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This paper proposes a framework for detecting the presence and assessing the severity of exudate formations in patients suffering from diabetic retinopathy. The proposed framework also localizes and detects the fovea, the knowledge of which aids in determining the severity of the impairment to visual function posed by the exudates. Results are presented using digital fundus images collected from both...
Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression...
Precise pupil features detection is an important factor for screening diabetic retinopathy. Some interferences caused by reflections, eyelashes and eyelids in pupil extraction need to be solved. This paper presents an algorithm to precisely estimate pupil features: pupil center and pupil radius. The system's hardware component allows for high frame rate image acquisition under infrared lighting conditions...
Retinal images are used for automated diagnosis of Diabetic Retinopathy. Preprocessing of retinal image is required prior to detection of features and abnormalities. The objective of preprocessing segmentation is to separate the background and noisy area from the overall image to enhance the quality of acquired retinal image. We present a method for colored retinal image preprocessing and enhancement...
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