Symmetric Non-rigid Registration: A Geometric Theory and Some Numerical Techniques

Tagare, Hemant D.; Groisser, David; Škrinjar, Oskar

doi:10.1007/s10851-008-0129-7

Cited by 33 publications

(39 citation statements)

References 29 publications

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“…Their limitations are because of the highly localized deformations that cannot be modelled due to stress deformation energy [45]. In the literature, there are reviews about the most promising non-linear registration strategies currently used in medical imaging, such as a novel curvature based registration technique that permits a faster image registration [51], the application of a deformable registration method in the automated hexahedral meshing of anatomical structures [52,53], symmetric nonrigid registration [54] and Brownian Warps, which is a diffeomorphism registration algorithm [55]. Also fluid registration and registration using optical flow are approaches that are equivalent to the equation of motion for incompressible flow [45].…”

Section: Geometric Transformationsmentioning

confidence: 99%

Computer Image Registration Techniques Applied to Nuclear Medicine Images

Alves¹,

Tavares²

2015

Lecture Notes in Computational Vision and Biomechanics

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Modern medicine has been using imaging as a fundamental tool in a wide range of applications. Consequently, the interest in automated registration of images from either the same or different modalities has increased. In this chapter, computer techniques of image registration are reviewed, and cover both their classification and the main steps involved. Moreover, the more common geometrical transforms, optimization and interpolation algorithms are described and discussed. The clinical applications examined emphases nuclear medicine.

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Section: Geometric Transformationsmentioning

confidence: 99%

Computer Image Registration Techniques Applied to Nuclear Medicine Images

Alves¹,

Tavares²

2015

Lecture Notes in Computational Vision and Biomechanics

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“…This asymmetry introduces undesirable bias upon any statistical analysis that follows registration because the registration result depends on the choice of the target domain. Symmetric algorithms have been proposed in order to tackle this shortcoming [5,13,56,79,84].…”

Section: Markov Random Field Registration Energymentioning

confidence: 99%

Graph-based Deformable Image Registration

Sotiras

Paragios

et al. 2015

Handbook of Biomedical Imaging

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Deformable image registration is a field that has received considerable attention in the medical image analysis community. As a consequence, there is an important body of works that aims to tackle deformable registration. In this chapter we review one class of these techniques that use discrete optimization, and more specifically Markov Random Field models. We begin the chapter by explaining how one can formulate the deformable registration problem as a minimal cost graph problem where the nodes of the graph corresponds to the deformation grid, the graph connectivity encodes regularization constraints, and the labels correspond to 3D displacements. We then explain the use of discrete models in intensitybased volumetric registration. In the third section, we detail the use of Gabor-based attribute vectors in the context of discrete deformable registration, demonstrating the versatility of the graph-based models. In the last section of the chapter, the case of landmark-based registration is discussed. We first explain the discrete graphical model behind establishing landmark correspondences, and then continue to show how one can integrate it with the intensity-based model towards creating enhanced models that combine the best of both worlds.

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“…In fact, this issue is omnipresent in nearly all the current methods used for registering 2D and 3D images. For instance, the paper by Tagare et al [123] solves the problem of asymmetry in the registration cost function but does not address this issue of the cost function changing values even when the registration remains the same. To our knowledge, Xie et al [143] is the only paper to address this issue.…”

Section: Deformable Templatesmentioning

confidence: 99%