130 research outputs found
A Computational Framework for Ultrastructural Mapping of Neural Circuitry
Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists
Human-Centric Machine Vision
Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans
Methods for Automated Neuron Image Analysis
Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure.
This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens
Interactive Learning for the Analysis of Biomedical and Industrial Imagery
In der vorliegenden Dissertation werden Methoden des ĂŒberwachten Lernens untersucht und auf die Analyse und die Segmentierung digitaler Bilddaten angewendet, die aus diversen Forschungsgebieten stammen. Die Segmentierung und die Klassifikation spielen eine wichtige Rolle in der biomedizinischen und industriellen Bildverarbeitung, hĂ€ufig basiert darauf weitere Erkennung und Quantifikation. Viele problemspezifische AnsĂ€tze existieren fĂŒr die unterschiedlichsten Fragestellungen und nutzen meist spezifisches Vorwissen aus den jeweiligen Bilddaten aus. In dieser Arbeit wird ein ĂŒberwachtes Lernverfahren vorgestellt, das mehrere Objekte und deren Klassen gleichzeitig segmentieren und unterscheiden kann. Die Methode ist generell genug um einen wichtigen Bereich von Anwendungen abzudecken, fĂŒr deren Lösung lokale Merkmale eine Rolle spielen. Segmentierungsergebnisse dieses Ansatzes werden auf verschiedenen DatensĂ€tzen mit unterschiedlichen Problemstellungen gezeigt. Die Resultate unterstreichen die Anwendbarkeit der Lernmethode fĂŒr viele biomedizinische und industrielle Anwendungen, ohne dass explizite Kenntnisse der Bildverarbeitung und Programmierung vorausgesetzt werden mĂŒssen. Der Ansatz basiert auf generellen Merkmalsklassen, die es erlauben lokal Strukturen wie Farbe, Textur und Kanten zu beschreiben. Zu diesem Zweck wurde eine interaktive Software implementiert, welche, fĂŒr gewöhnliche BildgröĂen, in Echtzeit arbeitet und es somit einem DomĂ€nenexperten erlaubt Segmentierungs- und Klassifikationsaufgaben interaktiv zu bearbeiten. DafĂŒr sind keine Kenntnisse in der Bildverarbeitung nötig, da sich die Benutzerinteraktion auf intuitives Markieren mit einem Pinselwerkzeug beschrĂ€nkt. Das interaktiv trainierte System kann dann ohne weitere Benutzerinteraktion auf viele neue Bilder angewendet werden. Der Ansatz ist auf Segmentierungsprobleme beschrĂ€nkt, fĂŒr deren Lösung lokale diskriminative Merkmale ausreichen. Innerhalb dieser EinschrĂ€nkung zeigt der Algorithmus jedoch erstaunlich gute Resultate, die in einer applikationsspezifischen Prozedur weiter verbessert werden können. Das Verfahren unterstĂŒtzt bis zu vierdimensionale, multispektrale Bilddaten in vereinheitlichter Weise. Um die Anwendbar- und Ăbertragbarkeit der Methode weiter zu illustrieren wurden mehrere echte AnwendungsfĂ€lle, kommend aus verschiedenen bildgebenden Bereichen, untersucht. Darunter sind u. A. die Segmentierung von Tumorgewebe, aufgenommen mittelsWeitfeldmikroskopie, die Quantifikation von Zellwanderungen in konfokalmikroskopischen Aufnahmen fĂŒr die Untersuchung der adulten Neurogenese, die Segmentierung von BlutgefĂ€Ăen in der Retina des Auges, das Verfolgen von KupferdrĂ€hten in einer Anwendung zur Produktauthentifikation und die QualitĂ€tskontrolle von Mikroskopiebildern im Kontext von Hochdurchsatz-Experimenten. Desweiteren wurde eine neue Klassifikationsmethode basierend auf globalen FrequenzschĂ€tzungen fĂŒr die Prozesskontrolle des Papieranlegers an Druckmaschinen entwickelt
A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging
Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools.
In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering.
In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract
Foreword and Acknowledgements
Overview and Contributions
Part 1 - Introduction
1 Fluorescence Microscopy
2 Introduction to Visual Processing
3 A Short Introduction to Cross Reality
4 Eye Tracking and Gaze-based Interaction
Part 2 - VR and AR for System Biology
5 scenery â VR/AR for Systems Biology
6 Rendering
7 Input Handling and Integration of External Hardware
8 Distributed Rendering
9 Miscellaneous Subsystems
10 Future Development Directions
Part III - Case Studies
C A S E S T U D I E S
11 Bionic Tracking: Using Eye Tracking for Cell Tracking
12 Towards Interactive Virtual Reality Laser Ablation
13 Rendering the Adaptive Particle Representation
14 sciview â Integrating scenery into ImageJ2 & Fiji
Part IV - Conclusion
15 Conclusions and Outlook
Backmatter & Appendices
A Questionnaire for VR Ablation User Study
B Full Correlations in VR Ablation Questionnaire
C Questionnaire for Bionic Tracking User Study
List of Tables
List of Figures
Bibliography
SelbststÀndigkeitserklÀrun
Doctor of Philosophy
dissertationElectron microscopy can visualize synapses at nanometer resolution, and can thereby capture the fine structure of these contacts. However, this imaging method lacks three key elements: temporal information, protein visualization, and large volume reconstruction. For my dissertation, I developed three methods in electron microscopy that overcame these limitations. First, I developed a method to freeze neurons at any desired time point after a stimulus to study synaptic vesicle cycle. Second, I developed a method to couple super-resolution fluorescence microscopy and electron microscopy to pinpoint the location of proteins in electron micrographs at nanometer resolution. Third, I collaborated with computer scientists to develop methods for semi-automated reconstruction of nervous system. I applied these techniques to answer two fundamental questions in synaptic biology. Which vesicles fuse in response to a stimulus? How are synaptic vesicles recovered at synapses after fusion? Only vesicles that are in direct contact with plasma membrane fuse upon stimulation. The active zone in C. elegans is broad, but primed vesicles are concentrated around the dense projection. Following exocytosis of synaptic vesicles, synaptic vesicle membrane was recovered rapidly at two distinct locations at a synapse: the dense projection and adherens junctions. These studies suggest that there may be a novel form of ultrafast endocytosis
Doctor of Philosophy
dissertationIt is imperative to obtain a complete network graph of at least one representative retina if we are to fully understand vertebrate vision. Synaptic connectomics endeavors to construct such graphs. Though previously prevented by hardware and software limitations, the creation of customized viewing and analysis software, affordable data storage, and advances in electron imaging platform control now permit connectome assembly and analysis. The optimal strategy for building complete connectomes utilizes automated transmission electron imaging with 2 nm or better resolution, molecular tags for cell identification, open access data volumes for navigation, and annotation with open source tools to build three-dimensional cell libraries, complete network diagrams, and connectivity databases. In a few years, the first retinal connectome analyses reveal that many well-studied cells participate in much richer networks than expected. Collectively, these results impel a refactoring of the inner plexiform layer, while providing proof of concept for connectomics as a game-changing approach for a new era of scientific discovery
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
- âŠ