Structural Image Restoration Through Deformable Templates

Amit, Yali; Grenander, Ulf; Piccioni, Mauro

doi:10.2307/2290581

Cited by 58 publications

(34 citation statements)

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“…The rationale behind building a population-specific atlas is described in (Mazziotta, Toga et al 2001)); the practical impact of such an atlas on the analysis of functional data is described in Good et al (Good, Johnsrude et al 2001), and its impact on the analysis of pediatric data is given in (Wilke M. 2002), (Wilke M. 2003), and (Kazemi, Moghaddam et al 2007). To reiterate the most important issues, an unbiased brain template is needed (1) to provide a registration target for automatic image processing techniques (e.g., those in (Evans, Collins et al 1993), (Collins, Neelin et al 1994), and (Thompson and Toga 2002)); (2) to act as an atlas for volume estimation of brain regions (Amit, Grenander et al 1991); (Christensen, Rabbitt et al 1994); (Collins, Zijdenbos et al 1999); (Mazziotta, Toga et al 2001); (Mazziotta, Toga et al 2001); (Toga and Thompson 2001); (Thompson and Toga 2002); (Essen and C. 2002) (Thompson and Toga 2002; Toga and Thompson 2007); (Essen and David 2005); (Seghers, D‘Agostino et al 2004); (Grabner, Janke et al 2006)); and (3) to function as a reference for a particular population group in order to study intra- and inter-group variability or growth (Thompson and Toga 2002; Gerig, Davis et al 2006). …”

Section: Methodsmentioning

confidence: 99%

Unbiased average age-appropriate atlases for pediatric studies

et al. 2011

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Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5 years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. . Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9 years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.

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Section: Methodsmentioning

confidence: 99%

Unbiased average age-appropriate atlases for pediatric studies

et al. 2011

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“…In contrast the probabilistic and statistical side of the deformable template framework has received less attention. In Amit et al (1991) a coherent probabilistic deformable model was proposed using Gaussian random vector fields to model the deformation process with independent and identically distributed (IID) noise added to the deformed template at the observation pixels, but no proposals were offered for estimating the relevant parameters. In general very little can be found on estimating a dense deformable model from a relatively small training set of images.…”

Section: Introductionmentioning

confidence: 99%

Towards a Coherent Statistical Framework for Dense Deformable Template Estimation

Allassonnière

Amit

Trouvé

2007

Journal of the Royal Statistical Society Series B: Statistical Methodology

175

299

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Abstract. The problem of estimating probabilistic deformable template models in the field of computer vision or of probabilistic atlases in the field of computational anatomy has not yet received a coherent statistical formulation and remains a challenge. In this paper, we provide a careful definition and analysis of a well defined statistical model based on dense deformable templates for gray level images of deformable objects. We propose a rigorous Bayesian framework for which we can derived an iterative algorithm for the effective estimation of the geometric and photometric parameters of the model in a small sample setting, together with an asymptotic consistency proof. The model is extended to mixtures of finite numbers of such components leading to a fine description of the photometric and geometric variations. We illustrate some of the ideas with images of handwritten digits, and apply the estimated models to classification through maximum likelihood.

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“…The second class of algorithms, known as active contour 13 or deformable models 14 , finds features in the image by evolving a contour under the action of internal and external forces (Fig. 1d).…”

Section: Isolating Cells From Images: Segmentationmentioning

confidence: 99%

Tools for analyzing cell shape changes during chemotaxis

Xiong

Iglesias

2010

Integr. Biol.

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Summary Chemotaxis refers to the ability of cells to sense the direction of external chemical gradients and respond by migrating towards the source. A thorough understanding of the chemotactic response of amoebae and neutrophils requires careful quantification of the cell shape changes observed during cell movement. The stochastic nature of this response calls for a statistical characterization of cellular morphology and this requires the processing of large data sets. For this reason, automatic image analysis algorithms are highly desirable and are becoming increasingly available. These usually include a combination of techniques from image segmentation, morphological transformations, as well as the incorporation of numerical algorithms and physical models. Here we review recent developments in the tracking and understanding of motile chemotaxing cells, with a particular emphasis on the description of pseudopodial activity in chemotactic Dictyostelium cells.

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Structural Image Restoration Through Deformable Templates

Cited by 58 publications

References 0 publications

Unbiased average age-appropriate atlases for pediatric studies

Unbiased average age-appropriate atlases for pediatric studies

Towards a Coherent Statistical Framework for Dense Deformable Template Estimation

Tools for analyzing cell shape changes during chemotaxis

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