Abstract
Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynaud's phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynaud's phenomenon, and those with potentially life-threatening systemic sclerosis. © 2014 Springer International Publishing.
Cite
CITATION STYLE
Berks, M., Tresadern, P., Dinsdale, G., Murray, A., Moore, T., Herrick, A., & Taylor, C. (2014). An automated system for detecting and measuring nailfold capillaries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8673 LNCS, pp. 658–665). Springer Verlag. https://doi.org/10.1007/978-3-319-10404-1_82
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
