276 research outputs found
A Review of Algorithms for Retinal Vessel Segmentation
oai:ojs.pkp.sfu.ca:article/41This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma. To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment
Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation
The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity
Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering
Screening of vision troubling eye diseases by segmenting fundus images eases the danger of loss of sight of people. Computer assisted analysis can play an important role in the forthcoming health care system universally. Therefore, this paper presents a clustering based method for extraction of retinal vasculature from ophthalmoscope images. The method starts with image enhancement by contrast limited adaptive histogram equalization (CLAHE) from which feature extraction is accomplished using Gabor filter followed by enhancement of extracted features with Hessian based enhancement filters. It then extracts the vessels using K-mean clustering technique. Finally, the method ends with the application of a morphological cleaning operation to get the ultimate vessel segmented image. The performance of the proposed method is evaluated by taking two different publicly available Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases using nine different performance matrices. It gives average accuracies of 0.952 and 0.951 for DRIVE and CHASE_DB1 databases, respectively. Â Â Â
Human treelike tubular structure segmentation: A comprehensive review and future perspectives
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed
Trainable COSFIRE filters for vessel delineation with application to retinal images
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe
A thresholding based technique to extract retinal blood vessels from fundus images
Retinal imaging has become the significant tool among all the medical imaging technology, due to its capability to extract many data which is linked to various eye diseases. So, the accurate extraction of blood vessel is necessary that helps the eye care specialists and ophthalmologist to identify the diseases at the early stages. In this paper, we have proposed a computerized technique for extraction of blood vessels from fundus images. The process is conducted in three phases: (i) pre-processing where the image is enhanced using contrast limited adaptive histogram equalization and median filter, (ii) segmentation using mean-C thresholding to extract retinal blood vessels, (iii) post-processing where morphological cleaning operation is used to remove isolated pixels. The performance of the proposed method is tested on and experimental results show that our method achieve an accuracies of 0.955 and 0.954 on Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases respectively
A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image
The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa
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 à s suas caracterÃsticas, 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 clÃnicas. 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 caracterÃsticas patológicas (lesões claras
e escuras) e anatómicas (disco ótico, vasos sanguÃneos e fóvea). Deste modo, foram
criados sistemas para a deteção do disco ótico e para a segmentação das lesões claras,
dos vasos sanguÃneos e das lesões escuras e, quando possÃvel, 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 constituÃdo 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
sanguÃneos 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
nÃvel. 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 cientÃfica 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 nÃvel 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
- …