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Journal ArticleDOI

Design and construction of a realistic digital brain phantom

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TLDR
The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.
Abstract
After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfils all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, the authors present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).

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Citations
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Journal ArticleDOI

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans

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Journal ArticleDOI

Reward motivation and neurostimulation interact to improve working memory performance in healthy older adults: A simultaneous tDCS-fNIRS study.

TL;DR: These results provide the first evidence of tDCS-dependent functional changes in PFC activity in healthy older adults during the execution of a WM task, and highlight the utility of combining reward motivation with prefrontal anodal tDCS, as a potential strategy to improve WM efficiency in low performinghealthy older adults.
Proceedings ArticleDOI

Lesion detection in noisy mr brain images using constrained gmm and active contours

TL;DR: Experimental results indicate that the proposed method performs healthy tissue segmentation using a probabilistic model for normal brain images outperforms previously suggested approaches especially for highly noisy data.
Proceedings ArticleDOI

Adaptive graph cuts with tissue priors for brain MRI segmentation

TL;DR: A novel framework for automatic brain MRI tissue segmentation using a graph cut/atlas-based registration methodology optimized within an iterative mode and validated in both simulated adult and real neonatal brain MR images corrupted by significant noise and intensity inhomogeneities.
Book ChapterDOI

Segmentation of brain images using adaptive atlases with application to ventriculomegaly

TL;DR: This work presents an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject, resulting in an adaptive atlas that shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly.
References
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Pattern Recognition with Fuzzy Objective Function Algorithms

TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging

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Journal ArticleDOI

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

TL;DR: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present, and is applied at an early stage in an automated data analysis, before a tissue model is available.
Journal ArticleDOI

Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space

TL;DR: A fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques and therefore does not suffer the drawbacks involved in user intervention.
Book

Introduction to artificial neural systems

TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
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