Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis

Baloch, Sajjad; Davatzikos, Christos

doi:10.1016/j.neuroimage.2008.10.048

Cited by 31 publications

(39 citation statements)

References 50 publications

(72 reference statements)

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“…Large differences between images are hard to be well represented by a diffeomorphism, and in practice we will have a residual between the warped and target image not captured by the transformation [14]. We account for this by concatenating the residual norm as a separate variate to the warp vector d ij 1 , weighted so that it is commensurate with the control point displacements.…”

Section: Deformable Registration Model and Proposed Methodsmentioning

confidence: 99%

Multiple Atlas Inference and Population Analysis Using Spectral Clustering

Sfikas

Nikou

2010

2010 20th International Conference on Pattern Recognition

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In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose a framework based on spectral clustering that is capable of partitioning an image population into sets that require a separate atlas, and identifying the most suitable templates to be used as coordinate reference frames. The spectral analysis step relies on pairwise distances that express anatomical differences between subjects as a function of the diffeomorphic warp required to match the one subject onto the other, plus residual information. The methodology is validated numerically on artificial and medical imaging data.

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Section: Deformable Registration Model and Proposed Methodsmentioning

confidence: 99%

Multiple Atlas Inference and Population Analysis Using Spectral Clustering

Sfikas

Nikou

2010

2010 20th International Conference on Pattern Recognition

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“…Specifically, a metric is defined to measure the distance between evergy pair of subjects in the population. Specifically, in ABSORB, the distance between evergy pair of subjects can be defined as the intensity difference or other applicable metrics [46][47][48][49][50][51][52][53][54][55][56] .…”

Section: Self-organized Registrationmentioning

confidence: 99%

Groupwise registration of brain magnetic resonance images: A review

Liu

Wang

2014

J. Shanghai Jiaotong Univ. (Sci.)

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A relatively new family of image registration methods, namely groupwise registration, has recently emerged and been widely investigated due to its fundamentally key role in analyzing image populations in terms of atlas-based analysis or clinical diagnostic systems. Compared with pairwise registration, groupwise registration is capable of handling a large-scale population of images simultaneously in an unbiased way. In this paper, a review of the latest research on groupwise registration is presented. First, the schemes of pairwise registration and groupwise registration are compared. Then, a classification of groupwise registration and several exemplar implementations of groupwise registration are illustrated, including their experimental results. Finally, typical applications of groupwise registration, e.g., infant atlas construction, and population-based anatomical variability evaluation, are discussed in this paper.

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“…The intermediate templates guided registration methods can be classified into two categories, intermediate template generation [25–28] and intermediate template selection [29, 31, 32], based on how to formulate the intermediate templates.…”

Section: Introductionmentioning

confidence: 99%

Directed graph based image registration

Jia

Wang

et al. 2012

Computerized Medical Imaging and Graphics

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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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Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis

Cited by 31 publications

References 50 publications

Multiple Atlas Inference and Population Analysis Using Spectral Clustering

Multiple Atlas Inference and Population Analysis Using Spectral Clustering

Groupwise registration of brain magnetic resonance images: A review

Directed graph based image registration

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