Design team members: Janine Cullen, Shane Pounder , Kim Whitear
Supervisors: Dr. Paul Fieguth, Systems Design, Dr. Trefford Simpson, Optometry
Background
There is a subjective element to any medical diagnosis. This is especially true in the field of optometry where professionals are constantly classifying ocular health based on experience rather than quantified characteristics. One specific symptom used to determine ocular health is the level of redness evident on the sclera of the eye. Excessive redness is known as conjunctival hyperaemia.
Currently, many optometrists classify the level of conjunctival hyperaemia based on a comparison of the eye with a set of control images showing different intensities of disorder. Unfortunately, there is not a fixed set of images that is universally accepted. There is also no scale that is used consistently by optometrists. Some practitioners grade the eye using a numerical classifier while others qualitatively classify the degree of redness in the eye. Furthermore, even if two optometrists grade redness on the same scale, there can still be a great deal of variation as they are still using a subjective grading method.
Project description
The goal of this project is to produce an automated classification system for grading eyes. The final product should provide a rating of the level of conjunctival hyperaemia for an image.
A number of criteria have been developed in order to better define the project goals. They are:
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The system must produce repeatable results that are not dependent on the user of the system.
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A scale that is easy to interpret must be used in order to increase the ease of interpretation of the results. A single scale will also allow for comparability between patients and over time.
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The system must be simple and easy to use.
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The system should also be extendable to a clinical environment.
Design methodology
In order to address the problem of subjective grading, it is necessary to learn more about how experts view redness of eyes. This will be accomplished through collecting expert data on our website. In parallel with the data collection an objective grading method for ocular redness will be developed. Using image processing and pattern recognition techniques, various features of the eye will be selected, such as redness and edge properties, and each image will be assigned an objective numeric value. Each of the selected features will then be compared separately and in parallel to the expert data in order to determine appropriate classifiers. The final algorithm could eventually be built into an optical imaging machine and be used by optomotrists everywhere to provide objective grading of ocular redness.