Journal ArticleDOI
Design and construction of a realistic digital brain phantom
D. L. Collins,Alex P. Zijdenbos,V. Kollokian,John G. Sled,Noor Jehan Kabani,Colin J. Holmes,Alan C. Evans +6 more
Reads0
Chats0
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).read more
Citations
More filters
Adaptive, nonparametric markov models and information-theoretic methods for image restoration and segmentation
Ross T. Whitaker,Suyash P. Awate +1 more
TL;DR: An adaptive Markov-random-field (MRF) image model that automatically learns the local statistical dependencies via data-driven nonparametric techniques and is applied to classify tissues in magnetic resonance (MR) images of the human brain by maximizing the mutual information between the classification labels and image data, capturing their mutual dependency.
Journal ArticleDOI
STRAPS: A fully data-driven spatio-temporally regularized algorithm for M/EEG patch source imaging
TL;DR: A fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices, which employs the recursive penalized least squares (RPLS) procedure to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing.
Journal ArticleDOI
Age-related occipito-temporal hypoactivation during visual search: relationships between mN2pc sources and performance.
Laura Lorenzo-López,Ricardo Gutiérrez,Stephan Moratti,Fernando Maestú,Fernando Cadaveira,Elena Amenedo +5 more
TL;DR: Findings suggest that the previously observed age-related changes in N2pc parameters are associated with a significant hypoactivation of occipito-temporal N2PC sources that is more marked in the right hemisphere.
Journal ArticleDOI
miLBP: a robust and fast modality-independent 3D LBP for multimodal deformable registration
TL;DR: A novel robust and fast modality-independent 3D binary descriptor, called miLBP, which integrates the principle of local self-similarity with a form of local binary pattern and can robustly extract the similar geometry features from 3D volumes across different modalities.
Journal ArticleDOI
An MRI digital brain phantom for validation of segmentation methods
Bruno Alfano,Marco Comerci,Michele Larobina,Anna Prinster,Joseph P. Hornak,S. Easter Selvan,Umberto Amato,Mario Quarantelli,Gioacchino Tedeschi,Arturo Brunetti,Marco Salvatore +10 more
TL;DR: A software procedure is presented for the construction of a realistic MRI digital brain phantom that was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field- echo (FFE) sequences.
References
More filters
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.
Book
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.