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    Automatic detection of exudates in retinal images based on threshold moving average models
    (2015-03-20)
    Wisaeng, Kittipol
    ;
    Hiransakolwong, Nualsawat
    ;
    Pothiruk, Ekkarat
    Since exudates diagnostic procedures require the attention of an expert ophthalmologist, as well as regular monitoring of the disease and the workload of expert ophthalmologists will eventually exceed the current screening capabilities. Retinal imaging technology is a current practice screening capability provide a great potential solution. In this paper, a fast and robust automatic detection of exudates based on moving average histogram models of the fuzzy image, and then derives the better histogram. After segmentation of candidate exudates, the true exudates were prune based on Sobel edge detector and automatic Otsu’s thresholding algorithm is presented that results in the accurate location of the exudates in digital retinal images. To compare the performance of exudates detection methods we have constructed a large database of digital retinal images. The method was trained on a set of 200 retinal images, and tested on a completely independent set of 1 220 retinal images. Results show that the exudates detection method performs overall best sensitivity, specificity, and accuracy are 90.42, 94.60, and 93.69%, respectively.
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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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    Automatic optic disc detection from low contrast retinal images
    (2012-10-16)
    Wisaeng, Kittipol
    ;
    Hiransakolwong, Nualsawat
    ;
    Pothiruk, Ekkarat
    On the research work leading to automatic analysis from low contrast retinal images the knowledge of optic disc (OD) detection is very essential, and a novel method to automatically locate OD is proposed in this paper. The method starts by following key preprocessing step, i.e., color normalization, contrast enhancement and removal noise. The OD detection algorithm is based on combination of mathematical morphology and Otsu's algorithms are presented. The proposed method was evaluated using the publicly available of the STARE project's dataset, containing 81 retinal images of both normal and diseased retinal. The OD was detected correctly in 91.35%. In addition, the OD was detected correctly in 97.61% using the local dataset.
      2
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    Automatic detection of exudates in diabetic retinopathy images
    (2012-08-17)
    Wisaeng, Kittipol
    ;
    Hiransakolwong, Nualsawat
    ;
    Pothiruk, Ekkarat
    Problem statement: Diabetic Retinopathy (DR) is globally the primary cause of visual impairment and blindness in diabetic patients. Retinal image is essential and crucial for ophthalmologists to diagnose diseases. Many of technique can achieve good performance on retinal feature are clearly visible. Unfortunately, it is a normal situation that the retinal images in Thailand are low-quality images. The existing algorithm cannot detect low-quality image. Therefore, this study is part of a larger effort to develop a new method for detection of exudates in low quality retinal image. Approach: In this study, we presented a new method towards the development for detecting exudates pathologies of DR. The color retinal images are segmented using Fuzzy C-Means (FCM) clustering and morphological methods and following key preprocessing step, i.e., color normalization, contrast enhancement, remove noise and color space selection. This enables its difference in our methods compared to other approach and the algorithm can achieve good performance even on low-quality retinal images. Result/Conclusion: The result shows that accuracy values increase when the FCM clustering is combined with morphological methods techniques. If any applications need to detect maximum number of exudates pixels or require execution speed, the FCM clustering technique could be used in isolation. However, if the applications require higher accuracy, the FCM clustering combined with morphological methods should be chosen. This system intends to help ophthalmologists in DR screening process to detect symptoms faster and more easily. This is not a final result application but it can be a preliminary diagnosis tool or decision support system for ophthalmologists. Human ophthalmologists are still needed for the cases where detection results are not very obvious. © 2012 Science Publications.
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    Efficient mode finding for retinal image segmentation via a new similarity measure
    (2012-07-24)
    Wisaeng, Kittipol
    ;
    Hiransakolwong, Nualsawat
    ;
    Pothiruk, Ekkarat
    Mean shift algorithm (MS) is an automatic method not only for finding mode of density for a given data but also for image segmentation. However, its weakness is that the MS is an expensive computation, especially in the retinal image which has very high data point. One obvious optimization is to avoid the redundancy computations. Hence, the main objective of this paper is to show that MS via a new similarity measure is most appropriate for retinal image segmentation. A new similarity evaluation of the retinal image was used for segmentation and then compared its results with the results from standard MS. Our results indicate that MS via a new similarity measure is a successful method in retinal image segmentation, reducing redundancy computation, and achieving speed up for 23.65 times faster than the standard MS.