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29
Landmarkbased elastic registration using approximating thinplate splines
 IEEE Trans. Med. Imag
, 2001
"... Abstract—We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thinplate splines. This approach is an extension of the original interpolating thinplate spline approach and allows to take into account landmark localization errors. The ..."
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Abstract—We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thinplate splines. This approach is an extension of the original interpolating thinplate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thinplate splines are special cases of this scheme. To localize landmarks we use a semiautomatic approach which is based on threedimensional (3D) differential operators. Experimental results are presented for two–dimensional as well as 3D tomographic images of the human brain. Index Terms—Anatomical landmarks, image matching, segmentation, splines. I.
Design of a Statistical Model of Brain Shape
, 1997
"... . This paper describes a statistical shape model of the brain extending through the whole organ. The variability in a normal population is described by global deformation modes. The model is based on the analysis of homologous deformations mapping similar structures in brain images. 1 Introduction ..."
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Cited by 17 (3 self)
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. This paper describes a statistical shape model of the brain extending through the whole organ. The variability in a normal population is described by global deformation modes. The model is based on the analysis of homologous deformations mapping similar structures in brain images. 1 Introduction Large variations exist across individuals in the morphology of the brain. Clinical diagnosis of diseases affecting the shape require the evaluation of the deviation from the normal anatomy. The large range of shape variations in a normal population (geometrical and topological) makes difficult the task of discriminating what is normal from what is pathological. To better address this problem, a shape model of normal variations must be identified. Although some shape variations can be explained by a mechanical model [1], a reliable model can only be inferred from a statistical study since the variations have morphogenic origins as well. The morphometry is concerned with the study and classifi...
Bayesian Approach to the Brain Image Matching Problem
, 1995
"... The application of image matching to the problem of localizing structural anatomy in images of the human brain forms the specific aim of our work. The interpretation of such images is a difficult task for human observers because of the many ways in which the identity of a given structure can be obsc ..."
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Cited by 14 (3 self)
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The application of image matching to the problem of localizing structural anatomy in images of the human brain forms the specific aim of our work. The interpretation of such images is a difficult task for human observers because of the many ways in which the identity of a given structure can be obscured. Our approach is based on the assumption that a common topology underlies the anatomy of normal individuals. To the degree that this assumption holds, the localization problem can be solved by determining the mapping from the anatomy of a given individual to some referential atlas of cerebral anatomy. Previous such approaches have in many cases relied on a physical interpretation of this mapping. In this paper, we examine a more general Bayesian formulation of the image matching problem and demonstrate the approach on twodimensional magnetic resonance images.
Elastic 3D Alignment of Rat Brain Histological Images
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2003
"... A threedimensional waveletbased algorithm for nonlinear registration of an elastic body model of the brain is developed. Surfaces of external and internal anatomic brain structures are used to guide alignment. The deformation field is represented with a multiresolution wavelet expansion and is mod ..."
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Cited by 12 (3 self)
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A threedimensional waveletbased algorithm for nonlinear registration of an elastic body model of the brain is developed. Surfaces of external and internal anatomic brain structures are used to guide alignment. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A progressive estimation of the registration parameters and the usage of an adaptive distance map reduce algorithm complexity, thereby providing computational flexibility that allows mapping of large, high resolution datasets. The performance of the algorithm was evaluated on rat brains. The waveletbased registration method yielded a twofold improvement over affine registration.
ParameterFree Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements
, 1999
"... A parameterfree approach for nonrigid image registration based on elasticity theory is presented. In contrast to traditional physicallybased numerical registration methods, no forces have to be computed from image data to drive the elastic deformation. Instead, displacements obtained with the ..."
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Cited by 12 (2 self)
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A parameterfree approach for nonrigid image registration based on elasticity theory is presented. In contrast to traditional physicallybased numerical registration methods, no forces have to be computed from image data to drive the elastic deformation. Instead, displacements obtained with the help of mapping boundary structures in the source and target image are incorporated as hard constraints into elastic image deformation. As a consequence, our approach does not contain any parameters of the deformation model such as elastic constants. The approach guarantees the exact correspondence of boundary structures in the images assuming that correct input data are available. The implemented incremental method allows to cope with large deformations. The theoretical background, the finite element discretization of the elastic model, and experimental results for 2D and 3D synthetic as well as real medical images are presented.
Numerical Methods for HighDimensional Warps
 in Chapter in Brain Warping
, 1998
"... Introduction The fundamental problem in brain warping is to define the class of admissible spatial transformations, which must be sufficiently broad to enable a reference anatomy to fit all subject anatomies, and to develop efficient, automated algorithms for the calculation of the appropriate tran ..."
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Cited by 11 (4 self)
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Introduction The fundamental problem in brain warping is to define the class of admissible spatial transformations, which must be sufficiently broad to enable a reference anatomy to fit all subject anatomies, and to develop efficient, automated algorithms for the calculation of the appropriate transformation. In this chapter, we focus on numerical methods for inferring spatial warps that are very high in dimension in order to accommodate the complex ways in which the neuroanatomy of normal individuals can vary. Specifically, the elastic matching technique described in a previous chapter is implemented. The warps therefore correspond to deformations in the continuum mechanics, and we require methods for solving boundaryvalue problems. Two approaches are standard and each involves a different way of discretizing the problem. The finite difference method , which operates directly on the motion equations, is easy to code and computationally fast, but the fi
TwoStep ParameterFree Elastic Image Registration with Prescribed Point Displacements
 In Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP '97
, 1997
"... A twostep parameterfree approach for nonrigid medical image registration is presented. Displacements of boundary structures are computed in the first step and then incorporated as hard constraints for elastic image deformation in the second step. In comparison to traditional nonparametric method ..."
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Cited by 8 (6 self)
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A twostep parameterfree approach for nonrigid medical image registration is presented. Displacements of boundary structures are computed in the first step and then incorporated as hard constraints for elastic image deformation in the second step. In comparison to traditional nonparametric methods, no driving forces have to be computed from image data. The approach guarantees the exact correspondence of certain structures in the images and does not depend on parameters of the deformation model such as elastic constants. Numerical examples with synthetic and real images are presented. 1 Introduction Numerous applications in modern medical imaging deal with nonrigid image registration. Examples are imageatlas as well as multimodality image registration in neurosurgery. There, a threedimensional image (deformable template) has to be completely transformed onto another one (study). One group of methods dealing with nonrigid image registration is the socalled nonparametric metho...
Explicit incorporation of prior anatomical information into a nonrigid registration of thoracic and abdominal CT and 18FDG wholebody emission PET images
 IEEE Trans. Med. Imag
, 2007
"... Abstract—The aim of this paper is to develop a registration methodology in order to combine anatomical and functional information provided by thoracic/abdominal computed tomography (CT) and wholebody positron emission tomography (PET) images. The proposed procedure is based on the incorporation of ..."
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Cited by 7 (2 self)
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Abstract—The aim of this paper is to develop a registration methodology in order to combine anatomical and functional information provided by thoracic/abdominal computed tomography (CT) and wholebody positron emission tomography (PET) images. The proposed procedure is based on the incorporation of prior anatomical information in an intensitybased nonrigid registration algorithm. This incorporation is achieved in an explicit way, initializing the intensitybased registration stage with the solution obtained by a nonrigid registration of corresponding anatomical structures. A segmentation algorithm based on a hierarchically ordered set of anatomyspecific rules is used to obtain anatomical structures in CT and emission PET scans. Nonrigid deformations are modeled in both registration stages by means of freeform deformations, the optimization of the control points being achieved by means of an original vector fieldbased approach instead of the classical gradientbased techniques, considerably reducing the computational time of the structure registration stage. We have applied the proposed methodology to 38 sets of images (33 provided by standalone machines and five by hybrid systems) and an assessment protocol has been developed to furnish a qualitative evaluation of the algorithm performance. Index Terms—Anatomical constraints, freeform deformations (FFD), nonrigid registration, oncology, thoracic and abdominal computed tomography (CT), wholebody positron emission tomography (PET. I.
A statistical partsbased model of anatomical variability
 Medical Imaging, IEEE Transactions on
"... Abstract—In this paper, we present a statistical partsbased model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a ..."
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Cited by 6 (0 self)
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Abstract—In this paper, we present a statistical partsbased model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence frequency are quantified statistically. The partsbased approach explicitly addresses the case where onetoone correspondence does not exist between all subjects in a population due to anatomical differences, as model parts are not required to appear in all subjects. The model is constructed through a fully automatic machine learning algorithm, identifying image patterns that appear with statistical regularity in a large collection of subject images. Parts are represented by generic scaleinvariant features, and the model can, therefore, be applied to a wide variety of image domains. Experimentation based on 2D MR slices shows that a PBM learned from a set of 102 subjects can be robustly fit to 50 new subjects with accuracy comparable to 3 human raters. Additionally, it is shown that unlike global models such as the AAM, PBM fitting is stable in the presence of unexpected, local perturbation. Index Terms—Intersubject variability, invariant feature, partsbased model, statistical appearance model. I.
Effect of Spatial Normalization on Analysis of Functional Data
, 1997
"... Conventional analysis of functional data often involves a normalization step in which the data are spatially aligned so that a measurement can be made across or between studies. Whether to enhance the signaltonoise ratio or to detect significant deviations in activation from normal, the method use ..."
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Cited by 6 (0 self)
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Conventional analysis of functional data often involves a normalization step in which the data are spatially aligned so that a measurement can be made across or between studies. Whether to enhance the signaltonoise ratio or to detect significant deviations in activation from normal, the method used to register the underlying anatomies clearly impacts the viability of the analysis. Nevertheless, it is common practice to infer only homogeneous transformations, in which all parts of the image volume undergo the same mapping. To detect subtle effects or to extend the analysis to anatomies that exhibit considerable morphological variation, higher dimensional mappings to allow more accurate alignment will be crucial. We describe a Bayesian volumetric warping approach to the normalization problem, which matches local image features between MRI brain volumes, and compare its performance with a standard method (SPM'96) as well as contrast its effect on the analysis of a set of functional MRI ...