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Author: search.filters.author.Maneerat, NoppadolSubject: search.filters.subject.Diabetic retinopathy

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    Retina blood vessel detection for diabetic retinopathy diagnosis
    (2019-03-28)
    Narkthewan, Athasart
    ;
    Maneerat, Noppadol
    Diabetes affects the microangiopathy in the retina which causes to retinal disorders such as blood vessel blockage then the abnormal blood vessel is occurred. The microvascular leakage will decrease or loss of sight. The aim of this research is to find the retinal blood vessel detection method for diagnosis of diabetic retinopathy. This study was carried out using the principle of image processing to analyze the retina image. The green channel was used for data processing. Consequently, several image processing techniques were applied to the green channel data as image enhancement, scaling, morphological operator and filter to extract the features of the retinal blood vessel in the retina image. The retinal blood vessel was extracted and displayed on the screen for diagnosis. The efficiency of algorithm for the retinal blood vessel detection was presented in this study. All different twenty retinal images from the DRIVE database were tested for blood vessel extraction. The error detection data was compared with the ground truth image. The results show that the maximum specificity and accuracy were 99.66% and 96.80%, respectively. It indicated that the proposed method could detect the blood vessel from retina image.
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    Classification of Cotton Wool Spots Using Principal Components Analysis and Support Vector Machine
    (2019-01-10)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Win, Khin Yadanar
    ;
    Hamamoto, Kazuhiko
    ;
    Panjaphongse, Ronakorn
    Diabetic retinopathy is a complication of the eye damage and can lead to being blindness if it is late for treatment. Microaneurysms, exudates, hemorrhages and cotton wool spots are the lesions associated with diabetic retinopathy. Numerous studies have been done on the detection of microaneurysms, and hemorrhages, as well as exudates whereas only a few research works for detection of cotton wool spots, mainly because of the fact that its appearances are difficult to filter out from the background and not clearly visible. In this paper, an algorithm is proposed to detect cotton wool spots based on integrating principal components analysis and support vector machine. First, preprocessing is performed to enhance the retinal images. Then adaptive thresholding method is used to roughly extract the cotton wool spot from the background. Support vector machine and principal components analysis are further applied respectively to select the important features from morphologies, first-order statistics, gray level occurrence matrix and lacunarity. The proposed method was evaluated with local and DIARETDB1 datasets containing 289 images. Given a success rate of accuracy 90.47 %, sensitivity 85.29%, and specificity 90.12% with the average computational time 16.47 seconds per image on cotton wool spots detection, this system performed better by comparing to the previous research works.
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    Automated diabetic retinopathy screening system using hybrid simulated annealing and ensemble bagging classifier
    (2018-07-22)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Hamamoto, Kazuhiko
    ;
    Panjaphongse, Ronakorn
    Diabetic Retinopathy (DR) is the leading cause of blindness in working-age adults globally. Primary screening of DR is essential, and it is recommended that diabetes patients undergo this procedure at least once per year to prevent vision loss. However, in addition to the insufficient number of ophthalmologists available, the eye examination itself is labor-intensive and time-consuming. Thus, an automated DR screening method using retinal images is proposed in this paper to reduce the workload of ophthalmologists in the primary screening process and so that ophthalmologists may make effective treatment plans promptly to help prevent patient blindness. First, all possible candidate lesions of DR were segmented from the whole retinal image using a combination of morphological-top-hat and Kirsch edge-detection methods supplemented by pre- and post-processing steps. Then, eight feature extractors were utilized to extract a total of 208 features based on the pixel density of the binary image as well as texture, color, and intensity information for the detected regions. Finally, hybrid simulated annealing was applied to select the optimal feature set to be used as the input to the ensemble bagging classifier. The evaluation results of this proposed method, on a dataset containing 1200 retinal images, indicate that it performs better than previous methods, with an accuracy of 97.08%, a sensitivity of 90.90%, a specificity of 98.92%, a precision of 96.15%, an F-measure of 93.45% and the area under receiver operating characteristic curve at 98.34%.