This paper presents a computationally efficient method for detection of optic nerve head in both color and fluorescein angiography retinal fundus images. It involves Radon transformation of multi-overlapping windows within an optimization framework in order to achieve computational efficiency as well as high detection rates in the presence of various structural, color, and intensity variations in such images. Three databases of STARE, DRIVE, and a local database have been examined. It is shown that this method provides high detection rates while achieving faster processing speeds than the existing methods that have reported comparable detection rates. For example, the detection rate for the STARE database which is the most widely used database is found to be 96.3% with a processing time of about 3s per image. 
Computationally efficient optic nerve head detection in retinal fundus images