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41
Unifying Maximum Likelihood Approaches in Medical Image Registration
, 1999
"... While intensitybased similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. The motivation of this paper is to clarify the assumptions on which a number of popular similarity measures rely. After formalizing r ..."
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Cited by 78 (21 self)
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While intensitybased similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. The motivation of this paper is to clarify 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 wellknown 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.
A Variational Approach to MultiModal Image Matching
, 2001
"... We address the problem of nonparametric multimodal image matching. We propose a generic framework which relies on a global variational formulation and show its versatility through three different multimodal registration methods : supervised registration by joint intensity learning, maximization o ..."
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Cited by 36 (3 self)
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We address the problem of nonparametric multimodal image matching. We propose a generic framework which relies on a global variational formulation and show its versatility through three different multimodal registration methods : supervised registration by joint intensity learning, maximization of the mutual information and maximization of the correlation ratio. Regularization is performed by using a functional borrowed from linear elasticity theory. We also consider a geometrydriven regularization method. Experiments on synthetic images and preliminary results on the realignment of MRI datasets are presented.
Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration
 In MICCAI
, 1999
"... While intensitybased 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 ..."
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Cited by 23 (6 self)
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While intensitybased 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 di#erent modeling assumptions and retrieve some wellknown measures (correlation coe#cient, 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.
Multimodal Image Registration by Minimising KullbackLeibler Distance
 H.M. CHAN AND A.C.S. CHUNG
, 2002
"... In this paper, we propose a multimodal image registration method based on the a priori knowledge of the expected joint intensity distribution estimated from aligned training images. The goal of the registration is to find the optimal transformation such that the discrepancy between the expected ..."
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Cited by 16 (3 self)
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In this paper, we propose a multimodal image registration method based on the a priori knowledge of the expected joint intensity distribution estimated from aligned training images. The goal of the registration is to find the optimal transformation such that the discrepancy between the expected and the observed joint intensity distributions is minimised. The difference between distributions is measured using the KullbackLeibler distance (KLD). Experimental results in 3D3D registration show that the KLD based registration algorithm is less dependent on the size of the sampling region than the Maximum logLikelihood based registration method. We have also shown that, if manual alignment is unavailable, the expected joint intensity distribution can be estimated based on the segmented and corresponding structures from a pair of novel images. The proposed method has been applied to 2D3D registration problems between digital subtraction angiograms (DSAs) and magnetic resonance angiographic (MRA) image volumes.
Learning Similarity Measure for MultiModal 3D Image Registration
, 2009
"... Multimodal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image ..."
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Cited by 10 (1 self)
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Multimodal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from maxmargin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CTMR/PETMR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multimodal medical image registration.
Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans
 in Proceedings of the 4th Int Conf on Medical Image Computing and ComputerAssisted Intervention (MICCAI
, 2001
"... Abstract. We developed an automated system that registers chest CT images temporally. Our registration method matches corresponding anatomical landmarks to obtain initial registration parameters. The initial pointtopoint registration is then generalized to an iterative surfacetosurface registrati ..."
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Cited by 9 (0 self)
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Abstract. We developed an automated system that registers chest CT images temporally. Our registration method matches corresponding anatomical landmarks to obtain initial registration parameters. The initial pointtopoint registration is then generalized to an iterative surfacetosurface registration method. Our “goodnessoffit ” measure is evaluated at each step in the iterative scheme until the registration performance is sufficient. We applied our method to register the 3D lung surfaces of 10 pairs of chest CT scans and report a promising registration performance. 1 1
Similarity Measures for Nonrigid Registration
 Medical Imaging 2001: Image Processing, volume 4322 of Proc. SPIE
"... Nonrigid mu ltimodal registrationrequtra similarity measuO with two important properties: locality and mu ltimodality. ..."
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Cited by 7 (4 self)
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Nonrigid mu ltimodal registrationrequtra similarity measuO with two important properties: locality and mu ltimodality.
A new & robust information theoretic measure and its application to image alignment
 In IPMI
, 2003
"... Abstract. In this paper we develop a novel measure of information in a random variable based on its cumulative distribution that we dub cumulative residual entropy (CRE). This measure parallels the well known Shannon entropy but has the following advantages: (1) it is more general than the Shannon E ..."
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Cited by 6 (4 self)
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Abstract. In this paper we develop a novel measure of information in a random variable based on its cumulative distribution that we dub cumulative residual entropy (CRE). This measure parallels the well known Shannon entropy but has the following advantages: (1) it is more general than the Shannon Entropy as its definition is valid in the discrete and continuous domains, (2) it possess more general mathematical properties and (3) it can be easily computed from sample data and these computations asymptotically converge to the true values. Based on CRE, we define the crossCRE (CCRE) between two random variables, and apply it to solve the image alignment problem for parameterized (3D rigid and affine) transformations. The key strengths of the CCRE over using the mutual information (based on Shannon’s entropy) are that the former has significantly larger tolerance to noise and a much larger convergence range over the field of parameterized transformations. We demonstrate these strengths via experiments on synthesized and real image data. 1
New method of probability density estimation with application to mutual information based image registration
 In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR
, 2006
"... We present a new, robust and computationally efficient method for estimating the probability density of the intensity values in an image. Our approach makes use of a continuous representation of the image and develops a relation between probability density at a particular intensity value and image g ..."
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Cited by 5 (1 self)
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We present a new, robust and computationally efficient method for estimating the probability density of the intensity values in an image. Our approach makes use of a continuous representation of the image and develops a relation between probability density at a particular intensity value and image gradients along the level sets at that value. Unlike traditional samplebased methods such as histograms, minimum spanning trees (MSTs), Parzen windows or mixture models, our technique expressly accounts for the relative ordering of the intensity values at different image locations and exploits the geometry of the image surface. Moreover, our method avoids the histogram binning problem and requires no critical parameter tuning. We extend the method to compute the joint density between two or more images. We apply our density estimation technique to the task of affine registration of 2D images using mutual information and show good results under high noise. 1.
Probability Density Estimation using Isocontours and Isosurfaces: Application to Information Theoretic Image Registration
"... We present a new, geometric approach for determining the probability density of the intensity values in an image. We drop the notion of an image as a set of discrete pixels, and assume a piecewisecontinuous representation. The probability density can then be regarded as being proportional to the ar ..."
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Cited by 5 (0 self)
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We present a new, geometric approach for determining the probability density of the intensity values in an image. We drop the notion of an image as a set of discrete pixels, and assume a piecewisecontinuous representation. The probability density can then be regarded as being proportional to the area between two nearby isocontours of the image surface. Our paper extends this idea to joint densities of image pairs. We demonstrate the application of our method to affine registration between two or more images using information theoretic measures such as mutual information. We show cases where our method outperforms existing methods such as simple histograms, histograms with partial volume interpolation, Parzen windows, etc. under fine intensity quantization for affine image registration under significant image noise. Furthermore, we demonstrate results on simultaneous registration of multiple images, as well as for pairs of volume datasets, and show some theoretical properties of our density estimator. Our approach requires the selection of only an image interpolant. The method neither requires any kind of kernel functions (as in Parzen windows) which are unrelated to the structure of the image in itself, nor does it rely on any form of sampling for density estimation. I.