title: An automated system for detecting and measuring nailfold capillaries.
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.
authors.forename: Michael
authors.initials: M
authors.lastname: Berks
authors.forename: Phil
authors.initials: P
authors.lastname: Tresadern
authors.forename: Graham
authors.initials: G
authors.lastname: Dinsdale
authors.forename: Andrea
authors.initials: A
authors.lastname: Murray
authors.forename: Tonia
authors.initials: T
authors.lastname: Moore
authors.forename: Ariane
authors.initials: A
authors.lastname: Herrick
authors.forename: Chris
authors.initials: C
authors.lastname: Taylor
mesh_terms: D000465:Algorithms; D002196:Capillaries / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D020678:Microscopic Angioscopy / Q000379:methods*; D009262:Nails / Q000098:blood supply* / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011928:Raynaud Disease / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique