Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

Analysis of Retinal Image Data to Support Glaucoma Diagnosis

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.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.

Artificial Intelligence and Deep Learning-Based System Design for Diabetic Retinopathy Classification

One of the biggest causes of avoidable blindness throughout the world is diabetic retinopathy (DR). There is a significant unmet need to test all diabetes patients for DR, and many instances of DR go undetected and untreated. In order to automate DR screening, this research aimed to create reliable diagnostic technologies. In order to reduce the pace of vision loss, it is important to refer eyes suspected of having DR to an ophthalmologist for further assessment and treatment. The primary goal of this research is to improve the classification accuracy for Diabetic Retinopathy (DR). In this script, we present a new neural network model for DR forecasting. The suggested model's accuracy in identifying DR phases was measured against that of regular and ensemble-based models. Various benchmark datasets, including MESSIDOR, IDRID, and APTOS, are used in the studies. The suggested DRPNN algorithm outperformed the competition in experiments assessed using industry-standard criteria

Retinal vessel segmentation using multi-scale textons derived from keypoints

This paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter parameters are derived from a smaller set of image features that we call keypoints. A Gabor filter bank, parameterised empirically by ROC analysis, is used to extract keypoints representing significant scale specific vessel features using an approach inspired by the SIFT algorithm. We first determine keypoints using a validation set and then derive seeds from these points to initialise a k-means clustering algorithm which builds a texton dictionary from another training set. During testing we use a simple 1-NN classifier to identify vessel/non-vessel pixels and evaluate our system using the DRIVE database. We achieve average values of sensitivity, specificity and accuracy of 78.12%, 96.68% and 95.05% respectively. We find that clusters of filter responses from keypoints are more robust than those derived from hand-labelled pixels. This, in turn yields textons more representative of vessel/non-vessel classes and mitigates problems arising due to intra and inter-observer variability

Detection and diabetic retinopathy grading using digital retinal images

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease

Développement et validation d’un système automatique de classification de la dégénérescence maculaire liée à l’âge

RÉSUMÉ La dégénérescence maculaire liée à l’âge (DMLA) est une des principales causes de déficience visuelle menant à une cécité irréversible chez les personnes âgées dans les pays industrialisés. Cette maladie regroupe une variété d’anomalies touchant la macula, se présentant sous diverses formes. Un des moyens les plus couramment utilisés pour rapidement examiner la rétine est la photographie de fond d’œil. À partir de ces images, il est déjà possible de détecter et de poser un diagnostic sur l’avancée de la maladie. Une classification recommandée pour évaluer la DMLA est la classification simplifiée de l’AREDS qui consiste à diviser la maladie en quatre catégories : non-DMLA, précoce, modérée, et avancée. Cette classification aide à déterminer le traitement spécifique le plus optimal. Elle se base sur des critères quantitatifs mais également qualitatifs, ce qui peut entrainer des variabilités inter- et intra-expert. Avec le vieillissement de la population et le dépistage systématique, le nombre de cas de DMLA à être examinés et le nombre d’images à être analysées est en augmentation rendant ainsi le travail long et laborieux pour les cliniciens. C’est pour cela, que des méthodes automatiques de détection et de classification de la DMLA ont été proposées, afin de rendre le processus rapide et reproductible. Cependant, il n’existe aucune méthode permettant une classification du degré de sévérité de la DMLA qui soit robuste à la qualité de l’image. Ce dernier point est important lorsqu’on travaille dans un contexte de télémédecine. Dans ce projet, nous proposons de développer et valider un système automatique de classification de la DMLA qui soit robuste à la qualité de l’image. Pour ce faire, nous avons d’abord établi une base de données constituée de 159 images, représentant les quatre catégories de l’AREDS et divers niveaux de qualité d’images. L’étiquetage de ces images a été réalisé par un expert en ophtalmologie et nous a servi de référence. Ensuite, une étude sur l’extraction de caractéristiques nous a permis de relever celles qui étaient pertinentes et de configurer les paramètres pour notre application. Nous en avons conclu que les caractéristiques de texture, de couleur et de contexte visuel semblaient les plus intéressantes. Nous avons effectué par après une étape de sélection afin de réduire la dimensionnalité de l’espace des caractéristiques. Cette étape nous a également permis d’évaluer l’importance des différentes caractéristiques lorsqu’elles étaient combinées ensemble.----------ABSTRACT Age-related macular degeneration (AMD) is the leading cause of visual deficiency and legal blindness in the elderly population in industrialized countries. This disease is a group of heterogeneous disorders affecting the macula. For eye examination, a common used modality is the fundus photography because it is fast and non-invasive procedure which may establish a diagnostic on the stage of the disease. A recommended classification for AMD is the simplified classification of AREDS which divides the disease into four categories: non-AMD, early, moderate and advanced. This classification is helpful to determine the optimal and specific treatment. It is based on quantitative criteria but also on qualitative ones, introducing inter- and intra-expert variability. Moreover, with the aging population and systematic screening, more cases of AMD must be examined and more images must be analyzed, rendering this task long and laborious for clinicians. To address this problem, automatic methods for AMD classification were then proposed for a fast and reproducible process. However, there is no method performing AMD severity classification which is robust to image quality. This last part is especially important in a context of telemedicine where the acquisition conditions are various. The aim of this project is to develop and validate an automatic system for AMD classification which is robust to image quality. To do so, we worked with a database of 159 images, representing the different categories at various levels of image quality. The labelling of these images is realized by one expert and served as a reference. A study on feature extraction is carried out to determine relevant features and to set the parameters for this application. We conclude that features based on texture, color and visual context are the most interesting. After, a selection is applied to reduce the dimensionality of features space. This step allows us to evaluate the feature relevance when all the features are combined. It is shown that the local binary patterns applied on the green channel are the most the discriminant features for AMD classification. Finally, different systems for AMD classification were modeled and tested to assess how the proposed method classifies the fundus images into the different categories. The results demonstrated robustness to image quality and also that our method outperforms the methods proposed in the literature. Errors were noted on images presenting diabetic retinopathy, visible choroidal vessels or too much degradation caused by artefacts. In this project, we propose the first AMD severities classification robust to image quality