Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.

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Medical image registration

A classification scheme for multimodal image matching is considered. The scope of the classification is restricted to methods that register data after acquisitions. The classification scheme may be used for any modality; not only for (2-D) projection images and (3-D) tomographic images, but also for other signal modalities that provide spatial insight into function or anatomy, e.g., EEG (electroencephalography) or MEG (magnetoencephalography) and for the real physical patient. The available literature on image matching is discussed and classified. >

Medical image matching-a review with classification

This paper presents a review of automated image registration methodologies that have been used in the medical field The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application The registration methodologies under review are classified into intensity or feature based The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described

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Medical image registration: a review.

We have developed a computational technique suitable for registration of sets of image data covering the whole brain volume which are translated and rotated with respect to one another. The same computational method may be used to register pairs of tomographic brain images which are rotated and translated in the transverse section plane. The technique is based on the classical theory of rigid bodies, employing as its basis the principal axes transformation. The performance of the method was studied by simulation and with image data from PET, XCT, and MRI. It was found that random errors in determining the brain contour are well tolerated. Progressively coarser axial sampling of data sets led to some degradation, but acceptable performance was obtained with axial sampling distances up to 10 mm. Given adequate digital sampling of the object volume, we conclude that registration by the principal axes transformation can be accomplished with typical errors in the range of approximately 1 mm. The advantages of the technique are simplicity and speed of computation.

https://jnm.snmjournals.org/content/jnumed/31/10/1717.full.pdf

The principal axes transformation : a method for image registration

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Five different algorithms and their modifications were tested on triangular meshes that represent tessellations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non-uniform rational B-spline (NURBs) surfaces, these meshes originated from. This work manifests the best algorithms suited for that indeed different algorithms should be employed to compute the Gaussian and mean curvatures.

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A comparison of Gaussian and mean curvatures estimation methods on triangular meshes

A tensor-based moment function method and a principal-axes method were investigated for registering 3-D test images to a standard image using translation, scale, and orientation. These methods were applied at two image resolutions to test discretization effects. At the higher resolution, both methods were found to perform well in cases where the test image could be described as an affine transform of the standard. At low resolutions, however, and when the test image was not an affine transform of the standard, only the principal-axes-based method performed adequately. The problem of quantifying the left ventricular function from gated blood pool single-photo emission computed tomographic images is considered. >

T.L. Faber

Papers

Orientation of 3-D structures in medical images