Image Registration by Maximization of Combined Mutual Information and Gradient Information

Pluim, Josien P. W.; Maintz, J. B. Antoine; Viergever, Max A.

doi:10.1007/978-3-540-40899-4_46

Cited by 194 publications

(227 citation statements)

References 15 publications

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“…Pluim et al (2000) combine a mutual information similarity measure with a gradient similarity measure to rigidly register clinical images of different modalities. Closely related to the high-magnitude gradients in the images, Maintz et al (1996a, b), study aligning medical images using the edges and the ridges.…”

Section: Methodsmentioning

confidence: 99%

Information Theoretic Deformable Registration Using Local Image Information

Karaçali

2006

Int J Comput Vision

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We present a deformable registration algorithm for multi-modality images based on information theoretic similarity measures at the scale of individual image voxels. We derive analytical expressions for the mutual information, the joint entropy, and the sum of marginal entropies of two images over a small neighborhood in terms of image gradients. Using these expressions, we formulate image registration algorithms maximizing local similarity over the whole image domain in an energy minimization framework. This strategy produces highly elastic image alignment as the registration is driven by voxel similarities between the images, the algorithms are easily implementable using the closed-form expressions for the derivative of the optimization function with respect to the deformation, and avoid estimation of joint and marginal probability densities governing the image intensities essential to conventional information theoretic image registration methods.

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Section: Methodsmentioning

confidence: 99%

Information Theoretic Deformable Registration Using Local Image Information

Karaçali

2006

Int J Comput Vision

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“…To determine if there is some correlation between the contribution of the shading to the shape of the scene, we analyzed the normalized mutual information [27] between the depth image and the shading contribution. Normalized mutual information is an information theoretic measure that is able to find correlations between images of different sources and has been successfully applied to register medical images from different sources (e.g., CT with MRI).…”

Section: Relationship Between Depth Map and Shadingmentioning

confidence: 99%

An experimental study on the effects of shading in 3D perception of volumetric models

Díaz¹,

Ropinski

Navazo

et al. 2015

Vis Comput

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Throughout the years, many shading techniques have been developed to improve the conveying of information in volume visualization. Some of these methods, usually referred to as realistic, are supposed to provide better cues for the understanding of volume data sets. While shading approaches are heavily exploited in traditional monoscopic setups, no previous study has analyzed the effect of these techniques in virtual reality. To further explore the influence of shading on the understanding of volume data in such environments, we carried out a user study in a desktop-based stereoscopic setup. The goals of the study were to investigate the impact of well-known shading approaches and the influence of real illumination on depth perception. Participants had to perform three different perceptual tasks when exposed to static visual stimuli. 45 participants took part in the study, giving us 1152 trials for each task. Results show that advanced shading techniques improve depth perception in stereoscopic volume visualization. As well, external lighting does not affect depth perception when these shading methods are applied. As a result, we derive some guidelines that may help the researchers when selecting illumination models for stereoscopic rendering.Peer ReviewedPostprint (published version

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“…The functional D measures the distance between the images and could be the sum of squares difference (SSD), mutual information (Collignon et al 1995;Wells et al 1996), normal gradient field (Pluim et al 2000;Haber and Modersitzki 2005), or any other distance measure at hand. In this paper, we focus on SSD,…”

Section: Summarizing Flirtmentioning

confidence: 99%

FLIRT with Rigidity—Image Registration with a Local Non-rigidity Penalty

Modersitzki

2007

Int J Comput Vis

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Registration is a technique nowadays commonly used in medical imaging. A drawback of most of the current registration schemes is that all tissue is being considered as non-rigid (Staring et al., Proceedings of the SPIE 2006, vol. 6144, pp. 1-10, 2006. Therefore, rigid objects in an image, such as bony structures or surgical instruments, may be transformed non-rigidly. In this paper, we integrate the concept of local rigidity to the FLexible Image Registration Toolbox (FLIRT) (Haber and Modersitzki, in SIAM J. Sci. Comput. 27(5):1594-1607, 2006; Modersitzki, Numerical Methods for Image Registration, 2004). The idea is to add a penalty for local non-rigidity to the cost function and thus to penalize non-rigid transformations of rigid objects.As our examples show, the new approach allows the maintenance of local rigidity in the desired fashion. For example, the new scheme can keep bony structures rigid during registration.We show, how the concept of local rigidity can be integrated in the FLIRT approach and present the variational backbone, a proper discretization, and a multilevel optimization scheme. We compare the FLIRT approach to the Bspline approach. As expected from the more general setting of the FLIRT approach, our examples demonstrate that the FLIRT results are superior: much smoother, smaller deformations, visually much more pleasing.

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Image Registration by Maximization of Combined Mutual Information and Gradient Information

Cited by 194 publications

References 15 publications

Information Theoretic Deformable Registration Using Local Image Information

Information Theoretic Deformable Registration Using Local Image Information

An experimental study on the effects of shading in 3D perception of volumetric models

FLIRT with Rigidity—Image Registration with a Local Non-rigidity Penalty

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