Applying fuzzy pattern trees for the assessment of corneal nerve tortuosity
The tortuosity of corneal nerve fibers 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 demonstrate the effectiveness of fuzzy pattern tree in IVCM image tortuosity assessment.
|Corresponding Author||Zhao，Yitian; Liu，Jiang|
Cited Times [WOS]:0
|Department||Department of Computer Science and Engineering|
1.Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo,China
2.School of Control and Computer Engineering,North China Electric Power University,Baoding,China
3.School of Computer Science University of Nottingham,Ningbo,China
4.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
5.School of Computing and Engineering,University of Huddersfield,Huddersfield,United Kingdom
|Corresponding Author Affilication||Department of Computer Science and Engineering|
Su，Pan,Zhang，Xuanhao,Qiu，Hao,et al. Applying fuzzy pattern trees for the assessment of corneal nerve tortuosity. 2021-05-04.
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