  Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are
the major causes of vision loss in developed countries. Alteration of retinal
layer structure and appearance of exudate are the most significant signs of
these diseases. With the aim of automatic classification of DME, AMD and normal
subjects from Optical Coherence Tomography (OCT) images, we proposed a
classification algorithm. The two important issues intended in this approach
are, not utilizing retinal layer segmentation which by itself is a challenging
task and attempting to identify diseases in their early stages, where the signs
of diseases appear in a small fraction of B-Scans. We used a histogram of
oriented gradients (HOG) feature descriptor to well characterize the
distribution of local intensity gradients and edge directions. In order to
capture the structure of extracted features, we employed different dictionary
learning-based classifiers. Our dataset consists of 45 subjects: 15 patients
with AMD, 15 patients with DME and 15 normal subjects. The proposed classifier
leads to an accuracy of 95.13%, 100.00%, and 100.00% for DME, AMD, and normal
OCT images, respectively, only by considering the 4% of all B-Scans of a volume
which outperforms the state of the art methods.
