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Efficient detection of wound-bed and peripheral skin with statistical colour models

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Abstract

A pressure ulcer is a clinical pathology of localised damage to the skin and underlying tissue caused by pressure, shear or friction. Reliable diagnosis supported by precise wound evaluation is crucial in order to success on treatment decisions. This paper presents a computer-vision approach to wound-area detection based on statistical colour models. Starting with a training set consisting of 113 real wound images, colour histogram models are created for four different tissue types. Back-projections of colour pixels on those histogram models are used, from a Bayesian perspective, to get an estimate of the posterior probability of a pixel to belong to any of those tissue classes. Performance measures obtained from contingency tables based on a gold standard of segmented images supplied by experts have been used for model selection. The resulting fitted model has been validated on a training set consisting of 322 wound images manually segmented and labelled by expert clinicians. The final fitted segmentation model shows robustness and gives high mean performance rates [(AUC: .9426 (SD .0563); accuracy: .8777 (SD .0799); F-score: 0.7389 (SD .1550); Cohen’s kappa: .6585 (SD .1787)] when segmenting significant wound areas that include healing tissues.

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Notes

  1. The authors guarantee that the study from which this paper has been motivated has adhered to all ethical aspects that ensure the privacy of all personal information from the individuals who participated in the research. The Ethical Commission of the Health District of Málaga (Spain) authorised the study, and all the participants were informed in writing on their participation in the study.

  2. The CIELUV colour space was adopted by the International Commission on Illumination (CIE) in 1976.

  3. Since the original JPEG pictures were taken in nonlinear sRGB, the usual procedure for sRGB\(\rightarrow\)CIELUV conversion was followed: after inverting gamma, the images were first converted to CIE XYZ and then to CIELUV.

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Acknowledgments

This research has been funded by Consejería de Salud y Bienestar Social, Servicio Andaluz de Salud, Junta de Andalucía, project PI-0027/2012.

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Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, 29071, Spain

    Francisco J. Veredas

  2. The Mediterranean Institute for the Advance of Biotechnology and Health Research, Málaga, 29010, Spain

    Héctor Mesa

  3. Escuela Universitaria de Enfermería, Diputación de Málaga, Málaga, 29071, Spain

    Laura Morente

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  1. Francisco J. Veredas
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Correspondence to Francisco J. Veredas.

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Veredas, F.J., Mesa, H. & Morente, L. Efficient detection of wound-bed and peripheral skin with statistical colour models. Med Biol Eng Comput 53, 345–359 (2015). https://doi.org/10.1007/s11517-014-1240-0

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