article · Microscopy Research and Technique
Manual diagnosis of diabetic maculopathy is time-consuming and labour-intensive for ophthalmologists. Identifying exudates in retinal images is essential for early diagnosis and timely clinical intervention. Two computational frameworks are presented to classify retinal images as normal or abnormal. The first framework applies fuzzy preprocessing to enhance contrast between features and the background. It then performs binarisation to segment and remove blood vessels and the optic disc from the image. Following this removal, gradient processing is conducted, generating a cumulative histogram of gradients that is compared against a calculated threshold to identify maculopathy. The second framework uses a convolutional neural network to automatically classify images into normal and abnormal categories.
Diabetic maculopathy can cause vision loss if it is not identified and managed promptly. Reviewing retinal photographs manually demands significant time and focus from specialist clinicians. Introducing automated image analysis tools can assist ophthalmologists by accelerating the screening process, helping detect subtle abnormalities such as exudates, and easing the diagnostic workload in eye health services.
This research could support diagnostic software developers creating automated screening tools for ophthalmologists and eye clinics. The abstract describes algorithmic workflows without reporting test metrics or clinical validation, placing the technology at an early stage of development. Moving towards practical use would require validation on large clinical datasets, user interface design, and regulatory clearance for medical software.
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Automatic detection of maculopathy disease is a very important step to achieve high-accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.
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DOI: 10.1002/jemt.23596
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