Classification of dry age-related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

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 exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning based classification is proposed. The two important issues intended in this approach are avoiding retinal layer segmentation and attempting to mimic the authors' understanding based on normal and abnormal region identifications, considering that the signs of diseases appear in a small fraction of B-Scans. The histogram of oriented gradients feature descriptor was utilized to characterize the distribution of local intensity gradients and edge directions. To capture the structure of extracted features, different dictionary learning-based classifiers are employed. The 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 4% of all B-Scans of a volume, which outperforms the state-of-the-art methods.