We present an automated algorithm for the detection of blood vessels in 2-D choroidal scan images followed by a measurement of the area of the vessels. The objective is to identify vessel parameters in the choroidal stroma that are affected by various abnormalities. The algorithm is divided into five stages. In the first stage, the image is denoised to remove sensor noise and facilitate further processing. In the second stage, the image is segmented in order to find the region of interest. In the third stage, three different contour detection methods are applied to address different challenges in vessel contour. In the fourth stage, the outputs of the three contour detection methods are combined to achieve refined vessel contour detection. In the fifth and final stage, the area of these contours are measured. The results have been evaluated by a practicing opthalmologist and performance of the algorithm relative to expert detection is reported. © 2013 IEEE.