Mr. Laurens Edo Hogeweg Profile

Laurens Edo Hogeweg

at Radboud Univ Nijmegen Medical Ctr

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Publications (6)

Proceedings Article | 18 March 2014 Paper

Effect of image variation on computer-aided detection systems

S. Rabbani, P. Maduskar, R. H. H. Philipsen, L. Hogeweg, B. van Ginneken

Proceedings Volume 9035, 90352H (2014) https://doi.org/10.1117/12.2043520

KEYWORDS: CAD systems, Image segmentation, Lung, Computer aided diagnosis and therapy, Computing systems, Imaging systems, Medical imaging, Chest imaging, Sensors, Image processing

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Proceedings Article | 29 March 2013 Paper

Automated localization of costophrenic recesses and costophrenic angle measurement on frontal chest radiographs

Pragnya Maduskar, Laurens Hogeweg, Rick Philipsen, Bram van Ginneken

Proceedings Volume 8670, 867038 (2013) https://doi.org/10.1117/12.2008239

KEYWORDS: Lung, Chest imaging, Image segmentation, Chest, Computer aided diagnosis and therapy, Diagnostics, Image analysis, Prototyping, Imaging systems, Image resolution

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Proceedings Article | 18 March 2013 Paper

Improved texture analysis for automatic detection of tuberculosis (TB) on chest radiographs with bone suppression images

Pragnya Maduskar, Laurens Hogeweg, Rick Philipsen, Steven Schalekamp, Bram van Ginneken

Proceedings Volume 8670, 86700H (2013) https://doi.org/10.1117/12.2008083

KEYWORDS: Chest imaging, CAD systems, Bone, Computer aided design, Computer aided diagnosis and therapy, Lung, Image segmentation, Prototyping, Image analysis, Diagnostics

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Proceedings Article | 28 February 2013 Paper

Normalization of chest radiographs

R.H.H.M. Philipsen, P. Maduskar, L. Hogeweg, B. van Ginneken

Proceedings Volume 8670, 86700G (2013) https://doi.org/10.1117/12.2008238

KEYWORDS: Image segmentation, Lung, Computer aided diagnosis and therapy, Chest imaging, CAD systems, Sensors, Chest, Radiography, Analog electronics, Databases

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Proceedings Article | 3 March 2011 Paper

Computer-aided detection as a decision assistant in chest radiography

Maurice Samulski, Peter Snoeren, Bram Platel, Bram van Ginneken, Laurens Hogeweg, Cornelia Schaefer-Prokop, Nico Karssemeijer

Proceedings Volume 7966, 796614 (2011) https://doi.org/10.1117/12.877968

KEYWORDS: Computer aided design, Computer aided diagnosis and therapy, Chest imaging, Chest, Radiography, CAD systems, Databases, Computing systems, Lung

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Background. Contrary to what may be expected, finding abnormalities in complex images like pulmonary nodules in chest radiographs is not dominated by time-consuming search strategies but by an almost immediate global interpretation. This was already known in the nineteen-seventies from experiments with briefly flashed chest radiographs. Later on, experiments with eye-trackers showed that abnormalities attracted the attention quite fast but often without further reader actions. Prolonging one's search seldom leads to newly found abnormalities and may even increase the chance of errors. The problem of reading chest radiographs is therefore not dominated by finding the abnormalities, but by interpreting them. Hypothesis. This suggests that readers could benefit from computer-aided detection (CAD) systems not so much by their ability to prompt potential abnormalities, but more from their ability to 'interpret' the potential abnormalities. In this paper, this hypothesis was investigated by an observer experiment. Experiment. In one condition, the traditional CAD condition, the most suspicious CAD locations were shown to the subjects, without telling them the levels of suspiciousness according to CAD. In the other condition, interactive CAD condition, levels of suspiciousness were given, but only when readers requested them at specified locations. These two conditions focus on decreasing search errors and decision errors, respectively. Results of reading without CAD were also recorded. Six subjects, all non-radiologists, read 223 chest radiographs in both conditions. CAD results were obtained from the OnGuard 5.0 system developed by Riverain Medical (Miamisburg, Ohio). Results. The observer data were analyzed by Location Response Operating Characteristic analysis (LROC). It was found that: 1) With the aid of CAD, the performance is significantly better than without CAD; 2) The performance with interactive CAD is significantly better than with traditional CAD at low false positive rates.

Proceedings Article | 9 March 2010 Paper

Rib suppression in chest radiographs to improve classification of textural abnormalities

Laurens Hogeweg, Christian Mol, Pim de Jong, Bram van Ginneken

Proceedings Volume 7624, 76240Y (2010) https://doi.org/10.1117/12.844409

KEYWORDS: Chest imaging, Computer aided diagnosis and therapy, CAD systems, Solid modeling, Medical imaging, Current controlled current source, Image segmentation, Statistical modeling, Image filtering, Gaussian filters

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Showing 5 of 6 publications

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