A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Abstract

Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation

“…14 The lattice Boltzmann (LB) method has found applications in various image processing tasks such as denoising, inpainting, registration and segmentation for its inherent parallelism and clear physical interpretation. [15][16][17] In the LB method, the fluid is discretized into particles distributed across nodes in a discrete space. During each time step, the particles within a node collide with each other and move to adjacent nodes until they reach an equilibrium state.…”