Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images

Abstract

We propose a novel convolutional neural network (CNN) based method for optic cup and disc segmentation. To reduce computational complexity, an entropy based sampling technique is introduced that gives superior results over uniform sampling. Filters are learned over several layers with the output of previous layers serving as the input to the next layer. A softmax logistic regression classifier is subsequently trained on the output of all learned filters. In several error metrics, the proposed algorithm outperforms existing methods on the public DRISHTI-GS data set.