Registration of Color and OCT Fundus Images Using Low-dimensional Step Pattern Analysis

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

doi:10.1007/978-3-319-24571-3_26

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…The B-scans affected by motion were aligned (translation) with their precedents using a iterative diamond search strategy [95]. Lastly, the corrected OCT en face image was registered to a color fundus image using a matching method based on the low-dimensional step pattern analysis [96]. Here, 28 pre-defined features were matched by Euclidean distance.…”

Section: [2015-2018]mentioning

confidence: 99%

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

et al. 2019

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Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.

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Section: [2015-2018]mentioning

confidence: 99%

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

et al. 2019

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“…In this paper, we register the OCT en face image to the color fundus using the matching method based on the Low-dimensional Step Pattern Analysis (LoSPA) [2]. LoSPA is the state-of-the-art key points based image registration algorithm.…”

Section: Multi-modality Registrationmentioning

confidence: 99%

“…It has been proven to be superior than traditional scale invariant feature transform based algorithms [10]. A very brief introduction of LoSPA method is given here while details of the algorithm can be find in [2]. In the LoSPA method, geometric corners are first extracted from edges that form a corner in both the OCT en face and color fundus images.…”

Section: Multi-modality Registrationmentioning

confidence: 99%

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Motion Correction in Optical Coherence Tomography for Multi-modality Retinal Image Registration

Cheng

Lee

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Optical coherence tomography (OCT) is a recently developed non-invasive imaging modality, which is often used in ophthalmology. Because of the sequential scanning in form of A-scans, OCT suffers from the inevitable eye movement. This often leads to mis-alignment especially among consecutive B-scans, which affects the analysis and processing of the data such as the registration of the OCT en face image to color fundus image. In this paper, we propose a novel method to correct the mis-alignment among consecutive B-scans to improve the accuracy in multi-modality retinal image registration. In the method, we propose to compute decorrelation from overlapping B-scans and to detect the eye movement. Then, the B-scans with eye movement will be re-aligned to its precedent scans while the rest of B-scans without eye movement are untouched. Our experiments results show that the proposed method improves the accuracy and success rate in the registration to color fundus images.

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“…Retinal registration can be divided into three categories: 1) fundus-fundus registration, which is useful to expand the effective field of view and analyze changes over time [11]; 2) fundus-OCT registration, which is a registration of 2D fundus image with 3D OCT image. It requires that the 3D OCT image be reduced to 2D image by z-direction projection [12]. Therefore, the problem of fundus-OCT registration becomes similar as fundus-fundus registration; 3) OCT-OCT registration, which is useful to access temporal changes of retinal layers and enlarge retinal coverage.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

Pan

Chen

2019

IEEE Access

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Medical image registration can be used for combining information from multiple imaging modalities, monitoring changes in size, shape or image intensity over time intervals. However, the development of such technique can be challenging for 3D spectral-domain optical coherence tomography (SD-OCT) imaging, because SD-OCT image is inherently noisy and its high resolution leads to high complexity of non-rigid registration. In this paper, a new segmentation guided approach is reported for registration of retinal OCT data. The proposed method models the 3D registration as a two-stage registration including x-y direction registration and z direction registration. In x-y direction registration, the vessel maps of OCT projection images between the template and the subject are registered to find out x-y direction displacement. The multi-scale vessel enhancement filter and morphological thinning methods are used to extract the vessel maps from the projection image of 3D OCT scans. And then x-y direction displacement is estimated by matching Speeded-Up Robust Features of the vessel maps. In z direction registration, using the tissue map instead of the original intensity image, A-scans are aligned to get the local displacements in z direction. The proposed method was evaluated on 45 longitudinal retinal OCT scans from 15 subjects. Experimental results show that the proposed method is accurate and very efficient.

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Registration of Color and OCT Fundus Images Using Low-dimensional Step Pattern Analysis

Cited by 5 publications

References 13 publications

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Motion Correction in Optical Coherence Tomography for Multi-modality Retinal Image Registration

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

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