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William M. Wells
Researcher at Brigham and Women's Hospital
Publications - 326
Citations - 29342
William M. Wells is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Image registration & Segmentation. The author has an hindex of 64, co-authored 315 publications receiving 27065 citations. Previous affiliations of William M. Wells include SRI International & Harvard University.
Papers
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Journal ArticleDOI
Alignment by Maximization of Mutual Information
Paul A. Viola,William M. Wells +1 more
TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.
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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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Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation
TL;DR: An expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE), which considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation.
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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.