Abnormality detection in automated mass screening system of diabetic retinopathy

An approach to abnormality detection from colour fundus images for an automated mass screening system is proposed in this paper, which uses the object-based colour difference image. Four colour models, viz. RGB, Luv, Lab and HVC, are evaluated based on hand-labelled feature maps, and Luv and Lab are selected for computing the colour difference because of their good performances in object classification. The object-based colour difference images of bright objects (e.g. exudates) and dark objects (e.g. haemorrhages and blood vessels) are obtained respectively according to the 2D histogram distribution on the L-u plane, and then a watershed transform is performed on the colour difference image to extract object candidates. A pre-thresholding and a post-verification procedure are performed to deal with the over-segmentation problem of the watershed transform.