Pan Su1,2, Tianhua Chen3, Jianyang Xie1, Yalin Zheng4, Hong Qi5, Davide Borroni6, Yitian Zhao1, Jiang Liu7. 1. Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315300, China. 2. School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China. 3. School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK. 4. Department of Eye and Vision Science, University of Liverpool, Liverpool, L69 3BX, UK. 5. Department of Ophthalmology, Peking University Third Hospital, Beijing, 100191, China. 6. St. Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, L69 3BX, UK. 7. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
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 (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.
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.
Authors: Matthew Oyeleye; Tianhua Chen; Sofya Titarenko; Grigoris Antoniou Journal: Int J Environ Res Public Health Date: 2022-02-19 Impact factor: 3.390