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
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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).read more
Citations
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Proceedings ArticleDOI
3D Segmentation by Maximally Stable Volumes (MSVs)
Michael Donoser,Horst Bischof +1 more
TL;DR: An efficient 3D segmentation concept based on extending the well-known maximally stable extremal region (MSER) detector to the third dimension is introduced and a very efficient way to detect the MSVs in quasi-linear time by analysis of the component tree is presented.
Journal ArticleDOI
Moral values are associated with individual differences in regional brain volume
TL;DR: It is demonstrated that variation in moral sentiment reflects individual differences in brain structure and suggested that there is a biological basis for moral sentiment, distributed across multiple brain regions.
Journal ArticleDOI
Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account.
TL;DR: This paper presents a new approach for segmenting regions of bone in MRI volumes using deformable models, which takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions.
Journal ArticleDOI
A Multilayer Grow-or-Go Model for GBM: Effects of Invasive Cells and Anti-Angiogenesis on Growth
TL;DR: A new mathematical model is derived that takes into account the ability of proliferative cells to become invasive under hypoxic conditions; model simulations generate the multilayer structure of GBM, namely proliferation, brain invasion, and necrosis.
Book ChapterDOI
Robust 3D Segmentation of Anatomical Structures with Level Sets
C. Baillard,Christian Barillot +1 more
TL;DR: The major contribution of this work is the design of a robust evolution model based on adaptive parameters depending on the data based on the level set formalism to segment anatomical structures in 3D medical images.
References
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A nonparametric method for automatic correction of intensity nonuniformity in MRI data
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Journal ArticleDOI
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
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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.