X-ray testing is based on the attenuation of X-rays when passing through matter. Image detectors acquire the X-ray information which is defined by the local penetrated wall thickness of the tested sample. By X-ray absorption in the detector and following read-out and digitization steps a digital image is generated. As detectors a radiographic film and film digitization, a storage phosphor imaging plate and a special Laser scanner (Computer Radiography - CR) or a digital detector array (DDA) can be used. The digital image in the computer can then be further analyzed using many types of image processing. In the presented work the automated evaluation of wall thickness profiles are investigated using a test steel pipe with 9 different wall thicknesses and various X-ray voltages and different filter materials at the tube port and intermediate between object and detector. In this way the influence of different radiation qualities on the accuracy of the automated wall thickness evaluation depending on the penetrated wall thickness of the steel pipe was investigated.
Detection and binarization of local objects of interest (defects and abnormalities) in radiographic images is considered with application to industrial (non-destructive testing) and medical diagnostic imaging. The known standard approaches such as the histogram-based binarization or the method of dynamic thresholding yield poor segmentation results on the images containing small low-contrast objects and noisy background. The proposed method for object detection using binary segmentation has the following advantageous features. A model-based approach is applied which exploits the object multi-scale morphological representation in order to perform a time-effective image analysis. The intensity function is modeled by a polynomial regression representation with the so- called conformable two-region model. The estimation of the model parameters is made by using a robust non-linear estimation procedure. The concept of a multi-scale relevance function has been introduced for rapid location of local objects invariantly to the object shape, size, and orientation. The relevance function is a function that has the local maximum at the location center of an object of interest or its relevant part such as the corner edge. The developed segmentation method has been comparatively tested on radiographic images in non-destructive testing of weld joins and medical images from chest radiography.
The goal of this study was to determine the influence of adsorbed molecules on the OH bending vibration, which cannot be directly measured in the fundamental region (750 - 1050 cm-1) because of the strong lattice vibrations of the substrate. The only way is to measure the combination vibrations in the near infrared. The Diffuse Reflectance Infrared Fourier Transform (DRIFT) technique was used to study the whole spectral range from the fundamental vibrations of OH groups to the overtones in the near infrared (3000 - 7500 cm-1) on the same sample. Several OH groups with different acidity on silica and crystalline alumosilica (zeolites) have been investigated.