Improved image registration by sparse patch-based deformation estimation

TL;DR: A fully convolutional network is designed that is subject to dual‐guidance: ground‐truth guidance using deformation fields obtained by an existing registration method; and image dissimilarity guidance using the difference between the images after registration.

TL;DR: Compared with the traditional SAR image registration methods, the proposed method is competent to improve the registration performance substantially and the local geometric consistency among a feature point and its nearest neighbor points is employed to remove the mismatches from the tentative matches.

TL;DR: This chapter will be dedicated to summarizing state-of-the-art learning-based registration algorithms, which can be employed for learning an appearance mapping model, learning an effective initialization for the optimization, etc.

TL;DR: Experimental results demonstrate that the MAD-RMM outperforms current state-of-the-art methods and shows the robustness to outliers on real retinal images.

TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.

TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

TL;DR: This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.

TL;DR: The decomposition of deformations by principal warps is demonstrated and the method is extended to deal with curving edges between landmarks to aid the extraction of features for analysis, comparison, and diagnosis of biological and medical images.