3ème Conférence Internationale sur
le Soudage, le CND et l’Industrie des Matériaux et Alliages (IC-WNDT-MI’12) Oran du 26 au 28 Novembre 2012,
http://www.csc.dz/ic-wndt-mi12/index.php 41
Extraction of weld defects dimension from radiographic images using the level set segmentation without re-initialization
N. RAMOU 1, Y. BOUTICHE 1 and M. HALIMI 1
1: Image and Signal Processing Laboratory, Welding and Control Research Center, Route de Dely Brahim B.P.64, Algiers Algeria
E-mail: naimramou@gmail.com
Abstract:
Radiographic images segmentation is the major interest for the weld defect diagnosis and monitoring in the field of industry. In this work we present a method that takes ownership of local segmentation geodesic active contours. The goal of the method presented in this paper is to automate the process of extracting dimension characteristics from radiographic images using level set segmentation to provide information which is used in the area of nondestructive testing (NDT). This method is found to be effective and robust.
Keywords: Level set segmentation, Radiographic weld defect, weld defects Dimension, NDT.
1 Introduction
Methods of nondestructive testing (NDT) have been the subject of much research and developments during the last thirty years. The methods of investigation radiology radiation (mainly X-ray), have significantly improved their performance. A chain of X-ray inspection includes
A source of X-rays.
A mechanical support for the object which can be rotated or translation if necessary.
A detector, which is associated with an imaging system (display screen) to get a two dimensional image of the object.
Radiographic inspection is widely used for weld inspection to provide image information about weld defect. Unfortunately these information are extremely difficult to be used in a quantitatively and objectively way. Consequently research has to offer new methods of image segmentation in order to increase the quality of information, speeding the diagnosis and optimizing their use. Several mathematical models have been developed to achieve image segmentation [1]. The last promising models to solve the image segmentation problem are based on level set and related to partial differential equations (PDE) based methods [2]. We have used the level set method without re-initialization in order to speed up the evolutionary process.
