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Image registration methods: a survey.
, 2003
"... Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrical ..."
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Cited by 760 (10 self)
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Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q
Mutual-information-based registration of medical images: a survey
- IEEE TRANSCATIONS ON MEDICAL IMAGING
, 2003
"... An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific ..."
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Cited by 302 (3 self)
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An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.
What is the Best Similarity Measure for Motion Correction In fMRI
, 2002
"... It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming ..."
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Cited by 51 (0 self)
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It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming at selecting a better similarity measure to drive functional magnetic resonance image registration. The behaviors of the Geman--McClure (GM) estimator, of the correlation ratio, and of the mutual information (MI) relative to activated areas are studied using simulated time series and actual data stemming from a 3T magnet. It is shown that these methods are more robust than the usual difference of squares measure. The results suggest also that the measures built from robust metrics like the GM estimator may be the best choice, while MI is also an interesting solution. Some more work, however, is required to compare the various robust metrics proposed in the literature.
Motion Correction Algorithms May Create Spurious Brain Activations in the Absence of Subject Motion
- NeuroImage
, 2001
"... This paper describes several experiments that prove that standard motion correction methods may induce spurious activations in some motion-free fMRI studies. This artifact stems from the fact that activated areas behave like biasing outliers for the difference of square-based measures usually drivin ..."
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Cited by 43 (2 self)
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This paper describes several experiments that prove that standard motion correction methods may induce spurious activations in some motion-free fMRI studies. This artifact stems from the fact that activated areas behave like biasing outliers for the difference of square-based measures usually driving such registration methods. This effect is demonstrated first using a motion-free simulated time series including artificial activation-like signal changes. Several additional simulations explore the influence of activation amplitude and extent. The effect is finally highlighted on an actual time series obtained from a 3-T magnet. All the experiments are performed using four different realignment methods, which allows us to show that the problem may be overcome by methods based on a robust similarity measure like mutual information. 2001 Academic Press Key Words: fMRI; motion correction; artifact; spurious activation; robust registration
Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images
, 2001
"... In this paper, we demonstrate a technique for modeling liver motion during the respiratory cycle using intensitybased free-form deformation registration of gated MR images. We acquired 3-D MR image sets (multislice 2-D) of the abdomen of four volunteers at end-inhalation, end-exhalation, and eight t ..."
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Cited by 37 (5 self)
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In this paper, we demonstrate a technique for modeling liver motion during the respiratory cycle using intensitybased free-form deformation registration of gated MR images. We acquired 3-D MR image sets (multislice 2-D) of the abdomen of four volunteers at end-inhalation, end-exhalation, and eight time points in between using respiratory gating. We computed the deformation field between the images using intensity-based rigid and non-rigid registration algorithms. The non-rigid transformation is a free-form deformation with B-spline interpolation between uniformlyspaced control points. The transformations between inhalation and exhalation were visually inspected. Much of the liver motion is cranial-caudal translation, and thus the rigid transformation captures much of the motion. However, there is still substantial residual deformation of up to 2 cm. The free-form deformation produces a motion field that appears on visual inspection to be accurate. This is true for the liver surface, internal liver structures such as the vascular tree, and the external skin surface. We conclude that abdominal organ motion due to respiration can be satisfactorily modeled using an intensity-based non-rigid 4-D image registration approach. This allows for an easier and potentially more accurate and patient-specific deformation field computation than physics-based models using assumed tissue properties and acting forces.
P.K.: Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation
- IEEE Trans. Medical Imaging
, 2003
"... Abstract—Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging ap-plications. To determine the MI between two images, the joint his-togram of the two images is required. In the literature, linear in-terpolation and partial volume interpolation ..."
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Cited by 23 (0 self)
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Abstract—Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging ap-plications. To determine the MI between two images, the joint his-togram of the two images is required. In the literature, linear in-terpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. It has been shown that joint histogram estimation through these two interpolation methods may introduce artifacts in the MI registra-tion function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new joint his-togram estimation scheme called generalized partial volume esti-mation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our ex-perimental results show that, by properly choosing the kernel func-tions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect reg-istration accuracy, the registration accuracy is improved. Index Terms—Image registration, interpolation-induced arti-facts, joint histogram estimation, mutual information, registration of brain CT and MR images. I.
F.Kruggel, “Computational cost of non-rigid registration algorithms based on fluid dynamics,” in
- IEEE Transactions on Image Medical Imaging
"... Abstract—Though fluid dynamics offer a good approach to non-rigid registration and give accurate results, even with large-scale deformations, its application is still very time consuming. We in-troduce and discuss different approaches to solve the core problem of nonrigid registration, the partial d ..."
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Cited by 21 (6 self)
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Abstract—Though fluid dynamics offer a good approach to non-rigid registration and give accurate results, even with large-scale deformations, its application is still very time consuming. We in-troduce and discuss different approaches to solve the core problem of nonrigid registration, the partial differential equation of fluid dynamics. We focus on the solvers, their computional costs and the accuracy of registration. Numerical experiments show that relax-ation is currently the best approach, especially when reducing the cost/iteration by focusing the updates on deformation spots. Index Terms—Computional cost, fluid dynamics, medical im-ages, registration. I.
Determining correspondence in 3-d MR brain images using attribute vectors as morphological signatures of voxels
- IEEE Transactions on Medical Imaging
, 2004
"... Abstract—Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on ..."
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Cited by 21 (9 self)
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Abstract—Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures. Index Terms—Computational anatomy, correspondence, deformable registration, image matching, wavelet transformations. I.
Intensity-Based Image Registration Using Robust Correlation Coefficients
- IEEE Transactions on Medical Imaging
, 2004
"... Abstract—The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of “outlier ” objects that appear in one image but not the other, such as surgical instruments, the pat ..."
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Cited by 16 (0 self)
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Abstract—The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of “outlier ” objects that appear in one image but not the other, such as surgical instruments, the patient table, etc., which can lead to biased registrations. This paper describes an intensity-based image registration technique that uses a robust correlation coefficient as a similarity measure. Relative to the ordinary sample correlation coefficient, the proposed similarity measure reduces the influence of outliers. We also compared the performance of the proposed method with the mutual informa-tion-based method. The robust correlation-based method should be useful for image registration in radiotherapy (KeV to MeV X-ray images) and image-guided surgery applications. We have investigated the properties of the proposed method by theoretical analysis, computer simulations, a phantom experiment, and with functional magnetic resonance imaging data. Index Terms—Image registration, mutual information, outlier, robust correlation coefficient, robustness. I.
Mutual information based image registration for remote sensing data
- International Journal of Remote Sensing
, 1998
"... Mutual information based image registration for remote sensing data ..."
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Cited by 12 (1 self)
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Mutual information based image registration for remote sensing data
