Enhancement of the asymmetry-based overlapping analysis through features extraction

In this paper, an enhanced algorithm is proposed to detect foot inflammation and, hence, predict ulcers before they can develop. This algorithm is based on an asymmetry analysis combined with a segmentation technique with a genetic algorithm to achieve higher efficiency in the detection of inflammation. The analysis involves several steps: segmentation, geometry transformation, overlapping, and abnormality identification. To enhance the results of this analysis, an additional step, features extraction, is performed. In this step, low and high order statistics are computed for each foot. Preliminary results show that the proposed algorithm combined with features extraction can be reliable and efficient to predict potential ulceration.