LAUSR

Optic disk (OD) detection and recognition is an important stage for developing automatic screening applications of diabetic retinopathy disease in color retinal image. However, the retinal image has a low resolution and was influenced by salt-and-paper noise. Therefore, a retinal image needs a preprocessing procedure (i.e., color image normalization, image enhancement and noise removal) prior to the use of the retinal images. Afterward, a combination of a maker-controlled watershed segmentation and mathematical morphology exiting that was applied to detect of OD. These two methods have complementary drawbacks and advantages, and this is the motivation for the hybrid method presented. These modifications enable the proposed methods to become more robust and accurate to detection of the OD regions. The methods were evaluated by applying to two-color retinal dataset [local dataset in Thailand and a public available diabetic retinopathy database (STARE)]. Although the retinal images in this paper are fairly low, the results showed the proposed method has the performance of the OD detection about 99.33% on 300 images from the local dataset and 95.06% of 81 images from the STARE dataset, taking an average computational time of 3.4 s per image. These results show effectiveness in both detections of the OD regions and boundary.