Research on fundus image augmentation algorithm

• Main Authors: Yu, Hui, Cai, Lanlan, Yan, Xue
• Format: Conference Proceeding
• Language: English
• Published: SPIE 23.11.2022
• ISBN: 9781510660571; 1510660577
• ISSN: 0277-786X
• DOI: 10.1117/12.2659857

Diabetic retinopathy is a serious ocular complication caused by diabetes, has now become the main reason for the workforce blinding, using digital fundus image for diabetic retinopathy screening regularly is the key to preventing blindness. However, in the process of image acquisition, generation and transmission, it is susceptible to low light conditions and Gaussian noise, and it is difficult to detect small lesions and blood vessels in fundus images, which greatly reduces the accuracy of computer-aided diagnosis. Therefore, this paper proposes an image enhancement algorithm for digital fundus images, using adaptive clipping based on improved Canny edge detection to perform square clipping around fundus region. The improved CLAHE technique was used for low light enhancement to highlight the details of the lesions in the image. Aiming at the additive white Gaussian noise caused by medical image digitalize process, the fundus image denoising algorithm based on self-supervised EM-GMM is used to suppress the influence of noise by imposing sparsity constraint on covariance eigenvalues. The experiment shows that our methods achieve good performance in DDR dataset.