23 research outputs found
Invariant Scattering Transform for Medical Imaging
Over the years, the Invariant Scattering Transform (IST) technique has become
popular for medical image analysis, including using wavelet transform
computation using Convolutional Neural Networks (CNN) to capture patterns'
scale and orientation in the input signal. IST aims to be invariant to
transformations that are common in medical images, such as translation,
rotation, scaling, and deformation, used to improve the performance in medical
imaging applications such as segmentation, classification, and registration,
which can be integrated into machine learning algorithms for disease detection,
diagnosis, and treatment planning. Additionally, combining IST with deep
learning approaches has the potential to leverage their strengths and enhance
medical image analysis outcomes. This study provides an overview of IST in
medical imaging by considering the types of IST, their application,
limitations, and potential scopes for future researchers and practitioners
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
Blood vessel segmentation in the analysis of retinal and diaphragm images
The segmentation and characterization of structures in medical images represents an
important part of the diagnostic and research procedures in medicine. This thesis focuses
on the characterization methods in two application fields that make use of two imaging
modalities. The first topic is the characterization of the blood vessel structure in the
human retina and the second is the characterization of diaphragm movement during
breathing. The imaged blood vessel structures are considered important landmarks in
both applications.
The framework for the retinal image processing and analysis starts with the testing
of five publicly available blood vessel segmentation methods for retinal images. The
parameters of the methods are optimized on five databases with the ground truth for
blood vessels. An approach for predicting the method parameters is proposed based on
the optimization results. The parameter prediction approach is then applied to obtain
vessel segmentation on a new database and an automatic approach to the blood vessel
classification and computation of the arteriovenous ratio is proposed and evaluated on
the new database.
The framework for the diaphragm image processing and analysis is based on the measurement
of diaphragm motion. The motion is characterized by a set of features quantifying
the amplitude and frequency of the breathing pattern, as well as a portion of the nonharmonic
movements that occur. In addition, a set of static features like the diaphragm
slope and height are proposed. Two approaches for the motion measurement are proposed
and compared. A statistical evaluation of the proposed features is performed by
comparing measurements from people with and without spinal findings.
The results from the retinal image processing and analysis revealed the possibility of the
successful prediction of the parameters of the blood vessel segmentation methods. The
automatic approach for the automatic arteriovenous ratio estimation revealed a stronger
association with blood pressure than the manually estimated ratio. The results from the
diaphragm image processing and analysis confirmed differences in the position, shape and
breathing patterns between the healthy people and people suffering from spinal findings.
The blood vessel structure was shown to be a reliable marker for characterizing the
diaphragm motion.Katedra kybernetik
Optic nerve head three-dimensional shape analysis
We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application
Detección automática de la presencia de patología ocular en retinografías empleando técnicas de procesado de imágenes
La vista es uno de los sentidos de mayor importancia para la vida humana. En los últimos años el número de enfermedades oculares ha aumentado y las predicciones de los científicos es que van a seguir aumentando en los próximos años. Existen enfermedades oculares que se han convertido en importantes causas de pérdida de visión a nivel mundial como la retinopatía diabética (RD), el glaucoma, la degeneración macular asociada a la edad (DMAE) y las cataratas. Estas enfermedades oculares suelen provocar alteraciones en el ojo humano, que pueden detectarse observando el ojo. Una de las técnicas más extendidas para observar el fondo del ojo es la retinografía, que es una imagen digital a color de la retina. Esta imagen es muy útil para el diagnóstico de enfermedades que afectan al ojo como RD y DMAE, entre otras. No obstante, la creciente incidencia de algunas enfermedades oculares y la escasez de oftalmólogos especialistas provoca que el análisis de las retinografías sea una tarea compleja y laboriosa.
El objetivo de este Trabajo Fin de Grado (TFG) ha sido el diseño y desarrollo de un método automático para diferenciar entre retinografías patológicas y no patológicas. Este método permitiría ayudar en el diagnóstico y cribado de los pacientes con enfermedades oculares y reducir la carga de trabajo a los oftalmólogos. Para ello, se partió de una base de datos (BD) formada por 1044 imágenes de calidad adecuada para su procesado automático. De ellas, 326 pertenecían a sujetos sanos y a 819 pacientes con algún tipo de patología. Estas imágenes se dividieron en un conjunto de entrenamiento (559 imágenes) y un conjunto de test (585 imágenes). En todos los casos, un oftalmólogo especialista indicó si las imágenes eran normales o patológicas.Grado en Ingeniería de Tecnologías de Telecomunicació
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
Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis
Background: Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making.
Method: Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma.
Results: The experimental results confirm that the combination of classifiers using the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path
Diabetic retinopathy diagnosis through multi-agent approaches
Programa Doutoral em Engenharia BiomédicaDiabetic retinopathy has been revealed as a serious public health problem in occidental
world, since it is the most common cause of vision impairment among people
of working age. The early diagnosis and an adequate treatment can prevent loss
of vision. Thus, a regular screening program to detect diabetic retinopathy in the
early stages could be efficient for the prevention of blindness. Due to its characteristics,
digital color fundus photographs have been the preferred eye examination
method adopted in these programs. Nevertheless, due to the growing incidence of
diabetes in population, ophthalmologists have to observe a huge number of images.
Therefore, the development of computational tools that can assist the diagnosis is
of major importance. Several works have been published in the recent past years
for this purpose; but an automatic system for clinical practice has yet to come. In
general, these algorithms are used to normalize, segment and extract information
from images to be utilized by classifiers which aim to classify the regions of the
fundus image. These methods are mostly based on global approaches that cannot
be locally adapted to the image properties and therefore, none of them perform as
needed because of fundus images complexity.
This thesis focuses on the development of new tools based on multi-agent approaches,
to assist the diabetic retinopathy early diagnosis. The fundus image automatic
segmentation concerning the diabetic retinopathy diagnosis should comprise both
pathological (dark and bright lesions) and anatomical features (optic disc, blood
vessels and fovea). In that way, systems for the optic disc detection, bright lesions
segmentation, blood vessels segmentation and dark lesions segmentation were implemented
and, when possible, compared to those approaches already described in
literature. Two kinds of agent based systems were investigated and applied to digital
color fundus photographs: ant colony system and multi-agent system composed of
reactive agents with interaction mechanisms between them. The ant colony system
was used to the optic disc detection and for bright lesion segmentation. Multi-agent
system models were developed for the blood vessel segmentation and for small dark
lesion segmentation. The multi-agent system models created in this study are not
image processing techniques on their own, but they are used as tools to improve
the traditional algorithms results at the micro level. The results of all the proposed approaches are very promising and reveal that the systems created perform better
than other recent methods described in the literature.
Therefore, the main scientific contribution of this thesis is to prove that multi-agent
systems based approaches can be efficient in segmenting structures in retinal images.
Such an approach overcomes the classic image processing algorithms that are limited
to macro results and do not consider the local characteristics of images. Hence,
multi-agent systems based approaches could be a fundamental tool, responsible for
a very efficient system development to be used in screening programs concerning
diabetic retinopathy early diagnosis.A retinopatia diabética tem-se revelado como um problema sério de saúde pública
no mundo ocidental, uma vez que é a principal causa de cegueira entre as pessoas
em idade ativa. Contudo, a perda de visão pode ser prevenida através da deteção
precoce da doença e de um tratamento adequado. Por isso, um programa regular
de rastreio e monitorização da retinopatia diabética pode ser eficiente na prevenção
da deterioração da visão. Devido às suas características, a fotografia digital colorida
do fundo do olho tem sido o exame adotado neste tipo de programas. No entanto,
devido ao aumento da incidência da diabetes na população, o número de imagens
a serem analisadas pelos oftalmologistas é elevado. Assim sendo, é muito importante
o desenvolvimento de ferramentas computacionais para auxiliar no diagnóstico
desta patologia. Nos últimos anos, têm sido vários os trabalhos publicados com
este propósito; porém, não existe ainda um sistema automático (ou recomendável)
para ser usado nas práticas clínicas. No geral, estes algoritmos são usados para
normalizar, segmentar e extrair informação das imagens que vai ser utilizada por
classificadores, cujo objetivo é identificar as regiões da imagem que se procuram.
Estes métodos são maioritariamente baseados em abordagens globais que não podem
ser localmente adaptadas às propriedades das imagens e, portanto, nenhum
apresenta a performance necessária devido à complexidade das imagens do fundo do
olho.
Esta tese foca-se no desenvolvimento de novas ferramentas computacionais baseadas
em sistemas multi-agente, para auxiliar na deteção precoce da retinopatia diabética.
A segmentação automática das imagens do fundo do olho com o objetivo de diagnosticar
a retinopatia diabética, deve englobar características patológicas (lesões claras
e escuras) e anatómicas (disco ótico, vasos sanguíneos e fóvea). Deste modo, foram
criados sistemas para a deteção do disco ótico e para a segmentação das lesões claras,
dos vasos sanguíneos e das lesões escuras e, quando possível, estes foram comparados
com abordagens já descritas na literatura. Dois tipos de sistemas baseados em
agentes foram investigados e aplicados nas imagens digitais coloridas do fundo do
olho: sistema de colónia de formigas e sistema multi-agente constituído por agentes
reativos e com mecanismos de interação entre eles. O sistema de colónia de formigas
foi usado para a deteção do disco ótico e para a segmentação das lesões claras. Modelos de sistemas multi-agente foram desenvolvidos para a segmentação dos vasos
sanguíneos e das lesões escuras. Os modelos multi-agentes criados ao longo deste
estudo não são por si só técnicas de processamento de imagem, mas são sim usados
como ferramentas para melhorar os resultados dos algoritmos tradicionais no baixo
nível. Os resultados de todas as abordagens propostas são muito promissores e revelam
que os sistemas criados apresentam melhor performance que outras abordagens
recentes descritas na literatura.
Posto isto, a maior contribuição científica desta tese é provar que abordagens baseadas
em sistemas multi-agente podem ser eficientes na segmentação de estruturas em imagens
da retina. Uma abordagem deste tipo ultrapassa os algoritmos clássicos de
processamento de imagem, que se limitam aos resultados de alto nível e não têm em
consideração as propriedades locais das imagens. Portanto, as abordagens baseadas
em sistemas multi-agente podem ser uma ferramenta fundamental, responsável pelo
desenvolvimento de um sistema eficiente para ser usado nos programas de rastreio
e monitorização da retinopatia diabética.Work supported by FEDER funds through the "Programa Operacional Factores de Competitividade – COMPETE" and by national funds by FCT- Fundação para a Ciência e a Tecnologia. C. Pereira thanks the FCT for the SFRH / BD / 61829 / 2009 grant
Computer-Assisted Algorithms for Ultrasound Imaging Systems
Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and
reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging
is considered to be safer, economical and can image the organs in real-time, which makes it widely
used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum
of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc.
Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are
in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of
an ultrasound system are constrained to hospitals and did not translate to its potential in remote
health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low
signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an
objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care
applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic
accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve
the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address
the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in
point-of-care and remote health-care applications