Benjamin Irving
University of Oxford
32 Papers
135 Citations
Benjamin Irving is an academic researcher from University of Oxford. The author has contributed to research in topics: Airway & Image segmentation. The author has an hindex of 11, co-authored 32 publications. Previous affiliations of Benjamin Irving include University of Cape Town & University College London.
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Papers
Extraction of Airways From CT (EXACT'09)
Pechin Lo,Bram van Ginneken,Joseph M. Reinhardt,Tarunashree Yavarna,Pim A. de Jong,Benjamin Irving,Catalin Fetita,Margarete Ortner,Romulo Pinho,Jan Sijbers,Marco Feuerstein,Anna Fabijańska,Christian Bauer,Reinhard Beichel,Carlos S. Mendoza,Rafael Wiemker,Jaesung Lee,Anthony P. Reeves,Silvia Born,Oliver Weinheimer,Eva M. van Rikxoort,Juerg Tschirren,Ken Mori,Benjamin L. Odry,David P. Naidich,Ieneke J. C. Hartmann,Eric A. Hoffman,Mathias Prokop,Jesper Holst Pedersen,Marleen de Bruijne +29 more
TL;DR: A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
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maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images
TL;DR: This work introduces maskSLIC an extension of SLIC to create supervoxels within regions-of-interest, and demonstrates that it overcomes issues that affect SLIC within an irregular mask and is more effective than voxel clustering.
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3D segmentation of the airway tree using a morphology based method
Benjamin Irving,Paul Taylor,Andrew Todd-Pokropek +2 more
- 01 Jan 2009
TL;DR: An automatic method for segmentation of the airway tree is outlined, which includes algorithms to detect the trachea, segment thetrachea and main bronchi by thresholding and region growing, and segment the remaining Bronchi by morphological filtering and reconstruction.
Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics.
Benjamin Irving,Amalia Cifor,Bartlomiej W. Papiez,Jamie Franklin,Ewan M. Anderson,Sir Michael Brady,Julia A. Schnabel +6 more
- 14 Sep 2014
TL;DR: There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods is proposed.
Paediatric dose measurement in a full-body digital radiography unit
TL;DR: The effective dose from the Statscan unit was well below that from the Shimadzu unit as well as that found in other radiological studies from around the world in children.
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