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identify a cornea with an accuracy of roughly 84%. This accuracy can be further improved by incorporating factors, such as the complexity of facial surface temperature due to aging and epicanthic fold, into target-tracing function.
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Chapter 6
Automatic Diagnosis
of Glaucoma Using Digital
Fundus Images
Rajendra Acharya, U. , Oliver Faust , Zhu Kuanyi , Tan Mei Xiu Irene , Boo Maggie , Sumeet Dua†, Tan Jen Hong‡ and Ng, E.Y.K.‡
Glaucoma is a progressive optic neuropathy that is caused by an increase of intraocular pressure (IOP) in eye. It mainly affects the optic disc by enlarging the cup size. If undiagnosed and not treated at an early stage, it can lead to blindness. Glaucoma is diagnosed through optical coherence tomography (OCT) and Heidelberg retinal tomography (HRT) and both methods are expensive. In this chapter, we present an improved method to diagnose glaucoma based on digital fundus images. This method makes use of digital image-processing techniques, such as preprocessing, image segmentation, and morphological operations, to detect both optic disc and blood vessels tree. Furthermore, these techniques are used to extract features such as cup- to-disc (c/d) ratio, blood vessels area, and the ratio that relates the blood vessels area in both inferior and superior sides to the blood vessel area in the nasal-temporal side. We validated these features with a Gaussian mixture model (GMM) classification system. This system was used to classify normal and glaucoma images. It identifies glaucoma with a sensitivity of 77% and a specificity of 88%.
Department of ECE, Ngee Ann Polytechnic, Singapore.
†Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA.
‡Department of Mechanical and Aerospace Engineering Nanyang Technological University, Singapore.
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