Mr. Maurice R. Samulski Profile

Maurice R. Samulski

System Developer at Datec Norge AS

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Publications (5)

Proceedings Article | 9 March 2011 Paper

Computer aided detection of breast masses in mammography using support vector machine classification

Jan Lesniak, Rianne Hupse, Michiel Kallenberg, Maurice Samulski, Rémi Blanc, Nico Karssemeijer, Gàbor Székely

Proceedings Volume 7963, 79631K (2011) https://doi.org/10.1117/12.878140

KEYWORDS: Breast, Mammography, Computer aided diagnosis and therapy, Neural networks, Image segmentation, Computer aided design, Databases, Receivers, Image classification, Cancer

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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 | 13 March 2009 Paper

Analysis of probed regions in an interactive CAD system for the detection of masses in mammograms

M. Samulski, A. Hupse, C. Boetes, G. den Heeten, N. Karssemeijer

Proceedings Volume 7263, 726314 (2009) https://doi.org/10.1117/12.813391

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Computer aided design, CAD systems, Cancer, Computing systems, Detection and tracking algorithms, Visualization, Visual process modeling, Breast cancer

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Proceedings Article | 17 March 2008 Paper

Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach

Maurice Samulski, Nico Karssemeijer

Proceedings Volume 6915, 69151M (2008) https://doi.org/10.1117/12.769792

KEYWORDS: Mammography, Image segmentation, Breast, Nipple, CAD systems, Computer aided diagnosis and therapy, Analytical research, Statistical analysis, Computer aided design, Tissues

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Proceedings Article | 30 March 2007 Paper

Classification of mammographic masses using support vector machines and Bayesian networks

Maurice Samulski, Nico Karssemeijer, Peter Lucas, Perry Groot

Proceedings Volume 6514, 65141J (2007) https://doi.org/10.1117/12.709679

KEYWORDS: Image segmentation, Principal component analysis, Statistical analysis, Breast, Mammography, Tissues, Image classification, Computer aided diagnosis and therapy, Feature extraction, Medical imaging

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