Detection and registration of vessels of fundus and OCT images using curevelet analysis

Golabbakhsh, Marzieh; Rabbani, Hossein; Esmaeili, Mahdad

doi:10.1109/bibe.2012.6399739

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…Previous work approaching OCT to en face fundus image registration relied on blood vessel segmentation [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Recent work on OCT to en face fluorescein angiography (FAG) registration used scanning laser ophthalmoscopy (SLO) as an intermediate image modality but still required blood vessel segmentation [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

et al. 2022

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Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.

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Section: Introductionmentioning

confidence: 99%

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

et al. 2022

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“…On the contrary, the feature‐based registration algorithms are widely used in the registration between different modalities of retinal fundus photographs. The feature‐based registration algorithms of retinal fundus photographs are mostly based on feature points [13–21] or vasculature skeletons [22–30]. Cattin et al.…”

Section: Introductionmentioning

confidence: 99%

“…Golabbakhsh et al. also proposed a registration method with OFI and CFP using a quadratic registration modal [26, 27]. The vasculature skeletons of both OFI and CFP were extracted based on a curvelet transform.…”

Section: Introductionmentioning

confidence: 99%

Iterative registration for multi‐modality retinal fundus photographs using directional vessel skeleton

Kong

Zang

Niu

et al. 2021

IET Image Processing

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This paper proposes an automated registration method for multi‐modality retinal fundus photographs based on the directional vessel skeleton. The main purpose is to register two retinal fundus photographs with different modalities of the same scanning region, which can provide multi‐modality information for clinicians to diagnose retinal diseases or to make a treatment decision. The directional vessel skeleton of each fundus image is first detected by bias field correction and Gabor filter. The final registered fundus photographs are then obtained by the iterative affine registration between the detected directional vessel skeletons of two photographs. In this work, four kinds of fundus photographs in the macular regions of the patient with diseases, consisting of 20 optical coherence tomography fundus images, 20 colour fundus photographs, 20 fluorescein fundus angiography images and 20 indocyanine green angiography images, are utilised to quantitatively evaluate the proposed method. The root‐mean‐square errors show an advantageous performance in both registration success rate and accuracy.

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“…However, to our best knowledge, there are not a lot of researches [4][5][6][7] on registration of SVPIs with…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [4] recently presented an algorithm to register SVPIs with CFPs; this method utilized the Euclidean distance and normal direction difference to overcome the above problem. However, the algorithms [6,7] used the global similarity to register the SVPIs and CFPs. The registration accuracy is relatively low because these methods do not consider the influence of distortion.…”

Section: Introductionmentioning

confidence: 99%

Registration of SD-OCT en-face images with color fundus photographs based on local patch matching

Niu

Chen

Shen

et al. 2014

Proceedings of the Ophthalmic Medical Image Analysis First International Workshop

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Detection and registration of vessels of fundus and OCT images using curevelet analysis

Cited by 3 publications

References 10 publications

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Iterative registration for multi‐modality retinal fundus photographs using directional vessel skeleton

Registration of SD-OCT en-face images with color fundus photographs based on local patch matching

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