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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Journal ArticleDOI
Multimodal Image Registration Through Simultaneous Segmentation
Iman Aganj,Bruce Fischl +1 more
TL;DR: This letter introduces a new noninformation-theoretical method for pairwise multimodal image registration, in which the error of segmentation—using both images—is considered as the registration cost function.
Proceedings ArticleDOI
Kullback-Leibler Distance Optimization for Non-rigid Registration of Echo-Planar to Structural Magnetic Resonance Brain Images
TL;DR: The results obtained indicate that the developed KLD-based non-rigid registration technique provides an effective way of correcting local distortions in echo-planar imaging.
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On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
TL;DR: In this article, a comparative study between polynomial and Gaussian surface fitting methods for bias field estimation from magnetic resonance imaging (MRI) images is presented, where the surface fitting is done on entire image and individual tissue regions.
Proceedings ArticleDOI
Continuous image representations avoid the histogram binning problem in mutual information based image registration
TL;DR: This paper reports results on affine registration of a pair of 2D medical images under high noise, and demonstrates the smoothness of various information-theoretic similarity measures such as joint entropy (JE) or MI w.r.t. the transformation.
Proceedings ArticleDOI
Efficient image registration using fast principal component analysis
TL;DR: A new efficient MI-based similarity measure is presented which applies Expectation Maximisation for Principal Component Analysis (EMPCA-MI), to afford significantly lower computational complexity, while providing analogous image registration performance with other feature-based MI solutions.
References
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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.