Nonrigid medical image registration technique as a composition of local warpings

Castellanos, N.P.; Angel, Pedro Luis del; Medina, V.

doi:10.1016/j.patcog.2003.09.019

Cited by 11 publications

(4 citation statements)

References 46 publications

(53 reference statements)

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“…The work presented in [46] tackles deformable registration. The authors propose a novel transformation model which is a composition of circular warpings of different size.…”

Section: Castellanos Et Al's Gamentioning

confidence: 99%

Evolutionary Intensity-based Medical Image Registration: A Review

Valsecchi¹,

Damas²,

Santamaría

2014

CMIR

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Metaheuristics are techniques that use approximate and intuitive strategies to quickly find near-optimal solutions of complex optimization problems. A number of outstanding examples belong to evolutionary computation, the class of methods inspired to biological and evolutionary phenomena. These techniques have been extensively and successfully applied to feature-based image registration in medicine. However, with the increase in computational power during the last decade, intensity-based (or voxel-based) image registration methods have been preferred in many medical imaging applications, due to their robustness, accuracy and applicability, in cases where landmarks or other features are not available or easy to detect. While traditional numerical optimization techniques are employed to solve the registration problem, a number of contributions in the literature support the use of metaheuristics to overcome the shortcomings of classic methods. The aim of the paper is to review the state of the art in the application of evolutionary computation and other metaheuristics to intensity-based medical image registration. The study considers both well-know techniques with a large number of references in the literature as well as recent, outstanding proposals. The analysis focuses on the design of the methods to highlight common and successful practices. In addition, recommendations and open research lines in the field are provided. 2.1.1 Transformation model The transformation model determines which kind of geometrical transformation can be applied to the scene image to reach the model. This also controls which geometrical properties (e.g. size, shape, position, orientation, etc.) are preserved through the process. Common models include rigid transform, which allows translations and rotations, similarity transform, which also admits scaling, and affine transformation, which can also represent shearing. These are examples of global transformations having respectively 6, 7 and 12 degrees of freedom for 3D images. At the other end of the spectrum there are nonrigid (also called elastic) transformations, such as B-spline and thin-plate splines transformations, able to represent local deformations (warping) using hundreds or even thousands of parameters.

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“…The work presented in [46] tackles deformable registration. The authors propose a novel transformation model which is a composition of circular warpings of different size.…”

Section: Castellanos Et Al's Gamentioning

confidence: 99%

Evolutionary Intensity-based Medical Image Registration: A Review

Valsecchi¹,

Damas²,

Santamaría

2014

CMIR

View full text Add to dashboard Cite

show abstract

“…These methods are based on the use of point interpolation techniques, together with a weighting of the deformation according to a distance function. Castellanos et al [8] developed a slightly different methodology, in which local non-rigid warpings are used to guarantee the continuity of the transformation.…”

Section: Related Work and Overview Of The Proposed Approachmentioning

confidence: 99%

CT and PET Registration Using Deformations Incorporating Tumor-Based Constraints

Moreno

Delso

Cámara

et al. 2005

Lecture Notes in Computer Science

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Registration of CT and PET thoracic images has to cope with deformations of the lungs during breathing. Possible tumors in the lungs usually do not follow the same deformations, and this should be taken into account in the registration procedure. We show in this paper how to introduce tumor-based constraints into a non-linear registration of thoracic CT and PET images. Tumors are segmented by means of a semi-automatic procedure and they are used to guarantee relevant deformations near the pathology. Results on synthetic and real data demonstrate a significant improvement of the combination of anatomical and functional images for diagnosis and for oncology applications.

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“…The situation is more complex for shapes and images. Different deformation models have been proposed, involving rigid deformations, small deformations [13] or deformation fields ruled by a differential equation; see [14].…”

Section: Introductionmentioning

confidence: 99%

Online EM for functional data

Maire¹,

Moulines²,

Lefèbvre³

2017

Computational Statistics & Data Analysis

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A novel approach to perform unsupervised sequential learning for functional data is proposed. Our goal is to extract reference shapes (referred to as templates) from noisy, deformed and censored realizations of curves and images. Our model generalizes the Bayesian dense deformable template model [1, 2], a hierarchical model in which the template is the function to be estimated and the deformation is a nuisance, assumed to be random with a known prior distribution. The templates are estimated using a Monte Carlo version of the online Expectation-Maximization (EM) algorithm, extending [3]. Our sequential inference framework is significantly more computationally efficient than equivalent batch learning algorithms, especially when the missing data is high-dimensional. Some numerical illustrations on curve registration problem and templates extraction from images are provided to support our findings.

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Nonrigid medical image registration technique as a composition of local warpings

Cited by 11 publications

References 46 publications

Evolutionary Intensity-based Medical Image Registration: A Review

Evolutionary Intensity-based Medical Image Registration: A Review

CT and PET Registration Using Deformations Incorporating Tumor-Based Constraints

Online EM for functional data

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