69 research outputs found
Retinal Image Analysis and its use in Medical Applications
Retina located in the back of the eye is not only a vital part of human sight, but also contains valuable information that can be used in biometric security applications, or for the diagnosis of certain diseases. In order to analyze this information from retinal images, its features of blood vessels, microaneurysms and the optic disc require extraction and detection respectively.
We propose a method to extract vessels called MF-FDOG. MF-FDOG consists of using two filters, Matched Filter (MF) and the first-order derivative of Gaussian (FDOG). The vessel map is extracted by applying a threshold to the response of MF, which is adaptively adjusted by the mean response of FDOG. This method allows us to better distinguish vessel objects from non-vessel objects.
Microaneurysm (MA) detection is accomplished with two proposed algorithms, Multi-scale Correlation Filtering (MSCF) and Dictionary Learning (DL) with Sparse Representation Classifier (SRC). MSCF is hierarchical in nature, consisting of two levels: coarse level microaneurysm candidate detection and fine level true microaneurysm detection. In the first level, all possible microaneurysm candidates are found while the second level extracts features from each candidate and compares them to a discrimination table for decision (MA or non-MA). In Dictionary Learning with Sparse Representation Classifier, MA and non-MA objects are extracted from images and used to learn two dictionaries, MA and non-MA. Sparse Representation Classifier is then applied to each MA candidate object detected beforehand, using the two dictionaries to determine class membership. The detection result is further improved by adding a class discrimination term into the Dictionary Learning model. This approach is known as Centralized Dictionary Learning (CDL) with Sparse Representation Classifier.
The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians, which are larger and have thicker vessels compared to Caucasians. We propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching.
The proposed extraction/detection approaches are tested in medical applications, specifically the case study of detecting diabetic retinopathy (DR). DR is a complication of diabetes that damages the retina and can lead to blindness. There are four stages of DR and is a leading cause of sight loss in industrialized nations. Using MF-FDOG, blood vessels were extracted from DR images, while DR images fed into MSCF and Dictionary and Centralized Dictionary Learning with Sparse Representation Classifier produced good microaneurysm detection results. Using a new database consisting of only Asian DR patients, we successfully tested our OD detection method. As part of future work we intend to improve existing methods such as enhancing low contrast microaneurysms and better scale selection. In additional, we will extract other features from the retina, develop a generalized OD detection method, apply Dictionary Learning with Sparse Representation Classifier to vessel extraction, and use the new image database to carry out more experiments in medical applications
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
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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Detection and removal of dust artifacts in retinal images via sparse-based inpainting
Dust particle artifacts are present in all imaging modalities but have more adverse consequences in
medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms.
We propose a method for detecting and accurately segmenting dust artifacts in retinal images based
on multi-scale template-matching on several input images and an iterative segmentation via an
inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsul
Detection and removal of dust artifacts in retinal images via sparse-based inpainting
Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule.The authors acknowledge the financial support from the Centre de Cooperació i Desenvolupament (CCD) at the Universitat Politècnica de Catalunya under project ref. CCD 2019-B004, and from the Universidad Tecnológica de Bolívar. Authors are grateful to Juan Luís Fuentes from the Miguel Servet University Hospital (Zaragoza, Spain) for providing the real images from clinical practice. E. Barrios thanks Minciencias and Sistema General de Regalías (Programa de Becas de Excelencia) for a PhD scholarship. E. Sierra thanks the Universidad Tecnológica de Bolívar for a post-graduate scholarship. Parts of this work were presented at the Pattern Recognition and Tracking XXX - SPIE DCS, 2019 [39]. L. Romero, A. Marrugo, and M.S. Millán thank the funds provided by the Spanish Ministerio de Ciencia e Innovación under the project reference PID2020-114582RB-I00.Peer ReviewedPostprint (published version
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