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Assessment of the repeatability in an automatic methodology for hyperemia grading in the bulbar conjunctiva

Brea, Luisa Sanchez, Rodriguez, Noelia Barreira, Gonzalez, Antonio Mosquera and Evans, Katharine 2017. Assessment of the repeatability in an automatic methodology for hyperemia grading in the bulbar conjunctiva. Presented at: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AL, USA, 14-19 May 2017. 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1673-1680. 10.1109/IJCNN.2017.7966052

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Abstract

When the vessels of the bulbar conjunctiva get congested with blood, a characteristic red hue appears in the area. This symptom is known as hyperemia, and can be an early indicator of certain pathologies. Therefore, a prompt diagnosis is desirable in order to minimize both medical and economic repercussions. A fully automatic methodology for hyperemia grading in the bulbar conjunctiva was developed, by means of image processing and machine learning techniques. As there is a wide range of illumination, contrast, and focus issues in the images that specialists use to perform the grading, a repeatability analysis is necessary. Thus, the validation of each step of the methodology was performed, analyzing how variations in the images are translated to the results, and comparing them to the optometrist's measurements. Our results prove the robustness of our methodology to various conditions. Moreover, the differences in the automatic outputs are similar to the optometrist's ones.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Optometry and Vision Sciences
Publisher: IEEE
Related URLs:
Date of First Compliant Deposit: 28 November 2017
Last Modified: 28 Nov 2017 13:50
URI: http://orca.cf.ac.uk/id/eprint/106485

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