Similar articles

Abstract: Methods for pressure sore monitoring remain both a clinical and research challenge. Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions. In this paper a technique is introduced for the assessment of pressure sores in guinea pigs, using captured color images. Sores were artificially induced, utilizing a system particularly developed for this purpose. Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation. Different segments of the color images were divided and labeled into three classes, based on their severity status. For quantitative analysis, a color based texture model, which is invariant against monotonic changes in illumination, is proposed. The texture model has been developed based on the local binary pattern operator. Tissue segments were classified, using the texture model and its features as inputs to a combination of neural networks. Our method is capable of discriminating tissue segments in different stages of pressure sore generation, and therefore can be a feasible tool for the early assessment of pressure sores.

[1]Ahonen, T., Hadid, A., Pietikainen, M., 2006. Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 28(12):2037-2041.

[2]Armanfard, A., Komeili, M., Kabir, E., 2008. TED: a Texture-Edge Descriptor Based on LBP for Pedestrian Detection. IEEE Int. Symp. on Telecommunications, p.643-648.

[3]Bader, D., Bouten, C., Colin, D., Oomens, C., 2005. Pressure Ulcer Research: Current and Future Perspectives. Springer Verlag, London, p.1-7.

[4]Belem, B., 2004. Non-invasive Wound Assessment by Image Analysis. PhD Thesis, University of Glamorgan, UK.

[5]Berriss, W.P., Sangwine, S.J., 1997. A Colour Histogram Clustering Technique for Tissue Analysis of Healing Skin Wounds. Proc. 6th Int. Conf. on Image Processing and Its Applications, p.693-697.

[6]Bianco, S., Gasparini, F., Russo, A., Schettini, R., 2007. A new method for RGB to XYZ transformation based on pattern search optimization. IEEE Trans. Consum. Electron., 53(3):1020-1028.

[7]Bon, F.X., Briand, E., Guichard, S., Couturaud, B., Revol, M., Servant, J.M., Dubertret, L., 2000. Quantitative and kinetic evolution of wound healing image analysis. IEEE Trans. Med. Imag., 19(7):767-772.

[8]Brown, M., Gogia, P.P., Sinacore, D.R., Menton, D.N., 1995. High-voltage galvanic stimulation on wound healing in guinea pigs: longer-term effects. Arch. Phys. Med. Rehabil., 76(12):1134-1137.

[9]Daniel, R.K., Priest, D.L., Wheatley, D.C., 1981. Etiologic factors in pressure sores: an experimental model. Arch. Phys. Med. Rehabil., 62:492-498.

[10]Diao, C.Y., Lu, D.M., Liu, G., 2005. Relighting multiple color textures. J. Zhejiang Univ. Sci., 6A(11):1284-1289.

[11]Galushka, M., Zheng, H., Patterson, D., Bradley, L., 2005. Case-Based Tissue Classification for Monitoring Leg Ulcer Healing. Proc. 18th IEEE Symp. on Computer-Based Medical Systems, p.353-358.

[12]Hansen, G.L., Sparrow, E.M., Kokate, J.Y., Leland, K.J., Iaizzo, P.A., 1997. Wound status evaluation using color image processing. IEEE Trans. Med. Imag., 16(1):78-86.

[13]Haralick, R.M., Shanmugan, K., Dinsten, I., 1973. Textural features for image classification. IEEE Tran. Syst. Man Cybern., 3(6):610-621.

[14]Heikkila, M., Pietikainen, M., 2006. A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell., 28(4):657-662.

[15]Herbin, M., Bon, F.X., Venot, A., Jeanlouis, F., Dubertret, M.L., Dubertret, L., Strauch, G., 1993. Assessment of healing kinetics through true color image processing. IEEE Trans. Med. Imag., 12(1):39-43.

[16]Jones, B.F., Plassmann, P., 1995. An instrument to measure the dimensions of skin wounds. IEEE Trans. Biomed. Eng., 42(5):464-470.

[17]Kolesnik, M., Fexa, A., 2005. Multi-dimensional color histograms for segmentation of wounds in images. LNCS, 3656:1014-1022.

[18]Lee, S.H., Choi, J.S., 2008. Design and implementation of color correction system for images captured by digital camera. IEEE Trans. Consum. Electron., 54(2):268-276.

[19]Mackiewicz, M., Berens, J., Fisher, M., 2008. Wireless capsule endoscopy color video segmentation. IEEE Trans. Med. Imag., 27(12):1769-1781.

[20]Malian, A., Azizi, A., van den Heuvel, F.A., Zolfaghari, M., 2005. Development of a robust photogrammetric metrology system for monitoring the healing of bedsores. Photogr. Rec., 20(111):241-273.

[21]Nischik, M., Forster, C., 1997. Analysis of skin erythema using true color images. IEEE Trans. Med. Imag., 16(6):711-715.

[22]Oduncu, H., Hoppe, A., Clark, M., Williams, R.J., Harding, K.J., 2004. Analysis of skin wound images using digital color image processing: a preliminary communication. Int. J. Lower Extrem. Wounds, 3(3):151-156.

[23]Ojala, T., Pietäikinen, M., Harwood, D., 1996. A comparative study of texture measures with classification based on feature distributions. Pattern Recogn., 29(1):51-59.

[24]Perez, A.A., Gonzaga, A., Alves, J.M., 2001. Segmentation and Analysis of Leg Ulcers Color Images. Proc. Int. Workshop on Medical Imaging and Augmented Reality, p.262-266.

[25]Plassmann, P., Jones, T.D., 1998. MAVIS: a non-invasive instrument to measure area and volume of wounds. Med. Eng. Phys., 20(5):332-338.

[26]Plassmann, P., Jones, B.F., Ring, E.F.J., 1995. A structured light system for measuring wounds. Photogr. Rec., 15(86):197-204.

[27]Salcido, R., Fisher, S.B., Donofrio, J.C., Bieschke, M., Knapp, C., Liang, R., LeGrand, E.K., Carney, J.M., 1995. An animal model and computer controlled surface pressure delivery system for the production of pressure ulcers. J. Rehabil. Res. Dev., 32(2):149-161.

[28]Salter, R., Love, H., Fright, R., Nixon, M., 2006. PDA-based, portable laser scanner measurement of wound size: accuracy and reproducibility. ANZ J. Surg., 76(Suppl 1):A59.

[29]Tajeripour, F., Kabir, E., Sheikhi, A., 2008. Fabric defect detection using modified local binary patterns. EURASIP J. Adv. Signal Process., 2008, Article ID 783898, p.1-12.

[30]Theodoridis, S., Koutroumbas, K., 2003. Pattern Recognition (2nd Ed.). Academic Press, London, p.270-272.

[31]Torkaman, G., Sharafi, A.A., Fallah, A., Katoozian, H.R., 2000. Biomechanical and Histological Studies of Experimental Pressure Sores in Guinea Pigs. Proc. 10th Int. Conf. on Biomedical Engineering, p.463-469.

[32]Treuillet, S., Albouy, B., Lucas, Y., 2009. Three-dimensional assessment of skin wounds using a standard digital camera. IEEE Trans. Med. Imag., 28(5):752-762.

[33]Zheng, H., Bradley, L., Patterson, D., Galushka, M., Winder, J., 2004. New Protocol for Leg Ulcer Classification from Colour Images. Proc. IEEE 26th Annual Int. Conf. on Engineering in Medicine and Biology Society, p.1389-1392.