Orientation-independent Feature Matching (OIFM) for Multimodal Retinal Image Registration

Li, Qiaoliang; Li, Shiyu; Wu, Yajie; Guo, Wei; Qi, Suwen; Huang, Gan; Chen, Siping; Liu, Zhong; Chen, Xin

doi:10.1016/j.bspc.2020.101957

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…The image registration based on this feature has advantages of less computation and faster registration speed, and it is robust to changes of gray image scale. However, its registration accuracy is usually not as high as that of gray-level-based image registration ( Li et al, 2020 ; Ran and Xu, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

Wang

Xiang²,

Chen³

et al. 2022

Front. Neurosci.

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In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.

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Section: Introductionmentioning

confidence: 99%

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

Wang

Xiang²,

Chen³

et al. 2022

Front. Neurosci.

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Medical image registration and its application in retinal images: a review

Nie,

Zhang,

et al. 2024

Vis. Comput. Ind. Biomed. Art

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Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, they have not systematically summarized the existing medical image registration methods. To this end, a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives, aiming to help audiences quickly understand the development of medical image registration. In particular, we review recent advances in retinal image registration, which has not attracted much attention. In addition, current challenges in retinal image registration are discussed and insights and prospects for future research provided.

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Orientation-independent Feature Matching (OIFM) for Multimodal Retinal Image Registration

Cited by 2 publications

References 37 publications

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

Medical image registration and its application in retinal images: a review

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