Eloi Canals Pascual
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Eloi Canals Pascual has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Akuroma Tolvanen
University of Eastern Finland, Faculty of Science, Forestry and Technology, Department of Technical Physics, Kuopio, Finland
1 shared publication Arash Mirhashemi
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
1 shared publication- Ervin Nippolainen
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
1 shared publication - Hamidreza Masjedi
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
1 shared publication - Isaac Oluwaseun Afara
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
1 shared publication - Johanna Päivikki Laakkonen
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
1 shared publication
Latest Publications
- Open Access
- 12 June 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. PP (02780062) , 1
Prediction of Articular Cartilage Markers with Multispectral Imaging
Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550–970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.
- 12 June 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. PP (02780062) , 1
Open AccessPrediction of Articular Cartilage Markers with Multispectral Imaging
Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550–970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.