Lattice Boltzmann Algorithm for Vessel Enhancement Problems

In this paper, a five-velocity lattice Boltzmann model with multiple non-constant relaxation times is applied for modeling anisotropic diffusion processes with applications to vessel enhancement problems. The proposed algorithm is as follows. First, the Frangi vesselness function is evaluated. In the non-vessel regions a lattice Boltzmann model for the Perona–Malik nonlinear diffusion equation is adopted in order to damp noise. In the regions with vessel-like structures a lattice Boltzmann model for coherence-enhancing anisotropic diffusion process is employed. It is shown in the numerical experiment that the proposed approach is efficient for noise removal, coherence enhancement and eventual binarization of vessel trees in synthetic images.

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Publication Details

Journal
Lobachevskii Journal of Mathematics
Published
2024-11-01
DOI
https://doi.org/10.1134/s1995080224606714
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
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article

Lattice Boltzmann Algorithm for Vessel Enhancement Problems

O. V. Ilyin
Lobachevskii Journal of Mathematics
Generative Adversarial Networks and Image Synthesis
article

Lattice Boltzmann Algorithm for Vessel Enhancement Problems

O. V. Ilyin
article en

Abstract

In this paper, a five-velocity lattice Boltzmann model with multiple non-constant relaxation times is applied for modeling anisotropic diffusion processes with applications to vessel enhancement problems. The proposed algorithm is as follows. First, the Frangi vesselness function is evaluated. In the non-vessel regions a lattice Boltzmann model for the Perona–Malik nonlinear diffusion equation is adopted in order to damp noise. In the regions with vessel-like structures a lattice Boltzmann model for coherence-enhancing anisotropic diffusion process is employed. It is shown in the numerical experiment that the proposed approach is efficient for noise removal, coherence enhancement and eventual binarization of vessel trees in synthetic images.

Lobachevskii Journal of MathematicsVol. 45(11)
Russian Academy of Sciences (RU)
Openalex Percentile: Top 72%
Generative Adversarial Networks and Image Synthesis
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