Abstract
Background and objective: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring. Methods: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects. Results: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin. Conclusions: The proposed recognition approach permits to achieve promising results.
Lingua originale | English |
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pagine (da-a) | 263-271 |
Numero di pagine | 9 |
Rivista | Computer Methods and Programs in Biomedicine |
Volume | 162 |
DOI | |
Stato di pubblicazione | Pubblicato - 2018 |
Keywords
- Case-Control Studies
- Computer Science Applications1707 Computer Vision and Pattern Recognition
- Diabetes Mellitus, Type 1
- Diagnosis, Computer-Assisted
- Erythrocyte Membrane
- Erythrocytes
- Feature extraction
- Female
- Glycated Hemoglobin A
- Health Informatics
- Humans
- Image Processing, Computer-Assisted
- Image processing
- Machine learning
- Male
- Membrane Fluidity
- Pattern Recognition, Automated
- Reproducibility of Results
- Software
- Two-photon microscopy
- Type 1 Diabetes