Automatic glaucoma detection based on the type of features used: A review

Abstract

Glaucoma is an eye disease that is the second most common cause of blindness in worldwide. The characteristic of glaucoma are high eye pressure, loss of vision gradually which can cause blindness and damage to the structure of retina. The damages which may occur for example are structural form changes of the Optic Nerve Head (ONH) and Retinal Nerve Fiber Layer (RNFL) thickness. The observable part of ONH which is the features of glaucoma such as disc, cup, neuroretinal rim, Parapapillary atrophy and blood vessels. The structure of the retina can be observed through a retinal image, where the image is produced from several types of equipment such funduscopy, Confocal Scanning Laser Ophthalmoscopy (CSLO), Heidelberg Retina Tomograph (HRT) and Optical Coherence Tomography (OCT). This paper discusses about the automatic feature extraction technique in retinal fundus images which can be used for detection or classification of glaucoma. The technique is divided into two groups, namely morphological and non-morphological based on the type of features used. This grouping aims to determine what type of features extraction technique can be used to represent the glaucoma characteristic.