Kushol Rafsanjany, Kabir Md Hasanul, Abdullah-Al-Wadud M, Islam Md Saiful
Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh.
Math Biosci Eng. 2020 Nov 6;17(6):7751-7771. doi: 10.3934/mbe.2020394.
The improper circulation of blood flow inside the retinal vessel is the primary source of most of the optical disorders including partial vision loss and blindness. Accurate blood vessel segmentation of the retinal image is utilized for biometric identification, computer-assisted laser surgical procedure, automatic screening, and diagnosis of ophthalmologic diseases like Diabetic retinopathy, Age-related macular degeneration, Hypertensive retinopathy, and so on. Proper identification of retinal blood vessels at its early stage assists medical experts to take expedient treatment procedures which could mitigate potential vision loss. This paper presents an efficient retinal blood vessel segmentation approach where a 4-D feature vector is constructed by the outcome of Bendlet transform, which can capture directional information much more efficiently than the traditional wavelets. Afterward, a bunch of ensemble classifiers is applied to find out the best possible result of whether a pixel falls inside a vessel or non-vessel segment. The detailed and comprehensive experiments operated on two benchmark and publicly available retinal color image databases (DRIVE and STARE) prove the effectiveness of the proposed approach where the average accuracy for vessel segmentation accomplished approximately 95%. Furthermore, in comparison with other promising works on the aforementioned databases demonstrates the enhanced performance and robustness of the proposed method.
视网膜血管内血流的异常循环是包括部分视力丧失和失明在内的大多数眼部疾病的主要根源。视网膜图像的精确血管分割可用于生物特征识别、计算机辅助激光手术、自动筛查以及糖尿病视网膜病变、年龄相关性黄斑变性、高血压性视网膜病变等眼科疾病的诊断。在早期正确识别视网膜血管有助于医学专家采取适当的治疗措施，从而减轻潜在的视力丧失。本文提出了一种高效的视网膜血管分割方法，通过Bendlet变换的结果构建一个四维特征向量，该向量能够比传统小波更有效地捕捉方向信息。随后，应用一组集成分类器来确定一个像素是落在血管段还是非血管段内的最佳可能结果。在两个基准且公开可用的视网膜彩色图像数据库（DRIVE和STARE）上进行的详细而全面的实验证明了该方法的有效性，其中血管分割的平均准确率约为95%。此外，与上述数据库上的其他有前景的工作相比，该方法展示了更高的性能和鲁棒性。
