A low-dimensional step pattern analysis algorithm with application to multimodal retinal image registration

Lee, Jimmy Addison; Cheng, Jun; Lee, Beng Hai; Ong, Ee Ping; Xu, Guozhen; Wong, Damon Wing Kee; Liu, Jiang; Laude, Augustinus; Lim, Tock Han

doi:10.1109/cvpr.2015.7298707

Cited by 26 publications

(21 citation statements)

References 33 publications

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“…On the other hand, the application of generic local shape descriptors is also limited by the differences among retinal image modalities, and usually requires preprocessing for multimodal scenarios [7]. Some proposals solve this issue with the design of domain-specific descriptors [8] [2], but they still rely on non-specific methods for the detection of interest points. The use of algorithms that aim at detecting common retinal structures can provide more representative and repeatable characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement

Hervella

Rouco

Novo

2018

Procedia Computer Science

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The analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and follow-up of relevant diseases, like hypertension or diabetes. This work presents a hybrid method for the multimodal registration of color fundus retinography and fluorescein angiography. The proposed method combines a feature-based approach, using domain-specific landmarks, with an intensity-based approach that employs a domainadapted similarity metric. The methodology is tested on a dataset of 59 image pairs containing both healthy and pathological cases. The results show a satisfactory performance of the proposed combined approach in this multimodal scenario, improving the registration accuracy achieved by the feature-based and the intensity-based approaches.

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Section: Introductionmentioning

confidence: 99%

Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement

Hervella

Rouco

Novo

2018

Procedia Computer Science

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“…Supervised evaluation metrics require manually labeled keypoint correspondences between the mutimodal images. Popular metrics includes maximum error (MAE) [10], [11], [17], [18], median error (MEE) [10], [11], [17], [18], root mean square error (RMSE) [11], [12], [17], [18], and percentage of correct keypoints (PCK) [23].…”

Section: B Objectivementioning

confidence: 99%

“…(4) The choice of threshold T is task dependent. For retinal image registration tasks, RMSE less 5 pixels is usually considered as success registration [11], [12], [17], [18], and the threshold T can be set to 5 pixel. To compute these metrics, we need to first manually label pairs of keypoint correspondences (generally 6 or more [10]- [12], [17], [18]) for all the multimodal images, where the keypoint locations should accurately lie on salient landmarks like vessel bifurcations, and uniformly distributed in the overlapping area.…”

Section: B Objectivementioning

confidence: 99%

“…GDB-ICP [8] and ED-DB-ICP [9] were initially applied for multimodal retinal image registration, but they were sensitive to changes in scale. Keypoint detection and feature description was later improved by descriptors designed for retinal images [10]- [12] or domain-specific landmarks [13], combined with better vessel extraction algorithms [11], [13], [14]. The robustness of keypoint matching was also improved by graph-based matching [15], spherical model [16], robust point matching algorithm [17], asymmetric Gaussian mixture model [18], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Study on Correlation Between Subjective and Objective Metrics for Multimodal Retinal Image Registration

Wang

Zhang

Cavichini³

et al. 2020

IEEE Access

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Retinal imaging is crucial in diagnosing and treating retinal diseases, and multimodal retinal image registration constitutes a major advance in understanding retinal diseases. Despite the fact that many methods have been proposed for the registration task, the evaluation metrics for successful registration have not been thoroughly studied. In this paper, we present a comprehensive overview of the existing evaluation metrics for multimodal retinal image registration, and compare the similarity between the subjective grade of ophthalmologists and various objective metrics. The Pearson's correlation coefficient and the corresponding confidence interval are used to evaluate metrics similarity. It is found that the binary and soft Dice coefficient on the segmented vessel can achieve the highest correlation with the subjective grades compared to other keypoint-supervised or unsupervised metrics. The paper established an objective metric that is highly correlated with the subjective evaluation of the ophthalmologists, which has never been studied before. The experimental results would build a connection between ophthalmology and image processing literature, and the findings may provide a good insight for researchers who investigate retinal image registration, retinal image segmentation and image domain transformation.

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“…We made our contribution by proposing a simplistic scheme that can decide the best transformation for each image pair, and have show in our results improvements for a considerable number of methods with insignificant increase in the computational effort. As future work, we are currently adapting our method to address multimodal fundus images registration (GHASSABI et al, 2013;WANG et al, 2015;LEE et al, 2015). Originally, VOTUS was not developed to deal with multimodal images, however, we achieve promising results with a slight change in our method.…”

Section: Discussionmentioning

confidence: 95%

Optimal transport applied to eye fundus image registration

Motta¹

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O transporte ótimo se tornou uma ferramenta promissora e eficaz para apoiar o processamento de imagens moderno, processamento geométrico e até aprendizado de máquina. De fato, a teoria do transporte ótimo permite uma grande flexibilidade na modelagem de problemas, pois diferentes recursos de otimização podem ser empregados enquanto se preserva uma propriedade relevante ao contexto que pode ser interpretada como massa. Nesta pesquisa, nós introduzimos uma nova técnica automática para o registro da imagem do fundo do olho que é baseada na teoria óptima do transporte, filtros de processamento de imagem, correspondência de grafos e transformações geométricas em uma estrutura concisa e unificada . Dadas duas imagens de fundo ocular, construímos grafos representativos que incorporam em suas estruturas informações espaciais e topológicas dos vasos sanguíneos do olho. Os grafos produzidos são usados como entrada pelos nossos modelo de transporte ótimo, a fim de estabelecer uma correspondência entre seus conjuntos de nós. Propomos também uma nova medida que estima a qualidade do registro e uma extensão de uma tecnica de removeção de outliers chamada DeSAC. Finalmente, transformações geométricas são realizadas entre as imagens para realizar adequadamente a tarefa de registro. Nosso método baseia-se em uma sólida base matemática, é fácil de implementar e funciona bem lidando com outliers criados durante o estágio de correspondência, produzindo soluções determinísticas e precisas. Demonstramos a exatidão e eficácia da metodologia proposta por meio de uma abordagem abrangente de comparações qualitativas e quantitativas contra vários métodos representativos do estado da arte em diferentes bases de dados de imagens de fundo de olho.Palavras-chave: Transporte ótimo, processamento de imagem, correspondência de grafos, registro, imagens médicas. ABSTRACT MOTTA, DANILO. Optimal transport applied to eye fundus image registration. 2019. 92 p. Tese (Doutorado em Ciências -Optimal transport has emerged as a promising and effective tool for supporting modern image processing, geometric processing, and even machine learning. Indeed, the optimal transport theory enables great flexibility in modeling problems, as different optimization resources can be successfully employed while preserving a context relevant property that can be interpreted as mass. In this research, we introduce a novel automatic technique for eye fundus image registration which is based on optimal transport theory, image processing filters, graph matching, and geometric transformations into a concise and unified framework. Given two ocular fundus images, we construct representative graphs which embed in their structures spatial and topological information from the eye's blood vessels. The graphs produced are then used as input by our optimal transport model in order to establish a correspondence between their sets of nodes. We also proposed a new measure that estimates the register quality and an extension of an outlier removal technique called DeSAC. Finally, the best ...

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A low-dimensional step pattern analysis algorithm with application to multimodal retinal image registration

Cited by 26 publications

References 33 publications

Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement

Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement

Study on Correlation Between Subjective and Objective Metrics for Multimodal Retinal Image Registration

Optimal transport applied to eye fundus image registration

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