Dilated Deep Neural Architectures for Improving Retinal Vessel Extraction

Abstract

Retinal vascular region is recognized as the promising anatomical region for the diagnosis of several commonly seen diseases including cardiovascular related and diabetes. In this paper we propose two novel deep neural architectures named as Dilated fully convolved convolutional neural network (FCNN) and dilated depth concatenated neural network (DCNN) to segment the retinal blood vessels. The proposed work is evaluated for both the proposed architectures with and without dilation. It is observed from the obtained results that dilation enhances the network performance. To eliminate the non-uniform illumination and low contrast differences effect the preprocessed images are used for training the architectures. The proposed methodologies are experimented on the two publicly available databases DRIVE and STARE database. The proposed dilated FCNN architecture can able to obtain high accuracy of about 95.39% which is high compared to the FCNN architecture. For the dilated DCNN architecture also, accuracy obtained is about 96.16% which is high compared to DCNN. The experimental results reveal the significance of dilation operation in improving the semantic segmentation of retinal blood vessels.