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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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Atlas-based segmentation and classification of magnetic resonance brain images

TL;DR: A new atlas-based segmentation method for pathological brains is proposed in this thesis as well as a validation method to assess this new approach and the importance of including prior knowledge in the medical image analysis framework and the indispensable role of registration techniques in this task is outlined.
Journal ArticleDOI

Nonrigid point registration for 2D curves and 3D surfaces and its various applications.

TL;DR: A nonrigid B-spline-based point-matching (BPM) method is proposed to match dense surface points that solves both the point correspondence andNonrigid transformation without features extraction.
Book ChapterDOI

Regional Structural Characterization of the Brain of Schizophrenia Patients

TL;DR: In this article, a set of cranial MRI's of 46 schizophrenia patients and age/gender matched healthy controls were used to study morphology and age-related changes in this disease, and the results showed significant positive correlation in the third ventricle and right thalamus of controls, but not patients.
Journal ArticleDOI

A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation

TL;DR: In this paper, the authors proposed a fully automated, data-driven image segmentation platform that addresses multiple difficulties in segmenting MR images such as varying inhomogeneity, non-standardness and noise, while producing a high-quality definition of different tissues.
Proceedings ArticleDOI

Validation of a new optimisation algorithm for registration tasks in medical imaging

TL;DR: A new optimisation algorithm is introduced (called NEWUOA) to address the above registration problems, and its robustness and accuracy properties are demonstrated.
References
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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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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.
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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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