Veredas Francisco J, Mesa Héctor, Morente Laura
Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, 29071, Spain,
Med Biol Eng Comput. 2015 Apr;53(4):345-59. doi: 10.1007/s11517-014-1240-0. Epub 2015 Jan 7.
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.
压疮是一种由压力、剪切力或摩擦力导致的皮肤及皮下组织局部损伤的临床病理现象。为了成功做出治疗决策，基于精确伤口评估的可靠诊断至关重要。本文提出了一种基于统计颜色模型的伤口面积检测计算机视觉方法。从由113张真实伤口图像组成的训练集开始，为四种不同组织类型创建颜色直方图模型。从贝叶斯角度出发，将颜色像素在这些直方图模型上的反向投影用于估计像素属于任何一种组织类别的后验概率。基于专家提供的分割图像金标准从列联表中获得的性能指标已用于模型选择。所得的拟合模型已在由专家临床医生手动分割和标记的322张伤口图像组成的训练集上进行了验证。最终的拟合分割模型显示出稳健性，并且在分割包括愈合组织在内的重要伤口区域时给出了较高的平均性能率[（曲线下面积：0.9426（标准差0.0563）；准确率：0.8777（标准差0.0799）；F值：0.7389（标准差0.1550）；科恩卡帕系数：0.6585（标准差0.1787）]。
