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
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Book ChapterDOI
Evaluation of Image Quality in Medical Volume Visualization: The State of the Art
Andreas Pommert,Karl Heinz Höhne +1 more
TL;DR: Various methods for evaluation of image quality are reviewed and are classified based on the fundamental terms of intelligibility and fidelity, and discussed with respect to the question what clues they provide on how to choose parameters, or improve imaging and visualization procedures.
Journal ArticleDOI
Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data
TL;DR: The accuracy and speed of this technique allow us to automatically create patient-specific finite element models within the operating room on a timely basis for application in image-guided updating of preoperative scans.
Book ChapterDOI
Multimodal evaluation for medical image segmentation
Rubén Cárdenes,Meritxell Bach,Ying Chi,Ioannis Marras,Rodrigo de Luis,Mats Anderson,P. M. M. Cashman,Matthieu Bultelle +7 more
TL;DR: This paper is a joint effort between five institutions that introduces several novel similarity measures and combines them to carry out a multimodal segmentation evaluation and shows experimentally that the combination of these measures improves the quality of the evaluation.
Journal ArticleDOI
On Removing Interpolation and Resampling Artifacts in Rigid Image Registration
TL;DR: This paper shows the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration and proposes the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing.
Journal ArticleDOI
Effect of phantom voxelization in CT simulations.
TL;DR: To study effects of phantom discretization, analytical CT simulations were run for a fan-beam geometry with phantom voxel sizes ranging from 0.0625 to 2 times the reconstructed pixel size and noise levels corresponding to 10(3) - 10(7) photons per detector pixel prior to attenuation.
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
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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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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.