Unsupervised diabetic foot monitoring techniques.

A significant amount of research, involving computerized methods, has been initiated the last few years regarding the identification and prevention of Diabetes Foot Ulceration (DFU). In this paper, the spatial analysis of the raw data is investigated. The major expectations were the indication of regions of interest and the extraction of a more reliable understanding, regarding the captured information. Towards this direction, unsupervised learning approaches were used for image segmentation purposes. According to the experimental results, high-level features can be used to segment coarse images, grouping together areas with skin irregularities on patient's foot. In practice, there are (or can be calculated) appropriate features, over RGB images, that will facilitate the detection of problematic/high-risk regions on a foot. Yet, unsupervised approaches should not be considered as viable monitoring solutions both in terms of time and accuracy. However, the proposed approach could potentially be used to assist the detection process resulted by supervised Deep Learning techniques.