Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-Resolution Image Registration
This work addresses the problem of efficient and reliable image registration for medical imaging applications, though it is incremental as it builds on existing sampling methods.
The paper tackled fast image registration by combining gradient-based and random pixel sampling, learning an optimal balance offline using particle swarm optimization. Results on the Vanderbilt RIRE dataset showed faster, more accurate, and robust 3D rigid registration compared to state-of-the-art methods.
This paper presents an approach to fast image registration through probabilistic pixel sampling. We propose a practical scheme to leverage the benefits of two state-of-the-art pixel sampling approaches: gradient magnitude based pixel sampling and uniformly random sampling. Our framework involves learning the optimal balance between the two sampling schemes off-line during training, based on a small training dataset, using particle swarm optimization. We then test the proposed sampling approach on 3D rigid registration against two state-of-the-art approaches based on the popular, publicly available, Vanderbilt RIRE dataset. Our results indicate that the proposed sampling approach yields much faster, accurate and robust registration results when compared against the state-of-the-art.