| Date: |
2022 |
| Abstract: |
Digital images are used for evaluation and diagnosis of a diabetic foot ulcer. Selecting the wound region (segmentation) in an image is a preliminary step for subsequent analysis. Most of the time, manual segmentation isn't very reliable because specialists could have different opinions over the ulcer border. This fact encourages researchers to find and test different automatic segmentation techniques. This paper presents a computer-aided ulcer region segmentation algorithm for diabetic foot images. The proposed algorithm has two stages: ulcer region segmentation, and post-processing of segmentation results. For the first stage, a trained machine learning model was selected to classify pixels inside the ulcer's region, after a comparison of five learning models. Exhaustive experiments have been performed with our own annotated dataset from images of Cuban patients. The second stage is needed because of the presence of some misclassified pixels. To solve this, we applied the DBSCAN clustering algorithm, together with dilation, and closing morphological operators. The best-trained model after the post-processing stage was the logistic regressor (Jaccard Index. |
| Note: |
Acknowledgements. We would like to thank the patients, volunteers, and co-workers of The Cuban National Institute of Angiology and Vascular Surgery (INACV, by its Spanish acronym), La Habana, Cuba, specially to Dr. Jose Ignacio Fernandez Montequín, Ph.D. The present study was reviewed and approved by the Ethics Committee of the INACV according to the ethical principles of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all healthy volunteers (without diabetes) and from all people with DFU included in the study. This research was developed as part of the project "Development of methodologies and algorithms aimed at supporting the imaging diagnosis of diabetic foot ulcer" from the National Program of Health Determinants, Risks and Prevention of Diseases in Vulnerable Groups, Code 1901100, National Institute of Hygiene and Epidemiology, Ministry of Public Health, Cuba, 2019-2021. |
| Rights: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Language: |
Anglès |
| Document: |
Article ; recerca ; Versió publicada |
| Subject: |
Diabetic foot ulcer ;
Supervised learning ;
Image segmentation ;
DFU |
| Published in: |
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 21 Núm. 2 (2022) , p. 22-39 (Regular Issue) , ISSN 1577-5097 |
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