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17
Fast parametric elastic image registration
 IEEE Transactions on Image Processing
, 2003
"... Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of so ..."
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Cited by 102 (8 self)
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Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. Particular attention has been paid to the factors influencing the speed of this algorithm. The Bspline deformation model is shown to be computationally more efficient than other alternatives. The algorithm has been successfully used for several twodimensional (2D) and threedimensional (3D) registration tasks in the medical domain, involving MRI, SPECT, CT, and ultrasound image modalities. We also present experiments in a controlled environment, permitting an exact evaluation of the registration accuracy. Test deformations are generated automatically using a random hierarchical fractional waveletbased generator. Index Terms—Elastic registration, image registration, landmarks, splines. I.
Iconic Feature Based Nonrigid Registration: The PASHA Algorithm
, 2004
"... In this paper, we first propose a new subdivision of the image information axis uis for the classification of nonrigid registration algorithms. Namely, we introdu) the notion of iconic featuy based (IFB) algorithms, which lie between geometrical and standard intensitybased algorithms fortheyuM b ..."
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Cited by 62 (20 self)
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In this paper, we first propose a new subdivision of the image information axis uis for the classification of nonrigid registration algorithms. Namely, we introdu) the notion of iconic featuy based (IFB) algorithms, which lie between geometrical and standard intensitybased algorithms fortheyuM both anintensitysimilaritymeasu and a geometrical distance. Then we present a new registration energyfor IFB registration that generalizes some of the existing techniquML We compareou algorithm with other registration approaches, and show the advantages of this energy. Besides, we also present a fasttechniqu for thecompukUy) of local statistics between images, which tuchou to beuyUM on pairs of images having a complex, nonstationaryrelationship between their intensities, as well as an hybridreguSkqy)qL scheme mixing elastic and fluy components. The potential of the algorithm is finallydemonstrated on a clinical application, namelydeep brainstimuMUqy of a Parkinsonian patient. Registration of pre and immediate postoperative MR images allow toquMSy)WS range of the deformationdu topneuU3y)W3Mflover the entire brain,thu yielding tomeasuMy)W3 of the deformation aroun the preoperatively computed stereotactic targets.
Reconstructing surfaces using anisotropic basis functions
 In International Conference on Computer Vision (ICCV) 2001
, 2001
"... Point sets obtained from computer vision techniques are often noisy and nonuniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing ..."
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Cited by 55 (5 self)
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Point sets obtained from computer vision techniques are often noisy and nonuniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing smooth and seamless 3D surfaces. Implicit functions are often formulated as a sum of weighted basis functions that are radially symmetric. Using radially symmetric basis functions inherently assumes, however, that the surface to be reconstructed is, everywhere, locally symmetric. Such an assumption is true only at planar regions, and hence, reconstruction using isotropic basis is insufficient to recover objects that exhibit sharp features. We preserve sharp features using anisotropic basis that allow the surface to vary locally. The reconstructed surface is sharper along edges and at corner points. We determine the direction of anisotropy at a point by performing principal component analysis of the data points in a small neighborhood. The resulting field of principle directions across the surface is smoothed through tensor filtering. We have applied the anisotropic basis functions to reconstruct surfaces from noisy synthetic 3D data and from real range data obtained from space carving. I.
Reconstructing Surfaces By Volumetric Regularization Using Radial Basis Functions
"... We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and no ..."
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Cited by 52 (4 self)
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We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to visionbased data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data; (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data; and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.
Comparison of detrending methods for optimal fMRI preprocessing
 NeuroImage
, 2002
"... Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological ..."
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Cited by 16 (1 self)
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Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological pulsations. However, the amount and direction of drift is difficult to predict, even between neighboring voxels. Further, there is no concensus on an optimal baseline drift removal algorithm. In this paper, five voxelbased detrending techniques were compared to each other and an autodetrending algorithm, which automatically selected the optimal method for a given voxel timeseries. For a significance level of P < 10 �6, linear and quadratic detrending moderately increased the percentage of activated voxels. Cubic detrending decreased activation, while a wavelet approach increased or decreased activation, depending on the dataset. Spline detrending was the best single algorithm. However, autodetrending (selecting the best algorithm or none, if detrending is not useful) appears to be the most judicious choice, particularly for analyzing fMRI data with weak activations in the presence
Robust shape tracking with multiple models in ultrasound images
 IEEE Transactions on Image Processing
, 2008
"... Abstract—This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing thei ..."
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Cited by 9 (7 self)
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Abstract—This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing their influence on the shape estimates. The problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem. The heart motion presents two phases (diastole and systole) with different dynamics, the multiple models used in this tracker try to solve this difficulty. In addition, ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models. Robust estimation techniques are used to address this difficulty. The multiple model data association (MMDA) tracker proposed in this paper is based on a bank of nonlinear filters, organized in a tree structure. The algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques. Index Terms—Image analysis, low level features, multiple model data association (MMDA), segmentation, tracking, ultrasound images. I.
Regularization in Image NonRigid Registration: I. Tradeoff between Smoothness and Intensity Similarity
, 2001
"... In this report, we first propose a new classification of nonrigid registration algorithms into three main categories: in one hand, the geometric algorithms, and in the other hand, intensity based methods that we split here into standard intensitybased (SIB) and pairandsmooth (P&S) algorithms ..."
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Cited by 8 (4 self)
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In this report, we first propose a new classification of nonrigid registration algorithms into three main categories: in one hand, the geometric algorithms, and in the other hand, intensity based methods that we split here into standard intensitybased (SIB) and pairandsmooth (P&S) algorithms. We then focus on the subset of SIB and P&S...
Full Motion and Flow Field Recovery From Echo Doppler Data
"... Abstract—We present a new computational method for reconstructing a vector velocity field from scattered, pulsedwave ultrasound Doppler data. The main difficulty is that the Doppler measurements are incomplete, for they do only capture the velocity component along the beam direction. We thus propos ..."
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Cited by 6 (2 self)
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Abstract—We present a new computational method for reconstructing a vector velocity field from scattered, pulsedwave ultrasound Doppler data. The main difficulty is that the Doppler measurements are incomplete, for they do only capture the velocity component along the beam direction. We thus propose to combine measurements from different beam directions. However, this is not yet sufficient to make the problem well posed because 1) the angle between the directions is typically small and 2) the data is noisy and nonuniformly sampled. We propose to solve this reconstruction problem in the continuous domain using regularization. The reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: 1) the sum of squared difference between the Doppler data and the projected velocities 2) a quadratic regularization functional that imposes some smoothness on the velocity field. We express our solution for this minimization problem in aspline basis, obtaining a sparse system of equations that can be solved efficiently. Using synthetic phantom data, we demonstrate the significance of tuning the regularization according to the a priori knowledge about the physical property of the motion. Next, we validate our method using real phantom data for which the ground truth is known. We then present reconstruction results obtained from clinical data that originate from 1) blood flow in carotid bifurcation and 2) cardiac wall motion. Index Terms—Color Doppler imaging, color flow imaging, echocardiography, nonuniform sampling, projected sampling, pulsed wave Doppler, regularized reconstruction, shiftinvariant spaces, tissue Doppler imaging, ultrasound Doppler, variational reconstruction, vector field reconstruction, velocity field reconstruction. I.
On Regularized Reconstruction of Vector Fields
"... Abstract—In this paper, we give a general characterization of regularization functionals for vector field reconstruction, based on the requirement that the said functionals satisfy certain geometric invariance properties with respect to transformations of the coordinate system. In preparation for ou ..."
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Cited by 4 (1 self)
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Abstract—In this paper, we give a general characterization of regularization functionals for vector field reconstruction, based on the requirement that the said functionals satisfy certain geometric invariance properties with respect to transformations of the coordinate system. In preparation for our general result, we also address some commonalities of invariant regularization in scalar and vector settings, and give a complete account of invariant regularization for scalar fields, before focusing on their main points of difference, which lead to a distinct class of regularization operators in the vector case. Finally, as an illustration of potential, we formulate and compare quadratic ( 2) and totalvariationtype ( 1) regularized denoising of vector fields in the proposed framework. Index Terms—Curl and divergence in higher dimensions, fractional Laplacian, fractional vector calculus, regularization, rotation invariance, scale invariance, total variation (TV), vector fields, vector spaces. I.
Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic Carm Data
"... Abstract—Anatomical and functional information about the cardiac chambers is a key component of future developments in the field of interventional cardiology. With the technology of Carm CT it is possible to reconstruct intraprocedural 3D images from angiographic projection data. Some approaches a ..."
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Cited by 1 (0 self)
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Abstract—Anatomical and functional information about the cardiac chambers is a key component of future developments in the field of interventional cardiology. With the technology of Carm CT it is possible to reconstruct intraprocedural 3D images from angiographic projection data. Some approaches attempt to add the temporal dimension (4D) by electrocardiogram (ECG)gating in order to distinguish physical states of the heart. However, for the left heart ventricle scanned during one single Carm sweep, this approach leads to insufficient projection data and thus to a degraded image reconstruction quality. In this paper, we evaluate the influence of different interpolation methods for a motion compensated reconstruction approach for the left heart ventricle based on a recently presented 3D dynamic surface model. The surface model results in a sparse motion vector field (MVF) defined at control points. However, to perform a motion compensated reconstruction a dense MVF is required. The dense MVF can be determined by different interpolation methods. In this paper, we evaluate thinplate splines (TPS), the Shepard’s method, simple averaging, and a smoothed weighting function as interpolation functions. The 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool is quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. Preliminary results on heart ventricle phantom data, as well as on clinical human data show the best results with the TPS interpolation. A. Purpose of this Work I.