Automatic evaluation of corneal nerve tortuosity in images from in vivo confocal microscopy.

@article{Scarpa2011AutomaticEO,
  title={Automatic evaluation of corneal nerve tortuosity in images from in vivo confocal microscopy.},
  author={Fabio Scarpa and Xiaodong Zheng and Yuichi Ohashi and Alfredo Ruggeri},
  journal={Investigative ophthalmology \& visual science},
  year={2011},
  volume={52 9},
  pages={
          6404-8
        }
}
PURPOSE. An algorithm and a computer program for the automatic grading of corneal nerve tortuosity are proposed and evaluated. METHODS. Thirty images of the corneal subbasal nerve plexus with different grades of tortuosity were acquired with a scanning laser confocal microscope in normal and pathologic subjects. Nerves were automatically traced with an algorithm previously developed, and a tortuosity measure was computed with the proposed method, based on the number of changes in the curvature… 
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62 Citations
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62 Citations

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Citation Type

Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy
TLDR
A fully automated method for image-level Tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification is proposed, which performs better than several selected state-of-the-art methods.
Development of Clinically Based Corneal Nerves Tortuosity Indexes
TLDR
A Linear Discriminant Analysis showed a very good capability (accuracy 85%) to differentiate healthy subjects from pathological ones by using both tortuosity indexes together.
A Fast and Efficient Technique for the Automatic Tracing of Corneal Nerves in Confocal Microscopy
TLDR
A novel fully automatic method capable of tracing the subbasal plexus nerves from human corneal confocal images using a support vector machines approach to minimize required computing time while still providing accurate results.
Corneal Nerve Tortuosity Grading via Ordered Weighted Averaging-based Feature Extraction.
TLDR
The simultaneous use of multiple aggregation operators could extract image-level features that lead to more stable and robust results compared with that using average and length-weighted average, and the OWA method could facilitate the explanation of derived aggregation behavior through stress functions.
A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images
U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures
TLDR
A novel U-Net segmented adjacent angle detection method was developed and identified the nerve interval lengths that optimised the correlation of tortuosity estimates with human grading and the optimised grading measurement was significantly correlated with the USAAD angle measurements.
Focused Tortuosity Definitions Based on Expert Clinical Assessment of Corneal Subbasal Nerves.
TLDR
Definitions of tortuosity specifying short or long-range tortUosity and considering only the most tortuous nerve in an image improved the agreement in Tortuosity grading among a group of expert observers.
Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
TLDR
An automated framework for tortuosity estimation for corneal nerve and retinal vessel images is proposed and achieves a promising performance compared with other state-of-the-art methods in terms of accuracy and generality.
Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity
TLDR
This chapter proposes a fuzzy pattern tree-based approach for the automated grading of corneal nerves’ tortuosity based on IVCM images, which starts with the deep learning-based image segmentation of corNeal nerves and then extracts several morphological Tortuosity measurements as features for further processing.
An Automatic Corneal Subbasal Nerve Registration System Using FFT and Phase Correlation Techniques for an Accurate DPN Diagnosis
TLDR
An automatic corneal subbasal nerve registration system is proposed that can be used to give ophthalmologists a summarized and objective description about a diabetic patient's health status using a sequence of CCM images that have been captured from different imaging devices and/or at different times.
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