52 research outputs found
Digital ocular fundus imaging: a review
Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a Ciência e TecnologiaFEDErPrograma COMPET
Psychiatric Case Record
Bipolar Disorder-Mania:
Patient was apparently normal one-month back, Then all of a sudden he developed sleep disturbances –mainly difficult in initiation of sleep. He also started abusing his family members for unwanted things. Subsequently, he started talking excessively and irritable. Sometimes he sings film songs and dances.
He used to say that God Supreme exists in himself and so he has all the powers of Almighty. With that superior power he says that he can solve all the problems in this world. He also says that he has invented herbs to keep people young.
For the past one week, he talks excessively without having an hour of sleep & wanders here and there & found excessively smoking. He becomes excessively spiritual and goes to near by villages for offering prayers to God. He takes only a little food everyday and he is very much keen in personal cleanliness.
Paranoid Schizophrenia:
She was apparently normal 8 months back, then she developed sleep disturbances in the form of difficult in falling asleep. She was found talking & smiling to herself at night & day with mirror gazing. She started saying that her neighbour & relatives are planning to kill herself by poisoning.
In this context she had frequent quarrels with them and she refused to take food prepared by her mother in law.
She left the home at night without informing any one and started wandering in the road side near her home. She was complaining that she hears voices as if her neighbour & relatives were talking about her among themselves
She was not doing house hold activities for past 6 months and she was not taking care of her child. Her personal hygiene was very much deteriorated slowly as she used to take bath & brush, only if she was asked to do so. She started abusing & assaulting the strangers and family members.
Generalised Anxiety Disorder:
Six months back he was apparently normal. He is working as a system analyst in a private bank . He had once, made a mistake in his bank work for which he was given charges by his employer, followed this event he becomes very tense and afraid whenever his boss called him. He is very cautious that he should not commit any mistakes. Even though he is not doing so, he fears that he may commit some mistake in his work. At that moment he develops palpitation, giddiness, breathlessness, excessive sweating over palms and soles. Slowly these symptoms present through out the day even when he was not in his office, and he could not control his
fearfulness. For the past 6 months he didn’t sleep well. His sleep is disturbed by bad dreams.
Recurrent Depressive Disorder:
Patient was apparently alright 2 months back. Then she developed sleep disturbances particularly early morning awakening, she use to wake up by 3.00 am and use to brood about herself and started crying. She was not doing her domestic work as before, as she felt excess tiredness and use to take frequent rests. She developed poor communication. She had lost her interest in pleasurable activities and was not interested in watching TV, and
attending family gatherings. She stayed aloof most of the time & calm, quiet and withdrawn. She was expressing her helplessness and hopelessness about the future.
She started to have decline in maintaining self care. 15 days back, she frequently expressed suicidal ideas and she had attempted suicide by hanging herself and was rescued by neighbours.
5 days back, she started talking in an irrelevant manner. She was smiling to self. She was assaulting her family members. She was suspicious that her neighbour had done black magic on her and also saying that people are talking about her. She reported hearing the voice of her neighbour
scolding and threatening her.
Organic Brain Syndrome – Dementia:
Ten months back he was apparently alright. Then his relatives noticed himself frequently misplaces things inside his home. Then he started behaving aggressively. He was beating his wife without reason. He was roaming here and there, running out of home and wandering aimlessly.
He was not able to come back home when he goes out. He was brought back to home by his relatives.
Slowly he developed fearfulness and tremulousness while he was staying alone.
He also started saying that his family members & neighbours were talking about himself, in this context he would make frequent quarrels with them. He also started hearing voices of known male voices abusing himself in third person.
He sleeps for few hour only. He is passing urine and motion inside the house. He is asking about his brother and mother-in-law who were expired long back. He behaves abnormally such as pouring water in the plate while eating. And his relatives found the symptoms were worsened by evening.
All these symptoms started insidiously, increased in severity through time and attained the present state.
No history of loss of appetite / crying spells / suicidal tendencies / convulsions / fever / head injury
Choroidal thickness and morphology analysed by optical coherence tomography as a method to approach diabetic ocular disease prognosis and progression
TÍTULO
ESPESOR COROIDEO Y MORFOLOGÍA ANALIZADO POR TOMOGRAFÍA DE COHERENCIA ÓPTICA COMO MÉTODO PARA ABORDAR LA PROGNOSIS Y LA PROGRESIÓN DE LA ENFERMEDAD OCULAR DIABÉTICA
INTRODUCCIÓN
La evidencia clínica in vivo de coroidopatía diabética (DC) ha sido difícil de demostrar clínicamente debido a la falta de medios para visualizar la coroides. Los avances de la tecnología de la tomografía de coherencia óptica (OCT) permitieron la visualización in vivo de la coroides. Los resultados controvertidos en la Diabetes para el grosor coroideo (CT) y la falta de identificación de los cambios morfológicos hacen que la DC identificada con OCT aún no se conozca.
PROPÓSITO
Evaluar si la medición por CT sola es un parámetro confiable para caracterizar la DC, para caracterizar los hallazgos morfológicos coroidales usando la OCT y la correlación con la CT y la retinopatía diabética (DR).
MÉTODOS
Estudio cohorte, prospectivo, longitudinal y observacional, donde se siguieron los controles diabéticos y sanos en visitas consecutivas. Los criterios de inclusión fueron: pacientes sanos y diabéticos sin o con cualquier estadio de RD, de 18 años o más, firmados voluntariamente con consentimiento informado. Los criterios de exclusión fueron: tratamiento previo de cualquier tipo para DR; enfermedad ocular previa o en curso o antecedentes de cirugía ocular; opacidades de medios relevantes; errores de refracción mayores que ± 6 dioptrías; medicamentos séricos crónicos o inmunosupresores sistémicos; cualquier condición sistémica grave e incontrolada. Se realizaron estudios previos para identificar los hallazgos morfológicos de la coroides en OCT que se utilizarán como método de clasificación. Evaluación cuantitativa del grosor de la coroides retiniana y subfoveal (SFCT), evaluación cualitativa de los hallazgos morfológicos coroidales y correlación con el estado de la enfermedad diabética retiniana. Evaluación de progresión y relevancia predictiva.
RESULTADOS
Se incluyeron 96 ojos diabéticos y setenta y ocho ojos sanos. A partir del análisis morfológico de la coroides, encontramos que el 78% de los sanos eran normales y las anormalidades encontradas siempre en las capas de grandes vasos con capas preservadas de coriocapilar / Sattler (ChS). La mayoría de los ojos diabéticos sin anomalías no tenían y las etapas iniciales de DR. Los cambios vasculares coroidales focales se correlacionaron con la enfermedad retiniana subyacente. La atrofia de la capa de ChS o la capa de grandes vasos estaba bien correlacionada con etapas más avanzadas de DR y maculopatía. La progresión de los cambios morfológicos coroidales se evidenció y se correlacionó con la progresión de la enfermedad retiniana. A partir del análisis estadístico: SFCT mostró una gran variabilidad con la dependencia multifactorial. La significación estadística de la correlación con SFCT se encontró solo para la edad para ambos grupos y las anomalías de las capas coroideas para los diabéticos y la presión arterial sistólica para la salud.
CONCLUSIÓN
SFCT mostró ser dependiente de variables multifactoriales con una amplia gama de valores que muestran que no es un parámetro confiable solo para la evaluación de DC. Con las imágenes de OCT podemos identificar y clasificar como hallazgos morfológicos coroidales normales o anormales. Los hallazgos anormales de la coroides están bien correlacionados con la enfermedad diabética retiniana suprayacente y pueden ser un marcador de evolución o progresión de la enfermedad del efecto del tratamiento.INTRODUCTION
Clinical evidence of diabetic choroidopathy (DC) in-vivo has been difficult to demonstrate clinically due to the lack of means to visualize the choroid. Advances of Optical Coherence Tomography (OCT) technology allowed in-vivo visualization of the choroid. Controversial results in Diabetes for choroidal thickness (CT) and lack in the identification of morphologic changes makes DC identified with OCT still to be understood.
PURPOSE
To evaluate whether CT measurement alone is a reliable parameter to characterize DC, to characterize choroidal morphological findings using the OCT and correlation with CT and the diabetic retinopathy (DR).
METHODS
A cohort, prospective, longitudinal and observational study, where diabetic and healthy controls were followed in consecutive visits. Inclusion criteria were: healthy and Diabetic patients without or with any stage of DR, aged 18 or more, signed voluntarily informed consent. Exclusion criteria were: previous treatment of any kind for DR; previous or ongoing ocular disease or history of ocular surgery; relevant media opacities; refractive errors greater than ± 6 Diopters; systemic chronic steroid or immunosuppressive medication; any serious and uncontrolled systemic condition. Previous studies were performed to identify choroidal morphological findings on OCT to be used as classification method. Quantitative assessment of retinal and subfoveal choroidal thickness (SFCT), qualitative assessment of choroidal morphological findings, and correlation with the retinal diabetic disease state. Evaluation of progression and predictive relevancy.
RESULTS
One hundred and ninety-six diabetic eyes and seventy-eight healthy eyes were included. From choroidal morphologic analysis we found: on healthy 78% were normal, and abnormalities found were always on the great vessels layers with preserved choriocapillaris/Sattler (ChS) layers. Most of diabetic eyes with no abnormalities had no and initial stages of DR. Focal choroidal vascular changes were correlated with the subjacent retinal disease. Atrophy of the ChS layer or the great vessels layer was well corelated with more advanced stages of DR and maculopathy. Progression of choroidal morphologic changes was well evidenced and correlated with progression of retinal disease. From statistical analysis: SFCT showed huge variability with multifactorial dependence. Statistical significance of correlation with SFCT was found only for age for both groups and choroidal layers abnormalities for diabetics and systolic blood pressure for healthy.
CONCLUSION
SFCT showed to be dependent on multifactorial variables with a wide range of values showing not to be a reliable parameter alone for DC evaluation. With OCT images we can identify and classify as normal or abnormal choroidal morphologic findings. Abnormal choroidal findings are well correlated with suprajacent retinal diabetic disease and can be a marker of evolution or progression of the disease of treatment effect
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
Incorporating spatial and temporal information for microaneurysm detection in retinal images
The retina of the human eye has the potential to reveal crucial information about several diseases such as diabetes. Several signs such as microaneurysms (MA) manifest themselves as early indicators of Diabetic Retinopathy (DR). Detection of these early signs is important from a clinical perspective in order to suggest appropriate treatment for DR patients. This work aims to improve the detection accuracy of MAs in colour fundus images. While it is expected that multiple images per eye are available in a clinical setup, proposed segmentation algorithms in the literature do not make use of these multiple images.
This work introduces a novel MA detection algorithm and a framework for combining spatial and temporal images. A new MA detection method has been proposed which uses a Gaussian matched filter and an ensemble classifier with 70 features for the detection of candidates. The proposed method was evaluated on three public datasets (171 images in total) and has shown improvement in performance for two of the sets when compared to a state-of-the-art method. For lesion-based performance, the proposed method has achieved Retinopathy Online Challenge (ROC) scores of 0.3923, 2109 and 0.1523 in the MESSIDOR, DIARETDB1 and ROC datasets respectively.
Based on the ensemble algorithm, a framework for the information combination is developed and consists of image alignment, detecting candidates with likelihood scores, matching candidates from aligned images, and finally fusing the scores from the aligned image pairs. This framework is used to combine information both spatially and temporally. A dataset of 320 images that consists of both spatial and temporal pairs was used for the evaluation. An improvement of performance by 2% is shown after combining spatial information. The framework is applied to temporal image pairs and the results of combining temporal information are analyzed and discussed
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