Next Article in Journal
Effects of Infrared Optical Trapping on Saccharomyces cerevisiae in a Microfluidic System
Previous Article in Journal
Mediator Preference of Two Different FAD-Dependent Glucose Dehydrogenases Employed in Disposable Enzyme Glucose Sensors
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2644; https://doi.org/10.3390/s17112644

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries

1
Centre of Excellent Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
2
Glasgow Centre for Ophthalmic Research, Gartnavel General Hospital, Glasgow G12 0YN, UK
*
Author to whom correspondence should be addressed.
Received: 19 August 2017 / Revised: 8 November 2017 / Accepted: 9 November 2017 / Published: 16 November 2017
(This article belongs to the Section Remote Sensors)

Abstract

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability. View Full-Text
Keywords: corneal epithelium; hyperspectral imaging; support vector machine; convolutional neural networks; image enhancement corneal epithelium; hyperspectral imaging; support vector machine; convolutional neural networks; image enhancement
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Md Noor, S.S.; Ren, J.; Marshall, S.; Michael, K. Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries. Sensors 2017, 17, 2644.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top