Retinal Image Quality Classification Using Saliency Maps and CNNs

  • Dwarikanath MahapatraEmail author
  • Pallab K. Roy
  • Suman Sedai
  • Rahil Garnavi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


Retinal image quality assessment (IQA) algorithms use different hand crafted features without considering the important role of the human visual system (HVS). We solve the IQA problem using the principles behind the working of the HVS. Unsupervised information from local saliency maps and supervised information from trained convolutional neural networks (CNNs) are combined to make a final decision on image quality. A novel algorithm is proposed that calculates saliency values for every image pixel at multiple scales to capture global and local image information. This extracts generalized image information in an unsupervised manner while CNNs provide a principled approach to feature learning without the need to define hand-crafted features. The individual classification decisions are fused by weighting them according to their confidence scores. Experimental results on real datasets demonstrate the superior performance of our proposed algorithm over competing methods.


Random Forest Human Visual System Convolutional Neural Network Salient Region Image Quality Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dwarikanath Mahapatra
    • 1
    Email author
  • Pallab K. Roy
    • 1
  • Suman Sedai
    • 1
  • Rahil Garnavi
    • 1
  1. 1.IBM ResearchMelbourneAustralia

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