Faculty of Engineering, University Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia.
J Digit Imaging. 2012 Jun;25(3):437-44. doi: 10.1007/s10278-011-9418-6.
Diabetic retinopathy has become an increasingly important cause of blindness. Nevertheless, vision loss can be prevented from early detection of diabetic retinopathy and monitor with regular examination. Common automatic detection of retinal abnormalities is for microaneurysms, hemorrhages, hard exudates, and cotton wool spot. However, there is a worse case of retinal abnormality, but not much research was done to detect it. It is neovascularization where new blood vessels grow due to extensive lack of oxygen in the retinal capillaries. This paper shows that various combination of techniques such as image normalization, compactness classifier, morphology-based operator, Gaussian filtering, and thresholding techniques were used in developing of neovascularization detection. A function matrix box was added in order to classify the neovascularization from natural blood vessel. A region-based neovascularization classification was attempted as a diagnostic accuracy. The developed method was tested on images from different database sources with varying quality and image resolution. It shows that specificity and sensitivity results were 89.4% and 63.9%, respectively. The proposed approach yield encouraging results for future development.
糖尿病性视网膜病变已成为导致失明的一个越来越重要的原因。然而，通过早期发现糖尿病性视网膜病变并定期检查，可以预防视力丧失。常见的视网膜异常的自动检测是针对微动脉瘤、出血、硬性渗出物和棉絮斑。然而，还有一种更严重的视网膜异常情况，但对其进行检测的研究并不多。这是由于视网膜毛细血管严重缺氧而导致新血管生长的新生血管形成。本文表明，在开发新生血管检测中使用了各种技术的组合，如图像归一化、紧致度分类器、基于形态的算子、高斯滤波和阈值技术。为了将新生血管与自然血管区分开来，添加了一个功能矩阵框。尝试了基于区域的新生血管分类作为诊断准确性。该方法在来自不同数据库来源的具有不同质量和图像分辨率的图像上进行了测试。结果表明，特异性和敏感性分别为 89.4%和 63.9%。该方法为未来的发展提供了令人鼓舞的结果。
