Dr. Henri A. Vrooman Profile

Dr. Henri A. Vrooman

at Erasmus MC

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Publications (6)

Proceedings Article | 17 March 2015 Paper

Interactive analysis of geographically distributed population imaging data collections over light-path data networks

Baldur van Lew, Charl Botha, Julien Milles, Henri Vrooman, Martijn van de Giessen, Boudewijn P. Lelieveldt

Proceedings Volume 9418, 941806 (2015) https://doi.org/10.1117/12.2081985

KEYWORDS: Clouds, Data storage, Network architectures, Network security, Computer security, Head, Genetics, Data centers, Image processing, Data analysis

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Proceedings Article | 26 March 2007 Paper

Automated localization of periventricular and subcortical white matter lesions

Fedde van der Lijn, Meike Vernooij, M. Arfan Ikram, Henri Vrooman, Daniel Rueckert, Alexander Hammers, Monique Breteler, Wiro Niessen

Proceedings Volume 6512, 651232 (2007) https://doi.org/10.1117/12.710532

KEYWORDS: Image segmentation, Magnetic resonance imaging, Tissues, Brain, Neuroimaging, Electronic filtering, Image registration, Image processing algorithms and systems, Detection and tracking algorithms, Surgery

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Proceedings Article | 15 March 2006 Paper

kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training

Henri Vrooman, Chris Cocosco, Rik Stokking, M. Arfan Ikram, Meike Vernooij, Monique Breteler, Wiro Niessen

Proceedings Volume 6144, 61443L (2006) https://doi.org/10.1117/12.650522

KEYWORDS: Tissues, Brain, Image segmentation, Magnetic resonance imaging, Electronic filtering, Image processing, Neuroimaging, Image classification, Brain mapping, Data acquisition

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Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue, requires laborious training on manually labeled subjects. In this work, the performance of kNN-based segmentation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using manual training is compared with a new method, in which training is automated using an atlas. From 12 subjects, standard T2 and PD scans and a high-resolution, high-contrast scan (Siemens T1-weighted HASTE sequence with reverse contrast) were used as feature sets. For the conventional kNN method, manual segmentations were used for training, and classifications were evaluated in a leave-one-out study. The performance as a function of the number of samples per tissue, and k was studied. For fully automated training, scans were registered to a probabilistic brain atlas. Initial training samples were randomly selected per tissue based on a threshold on the tissue probability. These initials were processed to keep the most reliable samples. Performance of the method for varying the threshold on the tissue probability method was studied. By measuring the percentage overlap (SI), classification results of both methods were validated. For conventional kNN classification, varying the number of training samples did not result in significant differences, while increasing k gave significantly better results. In the method using automated training, there is an overestimation of GM at the expense of CSF at higher thresholds on the tissue probability maps. The difference between the conventional method (k=45) and the observers was not significantly larger than inter-observer variability for all tissue types. The automated method performed slightly worse and performed equal to the observers for WM, and less for CSF and GM. From these results it can be concluded that conventional kNN classification may replace manual segmentation, and that atlas-based kNN segmentation has strong potential for fully automated segmentation, without the need of laborious manual training.

Proceedings Article | 20 April 2005 Paper

Improving the imaging of calcifications in CT by histogram-based selective deblurring

Empar Rollano-Hijarrubia, Frits van der Meer, Add van der Lugt, Harrie Weinans, Henry Vrooman, Albert Vossepoel, Rik Stokking

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.595491

KEYWORDS: Image restoration, Computed tomography, Deconvolution, 3D image processing, Point spread functions, In vitro testing, Tissues, X-ray computed tomography, Filtering (signal processing), Spatial resolution

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Proceedings Article | 9 May 2002 Paper

Additional processing for phase unwrapping of magnetic resonance thermometry imaging

S. Suprijanto, Frans Vos, M. Vogel, Albert Vossepoel, Henri Vrooman

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467062

KEYWORDS: Phase shifts, Image segmentation, Magnetic resonance imaging, Liver, Laser irradiation, Thermometry, Magnetism, Image processing, Image filtering, Image processing algorithms and systems

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Proceedings Article | 14 September 1993 Paper

Computer-simulated multiple beam equalization: image processing algorithm and research tool for Amber

Berend Stoel, Johan Reiber, Jorg Oestmann, Henri Vrooman, Henri Lemmers, Leo Schultze Kool

Proceedings Volume 1896, (1993) https://doi.org/10.1117/12.154595

KEYWORDS: X-rays, Optical simulations, Absorbance, Sensors, X-ray detectors, Computer simulations, Fluctuations and noise, Calibration, Physics, Radiography

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Showing 5 of 6 publications

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