title: Automatic assessment of macular edema from color retinal images.
abstract: Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.
authors.affiliation: Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, AP, India. sai.deepak@research.iiit.ac.in
authors.forename: K Sai
authors.initials: KS
authors.lastname: Deepak
authors.forename: Jayanthi
authors.initials: J
authors.lastname: Sivaswamy
mesh_terms: D016208:Databases, Factual; D003930:Diabetic Retinopathy; D003941:Diagnostic Techniques, Ophthalmological; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008269:Macular Edema / Q000175:diagnosis* / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity