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Volume estimation from sparse planar images using deformable models

Ruff, CF; Hughes, SW; Hawkes, DJ; (1999) Volume estimation from sparse planar images using deformable models. IMAGE AND VISION COMPUTING , 17 (8) 559 - 565.

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Abstract

In this article we will present Point Distribution Models (PDMs) constructed from Magnetic Resonance scanned foetal livers and will investigate their use in reconstructing 3D shapes from sparse data, as an aid to volume estimation. A solution of the model to data matching problem will be presented that is based on a hybrid Genetic Algorithm (GA). The GA has amongst its genetic operators, elements that extend the general Iterative Closest Point (ICP) algorithm to include deformable shape parameters. Results from using the GA to estimate volumes from two sparse sampling schemes will be presented. We will show how the algorithm can estimate liver volumes in the range of 10.26 to 28.84 cc with an accuracy of 0.17 +/- 4.44% when using only three sections through the liver volume. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

Type: Article
Title: Volume estimation from sparse planar images using deformable models
Location: UNIV ESSEX, COLCHESTER, ENGLAND
Keywords: 3D ultrasound, point distribution models, hybrid genetic algorithm, interative closest point algorithm, ULTRASOUND IMAGES, REGISTRATION, ORIENTATION, POSITION
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/155847
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