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Proceedings Paper

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images
Author(s): Siti Salwa Md Noor; Kaleena Michael; Stephen Marshall; Jinchang Ren
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Paper Abstract

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

Paper Details

Date Published: 19 December 2017
PDF: 11 pages
Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 106130A (19 December 2017); doi: 10.1117/12.2299820
Show Author Affiliations
Siti Salwa Md Noor, Univ. of Strathclyde (United Kingdom)
Kaleena Michael, Glasgow Ctr. for Ophthalamic Research (United Kingdom)
Stephen Marshall, Univ. of Strathclyde (United Kingdom)
Jinchang Ren, Univ. of Strathclyde (United Kingdom)


Published in SPIE Proceedings Vol. 10613:
2017 International Conference on Robotics and Machine Vision
Chiharu Ishii; Genci Capi; Jianhong Zhou, Editor(s)

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