Department of Medical Instrumentation Techniques Engineering, AL-Hussain University College, Karbala, Iraq.
J Digit Imaging. 2021 Jun;34(3):750-759. doi: 10.1007/s10278-021-00447-0. Epub 2021 Apr 22.
Digital images used in the field of ophthalmology are among the most important methods for automatic detection of certain eye diseases. These processes include image enhancement as a primary step to assist optometrists in identifying diseases. Therefore, many algorithms and methods have been developed for the enhancement of retinal fundus images, which may experience challenges that typically accompany enhancement processes, such as artificial borders and dim lighting that mask image details. To eliminate these problems, a new algorithm is proposed in this paper based on separating colour images into three channels (red, green, and blue). The green channel is passed through a Wiener filter and reinforced using the CLAHE technique before merging with the original red and blue channels. Reducing the green channel noise with this approach is proven effective over the other colour channels. Results from the Contrast Improvement Index (CII) and linear index of fuzziness (r) test indicate the success of the proposed algorithm compared with alternate algorithms in the application of improving blood vessel imagery and other details within ten test fundus images selected from the DRIVER database.
在眼科领域中,数字图像是自动检测某些眼部疾病的最重要方法之一。这些过程包括图像增强,作为辅助验光师识别疾病的首要步骤。因此,已经开发出许多用于增强视网膜眼底图像的算法和方法,这些图像可能会遇到增强过程中常见的挑战,例如人工边界和昏暗的光线会掩盖图像细节。为了解决这些问题,本文提出了一种新的算法,该算法基于将彩色图像分为三个通道(红色、绿色和蓝色)。绿色通道通过 Wiener 滤波器进行处理,并使用 CLAHE 技术进行增强,然后与原始的红色和蓝色通道合并。事实证明,与其他颜色通道相比,这种方法可以有效地减少绿色通道的噪声。对比度改善指数 (CII) 和模糊线性指数 (r) 测试的结果表明,与替代算法相比,该算法在应用于改善血管图像和从 DRIVER 数据库中选择的十个测试眼底图像中的其他细节方面取得了成功。