Automatic Diagnosis of Glaucoma Using Digital Fundus Images

or they are covered. These canals are blocked similarly to a sink being blocked by some objects covering the drain.4 In the case of angle-closure glaucoma, the iris is often not as wide and open as it should be. In such cases, the pupil dilates too much or too quickly (as when entering a dark room), and its outer edge compresses over the drainage canals. Patients diagnosed with this disease usually have very narrow drainage canals, which may be present at birth. Angle-closure glaucoma is usually treated with surgery. The surgeon cuts away a small portion of the outer iris edge; this helps to unblock the drainage canals.5

6.1.2. Diagnosis of Glaucoma

Many glaucoma tests are time consuming and require trained personal as well as special equipment. Glaucoma can be diagnosed through ophthalmoscopy, tonometry, and perimetry; a regular glaucoma checkup includes tonometry and ophthalmoscopy.4 New techniques to diagnose this ocular disease accurately at early stages are urgently needed.

Recent advances in computer-based systems improved the glaucomascreening process. Imaging systems, such as HRT, scanning laser polarimetry, OCT, and fundus cameras have been extensively used for ocular diagnosis.6 OCT, HCT, and confocal laser scanning tomography can indicate retinal nerve fiber damage before this damage has a negative effect on the visual field. However, many hospitals cannot afford such equipment, because of the high costs involved in purchasing, maintaining, and using such equipment. Hence, digital fundus cameras are often used as a cheaper alternative tool to diagnose glaucoma. These digital fundus cameras produce digital fundus images, which are subjected to image processing. This image processing yields features such as optic disk and blood vessels. These features can be used to diagnose the disease.6–9

Artificial intelligence (AI) has been incorporated into the above procedures in some investigations for the automated diagnosis of glaucoma. A fuzzy set concept was used to handle the uncertainty that is unavoidable in medical diagnosis,10 and a neuro-fuzzy method was developed to identify both the presence and the absence of glaucoma with a classification efficiency of 75.8%.11 A feed forward artificial neural network (ANN) was utilized to discriminate the images of the optic nerve heads of normal

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and glaucoma subjects.12 This method was able to provide an accuracy of 88.9% and a sensitivity (correct abnormal) of 84.4%. Other machinelearning algorithms, such as linear and quadratic discriminant analyses, support vector machine, and the mixture of Gaussian, multilayer perceptron, and Parzen windows, were also employed in the diagnosis of glaucoma.13

We compared the classifier performances by comparing their receiveroperating characteristic (ROC). To be specific, the parameter we used for the comparison was the area under the ROC curves and sensitivities at chosen specificities. They showed that both forward-selection and backwardelimination methodologies improved the identification rate. Therefore, we conclude that the proposed method has the potential to reduce testing time by diminishing the number of visual-field location measurements.

In this chapter, we present an automated system, which detects glaucoma based on three distinct features. The first feature is the c/d ratio, which quantifies changes in the cup area. Usually, a subject with glaucoma has a higher cup area, and, consequently, the optic nerve head shifts toward the nasal. This shift can be measured by taking the distance between the optic disc center and the nerve head under discussion. The second feature is defined as the ratio of this shift and the optic disc diameter. The last feature is defined as the ratio of the total blood vessel area in both inferior and superior sides of the optic disc in relation to the total blood vessel area in nasal and temporal areas; this ratio is the ISNT ratio. Figure 6.1 shows the functional block diagram of the system under discussion.

Fig. 6.1. The proposed glaucoma detection system (reprinted from Ref. [14], with kind permission from Journal of Medical Systems).

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