101 research outputs found
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
Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning
Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity
Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation
The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review
Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd
Retinal Vessels Segmentation Techniques and Algorithms: A Survey
Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.https://doi.org/10.3390/app802015
Mathematical Morphology for Quantification in Biological & Medical Image Analysis
Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology.
Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery.
Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios.
I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown.
This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis
A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding
Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images.
The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures
- âŠ