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Capillary nonperfusion (CNP), which is one of characteristic features in diabetic retinopathy patients, is an important judgement of the appearance of retinal diseases. This paper presents a novel, fast and hybrid method to detect CNP. This model combines region-based active contour model(ACM), Graph-Cut and Fuzzy Possibilistic C-Means(FPCM). We modify ACM with a new energy function to reduce suspicious...
This paper addresses a novel segmentation algorithm for detecting one of the diabetic retinopathy pathologies, called “exudates”. Exudates segmentation is ordinarily examined from retinal fundus images by various image processing techniques. Instead of carefully picking up the specific exudates features on the retinal images as has been done by the other works, our scheme is to observe global information...
Fundus Fluorescein Angiography (FA) is a powerful tool for imaging and evaluating Diabetic Macular Edema (DME), where the fluorescein dye leaks and accumulates in the diseased areas. Currently, the assessment of FA images is qualitative and suffers from large inter-observer variability. A necessary step towards quantitative assessment of DME is automatic segmentation of fluorescein leakage. In this...
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