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Robert A. McLaughlin
Researcher at University of Adelaide
Publications - 170
Citations - 4867
Robert A. McLaughlin is an academic researcher from University of Adelaide. The author has contributed to research in topics: Optical coherence tomography & Elastography. The author has an hindex of 39, co-authored 161 publications receiving 4208 citations. Previous affiliations of Robert A. McLaughlin include University of Oxford & University of Western Australia.
Papers
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Randomized Hough transform: improved ellipse detection with comparison
TL;DR: In this paper, the authors describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform (RHT) and compare it with three other Hough-based algorithms.
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Quantitative micro-elastography: imaging of tissue elasticity using compression optical coherence elastography.
Kelsey M. Kennedy,Lixin Chin,Robert A. McLaughlin,Bruce Latham,Christobel Saunders,David D. Sampson,David D. Sampson,Brendan F. Kennedy +7 more
TL;DR: It is shown that quantification of elasticity can improve the ability of compression OCE to distinguish between tissues, thereby extending the potential for inter-sample comparison and longitudinal studies of tissue elasticity.
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Intensity-based 2-D - 3-D registration of cerebral angiograms
John H. Hipwell,Graeme P. Penney,Robert A. McLaughlin,Kawal Rhode,Paul Summers,Tim C. S. Cox,James V. Byrne,J.A. Noble,David J. Hawkes +8 more
TL;DR: The most accurate and robust registrations were obtained using the pattern intensity, gradient difference, and gradient correlation similarity measures, when used in conjunction with the most sophisticated MRA segmentations.
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Strain estimation in phase-sensitive optical coherence elastography
Brendan F. Kennedy,Sze Howe Koh,Robert A. McLaughlin,Kelsey M. Kennedy,Peter R. T. Munro,David D. Sampson +5 more
TL;DR: A theoretical framework for strain estimation in optical coherence elastography (OCE) is presented, based on a statistical analysis of displacement measurements obtained from a mechanically loaded sample, and estimates of strain are derived using three methods: finite difference, ordinary least squares and weighted least squares.
Journal ArticleDOI
Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.
TL;DR: This paper parameterize the complete LV segmentation task in terms of the radial distances between the LV centerpoint and the endo‐ and epicardial contours in polar space and demonstrates the effectiveness of convolutional neural network regression paired with domain‐specific features in clinical segmentation.