Multi-class Posterior Atlas Formation via Unbiased Kullback-Leibler Template Estimation

Lorenzen, Peter; Davis, Bradley Moore; Gerig, Guido; Bullitt, Elizabeth; Joshi, Sarang

doi:10.1007/978-3-540-30135-6_12

Cited by 14 publications

(14 citation statements)

References 19 publications

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“…In [48], the minimum deformation target brain is defined as the average brain that minimizes the deformation of all the brains in the database to the average target brain. As alternative approaches, a Bayesian approach to minimize the deformation energy of a set of multimodality images is utilized in [53]- [55] and a constrained optimization problem is solved in [51] to maximize the similarity of images to the average brain while constraining the sum of all required deformations to zero. All of these techniques utilize high-dimensional nonrigid intersubject registration techniques that are covered in Section IV of this survey article.…”

Section: B Digital Brain Atlasesmentioning

confidence: 99%

Brain Functional Localization: A Survey of Image Registration Techniques

Gholipour

Kehtarnavaz

Briggs

et al. 2007

IEEE Trans. Med. Imaging

226

127

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Abstract-Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.Index Terms-Brain functional localization, fMRI image processing, functional imaging, survey of image registration techniques.

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Section: B Digital Brain Atlasesmentioning

confidence: 99%

Brain Functional Localization: A Survey of Image Registration Techniques

Gholipour

Kehtarnavaz

Briggs

et al. 2007

IEEE Trans. Med. Imaging

226

127

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show abstract

“…In the medical community, recently, there have been several approaches proposed [3,6,12,11,14]. One group of algorithms selects a standard coordinate frame (for example, based upon certain anatomical structures) and requires the algorithm to position all the inputs into that frame.…”

Section: Background and Previous Workmentioning

confidence: 99%

Efficient Population Registration of 3D Data

Zöllei

Learned-Miller

Grimson

et al. 2005

Computer Vision for Biomedical Image Applications

106

100

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We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradientbased stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.

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“…Atlases have broad application in medical image segmentation and registration and are often used in computer aided diagnosis to measure the shape of an object or detect the morphological differences between patient groups. Various techniques for atlas construction are developed for different human organs, like the heart [25,26,27] and especially the brain [28,29,30,31,32,33,34,35,36,37]. In this paper we use a statistical model as an atlas and an in-silico phantom model for evaluation.…”

Section: Introductionmentioning

confidence: 99%