Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity

Pan  Su; Xuanhao Zhang; Hao Qiu; Jianyang Xie; Yitian Zhao; Jiang  Liu; Tianhua Chen

doi:10.1007/978-3-030-66474-9_9

Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity

Pan Su, Xuanhao Zhang, Hao Qiu, Jianyang Xie, Yitian Zhao, Jiang Liu, Tianhua Chen

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The tortuosity of corneal nerve fibres is correlated with a number of diseases such as diabetic neuropathy. The assessment of corneal nerve tortuosity level in in vivo confocal microscopy (IVCM) images can inform the detection of early diseases and further complications. With the aim to assess the corneal nerve tortuosity accurately as well as to extract knowledge meaningful to ophthalmologists, this chapter proposes a fuzzy pattern tree-based approach for the automated grading of corneal nerves’ tortuosity based on IVCM images. The proposed method starts with the deep learning-based image segmentation of corneal nerves and then extracts several morphological tortuosity measurements as features for further processing. Finally, the fuzzy pattern trees are constructed based on the extracted features for the tortuosity grading. Experimental results on a public corneal nerve data set demonstrates the effectiveness of fuzzy pattern tree in IVCM image tortuosity assessment.

Original language	English
Title of host publication	Fuzzy Logic
Subtitle of host publication	Recent Applications and Developments
Editors	Jenny Carter, Francisco Chiclana, Arjab Singh Khuman, Tianhua Chen
Place of Publication	Cham
Publisher	Springer Nature Switzerland AG
Chapter	9
Pages	131-143
Number of pages	13
Edition	1
ISBN (Electronic)	9783030664749
ISBN (Print)	9783030664732, 9783030664763
DOIs	https://doi.org/10.1007/978-3-030-66474-9_9
Publication status	Published - 24 May 2021

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1 Editorial work

Springer Nature Switzerland AG (Publisher)
Jennifer Carter (Editor), Francisco Chiclana (Editor), Arjab Singh Khuman (Editor) & Tianhua Chen (Editor)
Oct 2019 → Oct 2020
Activity: Publication peer-review and editorial work types › Editorial work

Cite this

Su, Pan ; Zhang, Xuanhao ; Qiu, Hao ; Xie, Jianyang ; Zhao, Yitian ; Liu, Jiang ; Chen, Tianhua. / Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity. Fuzzy Logic: Recent Applications and Developments. editor / Jenny Carter ; Francisco Chiclana ; Arjab Singh Khuman ; Tianhua Chen. 1. ed. Cham : Springer Nature Switzerland AG, 2021. pp. 131-143

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abstract = "The tortuosity of corneal nerve fibres is correlated with a number of diseases such as diabetic neuropathy. The assessment of corneal nerve tortuosity level in in vivo confocal microscopy (IVCM) images can inform the detection of early diseases and further complications. With the aim to assess the corneal nerve tortuosity accurately as well as to extract knowledge meaningful to ophthalmologists, this chapter proposes a fuzzy pattern tree-based approach for the automated grading of corneal nerves{\textquoteright} tortuosity based on IVCM images. The proposed method starts with the deep learning-based image segmentation of corneal nerves and then extracts several morphological tortuosity measurements as features for further processing. Finally, the fuzzy pattern trees are constructed based on the extracted features for the tortuosity grading. Experimental results on a public corneal nerve data set demonstrates the effectiveness of fuzzy pattern tree in IVCM image tortuosity assessment.",

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Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity. / Su, Pan ; Zhang, Xuanhao; Qiu, Hao; Xie, Jianyang; Zhao, Yitian; Liu, Jiang ; Chen, Tianhua.

Fuzzy Logic: Recent Applications and Developments. ed. / Jenny Carter; Francisco Chiclana; Arjab Singh Khuman; Tianhua Chen. 1. ed. Cham : Springer Nature Switzerland AG, 2021. p. 131-143.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AB - The tortuosity of corneal nerve fibres is correlated with a number of diseases such as diabetic neuropathy. The assessment of corneal nerve tortuosity level in in vivo confocal microscopy (IVCM) images can inform the detection of early diseases and further complications. With the aim to assess the corneal nerve tortuosity accurately as well as to extract knowledge meaningful to ophthalmologists, this chapter proposes a fuzzy pattern tree-based approach for the automated grading of corneal nerves’ tortuosity based on IVCM images. The proposed method starts with the deep learning-based image segmentation of corneal nerves and then extracts several morphological tortuosity measurements as features for further processing. Finally, the fuzzy pattern trees are constructed based on the extracted features for the tortuosity grading. Experimental results on a public corneal nerve data set demonstrates the effectiveness of fuzzy pattern tree in IVCM image tortuosity assessment.

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Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity

Abstract

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