9 research outputs found
Detection and characterisation of vessels in retinal images.
Doctor of Philosophy in Mathematics, Statistics & Computer Science. University of KwaZulu-Natal, Durban
2015.As retinopathies such as diabetic retinopathy (DR) and retinopathy of
prematurity (ROP) continue to be the major causes of blindness globally,
regular retinal examinations of patients can assist in the early detection of
the retinopathies. The manual detection of retinal vessels is a very tedious
and time consuming task as it requires about two hours to manually detect
vessels in each retinal image. Automatic vessel segmentation has been helpful
in achieving speed, improved diagnosis and progress monitoring of these
diseases but has been challenging due to complexities such as the varying
width of the retinal vessels from very large to very small, low contrast of
thin vessels with respect to background and noise due to nonhomogeneous
illumination in the retinal images. Although several supervised and unsupervised
segmentation methods have been proposed in the literature, the
segmentation of thinner vessels, connectivity loss of the vessels and time
complexity remain the major challenges. In order to address these problems,
this research work investigated di erent unsupervised segmentation
approaches to be used in the robust detection of large and thin retinal vessels
in a timely e cient manner.
Firstly, this thesis conducted a study on the use of di erent global thresholding
techniques combined with di erent pre-processing and post-processing
techniques. Two histogram-based global thresholding techniques namely,
Otsu and Isodata were able to detect large retinal vessels but fail to segment
the thin vessels because these thin vessels have very low contrast and
are di cult to distinguish from the background tissues using the histogram
of the retinal images. Two new multi-scale approaches of computing global
threshold based on inverse di erence moment and sum-entropy combined
with phase congruence are investigated to improve the detection of vessels.
One of the findings of this study is that the multi-scale approaches of computing
global threshold combined with phase congruence based techniques
improved on the detection of large vessels and some of the thin vessels. They,
however, failed to maintain the width of the detected vessels. The reduction
in the width of the detected large and thin vessels results in low sensitivity
rates while relatively good accuracy rates were maintained. Another study
on the use of fuzzy c-means and GLCM sum entropy combined on phase
congruence for vessel segmentation showed that fuzzy c-means combined
with phase congruence achieved a higher average accuracy rates of 0.9431
and 0.9346 but a longer running time of 27.1 seconds when compared with
the multi-scale based sum entropy thresholding combined with phase congruence
with the average accuracy rates of 0.9416 and 0.9318 with a running
time of 10.3 seconds. The longer running time of the fuzzy c-means over the
sum entropy thresholding is, however, attributed to the iterative nature of
fuzzy c-means. When compared with the literature, both methods achieved
considerable faster running time.
This thesis investigated two novel local adaptive thresholding techniques for
the segmentation of large and thin retinal vessels. The two novel local adaptive
thresholding techniques applied two di erent Haralick texture features
namely, local homogeneity and energy. Although these two texture features
have been applied for supervised image segmentation in the literature, their
novelty in this thesis lies in that they are applied using an unsupervised
image segmentation approach. Each of these local adaptive thresholding
techniques locally applies a multi-scale approach on each of the texture
information considering the pixel of interest in relationship with its spacial
neighbourhood to compute the local adaptive threshold. The localised
multi-scale approach of computing the thresholds handled the challenge of
the vessels' width variation. Experiments showed significant improvements
in the average accuracy and average sensitivity rates of these techniques
when compared with the previously discussed global thresholding methods
and state of the art. The two novel local adaptive thresholding techniques
achieved a higher reduction of false vessels around the border of the optic
disc when compared with some of the previous techniques in the literature.
These techniques also achieved a highly improved computational time of 1.9
to 3.9 seconds to segment the vessels in each retinal image when compared
with the state of the art. Hence, these two novel local adaptive thresholding
techniques are proposed for the segmentation of the vessels in the retinal
images.
This thesis further investigated the combination of di erence image and kmeans
clustering technique for the segmentation of large and thin vessels in
retinal images. The pre-processing phase computed a di erence image and
k-means clustering technique was used for the vessel detection. While investigating
this vessel segmentation method, this thesis established the need
for a difference image that preserves the vessel details of the retinal image.
Investigating the di erent low pass filters, median filter yielded the best
di erence image required by k-means clustering for the segmentation of the
retinal vessels. Experiments showed that the median filter based di erence
images combined with k-means clustering technique achieved higher average
accuracy and average sensitivity rates when compared with the previously
discussed global thresholding methods and the state of the art. The median
filter based di erence images combined with k-means clustering technique
(that is, DIMDF) also achieved a higher reduction of false vessels around
the border of the optic disc when compared with some previous techniques
in the literature. These methods also achieved a highly improved computational
time of 3.4 to 4 seconds when compared with the literature. Hence,
the median filter based di erence images combined with k-means clustering
technique are proposed for the segmentation of the vessels in retinal images.
The characterisation of the detected vessels using tortuosity measure was
also investigated in this research. Although several vessel tortuosity methods
have been discussed in the literature, there is still need for an improved
method that e ciently detects vessel tortuosity. The experimental study
conducted in this research showed that the detection of the stationary points
helps in detecting the change of direction and twists in the vessels. The
combination of the vessel twist frequency obtained using the stationary
points and distance metric for the computation of normalised and nonnormalised
tortuosity index (TI) measure was investigated. Experimental
results showed that the non-normalised TI measure had a stronger correlation
with the expert's ground truth when compared with the distance
metric and normalised TI measures. Hence, a non-normalised TI measure
that combines the vessel twist frequency based on the stationary points and
distance metric is proposed for the measurement of vessel tortuosity
Deteksi Region dengan Completed Local Binary Pattern dan Color Feature untuk Segmentasi Exudate menggunakan Metode Saliency pada Retina Fundus Images
Salah satu pertanda awal dari penyakit tersebut adalah munculnya luka berupa exudates yang terjadi karena terdapat lipid atau lemak bocor pada pembuluh darah abnormal dan bisa menyebabkan kebutaan bila berada di sekitar daerah macula. Deteksi dini kemunculan exudates diharapkan mampu untuk menurunkan resiko kebutaan terhadap penderita penyakit diabetic retinopathy.
Salah satu tantangan dalam proses pendeteksian exudates adalah ukuran dari objek tersebut yang cukup kecil dibandingkan keseluruhan image. Dalam penelitian ini mengusulkan pendeteksian region atau daerah exudates menggunakan CLBP dan color feature untuk segmentasi exudates dengan saliency method. Terdapat tiga tahapan utama dalam penelitian ini, yaitu penghapusan optic disk, pendeteksian lokasi exudates, dan segmentasi exudates. Penghapusan optic disk dilakukan dengan menggunakan algoritma midpoint circle. Pada tahapan pendeteksian lokasi exudates, image akan dibagi menjadi sub-sub image yang lebih kecil kemudian diklasifikasikan menjadi exudates patch dan exudate-free patch berdasarkan fitur yang diperoleh dengan CLBP dan color feature. Sub image yang diklasifikasikan sebagai exudates patch kemudian disegmentasi dengan menggunakan saliency method dan renyi entropi thresholding.
Evaluasi metode dilakukan pada dataset diaretDB1 dengan menghitung nilai akurasi, sensitivity, dan specificity. Metode yang diajukan dapat mendeteksi exudates secara lebih akurat dengan rata-rata nilai akurasi 99.63 %, sensitivity 83.23%, dan specificity 99.57%.
==============================================================================================Diabetic retinopathy is a complication of diabetes that attacks the eye organs. One of the early signs of the disease is the appearance of an exudate wound that occurs because there is lipid or leaked fatty in the abnormal blood vessels and can cause blindness if it appears around the macula. Early detection of exudates is expected to reduce the risk of blindness to diabetic retinopathy patients.
One of the problems in the detection process of exudates is that the size of the object is quite small compared to the overall image. This study proposes to detect the exudate region using CLBP and color feature for segmentation exudates with saliency method. There are three main stages in this research, such as optical disk removal, location detection of exudates, and exudates segmentation. Optical disc removal is done by using midpoint circle algorithm. At the detection stage of the exudates location, the image will be divided into blocks then classified it into exudate patch and exudate-free patch based on features that obtained using CLBP and color features. Sub images that are classified as exudate patch then segmented by using the saliency method and renyi entropy thresholding.
The method evaluation is performed on the diaretDB1 dataset by calculating the accuracy, sensitivity, and specificity values. The proposed method can detect exudates more accurately as shown with the average of accuracy, sensitivity, and specificity value of 99.63%, 83.23% , and 99.57% respectively
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
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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
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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
Analysis of Retinal Image Data to Support Glaucoma Diagnosis
Fundus kamera je ĆĄiroce dostupnĂ© zobrazovacĂ zaĆĂzenĂ, kterĂ© umoĆŸĆuje relativnÄ rychlĂ© a nenĂĄkladnĂ© vyĆĄetĆenĂ zadnĂho segmentu oka â sĂtnice. Z tÄchto dĆŻvodĆŻ se mnoho vĂœzkumnĂœch pracoviĆĄĆ„ zamÄĆuje prĂĄvÄ na vĂœvoj automatickĂœch metod diagnostiky nemocĂ sĂtnice s vyuĆŸitĂm fundus fotografiĂ. Tato dizertaÄnĂ prĂĄce analyzuje souÄasnĂœ stav vÄdeckĂ©ho poznĂĄnĂ v oblasti diagnostiky glaukomu s vyuĆŸitĂm fundus kamery a navrhuje novou metodiku hodnocenĂ vrstvy nervovĂœch vlĂĄken (VNV) na sĂtnici pomocĂ texturnĂ analĂœzy. Spolu s touto metodikou je navrĆŸena metoda segmentace cĂ©vnĂho ĆeÄiĆĄtÄ sĂtnice, jakoĆŸto dalĆĄĂ hodnotnĂœ pĆĂspÄvek k souÄasnĂ©mu stavu ĆeĆĄenĂ© problematiky. Segmentace cĂ©vnĂho ĆeÄiĆĄtÄ rovnÄĆŸ slouĆŸĂ jako nezbytnĂœ krok pĆedchĂĄzejĂcĂ analĂœzu VNV. Vedle toho prĂĄce publikuje novou volnÄ dostupnou databĂĄzi snĂmkĆŻ sĂtnice se zlatĂœmi standardy pro ĂșÄely hodnocenĂ automatickĂœch metod segmentace cĂ©vnĂho ĆeÄiĆĄtÄ.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.