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Proceedings Paper

Segmentation of blurred objects using wavelet transform: application to x-ray images
Author(s): Cecile S. Barat; Christophe Ducottet; Anne Bilgot; Laurent Desbat
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Paper Abstract

First, we present a wavelet-based algorithm for edge detection and characterization, which is an adaptation of Mallat and Hwang’s method. This algorithm relies on a modelization of contours as smoothed singularities of three particular types (transitions, peaks and lines). On the one hand, it allows to detect and locate edges at an adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure its amplitude and smoothing size. The latter parameters represent respectively the contrast and the smoothness level of the edge point. Second, we explain that this method has been integrated in a 3D bone surface reconstruction algorithm designed for computer-assisted and minimal invasive orthopaedic surgery. In order to decrease the dose to the patient and to obtain rapidly a 3D image, we propose to identify a bone shape from few X-ray projections by using statistical shape models registered to segmented X-ray projections. We apply this approach to pedicle screw insertion (scoliosis, fractures...) where ten to forty percent of the screws are known to be misplaced. In this context, the proposed edge detection algorithm allows to overcome the major problem of vertebrae segmentation in the X-ray images.

Paper Details

Date Published: 27 February 2004
PDF: 12 pages
Proc. SPIE 5266, Wavelet Applications in Industrial Processing, (27 February 2004); doi: 10.1117/12.516058
Show Author Affiliations
Cecile S. Barat, Univ. Jean Monnet Saint-Etienne (France)
Christophe Ducottet, Univ. Jean Monnet Saint-Etienne (France)
Anne Bilgot, Univ. Joseph Fourier (France)
Laurent Desbat, Univ. Joseph Fourier (France)

Published in SPIE Proceedings Vol. 5266:
Wavelet Applications in Industrial Processing
Frederic Truchetet, Editor(s)

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