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1 Journal of Artificial Intelligence and Soft Computing Research

1 2012

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Automatic graph cut based segmentation of retinal optic disc by incorporating blood vessel compensation

Salazar-Gonzalez A., Li Y, Liu X.

Journal of Artificial Intelligence and Soft Computing Research

2012

Vol. 2, No. 3

235--245

Glaucoma is one of the main causes of blindness worldwide. Periodical retinal screening is highly recommended in order to detect any sign of the disease and apply the appropriated treatment. Different systems for the analysis of retinal images have been designed in order to assist this process. The segmentation of the optic disc is an important step in the development of a retinal screening system. In this paper we present an unsupervised method for the segmentation of the optic disc. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object. While many other methods have addressed this problem trying to eliminate the vessels, we have incorporated the blood vessel information into our formulation. The blood vessels inside of the optic disc are used to give continuity to the object to segment. Our approach is based on the graph cut technique, where the graph is constructed by considering the relationship between neighbouring pixels and by the likelihood of them belonging to the foreground and background from prior information. Our method was tested on two public datasets, DIARETDB1 and DRIVE. The performance of our method was measured by calculating the overlapping ratio (Oratio), sensitivity and the mean absolute distance (MAD) with respect to the manually labeled images.