Vessel Segmentation and Branching Detection using an Adaptive Profile Kalman Filter in Retinal Blood Vessel Structure Analysis

This paper presents an improved tracking based method for retinal vessel segmentation that uses blood vessel morphology to adapt the tracking parameters. The method includes branching detection and avoidance methods. A bi-level threshold method, based on local vessel information, is used for segmentation. Tracking is based on Kalman filtering. The results are compared with existing ground truth. It is concluded that ground truth segmentation is not easily comparable

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical Physics and Practic

A retinal vasculature tracking system guided by a deep architecture

Many diseases such as diabetic retinopathy (DR) and cardiovascular diseases show their early signs on retinal vasculature. Analysing the vasculature in fundus images may provide a tool for ophthalmologists to diagnose eye-related diseases and to monitor their progression. These analyses may also facilitate the discovery of new relations between changes on retinal vasculature and the existence or progression of related diseases or to validate present relations. In this thesis, a data driven method, namely a Translational Deep Belief Net (a TDBN), is adapted to vasculature segmentation. The segmentation performance of the TDBN on low resolution images was found to be comparable to that of the best-performing methods. Later, this network is used for the implementation of super-resolution for the segmentation of high resolution images. This approach provided an acceleration during segmentation, which relates to down-sampling ratio of an input fundus image. Finally, the TDBN is extended for the generation of probability maps for the existence of vessel parts, namely vessel interior, centreline, boundary and crossing/bifurcation patterns in centrelines. These probability maps are used to guide a probabilistic vasculature tracking system. Although segmentation can provide vasculature existence in a fundus image, it does not give quantifiable measures for vasculature. The latter has more practical value in medical clinics. In the second half of the thesis, a retinal vasculature tracking system is presented. This system uses Particle Filters to describe vessel morphology and topology. Apart from previous studies, the guidance for tracking is provided with the combination of probability maps generated by the TDBN. The experiments on a publicly available dataset, REVIEW, showed that the consistency of vessel widths predicted by the proposed method was better than that obtained from observers. Moreover, very noisy and low contrast vessel boundaries, which were hardly identifiable to the naked eye, were accurately estimated by the proposed tracking system. Also, bifurcation/crossing locations during the course of tracking were detected almost completely. Considering these promising initial results, future work involves analysing the performance of the tracking system on automatic detection of complete vessel networks in fundus images.Open Acces

A pilot study on discriminative power of features of superficial venous pattern in the hand

The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest

Vascular Tree Tracking and Bifurcation Points Detection in Retinal Images Using a Hierarchical Probabilistic Model

Background and Objective Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. Methods In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. Results The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. Conclusions This technique results in a classifier with high precision and recall when comparing it with Xu’s method

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

Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform, which is robust against noise, is subsequently used to compute the features and train the GPs. By learning the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is quantified. The combination of Radon features and GP results in a good performance in the presence of noise. The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and Matthew’s correlation coefficient (MCC). Experimental results on these datasets show that the proposed method reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the state-of-the-art