论文标题
预处理的辅助U-NET体系结构的参数缩放，用于简易视网膜分段
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation
论文作者
论文摘要
从视网膜底面图像中提取血管在诊断相关疾病的进展方面起着决定性的作用。在医学图像分析中，血管提取是一个语义二元分割问题，需要从背景中提取血管。在这里，我们提出了一种基于形态预处理的图像增强技术，再加上缩放的U-NET体系结构。尽管可训练的网络参数的数量相对较少，但U-NET体系结构的缩放版本提供了更好的性能与域中的其他方法相比。我们验证了驱动器数据库中视网膜底面图像的提出方法。与该域中的其他算法相比，从ROC曲线（> 0.9762）和分类精度（> 95.47％）的面积方面，可以显着改善。此外，所提出的方法对中央血管反射具有抗性，同时敏感地在存在背景项目的情况下检测血管。渗出液，视盘和凹fovea。
Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated the proposed method on retinal fundus images from the DRIVE database. A significant improvement as compared to the other algorithms in the domain, in terms of the area under ROC curve (>0.9762) and classification accuracy (>95.47%) are evident from the results. Furthermore, the proposed method is resistant to the central vessel reflex while sensitive to detect blood vessels in the presence of background items viz. exudates, optic disc, and fovea.
