Karadeniz Technical University, Trabzon, Turkey.
J Healthc Eng. 2017;2017:4897258. doi: 10.1155/2017/4897258. Epub 2017 Aug 3.
Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.
视网膜血管在糖尿病视网膜病变、青光眼、动脉硬化和高血压等各种视网膜疾病的诊断和治疗中具有重要作用。因此，为了帮助专家诊断和治疗系统性疾病，提取视网膜血管网络非常重要。在本文中，我们提出了一种新的方法来提取视网膜血管网络。我们的方法包括四个阶段：（1）预处理阶段，以便为分割做准备；（2）增强过程，包括 Gabor、Frangi 和高斯滤波器，分别在顶帽变换之前获得；（3）硬聚类和软聚类阶段，包括 K-means 和模糊 C 均值（FCM），以获得二值血管图；（4）后处理步骤，去除错误分割的孤立区域。该方法在 STARE 和 DRIVE 数据库中获得的彩色视网膜图像上进行了测试，这些数据库可在网上获得。结果表明，Gabor 滤波器后接 K-means 聚类方法对 STARE 和 DRIVE 数据库的准确率分别达到 95.94%和 95.71%，这对于诊断系统来说是可以接受的。
