Vessel Optimal Transport for Automated Alignment of Retinal Fundus Images

Motta, Danilo; Casaca, Wallace; Paiva, Afonso

doi:10.1109/tip.2019.2925287

Cited by 38 publications

(43 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alam et al proposed an adaptive detection-deletion mechanism for vessel tracking detection, in which a onedimensional filter defines the tracking path, and the segmented detected vessel segments are removed from the film to prevent overlapping detection, and the next tracking starting point is found from the remaining vessel branches, and the cycle is iterated to complete the detection [10]. Motta et al proposed a multiscale line tracking method with morphological assistance, in which the brightest points in the image pixels are selected as the initial seed points for tracking until the vessel profile does not satisfy the tracking conditions [11]. Lee et al proposed a blood vessel tracking method based on Bayesian theory and multiscale linear detection by considering all the features of blood vessel intersection and branching points as well as the cross-sectional direction and vertical grayscale profile of blood vessels [12].…”

Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

show abstract

Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…To compare the performance of various algorithms, the Graduated Assignment (GA) [38], Spectral Matching with Affine Constraints (SMAC) [39], Re‐weighted Random Walk Matching (RRWM) [40], Factorised Graph Matching (FGM) [41], and OT‐derived modulus (OT‐GM) [42] are employed. The AUC is used to measure the registration results.…”

Section: Methodsmentioning

confidence: 99%

Subpixel image registration regularised by and norms

Huang

Yang

2020

IET image process

View full text Add to dashboard Cite

“…Our x-y registration process follows a discrete optimal transport based graph matching model (OT-GM) [11]. Here, the cost function combines various similarity measures of new 3D cube descriptors (CD), more suitable for mouse OCT. We propose an Adaptive Weighted Vessel Graph Descriptors (AWVGD), which includes scaling, translation and rotation to achieve better performance while preserving computational efficiency.…”

Section: X-y Plane Registration For Projection Imagesmentioning

confidence: 99%

“…Defining X as a matrix of size N R × N M , with N R = |V R | and N M = |V M |, that matches the set of nodes V R to V M , the graphs produced are matched by solving the optimal transportation plan X to satisfy Equation (1) [11].…”

Section: Optimal Transport Based Graph Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Transport-based Graph Matching for 3D retinal OCT image registration

Tian¹,

Anantrasirichai²,

Nicholson³

et al. 2022

Preprint

View full text Add to dashboard Cite

Registration of longitudinal optical coherence tomography (OCT) images assists disease monitoring and is essential in image fusion applications. Mouse retinal OCT images are often collected for longitudinal study of eye disease models such as uveitis, but their quality is often poor compared with human imaging. This paper presents a novel but efficient framework involving an optimal transport based graph matching (OT-GM) method for 3D mouse OCT image registration. We first perform registration of fundus-like images obtained by projecting all b-scans of a volume on a plane orthogonal to them, hereafter referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence between nodes of graphs extracted from segmented vessels within the OCT projection images. The AWVGD comprises scaling, translation and rotation, which are computationally efficient, whereas CD exploits 3D spatial and frequency domain information. The OT-GM method subsequently performs the correct alignment in the x-y plane. Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and objective evaluation results demonstrate that our framework outperforms other well-established methods on mouse OCT images within a reasonable execution time.Retinal image registration aims to spatially align two or more retinal images captured at different times and from different viewpoints for clinical review of disease progression. Nowadays, non-invasive Optical Coherence Tomography (OCT), which enables 3-D retinal imaging, is widely used for in-vivo assessment of eye disease [1,2]. Data is generated by acquiring a series of A-scans that are combined to a 2D cross-sectional slice called a B scan. The summation of B-scans creates a fundus like projection image.An efficient longitudinal mouse OCT registration method has the potential to impact both early studies of disease mechanisms and treatment. It also helps generating strategies for analysis that are translatable to human imaging. However, imaging the mouse retina is more challenging and less well studied than is the case in humans. High-quality mouse OCT is usually expensive and hard to acquire. Low quality datasets suffer from major vascular dissimilarities,

show abstract

Vessel Optimal Transport for Automated Alignment of Retinal Fundus Images

Cited by 38 publications

References 70 publications

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Subpixel image registration regularised by and norms

Optimal Transport-based Graph Matching for 3D retinal OCT image registration

Contact Info

Product

Resources

About