Abstract
Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.
Highlights
IntroductionOphthalmology has become a core medical field. Ophthalmology treats severe diseases such as glaucoma and non-disease issues such as vision correction.Minor diseases such as conjunctivitis can be diagnosed visually or through simple examinations; severe diseases that may lead to vision loss cannot be diagnosed accurately without a detailed examination performed by a physician.An example of an ophthalmology examination is the retinal or fundus examination, in which a physician checks the interior of the eye through the pupil, including the vitreous, retina, retinal blood vessels, optic disc, and macula
As the population ages, ophthalmology has become a core medical field
An image synthesis approach was proposed in this study to improve the quality of retinal images, their vessel areas, to help physicians perform diagnoses after retinal examinations
Summary
Ophthalmology has become a core medical field. Ophthalmology treats severe diseases such as glaucoma and non-disease issues such as vision correction.Minor diseases such as conjunctivitis can be diagnosed visually or through simple examinations; severe diseases that may lead to vision loss cannot be diagnosed accurately without a detailed examination performed by a physician.An example of an ophthalmology examination is the retinal or fundus examination, in which a physician checks the interior of the eye through the pupil, including the vitreous, retina, retinal blood vessels, optic disc, and macula. Ophthalmology treats severe diseases such as glaucoma and non-disease issues such as vision correction. Minor diseases such as conjunctivitis can be diagnosed visually or through simple examinations; severe diseases that may lead to vision loss cannot be diagnosed accurately without a detailed examination performed by a physician. GLCM is a matrix that counts6how often different combinations of pixel brightness values (gray levels) occur in images, and it extracts a counts how often different combinations of pixel brightness values (gray levels) occur in images, and second-order statistical texture. If there are 4 known stages as stages 0–3, the GLCM is created as a 4 × 4 matrix
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