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Glaucoma Detection from smartphone-based ophthalmoscopic
images
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Development of Deep Learning algorithms for the analysis of fundus images
acquired with smartphone-based ophthalmoscopes. The algorithms autoamtically select the
region depicting the optic disc, segment the relevant structures and extract clinically
useful indexex for Glaucoma diagnosis, e.g. Vertical Cup to Disc Ratio. |
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Vessel Structure Tracking |
Identification and measurement of blood vessels in
retinal images could allow quantitative evaluation of clinical features,
which may allow early diagnosis and effective monitoring
of therapies in retinopathy. A new system is proposed for the
automatic extraction of the vascular structure in retinal images,
based on a sparse tracking technique. |
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Optic Disk Detection
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All retinal vessels originate from the optic
disc and then follow a parabolic course towards retinal edges. Thus, a geometrical
parametric
model was proposed to describe the direction of these vessels and two of the model
parameters
are just the coordinates of the optic disc center. |
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Non-Vascular Lesion Detection
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The most distinctive sign of diabetic retinopathy or severe hypertensive
retinopathy
are haemorrhages and microaneurysms (HM), hard exudates (HE) and cotton wool
spots (CWS). Automatic detection of their presence in the retina is thus of
paramount importance for assessing the presence of retinopathy. |
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Vessel Analysis: Tortuosity and AVR
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Tortuosity is among the first alterations in retinal vessel network
to appear in many retinopathies. Generalized arteriolar narrowing is an important sign of
hypertension.
It is expressed by AVR (Arteriolar-to-Venular diameter Ratio), which requires the long
and subjective manual measurement of caliber in many arteries and veins.
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Dynamic Fluorangiography
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In order to assess blood circulation in the
human retina, we studied a computerized system to automatically and
accurately measure the transit time of fluorescein dye between the user-selected
retinal locations. |