新生血管性青光眼( NVG )是由于糖尿病导致永久性视力丧失的人类眼部疾病。早期发现和治疗可防止视力进一步下降。因此,开发一个自动化系统对于帮助眼科医生早期检测NVG更为重要。本文利用分形几何的概念,提出了一种新的检测新生血管性青光眼的方法。分形几何是数学的一个分支。它适用于计算不规则、不对称和复杂自然物体的分形特征。本工作提出了基于分形特征的眼底图像新生血管性青光眼检测。它利用图像调整增强技术作为预处理方法,提高了NVG检测的精度,并利用分形几何的计盒技术估计分形维数。该系统在MESSIDOR和KMC数据集上进行测试,平均准确率为98 %。

Neovascular glaucoma (NVG) is a human eye disease due to diabetes that leads to permanent vision loss. Early detection and treatment of it prevent further vision loss. Hence the development of an automated system is more essential to help the ophthalmologist in detecting NVG at an earlier stage. In this paper, a novel approach is used for detection of Neovascular glaucoma using fractal geometry concepts. Fractal geometry is a branch of mathematics. It is useful in computing fractal features of irregular, asymmetrical, and complex natural objects. In this work, fractal feature-based Neovascular glaucoma detection from fundus images has been proposed. It utilizes the image adjustment enhancement technique as a preprocessing method to improve the accuracy of NVG detection and the box-counting technique of Fractal geometry to estimate the fractal dimension. The proposed system is tested over MESSIDOR and KMC datasets and yields an average accuracy of 98%.