Abstract

Diabetic retinopathy is one of the dangerous fundus diseases that leads to irreversible loss of vision. In the case of untimely or incorrect treatment, blindness occurs. Currently, laser coagulation is a common treatment method. An ophthalmologist uses a laser to apply a series of burns to the retina. The success of the operation depends entirely on the experience of the doctor. The automatic formation of a preliminary plan of coagulates allows us to solve a number of problems related to the operation, such as long manual placement of coagulates or adjustment of laser power. Thus, the probability of a doctor’s error is reduced, and the preparation time for the operation is significantly reduced. One of the key stages in the formation of the plan is the segmentation of the fundus image. This stage is carried out with the help of texture features, the calculation of which takes a long time. In relation to this, this study proposes high-performance algorithms for the segmentation of fundus images using CUDA technologies, which significantly speed up sequential versions and outperform parallel algorithms.

中文翻译：

---

使用图形处理单元开发用于眼底图像分割的高性能算法

摘要

糖尿病视网膜病变是一种危险的眼底疾病，会导致不可逆的视力丧失。如果治疗不及时或不正确，就会发生失明。目前，激光凝固是一种常见的治疗方法。眼科医生使用激光对视网膜进行一系列灼伤。手术的成功与否完全取决于医生的经验。凝结物初步计划的自动形成使我们能够解决许多与操作相关的问题，例如长时间手动放置凝结物或调整激光功率。从而降低了医生出错的概率，大大缩短了手术准备时间。计划形成的关键阶段之一是眼底图像的分割。这个阶段是在纹理特征的帮助下进行的，其计算需要很长时间。与此相关，本研究提出了使用 CUDA 技术分割眼底图像的高性能算法，该算法显着加快了顺序版本并优于并行算法。