One of the most causes of blindness in the world, particularly in the elderly, is glaucoma. The term "glaucoma" is a heterogeneous group of ocular diseases. Glaucoma is a disease that affects the optic nerve. The retina serves the function of light perception. For this, the light is absorbed by photoreceptors. The light pulses are converted into nerve signals and transmitted via the optic nerve; progressive damage of the optic nerve fibres leads to visual field defects with loss of visual function. Early detection is one of the essential factors for preventing optic nerve damage and blindness caused by glaucoma. Periodic check-ups and early diagnosis of the disease can prevent blindness. If the treatment starts early enough, it is possible to avoid loss of vision. Periodic check-ups, including imaging systems like OCT and image analysis by experts, check for rapid changes in the pattern of blood vessels and the development of different changes in the retina. This examination method is very time consuming, expensive and requires qualified personnel. Automated systems for the detection of glaucoma are necessary. However, problems such as lack of good quality retinal images, prevent a completely automated screening system. This is because the elements of anatomy and lesions in the retina are not visible on the poor-quality images. Optical coherence tomography (OCT) has become the golden standard in ophthalmic imaging and diagnostics capable to acquire tissue volume data non-invasively and with high resolution close to the level of histopathology. An important functional extension of OCT is OCT-angiography (OCTA), which enables display of retinal microvasculature without need of injecting contrast agents. Those angiographic maps introduce novel and promising biomarkers for early disease diagnostics including glaucoma. OCTA is naturally co-registered with OCT, since the OCTA vascular contrast is calculated directly from the OCT intensity mages. The main goal of this thesis, is to develop or improve a way of detecting glaucoma faster and earlier based on OCT images by applying advanced image processing methods. The aim of this work is to implement a segmentation algorithm for OCT angiography, which allows a proper extraction of different microvascular beds from the OCT tomogram. For the segmentation, the intensity tomogram is used to find the different retinal layers. This information will be transferred to the OCT angiography to extract the corresponding layers. Furthermore, a review of quantification and analysis of the de-noising algorithms for the OCT-angiography images will be performed. This should lead to the identification of biomarkers, based on the vascular structure for various eye diseases, which effect the micro vascular structure in the human eye.