Non-rigid image registration: theory and practice

Crum, William R.; Hartkens, Thomas; Hill, Derek L. G.

doi:10.1259/bjr/25329214

Cited by 610 publications

(454 citation statements)

References 119 publications

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“…This manual determination inevitably biases the registration and also subsequent analysis. In light of this, recently proposed groupwise registration algorithms consider the image population as a whole, by simultaneously deforming all individual images impartially, with the final goal of constructing an atlas which describes the population in an unbiased manner [1] [2]. Inferences drawn using an unbiased atlas can be expected to be more objective, and reflect more accurately the population characteristics.…”

Section: Introductionmentioning

confidence: 99%

Attribute Vector Guided Groupwise Registration

Yap

Shen

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Groupwise registration has been recently introduced for simultaneous registration of a group of images with the goal of constructing an unbiased atlas. To this end, direct application of information-theoretic entropy measures on image intensity has achieved various successes. However, simplistic voxelwise utilization of image intensity often neglects important contextual information, which can be provided by more comprehensive geometric and statistical features. In this paper, we employ attribute vectors, instead of image intensities, to guide groupwise registration. In particular, for each voxel, the attribute vector is computed from its multiple-scale neighborhoods to capture geometric information at different scales. Moreover, the probability density function (PDF) of each attribute in the vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure. For the purpose of registration, Jensen-Shannon (JS) divergence is used to measure the PDF dissimilarity of each attribute at corresponding locations of different individual images. By minimizing the overall JS divergence in the whole image space and estimating the deformation field of each image simultaneously, we can eventually register all images and build an unbiased atlas. Experimental results indicate that our method yields better registration quality, compared with a popular groupwise registration method.

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Section: Introductionmentioning

confidence: 99%

Attribute Vector Guided Groupwise Registration

Yap

Shen

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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“…There are several possible image metrics that are used in voxel similarity-based image registration (Crum et al, 2004;Hill et al, 2001;Maintz and Viergever, 1998;Rueckert and Schnabel, 2011;Zitova and Flusser, 2003): correlation coefficient, sum of squared differences, or mutual information (MI). MI (Maes et al, 1997;Mattes et al, 2003Mattes et al, , 2001Pluim et al, 2003;Wells et al, 1996) is one of the more successful medical image similarity measures.…”

Section: II State Of the Artmentioning

confidence: 99%

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Fernandez-de-Manuel

Wollny

Kybic

et al. 2014

Medical Image Analysis

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Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd-EOB-DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01).

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“…To quantitatively measure the directionality of a directed metric, we define the relative difference between two directed measurements as the directionality significance. For the directed measurement of d as defined above, its directionality significance on the image pair (S, T) is defined as (2) We can also define the directionality significance sig r on the directed measurement of r in a similar way. For illustration, the directionality significances sig d and sig r are calculated for all image pairs in a dataset of 18 elderly brains with large shape difference [43].…”

Section: Directionality In Registrationmentioning

confidence: 99%

“…Non-rigid image registration is one of the most important techniques in medical image analysis [1][2][3][4][5]. A large number of registration methods have been developed for pairwise image registration [6][7][8][9][10], where the subject is registered directly towards the template with the estimated spatial transformation by maximizing a certain similarity measure between the warped subject and the template.…”

Section: Introductionmentioning

confidence: 99%

Directed Graph Based Image Registration

Jia

Wang

et al. 2011

Machine Learning in Medical Imaging

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In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration.

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Non-rigid image registration: theory and practice

Cited by 610 publications

References 119 publications

Attribute Vector Guided Groupwise Registration

Attribute Vector Guided Groupwise Registration

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Directed Graph Based Image Registration

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