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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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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.
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Retinal image enhancement by indices of fuzziness
(2008-12-01)
Intajag, Sathit
;
Laohasongkram, Phiphat
;
Chatree, Pongtorn
Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes indices of fuzziness for solving the problems of retinal image enhancement. Histogram of the retinal image is stretched by estimating the optimal parameters of the indices of fuzziness in S-function, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of significant properties for contrast stretching, such as compressed the noise in background and produced a high contrast in a field of view or foreground that provides feasibility in diagnosis from a retinal image. © 2008 SICE.
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Retinal image enhancement by index of fuzziness in multi-mode histogram
(2008-12-01)
Intajag, Sathit
;
Chatthai, Rungrat
;
Tipsuwanporn, Vittaya
Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes histogram analysis for solving the problems of retinal image enhancement. The proposed method uses fuzzy set to enhance the images by partitioning the image histogram to multi-modes with derivative equation. Each mode of the histogram is contrasted by finding the optimal crossover point of S-function with an index of fuzziness, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of properties of an importance for contrast stretching, such as compressed the noise in background and produced a high contrast in the field of view that provide feasibility in diagnosis from a retinal image.
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Automatic segmentation of blood vessels in retinal image based on fuzzy K-median clustering
(2007-12-01)
Supot, Sookpotharom
;
Thanapong, Chaichana
;
Chuchart, Pintavirooj
;
Manas, Sangworasil
This paper presents an efficient method for automatic segmentation of blood vessels in retinal images. Specifically, we also delineate vascular intersections/crossovers. The proposed algorithm is composed of three steps: matched filter, fuzzy k-median (FKMED), and length filter. The segmentation results are compared with clinically generated vessel segmentation and are evaluated in terms of sensitivity and specificity. The results are encouraging and will be used for further application such as personal identification. © 2007 IEEE.
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