Dai Peishan, Sheng Hanwei, Zhang Jianmei, Li Ling, Wu Jing, Fan Min
Department of Biomedical Engineering, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Department of Education and Law, Hunan Women's University, Changsha 410004, China.
Int J Biomed Imaging. 2016;2016:5075612. doi: 10.1155/2016/5075612. Epub 2016 Sep 4.
Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.
眼底图像在视网膜相关疾病的诊断中起着重要作用。眼底图像的详细信息，如小血管、微动脉瘤和渗出物等，可能对比度较低，而视网膜图像增强通常有助于分析与眼底图像相关的疾病。当前的图像增强方法可能会导致人工边界、颜色水平的突然变化以及图像细节的丢失。为了避免这些副作用，提出了一种新的眼底图像增强方法。首先，利用具有域变换的归一化卷积算法对原始眼底图像进行处理，以获得具有背景基本信息的图像。然后，将具有背景基本信息的图像与原始眼底图像进行融合，得到增强的眼底图像。最后，采用包括四阶偏微分方程和松弛中值滤波器的两阶段去噪方法对融合后的图像进行去噪。使用包括DRIVE数据库、STARE数据库和DIARETDB1数据库在内的视网膜图像数据库来评估图像增强效果。结果表明，该方法能够显著增强眼底图像。而且，与其他一些眼底图像增强方法不同，该方法可以直接增强彩色图像。
