Computer Assisted Methods for Retinal Image Classification

Computer Assisted Methods for Retinal Image Classification

S. R. Nirmala (Gauhati University, India) and Purabi Sharma (Gauhati University, India)
DOI: 10.4018/978-1-4666-8493-5.ch010
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Diabetes maculopathy has become one of the rapidly increasing health threats worldwide. The complication of diabetes associated to retina of the eye is diabetic retinopathy. A patient with the disease has to undergo periodic screening of eye. The ophthalmologists use colour retinal images of a patient acquired from digital fundus camera for disease diagnosis. Limited number of ophthalmology specialists in most of the countries motivates the need for computer based analysis of retinal images using image processing techniques. The results of this process may be used in applications such as, to classify the retinal images into normal and diseased. This could reduce the workload of ophthalmologists, also aid in diagnosis, to make measurements and to look for a change in progression of disease. Some computer based retinal image analysis methods used for the application are briefed in this chapter.
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Modern medical imaging offers the potential for major advances in science and medicine as higher fidelity images are produced. It has developed into one of the most important fields within scientific imaging due to the rapid progress in computerized medical image visualization and advances in analysis methods (Dougherty 2011). Computer-aided diagnosis is a vital part of the early detection, diagnosis, and treatment of retinal pathologies. These accomplishments have allowed clinicians to make accurate and efficient diagnoses non-invasively. With medical imaging playing an increasingly prominent role in the diagnosis and treatment of diseases, the challenge is to effectively process and analyze the images in order to extract and interpret useful information about anatomical structures (Dougherty 2011). The number of people with eye diseases is increasing with an increase in the aged population worldwide. So, there is a relative decrease in ophthalmic services, especially in rural areas and developing countries. The World Health Organization (WHO) has launched “Vision 2020,” a global initiative for the prevention of avoidable visual impairment by the year 2020 (Wild, Roglic, Green, Sicree & King, 2004). The main causes of global blindness are age-related macular degeneration (ARMD), glaucoma, cataract and diabetic retinopathy (DR). If these diseases are detected early and treated, then significant reduction in the progression of eyesight loss is possible.

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