Abstract
The development of foot ulcers is associated with the Diabetic Foot (DF), which is a problem detected in patients with Diabetes Mellitus (DM). Several studies demonstrate that thermography is a technique that can be used to identify and monitor the DF problems, thus helping to analyze the possibility of ulcers arising, as tissue inflammation causes temperature variation.
There is great interest in developing methods to detect abnormal plantar temperature changes, since healthy individuals generally show characteristic patterns of plantar temperature variation and that the plantar temperature distribution of DF tissues does not follow a specific pattern, so temperature variations are difficult to measure. In this sequel, a methodology, that uses thermograms to analyze the diversity of thermal changes that exist in the plant of a foot and classifies it as being from an individual with possibility of ulcer arising or not, is presented in this paper. Therefore, the concept of clustering is used to propose binary classifiers with different descriptors, obtained using two clustering algorithms, to predict the risk of ulceration in a foot. Moreover, for each descriptor, a numerical indicator and a classification thresholder are presented. In addition, using a combination of two different descriptors, a hybrid quantitative indicator is presented. A public dataset (containing 90 thermograms of the sole of the foot healthy people and 244 of DM patients) was used to evaluate the performance of the classifiers; using the hybrid quantitative indicator and the k-means clustering, the following metrics were obtained: Accuracy = 80%, AUC = 87% and F-measure = 86%.
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Filipe, V., Teixeira, P., Teixeira, A. (2022). Two Clustering Methods for Measuring Plantar Temperature Changes in Thermal Images. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_19
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