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| Identifier |
000413413 |
| Title |
Retinal image registration through 3D eye modelling and pose estimation |
| Alternative Title |
Αντιστοίχιση εικόνων του αμφιβληστροειδή χιτώνα μέσω 3Δ μοντελοποίησης και εκτίμησης πόζας του οφθαλμού |
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Author
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Hernandez Matas Carlos
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Thesis advisor
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Αργυρός, Αντώνης
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Reviewer
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Ζαμπούλης, Ξενοφών
Μαριάς, Κωνσταντίνος
Τζιρίτας, Γεώργιος
Λουράκης, Μανόλης
Hunter, Andrew
Ruggeri, Alfredo
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| Abstract |
The in vivo assessment of small vessels can promote accurate diagnosis and progression monitoring of diseases related to vasculopathy, such as hypertension and
diabetes. Given that the human eye retina contains small vessels that can be directly
imaged via fundoscopy, the analysis of retinal structures becomes very important for
non-invasive approaches. This is also important for the diagnosis of illnesses that affect eyesight, such as macular edema, age-related macular degeneration or glaucoma.
This analysis can be greatly facilitated by accurate retinal image registration.
Image registration is applied upon a pair of images, the reference and the test
image. The goal is to warp the test image so that it images retinal points at the
same 2D locations as the reference image. This is a challenging task, mainly for
two reasons. The first is related to the perspective distortions due to the curved
shape of the retina and change of the camera pose relative to the eye between image
acquisitions. The second relates to potential changes in the retina that occur due to
retinopathy between temporally distant image acquisitions. In addition, the nature
of the application demands for high registration accuracy.
Registration methods may benefit from knowledge of the type of images to be
registered. In this work, we proposed a registration framework that simultaneously
estimates the relative pose of the cameras that acquired the retinal images as well
as the shape and the pose of the eye. The proposed framework, which has been
made publicly available, is evaluated quantitatively and is shown to outperform
state-of-the-art methods.
In the context of this work, we also developed a set of tools for generating realistic
3D eye models. These tools were used to render synthetic retinal image pairs, utilized
for testing and evaluating the proposed registration approach. Additionally, FIRE,
a dataset comprised of pairs of real retinal images has been compiled and made
publicly available. FIRE consists of three types of images, each one covering different
challenges in retinal image registration. To enable the experimental, quantitative
evaluation of the accuracy of a registration method, FIRE is annotated with ground
truth point correspondences.
In this work, we also explored the suitability of the proposed registration framework
for applications such as longitudinal studies, image mosaicing and super resolution.
Additionally, the fitness of the framework for performing eye shape estimation is studied. Pertinent experiments show encouraging results as well as ample room for further improvement.
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| Language |
English |
| Subject |
Medical imaging |
| Issue date |
2017-11-24 |
| Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
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Type of Work--Doctoral theses
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| Permanent Link |
https://elocus.lib.uoc.gr//dlib/4/7/1/metadata-dlib-1513768516-766056-9699.tkl
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| Views |
463 |
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