H
H. Neale Cardinal
Researcher at Robarts Research Institute
Publications - 13
Citations - 1448
H. Neale Cardinal is an academic researcher from Robarts Research Institute. The author has contributed to research in topics: Image segmentation & Nonlinear system. The author has an hindex of 11, co-authored 13 publications receiving 1378 citations. Previous affiliations of H. Neale Cardinal include University of Western Ontario.
Papers
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Journal ArticleDOI
Three-Dimensional Ultrasound Imaging
TL;DR: A review article describes the developments of a number of 3D ultrasound imaging systems using mechanical, free-hand and 2D array scanning techniques and the sources of errors in the reconstruction techniques as well as formulae relating design specification to geometric errors.
Journal ArticleDOI
Intra- and inter-observer variability and reliability of prostate volume measurement via two-dimensional and three-dimensional ultrasound imaging
Shidong Tong,Shidong Tong,H. Neale Cardinal,Raymond F. McLOUGHLIN,Raymond F. McLOUGHLIN,Donal B. Downey,Donal B. Downey,Aaron Fenster,Aaron Fenster,Aaron Fenster +9 more
TL;DR: In vivo prostate volume estimates from manual planimetry of 3D TR US images have much lower variability and higher reliability than HWL estimates from 2D TRUS images.
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An accurate method for direct dual-energy calibration and decomposition.
TL;DR: It is shown that direct approximation of the inverse dual-energy equations using the simple eight-term rational form of the conic surface equation provides an extremely fast decomposition algorithm, which is accurate, robust in the presence of noise, and which can be calibrated with as few as 9 calibration points, or robustly calibrated, with a built-in accuracy check, using only 16 calibration points.
Journal ArticleDOI
Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images.
TL;DR: Two methods for semiautomatic three-dimensional prostate boundary segmentation using 2-D ultrasound images are reported on, and it is concluded that the rotational segmentation method is superior.
Journal ArticleDOI
Automatic needle segmentation in three-dimensional ultrasound images using two orthogonal two-dimensional image projections
TL;DR: An algorithm to segment a needle from a three-dimensional (3D) ultrasound image by using two orthogonal two-dimensional image projections, which improves accuracy and robustness and uses volume cropping and Gaussian transfer functions to remove complex background from the 2D projection images.