A PYRAMID APPROACH TO SUBPIXEL REGISTRATION BASED ON INTENSITY PDF

Resources and Help A pyramid approach to subpixel registration based on intensity Abstract: We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images two-dimensional or volumes three-dimensional. It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy pyramid approach. The geometric deformation model is a global three-dimensional 3-D affine transformation that can be optionally restricted to rigid-body motion rotation and translation , combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography PET and functional magnetic resonance imaging fMRI data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum.

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Show Context Citation Context This method makes use of the same high-quality spline model for all aspects of the computation: image pyramid, geometric transform, and computation of the gradient of the criterion that is optimized Abstract—Based on the theory of approximation, this paper presents a unified analysis of interpolation and resampling techniques. An important issue is the choice of adequate basis functions.

We show that, contrary to the common belief, those that perform best are not interpolating. By opposition to By opposition to traditional interpolation, we call their use generalized interpolation; they involve a prefiltering step when correctly applied. We explain why the approximation order inherent in any basis function is important to limit interpolation artifacts.

The decomposition theorem states that any basis function endowed with approximation order can be expressed as the convolution of a B-spline of the same order with another function that has none.

This motivates the use of splines and spline-based functions as a tunable way to keep artifacts in check without any significant cost penalty. We discuss implementation and performance issues, and we provide experimental evidence to support our claims. Index Terms—Approximation constant, approximation order, B-splines, Fourier error kernel, maximal order and minimal support Moms , piecewise-polynomials. However, even with perfect knowledge of the ideal geometric transformation, a deficient interpolation can wash out these tiny differences.

The essence of interpolation is to represent an arbitrary c We propose a new method for the intermodal registration of images using a criterion known as mutual information. Our main contribution is an optimizer that we specifically designed for this criterion. We show that this new optimizer is well adapted to a multiresolution approach because it typically We show that this new optimizer is well adapted to a multiresolution approach because it typically converges in fewer criterion evaluations than other optimizers.

We have built a multiresolution image pyramid, along with an interpolation process, an optimizer, and the criterion itself, around the unifying concept of spline-processing. This ensures coherence in the way we model data and yields good performance.

We have tested our approach in a variety of experimental conditions and report excellent results. We claim an accuracy of about a hundredth of a pixel under ideal conditions. We are also robust since the accuracy is still about a tenth of a pixel under very noisy conditions.

In addition, a blind evaluation of our results compares very favorably to the work of several other researchers. We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general Maximum-Likelihood ML estimation of the transformation and the matches. We show that, in the specific case of a Gaussian noise, it corresponds to t This is usually implemented using a pyramid of blurred images with an increasing Gaussian kernel [8].

Using a Gaussian mixture model for representing the probability of measuring points follows esse Abstract—We present an algorithm for fast elastic multidimensional intensity-based image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard real-world problems, it is capable of accepting expert hints in the form of so In the case of hard real-world 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 B-spline deformation model is shown to be computationally more efficient than other alternatives. 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 wavelet-based generator. Index Terms—Elastic registration, image registration, landmarks, splines. This is because its higher cost per iteration is compensated for by a smaller number of iterations due to the quadratic convergence. An example of such a situation is shown in Fig.

Among Marquardt—Levenberg ML algorithms, we found the performance to be superior when using the full Hessian. Multiresolution A This paper presents a chronological overview of the developments in interpolation theory, from the earliest times to the present date. It brings out the connections between the results obtained in different ages, thereby putting the techniques currently used in signal and image processing into histo It brings out the connections between the results obtained in different ages, thereby putting the techniques currently used in signal and image processing into historical perspective.

A summary of the insights and recommendations that follow from relatively recent theoretical as well as experimental studies concludes the presentation. Keywords—Approximation, convolution-based interpolation, history, image processing, polynomial interpolation, signal processing, splines. It is not so much that thereby history may attribute to each man his own discoveries and others should be encouraged to earn like commendation, as that the art of making discoveries should be extended by considering noteworthy examples of it.

For more detailed information, the reader is referred to me This chapter presents a survey of interpolation and resampling techniques in the context of exact, separable interpolation of regularly sampled data. In this context, the traditional view of interpolation is to represent an arbitrary continuous function as a discrete sum of weighted and shifted syn In this context, the traditional view of interpolation is to represent an arbitrary continuous function as a discrete sum of weighted and shifted synthesis functions—in other words, a mixed convolution equation.

An important issue is the choice of adequate synthesis functions that satisfy interpolation properties. On the other hand, splines provide examples of infinite-support interpolation functions that can be realized exactly at a finite, surprisingly small computational cost.

We discuss implementation issues and illustrate the performance of each synthesis function. We also highlight several artifacts that may arise when performing interpolation, such as ringing, aliasing, blocking and blurring. We explain why the approximation order inherent in the synthesis function is important to limit these interpolation artifacts, which motivates the use of splines as a tunable way to keep them in check without any significant cost penalty.

Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics by Daniel Sage, Franck R. Neumann, Florence Hediger, Susan M. Abstract—We present a new, robust, computational procedure for tracking fluorescent markers in time-lapse microscopy.

The algorithm is optimized for finding the time-trajectory of single particles in very noisy dynamic two- or three-dimensional image sequences. It proceeds in three steps. First, t First, the images are aligned to compensate for the movement of the biological structure under investigation. Finally, the optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure.

We have used this software, which is implemented as a Java plug-in for the public-domain ImageJ software, to track the movement of chromosomal loci within nuclei of budding yeast cells.

Besides reducing trajectory analysis time by several fold, we achieve high reproducibility and accuracy of tracking. The application of the method to yeast chromatin dynamics reveals different classes of constraints on mobility of telomeres, reflecting differences in nuclear envelope association. Index Terms—Dynamic programming DP , fluorescence microscopy, image sequence analysis, living cell, particle tracking.

The proposed algorithm is entirely automatic; it is pixel based and does not require any landmarks. The algorithm is precise, reproducible and reasonably fast, thanks to the use of an efficient mult IEEE Int.

This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation.

The parameters are computed iteratively in The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage.

We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion. The work presented in this paper consists of two modules: log-polar registration followed by optimization-based affine registration. However, since it is based on optimization techniques, it may fail to register an image pair unless they are marginally misaligned.

We have addressed this problem by introducing a log-polar module t Powered by:.

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A pyramid approach to subpixel registration based on intensity.

Ruttimann, and Michael Unser, Senior Member, IEEE e Abstract— We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images two-dimensional or volumes three-dimensional. It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-? The geometric deformation model is a global three-dimensional 3-D af? It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography PET and functional magnetic resonance imaging fMRI data.

LEI 6815 ESTATUTO DO ESTRANGEIRO ATUALIZADA PDF

A Pyramid Approach to Subpixel Registration Based on Intensity

Abstract Abstract — We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images two-dimensional or volumes three-dimensional. It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy pyramid approach. The geometric deformation model is a global three-dimensional 3-D affine transformation that can be optionally restricted to rigid-body motion rotation and translation , combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography PET and functional magnetic resonance imaging fMRI data.

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