Automatic Vessel Segmentation on Retinal Images

Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber,branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hypertension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image.Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection,only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE(digital retinal images for vessel extraction)retinal database, which is much better than other methods.