Automated computer vision method for optic disc detection from non-uniform illuminated digital fundus images

Abstract

Accurate segmentation of optic disc from fundus images is an essential step to develop automatic screening device for eye pathologies. Automatic and correct segmentation of optic disc from non-uniformly illuminated or abnormal/affected fundus images is still a challenging issue. The proposed work presents a computer vision based method for optic disc segmentation from variety of normal, affected and blurred/non uniform illuminated fundus images. The methodology involves separation of super pixels from fundus images followed by removal of false positives like reflections, exudates, choroid vessels using analysis of geometrical features for correct optic disc segmentation. The proposed method was tested on both normal and abnormal/affected fundus images obtained from local eye hospital and achieved 90.78% overlapping ratio. The proposed OD segmentation method is robust and computationally cheap which makes it applicable for real time.