Conference Proceeding

Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images

Authors
  • Julian G. Zilly (ETH Zurich)
  • Joachim M. Buhmann (ETH Zurich)
  • Dwarikanath Mahapatra (ETH Zurich)

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.

How to Cite:

Zilly, J. G. & Buhmann, J. M. & Mahapatra, D., (2015) “Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 2(2015), 153-160. doi: https://doi.org/10.17077/omia.1039

Rights: Copyright © 2015 Julian G. Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra

Downloads:
Download pdf
View PDF

140 Views

44 Downloads

Published on
09 Oct 2015
Peer Reviewed