An improved robust hierarchical registration algorithm

Alexander, Murray E.; Scarth, Gordon B.; Somorjai, R. L.

doi:10.1016/s0730-725x(96)00384-0

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…Next, the image centroids were aligned to provide a good initial estimation for the matched image pairs. The centroid method provides an initial guess of sufficient accuracy for the iterative registration algorithm to allow accurate image registration with significant noise and/or large translation (ALEXANDER et al, 1997).…”

Section: Methodsmentioning

confidence: 99%

Robust image registration for functional magnetic resonance imaging of the brain

Hsu

Lee

2001

Med. Biol. Eng. Comput.

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Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5 degrees in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.

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Section: Methodsmentioning

confidence: 99%

Robust image registration for functional magnetic resonance imaging of the brain

Hsu

Lee

2001

Med. Biol. Eng. Comput.

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“…Timing tests indicate that the present method is significantly faster than a previous, iterative method (Alexander and Somorjai, 1996;Alexander et al, 1997), which uses feature matching at a small number of registration points strategically located at the strongest edge points in the image. Timing tests also demonstrate that significant computational savings are achieved by partially registering downsampled versions of input images rather than the full-size images.…”

Section: Introductionmentioning

confidence: 91%

“…Many registration algorithms are iterative (Alexander and Somorjai, 1996;Alexander et al, 1997;Woods et al, 1992), and their indeterminate computational time makes them unsuitable for realtime registration. Fourier techniques (Maas et al, 1997;Chen et al, 1994;Reddy and Chatterji, 1996), though noniterative, are restricted to rotations, translations, and uniform scaling and can be slow owing to the number of fast Fourier transforms (FFTs) required.…”

Section: Introductionmentioning

confidence: 99%

“…Fourier techniques (Maas et al, 1997;Chen et al, 1994;Reddy and Chatterji, 1996), though noniterative, are restricted to rotations, translations, and uniform scaling and can be slow owing to the number of fast Fourier transforms (FFTs) required. Furthermore, Fourier domain methods appear to be less robust to noise than spatial domain methods (Alexander and Somorjai, 1996;Alexander et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Fast hierarchical noniterative registration algorithm

Alexander

1999

Int. J. Imaging Syst. Technol.

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This note describes a fast algorithm for registering pairs of images from a time sequence of images. The algorithm solves a linear regression problem based on a linearization of the image matching equation, to obtain the registration coefficients. The problem of ill‐posedness caused by differentiation of a noisy image sampled on a finite lattice is solved by means of a patch algorithm. The algorithm uses an integrated form of the linearized displacement equation. Registration is simultaneously carried out on a set of computationally fast prefilters providing three downsampled bandpass images for each input image. The filters are multilevel and permit an efficient and versatile hierarchical registration procedure. Downsampling the images before registration significantly reduces computation time. As a final step, registration is repeated using full‐size images. Results for two‐dimensional (2D) images using a six‐parameter affine registration transformation and a 12‐parameter second‐order polynomial transformation indicate the method is 2.5 times faster (12.7 s/256 × 256 image pair) than a previous iterative method (28 s/pair) and, like the previous method, is robust to noise. The method may easily be generalized to 3D image registration and more general transformations, and is well‐suited to parallel processing. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 242–257, 1999

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“…Because the standard PAT of histologic images leads to unwanted trends in the registration result, we must recommend our robust version. Prior studies describe other algorithms which were developed for rigid transformations (Kuglin and Hines, 1975;de Castro and Morandi, 1987;Hibbard et al, 1992;Zhao et al, 1993;Alexander et al, 1997). However, these approaches are not as flexible as the technique investigated here with respect to subsequent optimizations.…”

Section: The Affine Linear Approachesmentioning

confidence: 99%

Image Registration of Sectioned Brains

Schmitt

Modersitzki

Heldmann

et al. 2006

Int J Comput Vision

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The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods.Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 μm thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often exhibit inhomogenities of the gray level distribution coming from staining and/or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground objects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rotation and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point.Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations.In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered rat brain 6 Schmitt et al.sections and the first one hundred images of serial histologic sections through both occipital lobes of a human brain (6112 images). Additionally, we will describe how a parallel implementation of the elastic registration was realized. Finally, the computed force fields have been applied here for the first time to the morphometrized data of cells determined automatically by an image analytic framework.

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An improved robust hierarchical registration algorithm

Cited by 7 publications

References 7 publications

Robust image registration for functional magnetic resonance imaging of the brain

Robust image registration for functional magnetic resonance imaging of the brain

Fast hierarchical noniterative registration algorithm

Image Registration of Sectioned Brains

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