Results 1  10
of
64
A Survey of Medical Image Registration
, 1998
"... The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of t ..."
Abstract

Cited by 541 (5 self)
 Add to MetaCart
The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. Keywords: registration, matching Received May 25, 1997
HAMMER: hierarchical attribute matching mechanism for elastic registration
 IEEE Trans. on Medical Imaging
, 2002
"... A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological cha ..."
Abstract

Cited by 269 (91 self)
 Add to MetaCart
(Show Context)
A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological characteristics in population studies. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e. a set of geometric moment invariants that is defined on each voxel in an image, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure. This is a fundamental deviation of our method from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, it employs a hierarchical deformation mechanism, which is initially influenced by parts of the anatomy that can be identified relatively more reliably than others. Moreover, the deformation mechanism involves a sequence of local smooth transformations, which do not update positions of individual voxels, but rather are based on evaluating a similarity of attribute vectors over a larger subvolume of a volumetric image. This renders this algorithm very robust to suboptimal solutions. A number of experiments in this paper have demonstrated excellent performance. 1.
Volumetric Transformation of Brain Anatomy
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 1997
"... This paper presents diffeomorphic transformations of threedimensional (3D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarc ..."
Abstract

Cited by 140 (11 self)
 Add to MetaCart
(Show Context)
This paper presents diffeomorphic transformations of threedimensional (3D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail. The initial lowdimensional registration is accomplished by constraining the transformation to be in a lowdimensional basis. The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The highdimensional large deformations are vector fields generated via the mismatch between the template and targetimage volumes constrained to be the solution of a NavierStokes fluid model. As part of this procedure, the Jacobian of the transformation is tracked, insuring the generation of diffeomorphisms. It is shown that transformations constrained by quadratic regularization methods such as the Laplacian, biharmonic, and linear elasticity models, do not ensure that the transformation maintains topology and, therefore, must only be used for coarse global registration.
Group Actions, Homeomorphisms, and Matching: A General Framework
, 2001
"... This paper constructs metrics on the space of images I defined as orbits under group actions G. The groups studied include the finite dimensional matrix groups and their products, as well as the infinite dimensional diffeomorphisms examined in Trouvé (1999, Quaterly of Applied Math.) and Dupuis et a ..."
Abstract

Cited by 133 (7 self)
 Add to MetaCart
(Show Context)
This paper constructs metrics on the space of images I defined as orbits under group actions G. The groups studied include the finite dimensional matrix groups and their products, as well as the infinite dimensional diffeomorphisms examined in Trouvé (1999, Quaterly of Applied Math.) and Dupuis et al. (1998). Quaterly of Applied Math.). Leftinvariant metrics are defined on the product G × I thus allowing the generation of transformations of the background geometry as well as the image values. Examples of the application of such metrics are presented for rigid object matching with and without signature variation, curves and volume matching, and structural generation in which image values are changed supporting notions such as tissue creation in carrying one image to another.
Medical image analysis: progress over two decades and the challenges ahead
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... AbstractThe analysis of medical images has been woven into the fabric of the Pattern Analysis and Machine Intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniquss lo another ..."
Abstract

Cited by 129 (6 self)
 Add to MetaCart
(Show Context)
AbstractThe analysis of medical images has been woven into the fabric of the Pattern Analysis and Machine Intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniquss lo another interesting dataset. However. over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new dlscipline in its own right. Examples of these include: the types of image information that are acquired, the fully threedimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the fiold over the last 20 years and suggest some of the challenges that remain for the years to come.
Magnetic resonance image tissue classification using a partial volume model
 NEUROIMAGE
, 2001
"... We describe a sequence of lowlevel operations to isolate and classify brain tissue within T1weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for imag ..."
Abstract

Cited by 128 (5 self)
 Add to MetaCart
We describe a sequence of lowlevel operations to isolate and classify brain tissue within T1weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic Bspline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensitynormalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average � indices of ��0.746 � 0.114 for gray matter (GM) and ��0.798 � 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average � indices �� 0.893 � 0.041 for GM and ��0.928 � 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute’s BrainWeb phantom.
Statistical Models of Appearance for Medical Image Analysis and Computer Vision
 In Proc. SPIE Medical Imaging
, 2001
"... Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the te ..."
Abstract

Cited by 114 (1 self)
 Add to MetaCart
Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the texture variation and a model of the correlations between shape and texture. With enough training examples such models should be able to synthesize any image of normal anatomy. By finding the parameters which optimize the match between a synthesized model image and a target image we can locate all the structures represented by the model. Two approaches to the matching will be described. The Active Shape Model essentially matches a model to boundaries in an image. The Active Appearance Model finds model parameters which synthesize a complete image which is as similar as possible to the target image. By using a `difference decomposition' approach the current difference between target image and synthesi...
Using Generative Models for Handwritten Digit Recognition
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1996
"... We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable Bsplines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maxi ..."
Abstract

Cited by 77 (7 self)
 Add to MetaCart
We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable Bsplines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of prenormalization of input images, but can ...
A Unified Framework for Atlas Matching using Active Appearance Models
, 1999
"... We propose to use statistical models of shape and texture as deformable anatomical atlases. By training on sets of labelled examples these can represent both the mean structure and appearance of anatomy in medical images, and the allowable modes of deformation. Given enough training examples such a ..."
Abstract

Cited by 72 (4 self)
 Add to MetaCart
(Show Context)
We propose to use statistical models of shape and texture as deformable anatomical atlases. By training on sets of labelled examples these can represent both the mean structure and appearance of anatomy in medical images, and the allowable modes of deformation. Given enough training examples such a model should be able synthesise any image of normal anatomy. By finding the parameters which minimise the difference between the synthesised model image and the target image we can locate all the modelled structure. This potentially time consuming step can be solved rapidly using the Active Appearance Model (AAM). In this paper we describe the models and the AAM algorithm and demonstrate the approach on structures in MR brain crosssections.
Computational anatomy: Shape, growth, and atrophy comparison via diffeomorphisms
 NeuroImage
, 2004
"... Computational anatomy (CA) is the mathematical study of anatomy I a I = I a BG, an orbit under groups of diffeomorphisms (i.e., smooth invertible mappings) g a G of anatomical exemplars Iaa I. The observable images are the output of medical imaging devices. There are three components that CA examine ..."
Abstract

Cited by 62 (2 self)
 Add to MetaCart
Computational anatomy (CA) is the mathematical study of anatomy I a I = I a BG, an orbit under groups of diffeomorphisms (i.e., smooth invertible mappings) g a G of anatomical exemplars Iaa I. The observable images are the output of medical imaging devices. There are three components that CA examines: (i) constructions of the anatomical submanifolds, (ii) comparison of the anatomical manifolds via estimation of the underlying diffeomorphisms g a G defining the shape or geometry of the anatomical manifolds, and (iii) generation of probability laws of anatomical variation P(d) on the images I for inference and disease testing within anatomical models. This paper reviews recent advances in these three areas applied to shape, growth, and atrophy.