Pixel classification for automated diabetic foot diagnosis
| Autor: | Kloeze, C., Almar Klein, Hazenberg, S., Ferdinand van der Heijden, Baal, J. G., Bus, S. A. |
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| Zdroj: | University of Twente Research Information (Pure Portal) Proceedings of the 4th Annual symposium of the IEEE-EMBS Benelux Chapter, 45-48 STARTPAGE=45;ENDPAGE=48;TITLE=Proceedings of the 4th Annual symposium of the IEEE-EMBS Benelux Chapter |
| Popis: | Worldwide, more than 180 million people suffer from diabetes mellitus. Approximately 50% of these patients will develop complications to their feet. Neuropathy, combined with poor blood supply and biomechanical changes results in a high risk for foot ulcers, which is a key problem in the diabetic foot; when these wounds become infected, this can ultimately result in lower extremity amputation, which has a serious effect on the quality of life of the patient, and causes a large economic burden on society. This was the motivation for a collaborate project (Vincent50) in which a photographic foot imaging device was developed. The system allows scanning of the foot soles on a daily basis which may lead to early recognition of foot problems. The goal of the present study is to determine whether pixel classification is a useful intermediate step towards automatically assessing the images of the foot soles for signs of diabetic foot disease. If successful, this approach will further relief health care professionals in assessing the foot and enable the placement of more devices in the future. The best agreement between automated recognition and expert diagnosis was achieved with a combination of RGB and derived features, proves that the RGB data is informative with respect to detection of ulcers. However, the automatic detection of pre-signs of ulcers and other anomalies needs more sophistication than pixel classification alone. Firstly, other physical features, such as hyperspectral data, infrared and/or textural features are expected to be more informative. Secondly, we expect to be able to boost the performance by using the context between neighboring pixels. Thirdly, an individualized and normalized classification process might help with the large variability in foot soles between individuals. |
| Databáze: | OpenAIRE |
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