Fusion of Colour and Depth Information to Enhance Wound Tissue Classification

Authors: Darren Thompson, Philip Morrow, Bryan Scotney, John Winder

Abstract:

Patients with diabetes are susceptible to chronic foot wounds which may be difficult to manage and slow to heal. Diagnosis and treatment currently rely on the subjective judgement of experienced professionals. An objective method of tissue assessment is required. In this paper, a data fusion approach was taken to wound tissue classification. The supervised Maximum Likelihood and unsupervised Multi-Modal Expectation Maximisation algorithms were used to classify tissues within simulated wound models by weighting the contributions of both colour and 3D depth information. It was found that, at low weightings, depth information could show significant improvements in classification accuracy when compared to classification by colour alone, particularly when using the maximum likelihood method. However, larger weightings were found to have an entirely negative effect on accuracy.

Keywords: Classification, data fusion, diabetic foot, stereophotogrammetry, tissue colour.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084374

References:

[1] S. Stremitzer, T. Wild and T. Hoelzenbein, "How precise is the evaluation of chronic wounds by health care professionals?" Int Wound J; vol 4(2): pp. 156-61, 2007.
[2] X. Liu, W. Kim, R. Schmidt, B. Drerup and J. Song, "Wound measurement by curvature maps: a feasibility study." Physiol Meas; vol 27(11): pp. 1107-23, 2006.
[3] M. Flanagan, "Wound measurement: Can it help us to monitor progression to healing?" J Wound Care; vol 12(5): pp. 189-94, 2003.
[4] P.C. Matthews, A.R. Berendt and B.A. Lipsky, "Clinical management of diabetic foot infection: diagnostics, therapeutics and the future." Expert Rev Anti Infect Ther; vol 5(1): pp. 117-27, 2007.
[5] D.G. Armstrong and B.A. Lipsky, "Diabetic foot infections: stepwise medical and surgical management." Int Wound J; vol 1(2): pp. 123-32, 2004.
[6] M.J. Levy and J. Valabhji, "The diabetic foot." Surgery (Oxford); vol 22(12): pp. 338-41, 2004.
[7] E.S. Papazoglou, L. Zubkov, X Mao, M. Neidrauer, N. Rannou and M.S. Weingarten. "Image analysis of chronic wounds for determining the surface area." Wound Repair Regen; vol 18(4): pp. 349-58, 2010.
[8] J. Shaw, C.M. Hughes, K.M. Lagan, P.M. Bell and M.R. Stevenson, "An evaluation of three wound measurement techniques in diabetic foot wounds." Diabetes Care; vol 30(10): pp. 2641-2, 2007.
[9] R.J. Winder, T.A. Darvann, W. McKnight, J.D. Magee and P. Ramsay- Baggs, "Technical validation of the Di3D stereophotogrammetry surface imaging system." Br J Oral Maxillofac Surg; vol 46(1): pp. 33-7, 2008.
[10] B. Khambay, N. Nairn, A. Bell, J. Miller, A. Bowman and A.F. Ayoub, "Validation and reproducibility of a high-resolution three-dimensional facial imaging system." Br J Oral Maxillofac Surg; vol 46(1): pp. 27-32, 2008.
[11] D.P. Thompson, J.H Cundell, D.A. McDowell and R.J. Winder, "Simulated wound volume measurement using 3D stereophotogrammetry." Proc. 14th annual Irish Machine Vision and Image Processing (IMVIP) Conference, Limerick, 2010, pp. 30-43.
[12] D.L. Pham, C. Xu and J.L. Prince, "Current Methods in Medical Image Segmentation." Annual Review of Biomedical Engineering, vol 2(1), pp. 315-337, 2000.
[13] R.O. Duda, P.E. Hart and D.G. Stork. Pattern Classification, 2nd Edn, 2001. New York: John Wiley & Sons.
[14] H. Wannous, Y. Lucas, S. Treuillet and B. Albouy. "A complete 3D wound assessment tool for accurate tissue classification and measurement." 15th IEEE International Conference on Image Processing, 2008; pp. 2928-31.
[15] H. Wannous, Y. Lucas and B. Albouy. "Enhanced assessment of the wound-healing process by accurate multiview tissue classification." IEEE Transactions on Medical Imaging; pp. 315-26, 2011.
[16] Y.J. Zhang, "A review of recent evaluation methods for image segmentation", Signal Processing and its Applications, Sixth International, Symposium on; pp. 148-51, 2001.
[17] X. Hong, S.I. McClean, B. Scotney and P.J. Morrow, "Model-based Segmentation of Multimodal Images", Lecture Notes in Computer Science, vol 4673, pp. 604-61, 2007.
[18] C. Fraley and A.E. Raftery, "How many clusters? Which clustering method? Answers via model-based cluster analysis." The Computer Journal; vol 41(8): pp. 578-88, 1998.
[19] B. Al Momani, "Classification of Remotely Sensed Imagery Using a Knowledge-Based Approach." PhD thesis, University of Ulster, 2008.