Diabetic retinopathy is the leading cause of blindness in adults

and the retinal vessels segmentation is the basis for the diagnosis of diabetic retinopathy. To improve the accuracy of retinal vessels segmentation

an automatic retinal vessel segmentation method based on multi-model fusion and region iterative growth is proposed. Firstly

the mathematical morphology

matched filter

scale-space analysis

multi-scale line-detection and neural network models were established to segment retinal vessels initially

and the mean value of the five segmentation results was taken as the preliminary output to reduce the noise. Secondly

after exudates and optic disc were separated with a mask developed

the white areas in the mask were replaced by the segmentation result of the mathematical morphology model

and then the combined result was generated with replaced mask and preliminary output fused. Finally

considering the prior knowledge of retinal vessels

final results were obtained after threshold and region iterative growth. The experimental results demonstrate that the accuracy

sensitivity and specificity for segmenting the retinal vessels in the DRIVE and STARE fundus image datasets are 0.9457

0.7843

0.9815 and 0.9472

0.7826 and 0.9803

respectively

which is superior to most classical algorithms.