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Subject: search.filters.subject.Exudates

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    Item type:Publication,
    Automated detection of exudates using histogram analysis for Digital Retinal Images
    (2017-01-18)
    Win, Khin Yadanar
    ;
    Choomchuay, Somsak
    Diabetic retinopathy is one of the serious vision-threating diabetes complications which can even lead to blindness if not diagnosed and cured at the early stage. Exudate is one of the early sign of diabetic retinopathy. Hence, proper method for exudates detection is very useful step in the screening of computer-aided diabetic retinopathy systems. This paper proposes an automatic algorithm to localize the optic disc and detect exudates from fundus retinal images. The algorithm uses histogram information. Optic disc occurs as the bright yellowish region in the retina image, which resembles exudates. It must be eliminated before exudates detection. Therefore, it is necessary to localize the center of the optic disc before it can be eliminated. Without the presence of the optic disc, exudates can be detected easily by using the histogram based thresholding technique. The DRIVE, DIARECTDB1, STARE, and 325 retinal fundus images from local dataset are examined for evaluating the proposed algorithm. The proposed method achieved the accuracy of 100%, 96%, 93%, and 97% respectively for optic disc detection. For exudates detection, the accurate rate is 99%, 90%, and 89% for DRIVE, DIARECTDB1 and local dataset respectively.
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    The clinical applications for automatic detection of exudates
    (2014-09-27)
    Wisaeng, K.
    ;
    Hiransakolwong, N.
    ;
    Pothiruk, E.
    Nowadays, the retinal imaging technology has been widely used for segmenting and detecting the exudates in diabetic retinopathy patients. Unfortunately, the retinal images in Thailand are poorquality images. Therefore, detecting of exudates in a large number by screening programs, are very expensive in professional time and may cause human error. In this study, the clinical applications for detection of exudates from the poor quality retinal image are presented. An application incorporating function, including retinal color normalization, contrast enhancement, noise removal, color space selection and removal of the optic disc, was also designed to standardize the workflow of retinal analysis. Afterward, detection of exudate based on optimal global thresholding and improved adaptive Otsu’s algorithm was applied. Two experiments were conducted to validate the detection performance with local databases and a publicly available DIARETDB1 database. The first experiment showed the average sensitivity, specificity and accuracy of 93.8, 95.3 and 94.9%, respectively. The cross validation results of the second experiment, 60% (53) of the retinal images were used for training and 40% (36) for testing, the sensitivity, specificity and accuracy are 84.2, 85.9 and 85.2%, respectively. This result indicates the proposed clinical application provides an effective tool in the screening of exudates.
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    Item type:Publication,
    Identification of exudates using fuzzy mathematical morphology
    (2014-01-01)
    Wisaeng, Kittipol
    ;
    Hiransakolwong, Nualsawat
    ;
    Pothiruk, Ekkarat
    Diabetic Retinopathy is the damage to the retina caused by complication and the most common cause of blindness in Thailand. Retinal image is essential for expert ophthalmologists to diagnose diseases. Several of method can achieve good performance on retinal feature are clearly visible. Unfortunately, the color retinal image in Thailand are low-resolution images. The existing method cannot identified lowresolution image. Therefore, this study is part of a larger effort to develop a new method for identification of exudates in low-resolution retinal image. In this study a fuzzy mathematical morphology based on fuzzy logical operator and mathematical morphology method is presented. The color retinal image are segmented by using fuzzy logical operator following key preprocessing step, i.e., color normalization, contrast enhancement, noise removal and color space selection. Afterward, a segmentation using mathematical morphology method was applied in this step. This enables its difference in our methods compared to other approach and the methods can achieve good performance even on lowresolution retinal images. Respect to the experimental results, the results obtained with fuzzy mathematical morphology better than the ones obtained with the fuzzy logical operator only method.