title: Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach.
abstract: Changes and variation in retinal vessel width are related to vascular risk factors and prospectively related to cardiovascular disease in later life. Hence, assessment of vessel width may be a useful physio-marker and potential predictor of cardiovascular status. However, measurement of vessel calibre from retinal images is a challenging process to automate. This paper proposes an automated system to measure vessel calibre in retinal images, which is demonstrated in images of multi-ethnic school children. The diameter measurement is based on the detection of the centreline pixels from a vessel probability map image, determining the vessel orientation at these pixels, extracting the vessel segments and later using a two-dimensional model, which is optimized to fit various types of intensity profiles of vessel segments. The width is then estimated from parameters of the optimized model. The method is also quantitatively analyzed using monochromatic representations of different colour spaces. The algorithm is evaluated on a recently introduced public database CHASE_DB1, which is a subset of retinal images of multi-ethnic children from the Child Heart and Health Study in England (CHASE) dataset. Moreover, the precise estimation of retinal vascular widths is critical for epidemiologists to identify the risk factors. This work also introduces an interactive software tool for epidemiologists, with which retinal vessel calibre can be precisely marked.
authors: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, United Kingdom. moazam_fraz@yahoo.com
authors: M M
authors: MM
authors: Fraz
authors: P
authors: P
authors: Remagnino
authors: A
authors: A
authors: Hoppe
authors: A R
authors: AR
authors: Rudnicka
authors: C G
authors: CG
authors: Owen
authors: P H
authors: PH
authors: Whincup
authors: S A
authors: SA
authors: Barman
mesh_terms: D000465:Algorithms; D002648:Child; D004739:England; D005006:Ethnicity; D005260:Female; D006801:Humans; D008297:Male; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D012171:Retinal Vessels / Q000033:anatomy & histology*; D012307:Risk Factors; D012984:Software; D011795:Surveys and Questionnaires