87 research outputs found
Upper airways segmentation using principal curvatures
Esta tesis propone una nueva técnica para segmentar las vías aéreas superiores. Esta propuesta
permite la extracción de estructuras curvilíneas usando curvaturas principales. La propuesta
permite la extracción de éstas estructuras en imágenes 2D y 3D. Entre las principales novedades
se encuentra la propuesta de un nuevo criterio de parada en la propagación del algoritmo de
realce de contraste (operador multi-escala de tipo sombrero alto). De la misma forma, el criterio
de parada propuesto es usado para detener los algoritmos de difusión anisotrópica. Además, un
nuevo criterio es propuesto para seleccionar las curvaturas principales que conforman las
estructuras curvilíneas, que se basa en los criterios propuestos por Steger, Deng et. al. y
Armande et. al. Además, se propone un nuevo algoritmo para realizar la supresión de nomáximos
que permite reducir la presencia de discontinuidades en el borde de las estructuras
curvilíneas. Para extraer los bordes de las estructuras curvilíneas, se utiliza un algoritmo de
enlace que incluye un nuevo criterio de distancia para reducir la aparición de agujeros en la
estructura final. Finalmente, con base en los resultados obtenidos, se utiliza un algoritmo
morfológico para cerrar los agujeros y se aplica un algoritmo de crecimiento de regiones para
obtener la segmentación final de las vías respiratorias superiores.This dissertation proposes a new approach to segment the upper airways. This proposal allows
the extraction of curvilinear structures based on the principal curvatures. The proposal
allows extracting these structures from 2D and 3D images. Among the main novelties is the
proposal of a new stopping criterion to stop the propagation of the contrast enhancement algorithm
(multiscale top-hat morphological operator). In the same way, the proposed stopping
criterion is used to stop the anisotropic diffusion algorithms. In addition, a new criterion is
proposed to select the principal curvatures that make up the curvilinear structures, which is
based on the criteria proposed by Steger, Deng et. al. and Armande et. al. Furthermore, a
new algorithm to perform the non-maximum suppression that allows reducing the presence
of discontinuities in the border of curvilinear structures is proposed. To extract the edges of
the curvilinear structures, a linking algorithm is used that includes a new distance criterion to
reduce the appearance of gaps in the final structure. Finally, based on the obtained results, a
morphological algorithm is used to close the gaps and a region growing algorithm to obtain
the final upper airways segmentation is applied.Doctor en IngenieríaDoctorad
Design and Development of Robotic Part Assembly System under Vision Guidance
Robots are widely used for part assembly across manufacturing industries to attain high productivity through automation. The automated mechanical part assembly system contributes a major share in production process. An appropriate vision guided robotic assembly system further minimizes the lead time and improve quality of the end product by suitable object detection methods and robot control strategies. An approach is made for the development of robotic part assembly system with the aid of industrial vision system. This approach is accomplished mainly in three phases. The first phase of research is mainly focused on feature extraction and object detection techniques. A hybrid edge detection method is developed by combining both fuzzy inference rule and wavelet transformation. The performance of this edge detector is quantitatively analysed and compared with widely used edge detectors like Canny, Sobel, Prewitt, mathematical morphology based, Robert, Laplacian of Gaussian and wavelet transformation based. A comparative study is performed for choosing a suitable corner detection method. The corner detection technique used in the study are curvature scale space, Wang-Brady and Harris method. The successful implementation of vision guided robotic system is dependent on the system configuration like eye-in-hand or eye-to-hand. In this configuration, there may be a case that the captured images of the parts is corrupted by geometric transformation such as scaling, rotation, translation and blurring due to camera or robot motion. Considering such issue, an image reconstruction method is proposed by using orthogonal Zernike moment invariants. The suggested method uses a selection process of moment order to reconstruct the affected image. This enables the object detection method efficient. In the second phase, the proposed system is developed by integrating the vision system and robot system. The proposed feature extraction and object detection methods are tested and found efficient for the purpose. In the third stage, robot navigation based on visual feedback are proposed. In the control scheme, general moment invariants, Legendre moment and Zernike moment invariants are used. The selection of best combination of visual features are performed by measuring the hamming distance between all possible combinations of visual features. This results in finding the best combination that makes the image based visual servoing control efficient. An indirect method is employed in determining the moment invariants for Legendre moment and Zernike moment. These moments are used as they are robust to noise. The control laws, based on these three global feature of image, perform efficiently to navigate the robot in the desire environment
Improved terrain type classification using UAV downwash dynamic texture effect
The ability to autonomously navigate in an unknown, dynamic environment, while at
the same time classifying various terrain types, are significant challenges still faced by
the computer vision research community. Addressing these problems is of great interest
for the development of collaborative autonomous navigation robots. For example, an
Unmanned Aerial Vehicle (UAV) can be used to determine a path, while an Unmanned
Surface Vehicle (USV) follows that path to reach the target destination. For the UAV to be
able to determine if a path is valid or not, it must be able to identify the type of terrain it
is flying over. With the help of its rotor air flow (known as downwash e↵ect), it becomes
possible to extract advanced texture features, used for terrain type classification.
This dissertation presents a complete analysis on the extraction of static and dynamic
texture features, proposing various algorithms and analyzing their pros and cons. A
UAV equipped with a single RGB camera was used to capture images and a Multilayer
Neural Network was used for the automatic classification of water and non-water-type
terrains by means of the downwash e↵ect created by the UAV rotors. The terrain type
classification results are then merged into a georeferenced dynamic map, where it is
possible to distinguish between water and non-water areas in real time.
To improve the algorithms’ processing time, several sequential processes were con verted into parallel processes and executed in the UAV onboard GPU with the CUDA
framework achieving speedups up to 10x. A comparison between the processing time
of these two processing modes, sequential in the CPU and parallel in the GPU, is also
presented in this dissertation.
All the algorithms were developed using open-source libraries, and were analyzed
and validated both via simulation and real environments. To evaluate the robustness of
the proposed algorithms, the studied terrains were tested with and without the presence
of the downwash e↵ect. It was concluded that the classifier could be improved by per forming combinations between static and dynamic features, achieving an accuracy higher
than 99% in the classification of water and non-water terrain.Dotar equipamentos moveis da funcionalidade de navegação autónoma em ambientes
desconhecidos e dinâmicos, ao mesmo tempo que, classificam terrenos do tipo água e
não água, são desafios que se colocam atualmente a investigadores na área da visão computacional. As soluções para estes problemas são de grande interesse para a navegação
autónoma e a colaboração entre robôs. Por exemplo, um veículo aéreo não tripulado (UAV)
pode ser usado para determinar o caminho que um veículo terrestre não tripulado (USV)
deve percorrer para alcançar o destino pretendido. Para o UAV conseguir determinar se o
caminho é válido ou não, tem de ser capaz de identificar qual o tipo de terreno que está
a sobrevoar. Com a ajuda do fluxo de ar gerado pelos motores (conhecido como efeito
downwash), é possível extrair características de textura avançadas, que serão usadas para
a classificação do tipo de terreno.
Esta dissertação apresenta uma análise completa sobre extração de texturas estáticas
e dinâmicas, propondo diversos algoritmos e analisando os seus prós e contras. Um UAV
equipado com uma única câmera RGB foi usado para capturar as imagens. Para classi ficar automaticamente terrenos do tipo água e não água foi usada uma rede neuronal
multicamada e recorreu-se ao efeito de downwash criado pelos motores do UAV. Os re sultados da classificação do tipo de terreno são depois colocados num mapa dinâmico
georreferenciado, onde é possível distinguir, em tempo real, terrenos do tipo água e não
água.
De forma a melhorar o tempo de processamento dos algoritmos desenvolvidos, vários processos sequenciais foram convertidos em processos paralelos e executados na
GPU a bordo do UAV, com a ajuda da framework CUDA, tornando o algoritmo até 10x
mais rápido. Também são apresentadas nesta dissertação comparações entre o tempo de
processamento destes dois modos de processamento, sequencial na CPU e paralelo na
GPU.
Todos os algoritmos foram desenvolvidos através de bibliotecas open-source, e foram
analisados e validados, tanto através de ambientes de simulação como em ambientes reais.
Para avaliar a robustez dos algoritmos propostos, os terrenos estudados foram testados
com e sem a presença do efeito downwash. Concluiu-se que o classificador pode ser melhorado realizando combinações entre as características de textura estáticas e dinâmicas,
alcançando uma precisão superior a 99% na classificação de terrenos do tipo água e não água
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
Computer aided diagnosis algorithms for digital microscopy
Automatic analysis and information extraction from an image is still a highly chal-
lenging research problem in the computer vision area, attempting to describe the
image content with computational and mathematical techniques. Moreover the in-
formation extracted from the image should be meaningful and as most discrimi-
natory as possible, since it will be used to categorize its content according to the
analysed problem. In the Medical Imaging domain this issue is even more felt
because many important decisions that affect the patient care, depend on the use-
fulness of the information extracted from the image. Manage medical image is even
more complicated not only due to the importance of the problem, but also because
it needs a fair amount of prior medical knowledge to be able to represent with data
the visual information to which pathologist refer.
Today medical decisions that impact patient care rely on the results of laboratory
tests to a greater extent than ever before, due to the marked expansion in the number
and complexity of offered tests. These developments promise to improve the care of
patients, but the more increase the number and complexity of the tests, the more
increases the possibility to misapply and misinterpret the test themselves, leading
to inappropriate diagnosis and therapies. Moreover, with the increased number of
tests also the amount of data to be analysed increases, forcing pathologists to devote
much time to the analysis of the tests themselves rather than to patient care and
the prescription of the right therapy, especially considering that most of the tests
performed are just check up tests and most of the analysed samples come from
healthy patients.
Then, a quantitative evaluation of medical images is really essential to overcome
uncertainty and subjectivity, but also to greatly reduce the amount of data and
the timing for the analysis. In the last few years, many computer assisted diagno-
sis systems have been developed, attempting to mimic pathologists by extracting
features from the images. Image analysis involves complex algorithms to identify
and characterize cells or tissues using image pattern recognition technology. This
thesis addresses the main problems associated to the digital microscopy analysis
in histology and haematology diagnosis, with the development of algorithms for the
extraction of useful information from different digital images, but able to distinguish
different biological structures in the images themselves. The proposed methods not
only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools
for skimming the number of samples to be analysed directly from the pathologist,
or as double check systems to verify the correct results of the automated facilities
used today
Computer aided diagnosis algorithms for digital microscopy
Automatic analysis and information extraction from an image is still a highly chal-
lenging research problem in the computer vision area, attempting to describe the
image content with computational and mathematical techniques. Moreover the in-
formation extracted from the image should be meaningful and as most discrimi-
natory as possible, since it will be used to categorize its content according to the
analysed problem. In the Medical Imaging domain this issue is even more felt
because many important decisions that affect the patient care, depend on the use-
fulness of the information extracted from the image. Manage medical image is even
more complicated not only due to the importance of the problem, but also because
it needs a fair amount of prior medical knowledge to be able to represent with data
the visual information to which pathologist refer.
Today medical decisions that impact patient care rely on the results of laboratory
tests to a greater extent than ever before, due to the marked expansion in the number
and complexity of offered tests. These developments promise to improve the care of
patients, but the more increase the number and complexity of the tests, the more
increases the possibility to misapply and misinterpret the test themselves, leading
to inappropriate diagnosis and therapies. Moreover, with the increased number of
tests also the amount of data to be analysed increases, forcing pathologists to devote
much time to the analysis of the tests themselves rather than to patient care and
the prescription of the right therapy, especially considering that most of the tests
performed are just check up tests and most of the analysed samples come from
healthy patients.
Then, a quantitative evaluation of medical images is really essential to overcome
uncertainty and subjectivity, but also to greatly reduce the amount of data and
the timing for the analysis. In the last few years, many computer assisted diagno-
sis systems have been developed, attempting to mimic pathologists by extracting
features from the images. Image analysis involves complex algorithms to identify
and characterize cells or tissues using image pattern recognition technology. This
thesis addresses the main problems associated to the digital microscopy analysis
in histology and haematology diagnosis, with the development of algorithms for the
extraction of useful information from different digital images, but able to distinguish
different biological structures in the images themselves. The proposed methods not
only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools
for skimming the number of samples to be analysed directly from the pathologist,
or as double check systems to verify the correct results of the automated facilities
used today
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