Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

Yitian Zhao, Jiong Zhang, ELLA PEREIRA, Yalin Zheng, Pan Su, Jianyang Xie, Yifan Zhao, Yonggang Shi, Hong Qi, Jiang Liu, YONGHUAI LIU

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Precise characterization and analysis of
corneal nerve fiber tortuosity are of great importance in
facilitating examination and diagnosis of many eye-related
diseases. In this paper we propose a fully automated
method for image-level tortuosity estimation, comprising
image enhancement, exponential curvature estimation, and
tortuosity level classification. The image enhancement
component is based on an extended Retinex model, which
not only corrects imbalanced illumination and improves
image contrast in an image, but also models noise explicitly
to aid removal of imaging noise. Afterwards, we take
advantage of exponential curvature estimation in the 3D
space of positions and orientations to directly measure
curvature based on the enhanced images, rather than relying
on the explicit segmentation and skeletonization steps
in a conventional pipeline usually with accumulated preprocessing
errors. The proposed method has been applied
over two corneal nerve microscopy datasets for the estimation
of a tortuosity level for each image. The experimental
results show that it performs better than several selected
state-of-the-art methods. Furthermore, we have performed
manual gradings at tortuosity level of four hundred and
three corneal nerve microscopic images, and this dataset
has been released for public access to facilitate other researchers
in the community in carrying out further research
on the same and related topics.
Original languageEnglish
JournalIEEE Transactions on Medical Imaging
Publication statusAccepted/In press - 13 Feb 2020


  • Corneal nerve
  • tortuosity
  • enhancement
  • segmentation
  • curvature

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