Burn depth analysis using multidimensional scaling applied to psychophysical experiment data

In this paper a psychophysical experiment and a multidimensional scaling (MDS) analysis are undergone to determine the physical characteristics that physicians employ to diagnose a burn depth. Subsequently, these characteristics are translated into mathematical features, correlated with these physical characteristics analysis. Finally, a study to verify the ability of these mathematical features to classify burns is performed. In this study, a space with axes correlated with the MDS axes has been developed. 74 images have been represented in this space and a k-nearest neighbor classifier has been used to classify these 74 images. A success rate of 66.2% was obtained when classifying burns into three burn depths and a success rate of 83.8% was obtained when burns were classified as those which needed grafts and those which did not. Additional studies have been performed comparing our system with a principal component analysis and a support vector machine classifier. Results validate the ability of the mathematical features extracted from the psychophysical experiment to classify burns into their depths. In addition, the method has been compared with another state-of-the-art method and the same database.