基于形态学和强度阈值的视网膜眼底图像视盘分割

Hayder Jaber Samawi, A. Al-Sultan, Enas Hamood Al-Saadi
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引用次数: 2

摘要

在许多影响视网膜的疾病中,有两种严重的疾病:糖尿病视网膜病变和青光眼。糖尿病视网膜病变(DR)是一种严重危及糖尿病患者视力的疾病。它的发生是由于糖尿病导致视网膜受损。青光眼是一种视网膜系统疾病,它会损害眼睛的视神经,并随着时间的推移而恶化。眼内压力的增加通常与此有关,因此它会损害将图像传递给大脑的视神经。如果高眼压引起的视神经损伤持续下去,青光眼会导致永久性视力丧失。早期诊断和治疗已被证明可以预防失明和视力丧失。与人工诊断方法相比,自动化视网膜分析系统有助于节省患者的时间、成本和视力。在这种情况下,诊断这些疾病的一个基本过程是在视网膜图像中精确有效地定位视盘(OD)。本文提出了一种鲁棒、高效的OD自动诊断方法。该方法首先去除图像中不需要的部分,如噪声、反射和模糊。其次,考虑强度阈值特征,通过形态学操作检测OD。该方法具有快速、鲁棒性好,即使在可见血管干扰下也能检测出OD。将该方法应用于Origa、Rim-One 3、Drishti、Messidor、Drions、Diaretdb0和DIARETDBI 7个数据集,结果准确率分别为98.46、97.48、97.03、98.75、97.27、95.38和95.45。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optic Disc Segmentation in Retinal Fundus Images Using Morphological Techniques and Intensity Thresholding
Among the many diseases that affect the retina, there are two serious diseases: Diabetic Retinopathy and Glaucoma. Diabetic Retinopathy (DR) is a sight-threatening risk inflicting disorder diabetic patient. It occurs due to damage in the retina as a result of diabetes. Glaucoma is a disease of the retinal system that damages the optic nerve of the eye and gets worse over time. A buildup of pressure inside the eye is often associated with it, so it can damage the optic nerve that transmits images to the brain. If damage to the optic nerve caused by high eye pressure continues, glaucoma causes permanent vision loss. Early diagnosis and treatment have been shown to prevent blindness and visual loss. Compared with the manual diagnostic methods, automated retinal analysis systems help save patients’ time, cost and vision. In this context, a fundamental process for diagnosing these diseases is the precise and effective localization of the Optic Disc (OD) in retinal images. This paper offers a robust and efficient method for automatic diagnosis of OD. The method starts with removing the undesirable portions of the image, such as noise, reflections and blur. Next, the OD has been detected by means of morphological operations considering the intensity threshold feature. The proposed method is fast and robustness to detect OD even if interrupted by the visible blood vessels. This method was applied on seven data sets which are the Origa, Rim-One 3, Drishti, Messidor, Drions, Diaretdb0 and DIARETDBI, and the resulted accuracy for these data set are 98.46, 97.48, 97.03, 98.75, 97.27, 95.38, 95.45 respectively.
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