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

Su, Pan, Chen, Tianhua, Xie, Jianyang ORCID: 0000-0002-4565-5807, Zheng, Yalin ORCID: 0000-0002-7873-0922, Qi, Hong, Borroni, Davide ORCID: 0000-0001-6952-5647, Zhao, Yitian and Liu, Jiang (2020) Corneal nerve tortuosity grading via ordered weighted averaging-based feature extraction. Medical Physics, 47 (10). pp. 4983-4996. ISSN 0094-2405, 2473-4209

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Abstract

<h4>Purpose</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusions</h4>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.

Item Type:	Article
Uncontrolled Keywords:	corneal nerve, feature extraction, ordered weighted averaging, tortuosity grading
Depositing User:	Symplectic Admin
Date Deposited:	29 Sep 2020 07:17
Last Modified:	07 Dec 2024 10:13
DOI:	10.1002/mp.14431
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3102950