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.

中文翻译：

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一种改进的足底区域划分算法用于早期糖尿病足的辅助诊断

糖尿病足早期是治疗的关键期，但患者并无明显症状。一旦发展成足部溃疡，就存在截肢的风险，治疗费用很高。在本研究中，将足底压力作为足部的重要生理参数，我们提出了辅助诊断早期糖尿病足的方法。收集早期糖尿病足患者的足底压力图像并进行去噪处理。提出了一种改进的足底压力图像自动区域划分算法。根据足底不同区域的最大压力点、压力中心点和压力值提取拉普拉斯谱特征，包括足底形状和触觉特征。最后，根据这些数据，设计了支持向量分类器，并采用序列最小优化算法对70名受试者左右脚的足底压力数据训练分类器，以识别早期糖尿病足。结果表明，该算法的平均识别率较高，为早期糖尿病足的诊断提供了重要参考。