) Mukherjee, R., et al., Automated tissue classification framework for reproducible chronic wound assessment. BioMed Research International, 2014. 2014: p. 1-9.
(12) Oduncu, H., et al., Analysis of skin wound images using digital color image processing: a preliminary communication. The International Journal of Lower Extremity Wounds, 2004. 3(3): p. 151-156.
(13) Pinero, B.A., C. Serrano, and J.I. Acha. Segmentation of burn images using the L* u* v* space and classification of their depths by color and texture imformation. in Medical Imaging Conference. 2002. International Society for Optics and Photonics.
(14) Veredas, F., H. Mesa, and L. Morente, Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Transactions on Medical Imaging, 2010. 29(2): p. 410-427.
(15) Wannous, H., Y. Lucas, and S. Treuillet, Enhanced assessment of the wound-healing process by accurate multiview tissue classification. IEEE Transactions on Medical Imaging, 2011. 30(2): p. 315-326.
(16) Jones, T.D. and P. Plassmann, An active contour model for measuring the area of leg ulcers. IEEE Transactions on Medical Imaging, 2000. 19(12): p. 1202-1210.
(17) Krouskop, T.A., R. Baker, and M.S. Wilson, A noncontact wound measurement system. Journal of Rehabilitation Research and Development, 2002. 39(3): p. 337-346.
(18) Liu, X., et al., Wound measurement by curvature maps: a feasibility study. Physiological Measurement, 2006. 27(11): p. 1107-1123.
(19) Moghimi, S., M.H.M. Baygi, and G. Torkaman, Automatic evaluation of pressure sore status by combining information obtained from high-frequency ultrasound and digital photography. Computers in Biology and Medicine, 2011. 41(7): p. 427-434.
(20) Moghimi, S., et al., Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images. Journal of Rehabilitation Research and Development, 2010. 47: p. 99-108.
(21) Romanelli, M., et al., Technological advances in wound bed measurements. WOUNDS-A COMPENDIUM OF CLINICAL RESEARCH AND PRACTICE, 2002. 14(2): p. 58-66.
(23) Tallman, P., et al., Initial rate of healing predicts complete healing of venous ulcers. Archives of dermatology, 1997. 133(10): p. 1231-1234.
(24) Kantor, J. and D. Margolis, A multicentre study of percentage change in venous leg ulcer area as a prognostic index of healing at 24 weeks. British Journal of Dermatology, 2000. 142(5): p. 960-964.
(25) Flanagan, M., Wound measurement: can it help us to monitor progression to healing?. Journal of Wound Care, 2003. 12(5): p. 189-194.
(26) Charles, H., Wound assessment: measuring the area of a leg ulcer. British Journal of Nursing, 1998. 7(13): p. 765-772.
(27) Gethin, G., The importance of continuous wound measuring. WOUNDS UK, 2006. 2(2): p. 60.
(28) Bulstrode, C., A. Goode, and P. Scott, Stereophotogrammetry for measuring rates of cutaneous healing: a comparison with conventional techniques. Clinical Science (Lond), 1986. 71(4): p. 437-43.
(29) Plassmann, P., Measuring wounds. Journal of Wound Care, 1995. 4(6): p. 269-272.
(30) Wendelken, M., et al., Wounds measured from digital photographs using photodigital planimetry software: validation and rater reliability. Wounds: A Compendium of Clinical Research and Practice, 2011. 23(9): p. 267-275.
(31) Cuzzell, J.Z., The new RYB color code. The American Journal of Nursing, 1988. 88(10): p. 1342-1346.
(32) Zheng, H., et al. New protocol for leg ulcer tissue classification from colour images. in 26th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. 2004. IEEE.
(33) Wannous, H., S. Treuillet, and Y. Lucas. Supervised tissue classification from color images for a complete wound assessment tool. in 29th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. 2007. IEEE.
(34) Jones, B.F. and P. Plassmann, An instrument to measure the dimensions of skin wounds. IEEE Transactions on Biomedical Engineering, 1995. 42(5): p. 464-470.
(35) Nischik, M. and C. Forster, Analysis of skin erythema using true-color images. IEEE Transactions on Medical Imaging, 1997. 16(6): p. 711-716.
(36) Bon, F.X., et al., Quantitative and kinetic evolution of wound healing through image analysis. IEEE Transactions on Medical Imaging, 2000. 19(7): p. 767-772.
(37) Perez, A.A., A. Gonzaga, and J.M. Alves. Segmentation and analysis of leg ulcers color images. in Medical Imaging and Augmented Reality, 2001. Proceedings. International Workshop on. 2001. IEEE.
(38) Berriss, W.P. and S.J. Sangwine A colour histogram clustering technique for tissue analysis of healing skin wounds. IET Conference Proceedings, 1997. 693-697.
(39) Malian, A., et al., Development of a robust photogrammetric metrology system for monitoring the healing of bedsores. The Photogrammetric Record, 2005. 20(111): p. 241-273.
(40) Hansen, G.L., et al., Wound status evaluation using color image processing. IEEE Transactions on Medical Imaging, 1997. 16(1): p. 78-86.
(41) Herbin, M., et al., Assessment of healing kinetics through true color image processing. IEEE Transactions on Medical Imaging, 1993. 12(1): p. 39-43.
(42) Plassmann, P. and T.D. Jones, Improved active contour models with application to measurement of leg ulcers. Journal of Electronic Imaging, 2003. 12(2): p. 317-326.
(43) Zhang, Z., W.V. Stoecker, and R.H. Moss, Border detection on digitized skin tumor images. IEEE Transactions on Medical Imaging, 2000. 19(11): p. 1128-1143.
(44) Karkanis, S.A., et al., Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE transactions on information technology in biomedicine, 2003. 7(3): p. 141-152.
(45) Cula, O.G., et al., Skin texture modeling. International Journal of Computer Vision, 2005. 62(1-2): p. 97-119.
(46) Resch, C.S., et al., Pressure sore volume measurement: a
technique to document and record wound healing. Journal of the American Geriatrics Society, 1988. 36(5): p. 444-446.
(47) Thomas, A.C. and A.B. Wysocki, The Healing Wound: A Comparison of Three Clinicially Useful Methods of Measurement. Advances in Skin & Wound Care, 1990. 3(1): p. 18.
(48) Langemo, D.K., et al., Comparison of 2 wound volume measurement methods. Advances in skin & wound care, 2001. 14(4, Part 1 of 2): p. 190-196.
(49) Boersma, S.M., et al., Photogrammetric wound measurement with a three-camera vision system. International Archives of Photogrammetry and Remote Sensing, 2000. 33(B5/1; PART 5): p. 84-91.
(50) Ozturk, C., et al. A new structured light method for 3-D wound measurement. in IEEE Twenty-Second Annual Northeast Bioengineering Conference. 1996. IEEE.
(51) Callieri, M., et al. Derma: Monitoring the Evolution of Skin Lesions with a 3D System. in VMV. 2003.
(52) Albouy, B., Y. Lucas, and S. Treuillet. 3D Modeling from uncalibrated color images for a complete wound assessment tool. in 29th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. 2007. IEEE.