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Corneal nerve tortuosity grading via ordered weighted averaging-based feature extraction.

Pan Su1,2, Tianhua Chen3, Jianyang Xie1

  • 1Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315300, China.

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|August 8, 2020
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Summary

This study introduces an automated method for assessing corneal nerve fiber tortuosity using in vivo confocal microscopy (IVCM). The new approach improves accuracy in grading nerve fiber tortuosity, crucial for diagnosing various diseases.

Keywords:
corneal nervefeature extractionordered weighted averagingtortuosity grading

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Corneal nerve fiber tortuosity is a key indicator for numerous ocular diseases.
  • Manual assessment of nerve tortuosity is time-consuming and subjective.
  • Automated methods are needed for objective and efficient tortuosity evaluation.

Purpose of the Study:

  • To develop an automated method for extracting image-level features for corneal nerve tortuosity grading.
  • To propose novel aggregation approaches for quantifying nerve fiber tortuosity from in vivo confocal microscopy (IVCM) images.
  • To enhance the objectivity and efficiency of corneal nerve tortuosity assessment.

Main Methods:

  • Segmentation of corneal nerve fibers from IVCM images.
  • Calculation of tortuosity using morphological measures.
  • Application of Ordered Weighted Averaging (OWA) and k-Nearest-Neighbor guided dependent OWA (kNNDOWA) for feature aggregation.
  • Supervised feature selection using the Wrapper method.

Main Results:

  • The proposed method achieved superior accuracy compared to conventional averaging techniques (15.44% and 14.34% gains).
  • Validation on public and in-house datasets confirmed the method's effectiveness.
  • Demonstrated improved performance in automated corneal nerve tortuosity grading.

Conclusions:

  • Simultaneous use of multiple aggregation operators yields more stable and robust image-level features.
  • OWA facilitates explanation of aggregation behavior via stress functions.
  • kNNDOWA effectively mitigates outlier effects in feature extraction, enhancing reliability.