Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging Academic Article uri icon


MeSH Major

  • Algorithms
  • Cornea
  • Diabetic Retinopathy
  • Image Interpretation, Computer-Assisted
  • Microscopy, Confocal
  • Nerve Fibers
  • Pattern Recognition, Automated


  • Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation.

publication date

  • October 2011



  • Academic Article



  • eng

Digital Object Identifier (DOI)

  • 10.1016/

PubMed ID

  • 21719344

Additional Document Info

start page

  • 738

end page

  • 47


  • 15


  • 5