Corneal nerve tortuosity grading via ordered weighted averaging‐based feature extraction

Pan Su, Tianhua Chen, Jianyang Xie, Yalin Zheng, Hong Qi, Davide Borroni, Yitian Zhao, Jiang Liu

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Purpose: Tortuosity of corneal nerve fibers acquired by in vivo Confocal Microscopy (IVCM) are closely correlated to numerous diseases. While tortuosity assessment has conventionally been conducted through labor-intensive manual evaluation, this warrants an automated and objective tortuosity assessment of curvilinear structures. This paper proposes a method that extracts the image-level features for corneal nerve tortuosity grading. Methods: For an IVCM image, all corneal nerve fibers are first segmented and then, their tortuosity are calculated by morphological measures. The ordered weighted averaging (OWA) approach, and the k-Nearest-Neighbor guided dependent ordered weighted averaging (kNNDOWA) approach are proposed to aggregate the tortuosity values and form a set of extracted features. This is followed by running the Wrapper method, a supervised feature selection, with an aim to identify the most informative attributes for tortuosity grading. Results: Validated on a public and an in-house benchmark data sets, experimental results demonstrate superiority of the proposed method over the conventional averaging and length-weighted averaging methods with performance gain in accuracy (15.44% and 14.34%, respectively). Conclusions: The simultaneous use of multiple aggregation operators could extract the image-level features that lead to more stable and robust results compared with that using average and length-weighted average. The OWA method could facilitate the explanation of derived aggregation behavior through stress functions. The kNNDOWA method could mitigate the effects of outliers in the image-level feature extraction.

Original languageEnglish
Pages (from-to)4983-4996
Number of pages14
JournalMedical Physics
Issue number10
Early online date5 Aug 2020
Publication statusPublished - 1 Oct 2020


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