R
Ron Kikinis
Researcher at Brigham and Women's Hospital
Publications - 705
Citations - 68915
Ron Kikinis is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Segmentation & Diffusion MRI. The author has an hindex of 126, co-authored 684 publications receiving 63398 citations. Previous affiliations of Ron Kikinis include University of Zurich & University of Tokyo.
Papers
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Journal ArticleDOI
3D Slicer as an image computing platform for the Quantitative Imaging Network.
Andriy Fedorov,Reinhard Beichel,Jayashree Kalpathy-Cramer,Julien Finet,Jean-Christophe Fillion-Robin,Sonia Pujol,Christian Bauer,Dominique Jennings,Fiona M. Fennessy,Milan Sonka,John M. Buatti,Stephen R. Aylward,James V. Miller,Steve Pieper,Ron Kikinis +14 more
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
Journal ArticleDOI
Multi-modal volume registration by maximization of mutual information
William M. Wells,William M. Wells,Paul A. Viola,Paul A. Viola,Hideki Atsumi,Shin Nakajima,Ron Kikinis +6 more
TL;DR: In this paper, an information-theoretic approach for finding the registration of volumetric medical images of differing modalities is presented, which is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized.
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Statistical validation of image segmentation quality based on a spatial overlap index.
Kelly H. Zou,Kelly H. Zou,Simon K. Warfield,Aditya Bharatha,Clare M. Tempany,Michael Kaus,Steven Haker,William M. Wells,William M. Wells,Ferenc A. Jolesz,Ron Kikinis +10 more
TL;DR: The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation, and may be adapted for similar validation tasks.
Journal ArticleDOI
Adaptive segmentation of MRI data
TL;DR: Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging data, that has proven to be effective in a study that includes more than 1000 brain scans.
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Nonlinear anisotropic filtering of MRI data
TL;DR: In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed, which overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details.