Optical coherence tomography (OCT), which is routinely used in ophthalmology, enables transverse optical imaging of the retina and, hence, the identification of the different neuronal layers. Directional OCT (D-OCT) extends this technology by acquiring sets of images at different incidence angles of the light beam. In this way, reflectance properties of photoreceptor substructures are highlighted, enabling physicians to study their orientation, which is potentially an interesting biomarker for retinal diseases. Nevertheless, commercial OCT devices equipped to automate D-OCT acquisition do not yet exist, meaning that physicians manually deviate the light beam to acquire a set of D-OCT images sequentially. Therefore, the intensities in the stack of images are not directly comparable, and a normalization step is required before differential analysis. In this paper, we present advanced image processing methods to perform differential analysis of a set of D-OCT images and extract the angle-dependent retinal substructures. Our approach relies on a robust and accurate normalization algorithm followed by a classification that is spatially regularized. We also propose a robust color representation that facilitates interpretation of D-OCT data in general, by detecting and highlighting angle-dependent structures in healthy and diseased eyes. Experimental results show evidence of photoreceptor disarray in a variety of retinal diseases, demonstrating the potential medical interest of the approach.