基于结构相似约束生成对抗网络的视网膜OCT图像去噪算法

Retinal OCT image denoising based on structural similarity constrained generative adversarial network

光学相干断层扫描(OCT)图像中存在的散斑噪声会掩盖视网膜重要的形态学细节,妨碍视网膜病变的观察和临床诊断。提出了一种基于结构相似约束生成对抗网络的视网膜OCT图像去噪算法,基于残差策略改进生成对抗网络模型结构,并融合结构相似性损失约束模型优化,实现散斑噪声抑制,同时增强对视网膜结构细节的保留。在杜克大学发布的SD-OCT公开数据集上的实验表明,所提算法的峰值信噪比和边缘保持指数分别为28.08和0.960,优于所对比的其他去噪方法,且适用于其他来自A2A SD-OCT研究的公开数据集。

Speckle noise in optical coherence tomography(OCT)images obscures important morphological details,and hinders the clinical observation and diagnosis of retinal lesions.A structural similarity constrained generative adversarial network(SSGAN)is proposed for retinal OCT image denoising.The proposed SSGAN utilizes the residual strategy to improve the structure of original generative adversarial network,and incorporates the structural similarity index measure loss into the objective function to achieve more structural constrains while suppressing speckle noise.The experiments on the SD-OCT public dataset published by Duke University show that the peak signal-to-noise ratio and edge preserve index of the proposed method are 28.08 and 0.960 respectively,outperforming the other denoising comparison methods.Further experiments demonstrate that the proposed method can be easily applied to other public datasets from the A2A SD-OCT study.