AIMS: The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). METHODS: High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. RESULTS: The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. CONCLUSIONS: Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.
AIMS: The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). METHODS: High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. RESULTS: The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. CONCLUSIONS: Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.
Authors: S Philip; A D Fleming; K A Goatman; S Fonseca; P McNamee; G S Scotland; G J Prescott; P F Sharp; J A Olson Journal: Br J Ophthalmol Date: 2007-05-15 Impact factor: 4.638
Authors: Michael F Chiang; Rony Gelman; Steven L Williams; Joo-Yeon Lee; Daniel S Casper; M Elena Martinez-Perez; John T Flynn Journal: Invest Ophthalmol Vis Sci Date: 2008-04-11 Impact factor: 4.799
Authors: Emanuele Trucco; Alfredo Ruggeri; Thomas Karnowski; Luca Giancardo; Edward Chaum; Jean Pierre Hubschman; Bashir Al-Diri; Carol Y Cheung; Damon Wong; Michael Abràmoff; Gilbert Lim; Dinesh Kumar; Philippe Burlina; Neil M Bressler; Herbert F Jelinek; Fabrice Meriaudeau; Gwénolé Quellec; Tom Macgillivray; Bal Dhillon Journal: Invest Ophthalmol Vis Sci Date: 2013-05-01 Impact factor: 4.799