An Improved Plantar Regional Division Algorithm for Aided Diagnosis of Early Diabetic Foot
| Autor: | Haizhen Wang, Jingyou Li, Zuozheng Lian, Mingjun Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
010504 meteorology & atmospheric sciences business.industry medicine.medical_treatment medicine.disease 01 natural sciences Diabetic foot Treatment period 030218 nuclear medicine & medical imaging Surgery 03 medical and health sciences 0302 clinical medicine Amputation Artificial Intelligence medicine Computer Vision and Pattern Recognition Artificial intelligence Foot ulcers business Software 0105 earth and related environmental sciences Aided diagnosis |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 34:2057006 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001420570062 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|