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A Review of Medical Image Registration
 Interactive imageguided neurosurgery
, 1993
"... Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergist ..."
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Cited by 29 (0 self)
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Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, Xray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while softtissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio
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 12 (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.
Elastically Deforming a ThreeDimensional Atlas to Match Anatomical Brain Images
 J. Comput. Assist. Tomogr
, 1993
"... To evaluate our system for elastically deforming a threedimensional atlas to match anatomical brain images, six deformed versions of an atlas were generated. The deformed atlases were created by elastically mapping an anatomical brain atlas onto different MRI brain image volumes. The mapping matche ..."
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Cited by 11 (0 self)
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To evaluate our system for elastically deforming a threedimensional atlas to match anatomical brain images, six deformed versions of an atlas were generated. The deformed atlases were created by elastically mapping an anatomical brain atlas onto different MRI brain image volumes. The mapping matches the edges of the ventricles and the surface of the brain; the resultant deformations are propagated through the atlas volume, deforming the remainder of the structures in the process. The atlas was then elastically matched to its deformed versions. The accuracy of the resultant matches was evaluated by determining the correspondence of 32 cortical and subcortical structures. The system on average matched the centroid of a structure to within 1 mm of its true position and fit a structure to within 11% of its true volume. The overlap between the matched and true structures, defined by the ratio between the volume of their intersection and the volume of their union, averaged 66%. When the gra...
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 9 (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
Flexible prior models in Bayesian image analysis
 Maximum Entropy and Bayesian Methods
, 1993
"... ABSTRACT. A new class of prior models is proposed for Bayesian image analysis. This class of priors provides an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the object under analysis. Thus prior ..."
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Cited by 7 (7 self)
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ABSTRACT. A new class of prior models is proposed for Bayesian image analysis. This class of priors provides an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the object under analysis. Thus prior morphological information about the object being reconstructed may be adapted to various degrees to match the available measurements. An example of tomographic reconstruction illustrates the potential of this approach. 1.
Towards Automatic Registration of Magnetic Resonance Images of the Brain Using Neural Networks. Part 2
, 1998
"... put of the detector plane of (c) is shown in (e). The entire surface is smoother than (d). The uncorrupted corner and the blurred feature give a less pronounced peak; the position of the corrupted corner cannot be detected with confidence and several likely locations are indicated by the smooth hill ..."
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Cited by 1 (1 self)
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put of the detector plane of (c) is shown in (e). The entire surface is smoother than (d). The uncorrupted corner and the blurred feature give a less pronounced peak; the position of the corrupted corner cannot be detected with confidence and several likely locations are indicated by the smooth hill. Thus, detection and placement can be improved by using sharp feature representations. The aim of this chapter is to develop feature sets with sharp contours. Three amendments to the previously proposed architecture are proposed: the use of spatial competition during training is outlined in x6.2, the selection of a subset of features from a larger set is suggested in x6.3, and the application of thresholdlike, feature postprocessing is discussed in x6.4. First a description of the three methods is given which is followed by an experimental investigation in x6.5. The new feature types of the three methods are given in
Advances in Elastic Matching Theory and its Implementation
, 1997
"... . Computational anatomy via the deformable modeling or elastic matching paradigm is gaining increased prominence in medical imaging research. Our work in atlasbased localization of neuroanatomy has progressed toward statistical approaches that subsume the original elastic matching while retaining i ..."
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. Computational anatomy via the deformable modeling or elastic matching paradigm is gaining increased prominence in medical imaging research. Our work in atlasbased localization of neuroanatomy has progressed toward statistical approaches that subsume the original elastic matching while retaining its practical flavor. In view of the complex geometries involved and the sparsity of image features in the localization problem, elastic matching is reformulated using variational principles to facilitate its numerical solution by the finite element method. The variational formulation in addition exposes the means by which Gibbs modeling and, thus, Bayesian analysis can be applied to the problem. In this paper, we review these developments and demonstrate the methods on MRI data, including the computation of interval estimates. 1 Introduction In 1981, Broit in collaboration with Bajcsy introduced a method for the "optimal registration of deformed images" [1], innovating the physicsbased app...
Vol. 10, No. 5/May 1993/J. Opt. Soc. Am. A 997 Bayesian reconstruction based on flexible prior models
, 1992
"... A new approach to Bayesian reconstruction is proposed that endows the prior probability distribution with an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the reconstruction. With this warping, pr ..."
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A new approach to Bayesian reconstruction is proposed that endows the prior probability distribution with an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the reconstruction. With this warping, prior morphological information regarding the object that is being reconstructed may be adapted to various degrees to match the available measurements. The extent of warping is readily controlled through the prior probability distributions that are specified for the warp parameters. The complete reconstruction consists of a warped version of the prior model plus an estimated deviation from the warped model. Examples of tomographic reconstructions demonstrate the power of this approach. 1.
Proc.ofSPIEVol.1652,MedicalImagingVI:ImageProcessing,ed.MHLoew(Jun1992)CopyrightSPIE W Reconstruction
"... A new approach to Bayesian reconstruction is introduced in which the prior probability distribution is endowed with an inherent geometrical flexibility. This flexibility is achieved through a warping of the coordinate system of the prior distribution into that of the reconstruction. This warping all ..."
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A new approach to Bayesian reconstruction is introduced in which the prior probability distribution is endowed with an inherent geometrical flexibility. This flexibility is achieved through a warping of the coordinate system of the prior distribution into that of the reconstruction. This warping allows various degrees of mismatch between the assumed prior distribution and the actual distribution corresponding to the available measurements. The extent of the mismatch is readily controlled through constraints placed on the warp parameters. 1.
Abstract Edge Preserving Image Compression for Magnetic Resonance Images Using DANNBased Neural Networks
"... With the tremendous growth in imaging applications and the development of filmless radiology, the need for compression techniques which can achieve high compression ratios with user specified distortion rates become necessary. Boundaries and edges in the tissue structures are vital for detection of ..."
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With the tremendous growth in imaging applications and the development of filmless radiology, the need for compression techniques which can achieve high compression ratios with user specified distortion rates become necessary. Boundaries and edges in the tissue structures are vital for detection of lesions and tumors, which in turn requires the preservation of edges in the image. Unlike existing lossy transformbased compression techniques such as FFT and DCT, edge preservation is addressed in this new compression scheme. The proposed Edge Preserving Image Compressor (EPIC) combines lossless compression of edges with neural network compression techniques based on Dynamic Associative Neural Networks (DANN), to provide high compression ratios with user specified distortion rates in an adaptive compression system wellsuited to parallel implementations. Improvements to DANNbased training through the use of a variance classifier for controlling a bank of neural networks speed convergence and allow the use of higher compression ratios for “simple ” patterns. The adaptation and generalization capabilities inherent in EPIC also facilitate progressive transmission of images through varying the number of quantization levels used to represent compressed patterns. EPIC was able to achieve average compression ratios of 7.51:1 with an averaged Average Mean Squared Error (AMSE) of 0.0147. I.