Corneal nerve tortuosity grading via ordered weighted averaging‐based feature extraction. Issue 10 (28th August 2020)

• Record Type: Journal Article
• Authors: Su, Pan; Chen, Tianhua; Xie, Jianyang; Zheng, Yalin; Qi, Hong; Borroni, Davide; Zhao, Yitian; Liu, Jiang
• Is Part Of: Medical physics. Volume 47:Issue 10(2020)
• Page Start: 4983
• Page End: 4996
• Publication Date: 2020-08-28
• Subjects: corneal nerve -- feature extraction -- ordered weighted averaging -- tortuosity grading; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14431
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 ( k NNDOWA) 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. Abstract :