Yes / Corneal images can be acquired using confocal microscopes which provide detailed images of
the different layers inside the cornea. Most corneal problems and diseases occur in one or more of the
main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically
extracting clinical information associated with corneal diseases, or evaluating the normal cornea, it is
important also to be able to automatically recognise these layers easily. Artificial intelligence (AI)
approaches can provide improved accuracy over the conventional processing techniques and save a
useful amount of time over the manual analysis time required by clinical experts. Artificial neural
networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS), are powerful AI techniques,
which have the capability to accurately classify the main layers of the cornea. The use of an ANFIS
approach to analyse corneal layers is described for the first time in this paper, and statistical features
have been also employed in the identification of the corneal abnormality. An ANN approach is then
added to form a combined committee machine with improved performance which achieves an
accuracy of 100% for some classes in the processed data sets. Three normal data sets of whole corneas,
comprising a total of 356 images, and seven abnormal corneal images associated with diseases have
been investigated in the proposed system. The resulting system is able to pre-process (quality
enhancement, noise removal), classify (whole data sets, not just samples of the images as mentioned in
the previous studies), and identify abnormalities in the analysed data sets. The system output is
visually mapped and the main corneal layers are displayed. 3D volume visualisation for the processed
corneal images as well as for each individual corneal cell is also achieved through this system. Corneal
clinicians have verified and approved the clinical usefulness of the developed system especially in
terms of underpinning the expertise of ophthalmologists and its applicability in patient care.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/8303 |
| Date | 31 July 2015 |
| Creators | Qahwaji, Rami S.R., Ipson, Stanley S., Sharif, Mhd Saeed, Brahma, A. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted Manuscript |
| Rights | (c) 2015 Elsevier B.V. Full-text reproduced in accordance with the publisher's self-archiving policy. |
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