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Proceedings Paper

Estimation of discontinuous optical flow using a neural network for echocardiographic images
Author(s): Vincent Devlaminck; L. Lelaurin; A. Yacoubi; Jean-Paul Dubus
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Paper Abstract

The purpose is to develop a method of estimation of the field of vectors velocity to evaluate motion of the heart in view of an assistance with the medical diagnosis. This method is based on an application of neural network to optical flow estimation in a sequence of echocardiographic images. The network is discrete, deterministic and locally connected. It contains redundant neurons for representing w(i,j)x and w(i,j)y the components of the velocity vector at point (i,j). We formulate the motion estimation problem as one of minimizing an error function of three terms. The first term is a least-squares penalty expression which coerce the flow field into satisfying the flow equation; the second is a smoothness constraint used for obtaining a smooth optical flow and the third term is a line process to weaken the smoothness constraint and to detect motion discontinuities. The minimization of this function is obtained with the help from a neural network. The model consist of 2*L*M Hopfield sub-networks for an image of size L*M. Each sub-network is totally interconnected. Results provide a good coherence between the homogeneous area cartography of motion and textured regions present in the image.

Paper Details

Date Published: 12 May 1995
PDF: 7 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208716
Show Author Affiliations
Vincent Devlaminck, Univ. des Sciences et Technologies de Lille (France)
L. Lelaurin, Univ. des Sciences et Technologies de Lille (France)
A. Yacoubi, Univ. des Sciences et Technologies de Lille (France)
Jean-Paul Dubus, Univ. des Sciences et Technologies de Lille (France)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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