Basit A, Fraz Muhammad Moazam
Appl Opt. 2015 Apr 10;54(11):3440-7. doi: 10.1364/AO.54.003440.
With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.
随着数字图像处理、分析和建模技术的发展，自动视网膜图像分析正成为早期检测糖尿病视网膜病变和青光眼等眼科疾病的重要筛查工具。本文提出了一种用于视盘检测和视盘边界提取的稳健方法，以助力此类眼科疾病的计算机辅助诊断和治疗的发展。所提出的方法基于形态学操作、平滑滤波器和标记控制的分水岭变换。使用内部和外部标记首先修改梯度幅值图像，然后将分水岭变换应用于该修改后的梯度幅值图像进行边界提取。该方法在视盘检测和边界提取方面比现有方法有显著改进。对于DRIVE、Shifa、CHASE_DB1和DIARETDB1数据库，所提出的方法的视盘检测成功率分别为100%、100%、100%和98.9%。对于这些数据库，视盘边界检测的平均空间重叠率分别为61.88%、70.96%、45.61%和54.69%，高于当前方法。
