Optic nerve head segmentation using genetic active contours

As one of the leading causes of untreatable blindness worldwide, glaucoma is more likely to occur in persons of African or Asian descends as compared to Caucasians. Glaucoma is a manifestation of a group of conditions originated from different causes resulting in an increased pressure inside the eye. The pressure causes the blood vessels and retinal nerves to atrophy and leads to an eventual loss of vision. Early detection of glaucoma is important because it can minimise damage and allow for prompt and adequate treatment in avoiding blindness. The segmentation and evaluation of the optic nerve head (ONH) plays a significant role in the diagnosis of glaucoma. This paper reviews segmentation approaches using active contours (snakes) for locating and detecting the ONH in terms of the representation of the contours and the energy formulation used. It proposes an algorithm that combines active contour models and genetic algorithm (GA); coupled with the modified energy terms and the incorporation of the energy minimization procedure using GA, the proposed approach can greatly affect the performance of active contour models in medical image segmentation. Preliminary results indicates that an improved algorithm may improve the manual glaucoma screening efficiency.