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 proceedingChapter

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 languageEnglish
Title of host publicationFuzzy Logic
Subtitle of host publicationRecent Applications and Developments
EditorsJenny Carter, Tianhua Chen, Francisco Chiclana Parilla, Arjab Singh Khuman
PublisherSpringer, Cham
Publication statusAccepted/In press - 16 Oct 2020

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