294 research outputs found
Retinal Image Matching Using Hierarchical Vascular Features
We propose a method for retinal image matching that can be used in image matching for person
identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image, and a feature vector is constructed for each of the vessel segments in the retinal blood vessels. The feature
vectors are represented in a tree structure with maintaining the vessel segments actual hierarchical positions.
Using these feature vectors, corresponding images are matched. The method identifies the same vessel in the
corresponding images for comparing the desired feature(s). Initial results are encouraging and demonstrate
that the proposed method is suitable for image matching and patient longitudinal study
Automatic generation of synthetic retinal fundus images:Vascular network
AbstractThis work is part of an ongoing project aimed to generate synthetic retinal fundus images. This paper concentrates on the generation of synthetic vascular networks with realistic shape and texture characteristics. An example-based method, the Active Shape Model, is used to synthesize reliable vesselsâ shapes. An approach based on Kalman Filtering combined with an extension of the Multiresolution Hermite vascular cross-section model has been developed for the simulation of vesselsâ textures. The proposed method is able to generate realistic synthetic vascular networks with morphological properties that guarantee the correct flow of the blood and the oxygenation of the retinal surface observed by fundus cameras. The validity of our synthetic retinal images is demonstrated by qualitative assessment and quantitative analysis
Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging
Programa Oficial de Doutoramento en ComputaciĂłn . 5009V01[Abstract]
Medical imaging plays a prominent role in modern clinical practice for numerous
medical specialties. For instance, in ophthalmology, different imaging techniques are
commonly used to visualize and study the eye fundus. In this context, automated
image analysis methods are key towards facilitating the early diagnosis and adequate
treatment of several diseases. Nowadays, deep learning algorithms have already
demonstrated a remarkable performance for different image analysis tasks. However,
these approaches typically require large amounts of annotated data for the training
of deep neural networks. This complicates the adoption of deep learning approaches,
especially in areas where large scale annotated datasets are harder to obtain, such
as in medical imaging.
This thesis aims to explore novel approaches for the automated analysis of medical
images, particularly in ophthalmology. In this regard, the main focus is on
the development of novel deep learning-based approaches that do not require large
amounts of annotated training data and can be applied to high resolution images.
For that purpose, we have presented a novel paradigm that allows to take advantage
of unlabeled complementary image modalities for the training of deep neural
networks. Additionally, we have also developed novel approaches for the detailed
analysis of eye fundus images. In that regard, this thesis explores the analysis of
relevant retinal structures as well as the diagnosis of different retinal diseases. In
general, the developed algorithms provide satisfactory results for the analysis of the
eye fundus, even when limited annotated training data is available.[Resumen]
Las tĂ©cnicas de imagen tienen un papel destacado en la prĂĄctica clĂnica moderna
de numerosas especialidades mĂ©dicas. Por ejemplo, en oftalmologĂa es comĂșn el uso
de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este
contexto, los métodos automåticos de anålisis de imagen son clave para facilitar
el diagnĂłstico precoz y el tratamiento adecuado de diversas enfermedades. En la
actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable
rendimiento en diferentes tareas de anålisis de imagen. Sin embargo, estos métodos
suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de
las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje
profundo, especialmente en ĂĄreas donde los conjuntos masivos de datos etiquetados
son mĂĄs difĂciles de obtener, como es el caso de la imagen mĂ©dica.
Esta tesis tiene como objetivo explorar nuevos mĂ©todos para el anĂĄlisis automĂĄtico de imagen mĂ©dica, concretamente en oftalmologĂa. En este sentido, el foco
principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no
requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan
aplicarse a imĂĄgenes de alta resoluciĂłn. Para ello, hemos presentado un nuevo
paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas
para el entrenamiento de redes neuronales profundas. Ademås, también
hemos desarrollado nuevos métodos para el anålisis en detalle de las imågenes del
fondo de ojo. En este sentido, esta tesis explora el anĂĄlisis de estructuras retinianas
relevantes, asĂ como el diagnĂłstico de diferentes enfermedades de la retina. En
general, los algoritmos desarrollados proporcionan resultados satisfactorios para el
anĂĄlisis de las imĂĄgenes de fondo de ojo, incluso cuando la disponibilidad de datos
de entrenamiento etiquetados es limitada.[Resumo]
As tĂ©cnicas de imaxe teñen un papel destacado na prĂĄctica clĂnica moderna de
numerosas especialidades mĂ©dicas. Por exemplo, en oftalmoloxĂa Ă© comĂșn o uso
de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste
contexto, os métodos automåticos de anålises de imaxe son clave para facilitar o
diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade,
os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento
en diferentes tarefas de anålises de imaxe. Con todo, estes métodos adoitan necesitar
grandes cantidades de datos etiquetos para o adestramento das redes neuronais
profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente
en ĂĄreas onde os conxuntos masivos de datos etiquetados son mĂĄis difĂciles
de obter, como é o caso da imaxe médica.
Esta tese ten como obxectivo explorar novos métodos para a anålise automåtica
de imaxe mĂ©dica, concretamente en oftalmoloxĂa. Neste sentido, o foco principal
é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non
requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse
a imaxes de alta resoluciĂłn. Para iso, presentamos un novo paradigma que
permite aproveitar modalidades de imaxe complementarias non etiquetadas para o
adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos
métodos para a anålise en detalle das imaxes do fondo de ollo. Neste sentido, esta
tese explora a anĂĄlise de estruturas retinianas relevantes, asĂ como o diagnĂłstico de
diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan
resultados satisfactorios para a anĂĄlise das imaxes de fondo de ollo, mesmo
cando a dispoñibilidade de datos de adestramento etiquetados é limitada
Digital ocular fundus imaging: a review
Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a CiĂȘncia e TecnologiaFEDErPrograma COMPET
Simulation and Synthesis in Medical Imaging
This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published in this issue covering both mechanistic (5) and phenomenological (7) medical image generation. This rich selection of papers covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology. It also covers all mainstream diagnostic medical imaging modalities. We conclude the editorial with a personal view on the field and highlight some existing challenges and future research opportunities
End-to-End Adversarial Retinal Image Synthesis.
In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality
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Global energy minimisation of arterial trees with application to embolic stroke
Computer generation of optimal arterial trees has previously been limited to the production of locally optimal configurations. The application of a global optimisation algorithm allows for the generation of vasculatures with consistent structure. Comparison of this structure to that of in-vivo vasculatures allows the determination of to what extent the vascular structure is the result of energy minimisation. In this thesis an algorithm capable of generation globally optimal vascular trees in geometries derived from medical imaging is developed.
We begin by outlining a small set of constraints which capture physiological principles guiding the organisation of arterial trees. The constraints are then used to produce an algorithm capable of finding the minimal energy configuration of a given arterial tree. The algorithm is used to produce both coronary and cerebral vasculature, and the latter is generated in geometries segmented from MRI data of a human brain. The trees are compared both morphologically and structurally to those found in-vivo. The morphological comparisons for the coronary vasculature show excellent agreement with experiment. The positions of the larger coronary arteries in the generated trees agree extremely well with experiment, suggesting that structure of the coronary vasculature is the result of energy minimisation.
The generated cerebral vasculature approximates the vascular territories of the major cerebral arteries, however the morphological comparisons show that the structure of the cerebral arteries is likely not the result of energy minimisation. The cerebral vasculatures is used to extend a statistical model of embolic stroke to include the effects of branching asymmetry, and an analytic approximation to the statistical model of embolic stroke is developed and validated. It is found that branching asymmetry produces an overall reduction in the level of blockage occuring during an embolic event
Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach
RĂSUMĂ
La rétine permet de visualiser facilement une partie du réseau vasculaire humain. Elle offre
ainsi un aperçu direct sur le développement et le résultat de certaines maladies liées au réseau
vasculaire dans son entier. Chaque complication visible sur la rétine peut avoir un impact sur
la capacité visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premiÚres
structures anatomiques affectĂ©es par la progression dâune maladie, ĂȘtre capable de les analyser
est donc crucial. Les changements dans lâĂ©tat, lâaspect, la morphologie, la fonctionnalitĂ©, ou
mĂȘme la croissance des petits vaisseaux indiquent la gravitĂ© des maladies.
Le diabÚte est une maladie métabolique qui affecte des millions de personnes autour
du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements
pathologiques dans diffĂ©rents organes du corps humain. La rĂ©tinopathie diabĂ©tique dĂ©crit lâen-
semble des conditions et conséquences du diabÚte au niveau de la rétine. Les petits vaisseaux
jouent un rÎle dans le déclenchement, le développement et les conséquences de la rétinopa-
thie. Dans les derniĂšres Ă©tapes de cette maladie, la croissance des nouveaux petits vaisseaux,
appelée néovascularisation, présente un risque important de provoquer la cécité. Il est donc
crucial de détecter tous les changements qui ont lieu dans les petits vaisseaux de la rétine
dans le but de caractériser les vaisseaux sains et les vaisseaux anormaux. La caractérisation
en elle-mĂȘme peut faciliter la dĂ©tection locale dâune rĂ©tinopathie spĂ©cifique.
La segmentation automatique des structures anatomiques comme le réseau vasculaire est
une Ă©tape cruciale. Ces informations peuvent ĂȘtre fournies Ă un mĂ©decin pour quâelles soient
considĂ©rĂ©es lors de son diagnostic. Dans les systĂšmes automatiques dâaide au diagnostic, le
rÎle des petits vaisseaux est significatif. Ne pas réussir à les détecter automatiquement peut
conduire à une sur-segmentation du taux de faux positifs des lésions rouges dans les étapes
ultĂ©rieures. Les efforts de recherche se sont concentrĂ©s jusquâĂ prĂ©sent sur la localisation
précise des vaisseaux de taille moyenne. Les modÚles existants ont beaucoup plus de difficultés
Ă extraire les petits vaisseaux sanguins. Les modĂšles existants ne sont pas robustes Ă la grande
variance dâapparence des vaisseaux ainsi quâĂ lâinterfĂ©rence avec lâarriĂšre-plan. Les modĂšles de
la littĂ©rature existante supposent une forme gĂ©nĂ©rale qui nâest pas suffisante pour sâadapter
à la largeur étroite et la courbure qui caractérisent les petits vaisseaux sanguins. De plus, le
contraste avec lâarriĂšre-plan dans les rĂ©gions des petits vaisseaux est trĂšs faible. Les mĂ©thodes
de segmentation ou de suivi produisent des résultats fragmentés ou discontinus. Par ailleurs,
la segmentation des petits vaisseaux est gĂ©nĂ©ralement faite aux dĂ©pends de lâamplification
du bruit. Les modÚles déformables sont inadéquats pour segmenter les petits vaisseaux. Les
forces utilisées ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et
vii
la variance des vaisseaux. Enfin, les approches de type apprentissage machine nécessitent un
entraĂźnement avec une base de donnĂ©es Ă©tiquetĂ©e. Il est trĂšs difficile dâobtenir ces bases de
données dans le cas des petits vaisseaux.
Cette thÚse étend les travaux de recherche antérieurs en fournissant une nouvelle mé-
thode de segmentation des petits vaisseaux rétiniens. La détection de ligne à échelles multiples
(MSLD) est une méthode récente qui démontre une bonne performance de segmentation dans
les images de la rétine, tandis que le vote tensoriel est une méthode proposée pour reconnecter
les pixels. Une approche combinant un algorithme de détection de ligne et de vote tensoriel est
proposĂ©e. Lâapplication des dĂ©tecteurs de lignes a prouvĂ© son efficacitĂ© Ă segmenter les vais-
seaux de tailles moyennes. De plus, les approches dâorganisation perceptuelle comme le vote
tensoriel ont dĂ©montrĂ© une meilleure robustesse en combinant les informations voisines dâune
maniÚre hiérarchique. La méthode de vote tensoriel est plus proche de la perception humain
que dâautres modĂšles standards. Comme dĂ©montrĂ© dans ce manuscrit, câest un outil pour
segmenter les petits vaisseaux plus puissant que les méthodes existantes. Cette combinaison
spécifique nous permet de surmonter les défis de fragmentation éprouvés par les méthodes de
type modĂšle dĂ©formable au niveau des petits vaisseaux. Nous proposons Ă©galement dâutiliser
un seuil adaptatif sur la rĂ©ponse de lâalgorithme de dĂ©tection de ligne pour ĂȘtre plus robuste
aux images non-uniformes. Nous illustrons Ă©galement comment une combinaison des deux
méthodes individuelles, à plusieurs échelles, est capable de reconnecter les vaisseaux sur des
distances variables. Un algorithme de reconstruction des vaisseaux est également proposé.
Cette derniĂšre Ă©tape est nĂ©cessaire car lâinformation gĂ©omĂ©trique complĂšte est requise pour
pouvoir utiliser la segmentation dans un systĂšme dâaide au diagnostic.
La segmentation a Ă©tĂ© validĂ©e sur une base de donnĂ©es dâimages de fond dâoeil Ă haute
résolution. Cette base contient des images manifestant une rétinopathie diabétique. La seg-
mentation emploie des mesures de désaccord standards et aussi des mesures basées sur la
perception. En considérant juste les petits vaisseaux dans les images de la base de données,
lâamĂ©lioration dans le taux de sensibilitĂ© que notre mĂ©thode apporte par rapport Ă la mĂ©thode
standard de détection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basées
sur la perception, lâamĂ©lioration est de 7.8%.
Dans une seconde partie du manuscrit, nous proposons également une méthode pour
caractériser les rétines saines ou anormales. Certaines images contiennent de la néovascula-
risation. La caractérisation des vaisseaux en bonne santé ou anormale constitue une étape
essentielle pour le dĂ©veloppement dâun systĂšme dâaide au diagnostic. En plus des dĂ©fis que
posent les petits vaisseaux sains, les néovaisseaux démontrent eux un degré de complexité
encore plus élevé. Ceux-ci forment en effet des réseaux de vaisseaux à la morphologie com-
plexe et inhabituelle, souvent minces et Ă fortes courbures. Les travaux existants se limitent
viii
Ă lâutilisation de caractĂ©ristiques de premier ordre extraites des petits vaisseaux segmentĂ©s.
Notre contribution est dâutiliser le vote tensoriel pour isoler les jonctions vasculaires et dâuti-
liser ces jonctions comme points dâintĂ©rĂȘts. Nous utilisons ensuite une statistique spatiale
de second ordre calculée sur les jonctions pour caractériser les vaisseaux comme étant sains
ou pathologiques. Notre méthode améliore la sensibilité de la caractérisation de 9.09% par
rapport Ă une mĂ©thode de lâĂ©tat de lâart.
La mĂ©thode dĂ©veloppĂ©e sâest rĂ©vĂ©lĂ©e efficace pour la segmentation des vaisseaux rĂ©ti-
niens. Des tenseurs dâordre supĂ©rieur ainsi que la mise en Ćuvre dâun vote par tenseur via
un filtrage orientable pourraient ĂȘtre Ă©tudiĂ©s pour rĂ©duire davantage le temps dâexĂ©cution et
résoudre les défis encore présents au niveau des jonctions vasculaires. De plus, la caractéri-
sation pourrait ĂȘtre amĂ©liorĂ©e pour la dĂ©tection de la rĂ©tinopathie prolifĂ©rative en utilisant
un apprentissage supervisé incluant des cas de rétinopathie diabétique non proliférative ou
dâautres pathologies. Finalement, lâincorporation des mĂ©thodes proposĂ©es dans des systĂšmes
dâaide au diagnostic pourrait favoriser le dĂ©pistage rĂ©gulier pour une dĂ©tection prĂ©coce des
rĂ©tinopathies et dâautres pathologies oculaires dans le but de rĂ©duire la cessitĂ© au sein de la
population.----------ABSTRACT
As an easily accessible site for the direct observation of the circulation system, human retina
can offer a unique insight into diseases development or outcome. Retinal vessels are repre-
sentative of the general condition of the whole systematic circulation, and thus can act as
a "window" to the status of the vascular network in the whole body. Each complication on
the retina can have an adverse impact on the patientâs sight. In this direction, small vesselsâ
relevance is very high as they are among the first anatomical structures that get affected
as diseases progress. Moreover, changes in the small vesselsâ state, appearance, morphology,
functionality, or even growth indicate the severity of the diseases.
This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease
affecting millions of people around the world. This disorder disturbs the natural blood glucose
levels causing various pathophysiological changes in different systems across the human body.
Diabetic retinopathy is the medical term that describes the condition when the fundus and
the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial
role in the onset, the development, and the outcome of the retinopathy. More importantly,
at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of
significant risk for blindness. Therefore, there is a need to detect all the changes that occur
in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal.
The characterization, in turn, can facilitate the detection of a specific retinopathy locally,
like the sight-threatening proliferative diabetic retinopathy.
Segmentation techniques can automatically isolate important anatomical structures like
the vessels, and provide this information to the physician to assist him in the final decision. In
comprehensive systems for the automatization of DR detection, small vessels role is significant
as missing them early in a CAD pipeline might lead to an increase in the false positive rate
of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the
accurate localization of the medium range vessels. In contrast, the existing models are weak
in case of the small vessels. The required generalization to adapt an existing model does not
allow the approaches to be flexible, yet robust to compensate for the increased variability in
the appearance as well as the interference with the background. So far, the current template
models (matched filtering, line detection, and morphological processing) assume a general
shape for the vessels that is not enough to approximate the narrow, curved, characteristics
of the small vessels. Additionally, due to the weak contrast in the small vessel regions,
the current segmentation and the tracking methods produce fragmented or discontinued
results. Alternatively, the small vessel segmentation can be accomplished at the expense of
x
background noise magnification, in the case of using thresholding or the image derivatives
methods. Furthermore, the proposed deformable models are not able to propagate a contour
to the full extent of the vasculature in order to enclose all the small vessels. The deformable
model external forces are ineffective to compensate for the low contrast, the low width, the
high variability in the small vessel appearance, as well as the discontinuities. Internal forces,
also, are not able to impose a global shape constraint to the contour that could be able to
approximate the variability in the appearance of the vasculature in different categories of
vessels. Finally, machine learning approaches require the training of a classifier on a labelled
set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In
the case of the unsupervised methods, the user has to predefine the number of clusters and
perform an effective initialization of the cluster centers in order to converge to the global
minimum.
This dissertation expanded the previous research work and provides a new segmentation
method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method
that demonstrates good segmentation performance in the retinal images, while tensor voting
is a method first proposed for reconnecting pixels. For the first time, we combined the
line detection with the tensor voting framework. The application of the line detectors has
been proved an effective way to segment medium-sized vessels. Additionally, perceptual
organization approaches like tensor voting, demonstrate increased robustness by combining
information coming from the neighborhood in a hierarchical way. Tensor voting is closer than
standard models to the way human perception functions. As we show, it is a more powerful
tool to segment small vessels than the existing methods. This specific combination allows us
to overcome the apparent fragmentation challenge of the template methods at the smallest
vessels. Moreover, we thresholded the line detection response adaptively to compensate for
non-uniform images. We also combined the two individual methods in a multi-scale scheme
in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels
from their extracted centerlines based on pixel painting as complete geometric information
is required to be able to utilize the segmentation in a CAD system.
The segmentation was validated on a high-resolution fundus image database that in-
cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as
perceptual-based measures. When only the smallest vessels are considered, the improve-
ments in the sensitivity rate for the database against the standard multi-scale line detection
method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the
basic method.
The second objective of the thesis was to implement a method for the characterization of
isolated retinal areas into healthy or abnormal cases. Some of the original images, from which
xi
these patches are extracted, contain neovascularizations. Investigation of image features
for the vessels characterization to healthy or abnormal constitutes an essential step in the
direction of developing CAD system for the automatization of DR screening. Given that the
amount of data will significantly increase under CAD systems, the focus on this category of
vessels can facilitate the referral of sight-threatening cases to early treatment. In addition
to the challenges that small healthy vessels pose, neovessels demonstrate an even higher
degree of complexity as they form networks of convolved, twisted, looped thin vessels. The
existing work is limited to the use of first-order characteristics extracted from the small
segmented vessels that limits the study of patterns. Our contribution is in using the tensor
voting framework to isolate the retinal vascular junctions and in turn using those junctions
as points of interests. Second, we exploited second-order statistics computed on the junction
spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the
second-order spatial statistics extracted from the junction distribution are combined with
widely used features to improve the characterization sensitivity by 9.09% over the state of
art.
The developed method proved effective for the segmentation of the retinal vessels. Higher
order tensors along with the implementation of tensor voting via steerable filtering could
be employed to further reduce the execution time, and resolve the challenges at vascular
junctions. Moreover, the characterization could be advanced to the detection of prolifera-
tive retinopathy by extending the supervised learning to include non-proliferative diabetic
retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into
CAD systems could facilitate screening for the effective reduction of the vision-threatening
diabetic retinopathy rates, or the early detection of other than ocular pathologies
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