Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification

Abstract

The pathological changes in the eye fundus image, especially around Optic Disc (OD) and Optic Cup (OC) may indicate eye diseases such as glaucoma. Therefore, accurate OD and OC segmentation is essential. The variety in images caused by different eye fundus cameras makes the complexity for the existing deep learning (DL) networks in OD and OC segmentation. In most research cases, experiments were conducted on individual data sets only and the results were obtained for that specific data sample. Our future goal is to develop a DL method that segments OD and OC in any kind of eye fundus image but the application of the mixed training data strategy is in the initiation stage and the image preprocessing is not discussed. Therefore, the aim of this paper is to evaluate the mage preprocessing impact on OD and OC segmentation in different eye fundus images aligned by size. We adopted a mixed training data strategy by combining images of DRISHTI-GS, REFUGE, and RIM-ONE datasets, and applied image resizing incorporating various interpolation methods, namely bilinear, nearest neighbor, and bicubic for image resolution alignment. The impact of image preprocessing on OD and OC segmentation was evaluated using three convolutional neural networks Attention U-Net, Residual Attention U-Net (RAUNET), and U-Net++. The experimental results show that the most accurate segmentation is achieved by resizing images to a size of 512 x 512 px and applying bicubic interpolation. The highest Dice of 0.979 for OD and 0.877 for OC are achieved on  RISHTI-GS test dataset, 0.973 for OD and 0.874 for OC on the REFUGE test dataset, 0.977 for OD and 0:855 for OC on RIM-ONE test dataset. Anova and Levene’s tests with statistically significant evidence at α = 0.05 show that the chosen size in image resizing has impact on the OD and OC segmentation results, meanwhile, the interpolation method does influent OC segmentation only.