  Optical coherence tomography angiography (OCTA) is an imaging technique that
allows for non-invasive investigation of the microvasculature in the retina.
OCTA uses laser light reflectance to measure moving blood cells. Hereby, it
visualizes the blood flow in the retina and can be used for determining regions
with more or less blood flow. OCTA images contain the capillary network
together with larger blood vessels, and in this paper we propose a method that
segments larger vessels, capillaries and background. The segmentation is
obtained using a dictionary-based machine learning method that requires
training data to learn the parameters of the segmentation model. Here, we give
a detailed description of how the method is applied to OCTA images, and we
demonstrate how it robustly labels capillaries and blood vessels and hereby
provides the basis for quantifying retinal blood flow.
