114 research outputs found
Automatic Classification of Bright Retinal Lesions via Deep Network Features
The diabetic retinopathy is timely diagonalized through color eye fundus
images by experienced ophthalmologists, in order to recognize potential retinal
features and identify early-blindness cases. In this paper, it is proposed to
extract deep features from the last fully-connected layer of, four different,
pre-trained convolutional neural networks. These features are then feeded into
a non-linear classifier to discriminate three-class diabetic cases, i.e.,
normal, exudates, and drusen. Averaged across 1113 color retinal images
collected from six publicly available annotated datasets, the deep features
approach perform better than the classical bag-of-words approach. The proposed
approaches have an average accuracy between 91.23% and 92.00% with more than
13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28,
2017
Automated Identification of Diabetic Retinopathy: A Survey
Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy
Bright Lesion Detection in Color Fundus Images Based on Texture Features
In this paper a computer aided screening system for the detection of bright lesions or exudates using color fundus images is proposed. The proposed screening system is used to identify the suspicious regions for bright lesions. A texture feature extraction method is also demonstrated to describe the characteristics of region of interest. In final stage the normal and abnormal images are classified using Support vector machine classifier. Our proposed system obtained the effective detection performance compared to some of the state–of–art methods
Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images
Identification and characterization of drusen is essential for the severity assessment of age-related macular degeneration (AMD). Presented here is a novel super-candidate based approach, combined with robust preprocessing and adaptive thresholding for detection of drusen, resulting in accurate segmentation with the mean lesion-level overlap of 0.75, even in cases with non-uniform illumination, poor contrast and con- founding anatomical structures. We also present a feature based lesion- level discrimination analysis between hard and soft drusen. Our method gives sensitivity of 80% for high specificity above 90% and high sensitivity of 95% for specificity of 70% on representative pathological databases (STARE and ARIA) for both detection and discrimination
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening.
Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms
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
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