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Subject: search.filters.subject.Diabetic Retinopathy

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    Optic Disk and Fovea Localization by Using the Direction of Blood Vessels and Morphology Operation
    (2021-03-17)
    Kanjanasurat, Isoon
    ;
    Satayarak, Nitjaree
    This paper presents the optic disk localization by using the matrix that extracted the blood vessels' direction and finding the fovea position using morphology operation in diabetic retinopathy. Our approach begins with blood vessel extraction for locating the optic disk area. Next process, the blood vessel structure was used to estimate the location of the optic disk. Next step, the morphology operator, including erosion and dilation, was used to prepare for attaining the fovea region. Finally, the location of the fovea was estimated by using the position of the optic disk, and specific characteristics of the fovea spot. The proposed method was tested on the DRIVE, DIARETDB0, and DIARETDB1 that is a public diabetic retinal image dataset. The results of the optic disk and fovea localization were compared with the ground truth image. This method can locate optic disk and fovea on DRIVE 100%. In DIARETDB0 and DIARETDB1, this algorithm can achieve optic disk 96.15% and 98.87%, respectively, and locate fovea more than 90%.
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    Detection of hard exudate for diabetic retinopathy using unsupervised classification method
    (2020-07-01)
    Maneerat, Noppadol
    ;
    Thongpasri, Teerapon
    ;
    Narkthewan, Athasart
    ;
    Kimpan, Chom
    Diabetic retinopathy (DR) causes retinal disorders such as blood vessel blockage, the leaks of blood, and the proteins in water bleeding into the tissues of retina. All of the symptoms lead to the destruction of retina resulting in reduced visibility or finally lose vision. Therefore, this study presents an image processing method to extract hard exudates in the retinal image, which is a serious symptom of diabetic retinopathy using an unsupervised classification method. The proposed hard exudates extraction method composes of 3 steps. Firstly, the optic disc similar to hard exudate is eliminated from the retinal image. Subsequently, the green channel of the RGB color model is selected for data analysis because it represents all hard exudates better than the red and blue channels. The features of hard exudates in the retinal image are then extracted by various methods such as dilation, erosion, entropy analysis, and standard deviation analysis and it also appeared in many dimensions. Finally, the proposed method uses k-mean, which is an unsupervised classification technique for hard exudates clustering. The determination of hard exudates from the retinal image is achieved using two datasets (DIARETDB0 and DIARETDB1). These datasets are usually used for algorithm efficiency analysis to retinal image evaluation. The results show that the maximum specificity is approximate 97%. It indicates that the proposed method can be applied for the automatic detection of diabetic retinopathy.
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    Automatic localization of optic disc in fundus image using iterative background removal
    (2019-07-01)
    Narkthewan, Athasart
    ;
    Maneerat, Noppadol
    Diabetic retinopathy (DR) is leading to cause of blood clot stimulating the formation of abnormal new blood vessels. hemorrhage and protein secretion from blood vessels to retinal tissue. The retinal damage causes the loss of sight thus the localization of Optic Disc is necessary for analysis of the abnormal retinal image. However, the data located at the Optic Disc image are similar to Hard Exudates in the retinal image. Therefore, the aim of the present study is to detect Optic Disc using the technique of background removal. The principle of image processing is also applied for the retinal image. The image data from Fundus camera are recorded in RGB color model separated into 3 channels: Red, green and blue channel. The data of all channels are evaluated by the preprocessing algorithm to make a more clear appearance of Optic Disc. Subsequently, the preprocessing image is analyzed by iterative background removal using entropy evaluation of the image data. Finally., the variance analysis of data intensity is performed in both the horizontal and vertical axis to determine the localization and size of Optic Disc. The result shows that the accuracy of localization and size of Optic Disc is approximately 98%. It indicates that this method is useful for retinal image analysis of diabetic retinopathy.