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

  • Julian G. Zilly
  • Joachim M. Buhmann
  • Dwarikanath MahapatraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

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.

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Julian G. Zilly
    • 1
  • Joachim M. Buhmann
    • 2
  • Dwarikanath Mahapatra
    • 2
    Email author
  1. 1.Department of Mechanical EngineeringETH ZurichZurichSwitzerland
  2. 2.Department of Computer ScienceETH ZurichZurichSwitzerland

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