Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, AP, India.
IEEE Trans Med Imaging. 2012 Mar;31(3):766-76. doi: 10.1109/TMI.2011.2178856. Epub 2011 Dec 8.
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.
糖尿病性黄斑水肿（DME）是糖尿病性视网膜病变的晚期症状，可导致不可逆转的视力丧失。本文提出了一种用于从眼底彩色图像中检测和分类 DME 严重程度的两阶段方法。通过使用正常眼底图像的监督学习方法进行 DME 检测。引入了一种特征提取技术来捕获眼底图像的全局特征，并区分正常和 DME 图像。通过检查黄斑区域的对称性，使用旋转不对称性度量来评估疾病的严重程度。针对几个公开可用的数据集评估了所提出的方法和特征的性能。检测性能的灵敏度为 100%，特异性在 74%至 90%之间。具有 100%灵敏度和 97%特异性的病例可检测到需要立即转诊的情况。中度病例的严重程度分类准确率为 81%，严重病例的准确率为 100%。这些结果证明了所提出的解决方案的有效性。
