Detection of optic disc area is complex because it is located in an area that is considered as pathological blood vessels when in segmentation and thus require a method to detect the area of the optic disc, this paper proposed the optic disc segmentation using a method that has not been used before, and this method is very simple, K-means clustering is a proposed Method in this paper to detect the optic disc area with perfected using adaptive morphology. This paper successfully detect optic disc area quickly and segmented blood vessels more quickly.

The morphological changes of retinal blood vessels are important indicators used to diagnose and monitor the progression of various diseases. A number of retinal blood vessel segmentation methods have been introduced, including the line operator based methods, which have shown satisfactory results. However, the basic line operator method cannot discriminate the pixels around the retinal optic disc, resulting in false classification of those pixels. In this paper, we integrate the detection of pixels around the retinal optic disc to the line operator method so that those pixels can be excluded from the vessel pixels. The method is evaluated on the widely used retinal dataset, the DRIVE dataset. The results demonstrate that the proposed method has made improvements over the basic and the multi-scale line detector with accuracy and area under curve of 0.942 and 0.9521, respectively.