Abstract
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue with high prevalence rates in aged people. Diagnosis and treatment of pressure ulcers involve high costs for sanitary systems. Accurate wound-state evaluation is a critical task for optimizing the effectiveness of treatments. Reliable trace of wound-state evolution can be done by precisely registering the wound area. Clinicians estimate the wound area with often subjective and imprecise manual methods. This article presents a computer-vision approach based on machine hybrid-learning techniques to precise automatic estimation of wound dimensions on pressure ulcer real images taken under non-controlled illumination conditions. The system combines neural networks and Bayesian classifiers to effectively recognize and separate skin and healing regions from wound-tissue regions to be measured. This tissue-recognition approach to wound area estimation gives high performance rates and operates better than a widespread clinical method when approximating real wound areas of variable size.
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References
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space anaylsis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)
Cula, O., Dana, K., Murphy, F., Rao, B.: Skin texture modeling. Int. J. Comput. Vision 62(1-2), 97–119 (2005)
Jones, T.D., Plassmann, P.: An active contour model for measuring the area of leg ulcers. IEEE Trans. Med. Imaging 19(12), 1202–1210 (2000)
Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Technol. Biomed. 7(3), 141–152 (2003)
Kosmopoulos, D., Tzevelekou, F.: Automated pressure ulcer lesion diagnosis for telemedicine systems. IEEE Eng. Med. Biol. Mag. 26(5), 18–22 (2007)
Liu, Y.: Create stable neural networks by cross-validation. In: Proc. of the IEEE International Joint Conference on Neural Networks, IJCNN 2006, Vancouver, BC, Canada, pp. 3925–3928 (2006)
Mesa, H., Veredas, F., Morente, L.: Tissue recognition for pressure ulcer evaluation. In: Proceedings of the 4th European Conference of the IFMBE (MBEC 2008), Antwerp, Belgium, pp. 1524–1527. Springer, Heidelberg (2008)
Stacy, M., Burnand, K., Layer, G., Pattison, N.: Measurement of the healing of venous ulcer. ANZ Journal of Surgery 61(11), 844–848 (1991)
Tannen, A., Dassen, T., Bours, G., Halfens, R.: A comparison of pressure ulcer prevalence: concerted data collection in the netherlands and germany. International Journal of Nursing Studies 41(6), 607–612 (2004)
Tresp, V.: A bayesian committee machine. Neural Comput. 12, 2719–2741 (2000)
Wannous, H., Treuillet, S., Lucas, Y.: Supervised tissue classification from color images for a complete wound assessment tool. In: Proc. of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, pp. 6031–6034 (2007)
Zhang, H.: The optimality of Naïve Bayes. In: Proc. 17th Internat. FLAIRS Conf., Florida, USA (2004)
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Veredas, F.J., Mesa, H., Morente, L. (2009). Tissue Recognition Approach to Pressure Ulcer Area Estimation with Neural Networks. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_131
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DOI: https://doi.org/10.1007/978-3-642-02478-8_131
Publisher Name: Springer, Berlin, Heidelberg
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