Regional classification-guided wavelet Y-Net network for hard exudate segmentation in fundus images

Abstract

We propose an hard exudate(EX)segmentation algorithm based on regional classification-guided wavelet Y-Net network to eliminate the influence of optic disc on EX segmentation process. The wavelet Y-Net network was an end-to-end fundus image EX segmentation network, which combined the regional detection of optic disc and hard exudates segmentation by regional classification-guided EX segmentation to effectively reduce the interference of optic disc in EX segmentation.To avoid failure of small EX region segmentation caused by information loss due to down-sampling operation, discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) were introduced to replace the traditional pooling down-sampling and up-sampling operations.Meanwhile, the inception module based on residual connection was used to obtain the multi-scale features.The proposed algorithm was trained and tested on the IDRiD and e-ophtha EX datasets and evaluated at the pixel level. For IDRiD and e-ophtha EX datasets, the proposed algorithm achieved accuracy rates of 0.9858 and 0.9938 with AUC values of 0.9880 and 0.9986, respectively. The proposed method can effectively avoid the influence of the optic disc, retain the image details, and improve the effect of EX segmentation.