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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

3D MR image denoising using rough set and kernel PCA method.

TL;DR: A two stage method, using kernel principal component analysis (KPCA) and rough set theory (RST), for denoising volumetric MRI data, motivated by idea that under Rician noise MRI data may be non-linear and kernel mapping will help to define linear separator between these clusters/basis vectors thus used for image denoised.
Book ChapterDOI

Many heads are better than one: jointly removing bias from multiple MRIs using nonparametric maximum likelihood

TL;DR: This work combines statistics from the same location across different patients' images, rather than within an image, to eliminate bias fields from all of the images simultaneously, and presents a variety of "two-dimensional" experimental results showing how the method overcomes serious problems experienced by other methods.
Journal ArticleDOI

Evaluation of accuracy in MS lesion volumetry using realistic lesion phantoms

TL;DR: Results clearly show the importance of an improved gold standard in lesion volumetry beyond voxel counting.

Morphological Volumetry : Theory, Concepts, and Application to Quantitative Medical Imaging

TL;DR: In this chapter, a robust method for the removal of non-cerebral tissue in T1 weighted magnetic resonance (MR) brain images is presented, using a modified three-dimensional Fast Watershed Transform (FWT) that is perfectly suited to locate the boundaries of the brain, including the cerebellum and the spinal cord.
Proceedings ArticleDOI

Making 3D binary digital images well-composed

TL;DR: A new randomized algorithm for making 3D binary digital images that are not well-composed into well-Composed ones is introduced, the complexity and convergence of the algorithm are analyzed, and experimental evidence of its effectiveness when faced with practical medical imaging data is presented.
References
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Book

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

TL;DR: Direct and Indirect Radiologic Localization Reference System: Basal Brain Line CA-CP Cerebral Structures in Three-Dimensional Space Practical Examples for the Use of the Atlas in Neuroradiologic Examinations Three- Dimensional Atlas of a Human Brain Nomenclature-Abbreviations Anatomic Index Conclusions.
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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