Rigid registration of CT, MR and cryosection images using a GLCM framework

Bro-Nielsen, Morten

doi:10.1007/bfb0029236

Cited by 12 publications

(11 citation statements)

References 18 publications

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“…The heuristic criterion devised by Woods et al [21] was originally intended for PET-MR registration, but it has also been used with other modalities [2]. This turns out to be very similar to the correlation ratio.…”

Section: Woods Criterionmentioning

confidence: 84%

“…Even so, 2 we can identify two differences. First, the correlation ratio sums variances, a~ whereas the Woods criterion sums normalized standard deviations, aJmi.…”

Section: Woods Criterionmentioning

confidence: 99%

“…A common technique consists of normalizing the image pair 2D-histogram [4,5,2]. Then, the images may be seen as discrete random variables [11].…”

Section: Application To Registrationmentioning

confidence: 99%

“…As the measure is assumed to be maximal when the images are correctly aligned, these approaches are often implemented using an optimization scheme, or simulating a dynamic process [7]. Many similarity measures have been proposed in the literature (see [3,15,2,6] for reviews). Considering the elementary problem of aligning two similar images, the first idea was to use a least squares criterion.…”

Section: Introductionmentioning

confidence: 99%

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The correlation ratio as a new similarity measure for multimodal image registration

Roche

Malandain

Pennec

et al. 1998

Lecture Notes in Computer Science

409

330

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Abstract. Over the last five years, new "voxel-based" approaches have allowed important progress in multimodal image registration, notably due to the increasing use of information-theoretic similarity measures. Their wide success has led to the progressive abandon of measures using standard image statistics (mean and variance). Until now, such measures have essentially been based on heuristics. In this paper, we address the determination of a new measure based on standard statistics from a theoretical point of view. We show that it naturally leads to a known concept of probability theory, the correlation ratio. In our derivation, we take as the hypothesis the functional dependence between the image intensities. Although such a hypothesis is not as general as possible, it enables us to model the image smoothness prior very easily. We also demonstrate results of multimodal rigid registration involving Magnetic Resonance (MR.), Computed Tomography (CT), and Positron Emission Tomography (PET) images. These results suggest that the correlation ratio provides a good trade-off between accuracy and robustness.

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Section: Woods Criterionmentioning

confidence: 84%

“…Even so, 2 we can identify two differences. First, the correlation ratio sums variances, a~ whereas the Woods criterion sums normalized standard deviations, aJmi.…”

Section: Woods Criterionmentioning

confidence: 99%

“…A common technique consists of normalizing the image pair 2D-histogram [4,5,2]. Then, the images may be seen as discrete random variables [11].…”

Section: Application To Registrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The correlation ratio as a new similarity measure for multimodal image registration

Roche

Malandain

Pennec

et al. 1998

Lecture Notes in Computer Science

409

330

View full text Add to dashboard Cite

show abstract

“…This category is not fundamentally different from the previous one, as the ideal case is still perfect functional dependence; mutual information is however theoretically more robust to variations with respect to this ideal situation. A number of comparison studies have shown that similarity measures yield different performances depending on the considered modality combinations [22,2,13,10,15]. There is probably no universal measure and, for a specific problem, the point is rather to choose the one that is best adapted to the nature of the images.…”

Section: ρ(I J) = Cov(i J) Var(i) Var(j)mentioning

confidence: 99%

Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration

Roche

Malandain

Ayache

et al. 1999

Medical Image Computing and Computer-Assisted Intervention – MICCAI’99

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Abstract. While intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the physics of imaging. The motivation of this paper is to determine what are the assumptions corresponding to a number of popular similarity measures in order to better understand their use, and finally help choosing the one which is the most appropriate for a given class of problems. After formalizing registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well-known measures (correlation coefficient, correlation ratio, mutual information). Finally, we present results of registration between 3D MR and 3D Ultrasound images to illustrate the importance of choosing an appropriate similarity measure.

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Unifying maximum likelihood approaches in medical image registration

Roche¹,

Malandain²,

Ayache³

2000

Int. J. Imaging Syst. Technol.

179

109

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Although intensity‐based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. This paper clarifies the assumptions on which a number of popular similarity measures rely. After formalizing registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well‐known measures (correlation coefficient, correlation ratio, mutual information). Finally, we present results of rigid registration between several image modalities to illustrate the importance of choosing an appropriate similarity measure.© 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 71–80, 2000

show abstract

Rigid registration of CT, MR and cryosection images using a GLCM framework

Cited by 12 publications

References 18 publications

The correlation ratio as a new similarity measure for multimodal image registration

The correlation ratio as a new similarity measure for multimodal image registration

Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration

Unifying maximum likelihood approaches in medical image registration

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