Segmentation of Welding Defects using Level Set Methods

Abstract

-Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the reinitialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation

“…The first dataset comprises MR brain images, which include both real and simulated images [4,34]. The second dataset consists of real x-ray images that include defects occurring during the welding process, used in nondestructive testing [8,35,36]. Specifically, this dataset is subdivided into:…”

“…• Radiographic images of welding that include defects that could happen during the welding operation (Lacks of penetration) obtained from images data used in [8,35].…”

“…Figures 11 and 12 show the comparative behaviour of the FCMC, KFCMC, NBWTFCM, FCMC-NBA and the proposed EIFCMNB method in terms of VPE, VPC for a number of iterations (15, 30, 50, 80 and 100) with an image corrupted by the Gaussian noise (σ = 20). The test images used are simulated and MR brain images [4,32] and x-ray welding images [8,35,36].…”