Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

Computational Analysis of Fundus Images: Rule-Based and Scale-Space Models

Fundus images are one of the most important imaging examinations in modern ophthalmology because they are simple, inexpensive and, above all, noninvasive. Nowadays, the acquisition and storage of highresolution fundus images is relatively easy and fast. Therefore, fundus imaging has become a fundamental investigation in retinal lesion detection, ocular health monitoring and screening programmes. Given the large volume and clinical complexity associated with these images, their analysis and interpretation by trained clinicians becomes a timeconsuming task and is prone to human error. Therefore, there is a growing interest in developing automated approaches that are affordable and have high sensitivity and specificity. These automated approaches need to be robust if they are to be used in the general population to diagnose and track retinal diseases. To be effective, the automated systems must be able to recognize normal structures and distinguish them from pathological clinical manifestations. The main objective of the research leading to this thesis was to develop automated systems capable of recognizing and segmenting retinal anatomical structures and retinal pathological clinical manifestations associated with the most common retinal diseases. In particular, these automated algorithms were developed on the premise of robustness and efficiency to deal with the difficulties and complexity inherent in these images. Four objectives were considered in the analysis of fundus images. Segmentation of exudates, localization of the optic disc, detection of the midline of blood vessels, segmentation of the vascular network and detection of microaneurysms. In addition, we also evaluated the detection of diabetic retinopathy on fundus images using the microaneurysm detection method. An overview of the state of the art is presented to compare the performance of the developed approaches with the main methods described in the literature for each of the previously described objectives. To facilitate the comparison of methods, the state of the art has been divided into rulebased methods and machine learningbased methods. In the research reported in this paper, rulebased methods based on image processing methods were preferred over machine learningbased methods. In particular, scalespace methods proved to be effective in achieving the set goals. Two different approaches to exudate segmentation were developed. The first approach is based on scalespace curvature in combination with the local maximum of a scalespace blob detector and dynamic thresholds. The second approach is based on the analysis of the distribution function of the maximum values of the noise map in combination with morphological operators and adaptive thresholds. Both approaches perform a correct segmentation of the exudates and cope well with the uneven illumination and contrast variations in the fundus images. Optic disc localization was achieved using a new technique called cumulative sum fields, which was combined with a vascular enhancement method. The algorithm proved to be reliable and efficient, especially for pathological images. The robustness of the method was tested on 8 datasets. The detection of the midline of the blood vessels was achieved using a modified corner detector in combination with binary philtres and dynamic thresholding. Segmentation of the vascular network was achieved using a new scalespace blood vessels enhancement method. The developed methods have proven effective in detecting the midline of blood vessels and segmenting vascular networks. The microaneurysm detection method relies on a scalespace microaneurysm detection and labelling system. A new approach based on the neighbourhood of the microaneurysms was used for labelling. Microaneurysm detection enabled the assessment of diabetic retinopathy detection. The microaneurysm detection method proved to be competitive with other methods, especially with highresolution images. Diabetic retinopathy detection with the developed microaneurysm detection method showed similar performance to other methods and human experts. The results of this work show that it is possible to develop reliable and robust scalespace methods that can detect various anatomical structures and pathological features of the retina. Furthermore, the results obtained in this work show that although recent research has focused on machine learning methods, scalespace methods can achieve very competitive results and typically have greater independence from image acquisition. The methods developed in this work may also be relevant for the future definition of new descriptors and features that can significantly improve the results of automated methods.As imagens do fundo do olho são hoje um dos principais exames imagiológicos da oftalmologia moderna, pela sua simplicidade, baixo custo e acima de tudo pelo seu carácter nãoinvasivo. A aquisição e armazenamento de imagens do fundo do olho com alta resolução é também relativamente simples e rápida. Desta forma, as imagens do fundo do olho são um exame fundamental na identificação de alterações retinianas, monitorização da saúde ocular, e em programas de rastreio. Considerando o elevado volume e complexidade clínica associada a estas imagens, a análise e interpretação das mesmas por clínicos treinados tornase uma tarefa morosa e propensa a erros humanos. Assim, há um interesse crescente no desenvolvimento de abordagens automatizadas, acessíveis em custo, e com uma alta sensibilidade e especificidade. Estas devem ser robustas para serem aplicadas à população em geral no diagnóstico e seguimento de doenças retinianas. Para serem eficazes, os sistemas de análise têm que conseguir detetar e distinguir estruturas normais de sinais patológicos. O objetivo principal da investigação que levou a esta tese de doutoramento é o desenvolvimento de sistemas automáticos capazes de detetar e segmentar as estruturas anatómicas da retina, e os sinais patológicos retinianos associados às doenças retinianas mais comuns. Em particular, estes algoritmos automatizados foram desenvolvidos segundo as premissas de robustez e eficácia para lidar com as dificuldades e complexidades inerentes a estas imagens. Foram considerados quatro objetivos de análise de imagens do fundo do olho. São estes, a segmentação de exsudados, a localização do disco ótico, a deteção da linha central venosa dos vasos sanguíneos e segmentação da rede vascular, e a deteção de microaneurismas. De acrescentar que usando o método de deteção de microaneurismas, avaliouse também a capacidade de deteção da retinopatia diabética em imagens do fundo do olho. Para comparar o desempenho das metodologias desenvolvidas neste trabalho, foi realizado um levantamento do estado da arte, onde foram considerados os métodos mais relevantes descritos na literatura para cada um dos objetivos descritos anteriormente. Para facilitar a comparação entre métodos, o estado da arte foi dividido em metodologias de processamento de imagem e baseadas em aprendizagem máquina. Optouse no trabalho de investigação desenvolvido pela utilização de metodologias de análise espacial de imagem em detrimento de metodologias baseadas em aprendizagem máquina. Em particular, as metodologias baseadas no espaço de escalas mostraram ser efetivas na obtenção dos objetivos estabelecidos. Para a segmentação de exsudados foram usadas duas abordagens distintas. A primeira abordagem baseiase na curvatura em espaço de escalas em conjunto com a resposta máxima local de um detetor de manchas em espaço de escalas e limiares dinâmicos. A segunda abordagem baseiase na análise do mapa de distribuição de ruído em conjunto com operadores morfológicos e limiares adaptativos. Ambas as abordagens fazem uma segmentação dos exsudados de elevada precisão, além de lidarem eficazmente com a iluminação nãouniforme e a variação de contraste presente nas imagens do fundo do olho. A localização do disco ótico foi conseguida com uma nova técnica designada por campos de soma acumulativos, combinada com métodos de melhoramento da rede vascular. O algoritmo revela ser fiável e eficiente, particularmente em imagens patológicas. A robustez do método foi verificada pela sua avaliação em oito bases de dados. A deteção da linha central dos vasos sanguíneos foi obtida através de um detetor de cantos modificado em conjunto com filtros binários e limiares dinâmicos. A segmentação da rede vascular foi conseguida com um novo método de melhoramento de vasos sanguíneos em espaço de escalas. Os métodos desenvolvidos mostraram ser eficazes na deteção da linha central dos vasos sanguíneos e na segmentação da rede vascular. Finalmente, o método para a deteção de microaneurismas assenta num formalismo de espaço de escalas na deteção e na rotulagem dos microaneurismas. Para a rotulagem foi utilizada uma nova abordagem da vizinhança dos candidatos a microaneurismas. A deteção de microaneurismas permitiu avaliar também a deteção da retinopatia diabética. O método para a deteção de microaneurismas mostrou ser competitivo quando comparado com outros métodos, em particular em imagens de alta resolução. A deteção da retinopatia diabética exibiu um desempenho semelhante a outros métodos e a especialistas humanos. Os trabalhos descritos nesta tese mostram ser possível desenvolver uma abordagem fiável e robusta em espaço de escalas capaz de detetar diferentes estruturas anatómicas e sinais patológicos da retina. Além disso, os resultados obtidos mostram que apesar de a pesquisa mais recente concentrarse em metodologias de aprendizagem máquina, as metodologias de análise espacial apresentam resultados muito competitivos e tipicamente independentes do equipamento de aquisição das imagens. As metodologias desenvolvidas nesta tese podem ser importantes na definição de novos descritores e características, que podem melhorar significativamente o resultado de métodos automatizados

Using MAS to detect retinal blood vessels

The segmentation of retinal vasculature by color fundus images analysis is crucial for several medical diagnostic systems, such as the diabetic retinopathy early diagnosis. Several interesting approaches have been done in this field but the obtained results need to be improved. We propose therefore a new approach based on an organization of agents. This multi-agent approach is preceded by a preprocessing phase in which the fundamental filter is an improved version of the Kirsch derivative. This first phase allows the construction of an environment where the agents are situated and interact. Then, edges detection emerged from agents’ interaction. With this study, competitive results as compared with those present in the literature were achieved and it seems that a very efficient system for the diabetic retinopathy diagnosis could be built using MAS mechanisms.Fundação para a Ciência e a Tecnologia (FCT

Registration of retinal images by a MAS-ICP approach - a preliminary study

Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age. For monitoring the pathology image registration algorithms applied to retinal images is very useful. In this work, a novel vessel-based retinal image registration approach is proposed. The segmentation of the vasculature is performed by a multi-agent system model. All these information is then used in a Robust Point Matching Iterative Closest Point algorithm improved by a Region Bootstrap approach. With this preliminary study, the novelty of integrating all these algorithms for image registration preceded by a multi-agents system for image edges detection seems to be efficient for temporal retinal image registration. Consequently, a system developed on basis of this approach could help in screening programs for the diabetic retinopathy prevention.C. P. thanks the Fundacao para a Ciencia e Tecnologia (FCT), Portugal for the Ph.D. Grant SFRH/BD/ 61829/2009

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field