An Improved Plantar Regional Division Algorithm for Aided Diagnosis of Early Diabetic Foot

Abstract

The early stages of diabetic foot represent a critical treatment period, but patients show no obvious symptoms. Upon the development into foot ulcers, a risk of amputation exists for which treatment costs are high. In this study, considering the plantar pressure as an important physiological parameter of the foot, we proposed methods to assist the diagnosis of early diabetic foot. Plantar pressure images of early diabetic foot patients were collected and de-noised. An improved automatic regional division algorithm of plantar pressure images was proposed. Laplacian spectrum features were extracted according to the maximum pressure point, pressure center point, and pressure values of the different plantar regions, including plantar shape and tactile features. Finally, based on these data, a support vector classifier was designed and sequential minimal optimization algorithms were used to train the classifier on the plantar pressure data of the left and right foot in 70 subjects to identify early diabetic foot. The results showed that the average recognition rates of the algorithm were high, providing an important reference for the diagnosis of early diabetic foot.