Top PDF Automatic x ray image characterisation for non destructive evaluation

Automatic x ray image characterisation for non destructive evaluation

Automatic x ray image characterisation for non destructive evaluation

The second and also significant step in the proposed system is image pre-processing. In our specific application, the main task for image pre-processing is defect segmentation. All subsequent tasks including feature extraction and identification depend on the qualities of the image segmentation [11]. Regardless of what defect properties are taken as the judgement scale for defect identification, the defect areas should be segmented from the background image first. The most commonly used feature-domain approaches [8-10] in image segmentation include thresholding, edge detection, morphological processing, watershed transform and region growing techniques. For the proposed automatic defect inspection system, the defect segmentation can’t be implemented as readily as other common applications, because of the low and various image qualities due to different scanning conditions, occurrence of multiple defects and noise in one image. It is intended to design a defect segmentation approach based on the association of adaptive morphological filters, edge detection and morphological processing. This novel and innovative image segmentation approach is outlined in the following sections.
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Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

Background: Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results: In this study we describe the development of a robust method for the accurate extraction and measure- ment of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, per- forms image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress.
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Improved non-destructive 2D and 3D X-ray imaging of leaf venation

Improved non-destructive 2D and 3D X-ray imaging of leaf venation

Leaf venation networks of entire leaves were visual- ized using high-resolution X-ray technology. Well-pre- served, ideally flat leaves were placed on flexible image plates with a resolution of 25 µm, which is the industrial standard (as certified by the German Federal Institute for Materials Research and Testing, BAM, Berlin), and scanned in a Faxitron X-ray system (Hewlett-Packard) with a field of view of maximally 300  mm in diameter. Depending on the individual leaf size, up to 10 (typically 6-8) leaves were placed on an image plate. To achieve high-quality images, only leaves that provide direct con- tact with the image plate (the distance between both must not surmount 4–5 mm) were selected. In a first step, X-ray conditions were optimized using a test series with varying exposure times and photon energies. Because leaves contain elements with low atomic numbers, best contrast is achieved at low photon energies. Therefore, test images were generated with tube voltages between 12 (i.e. the lowest feasible tube voltage) and 30  kV and exposure times between 30 and 1020  s. Highest con- trast was generally obtained with tube voltages close to the lowest limit and 600–1020  s. Image data were read with a semi-automatic image plate scanner (Dürr, Ger- many) and stored as 16-bit RAW and 8-bit BMP files (the latter for image size definition of the RAW import). To minimize deleterious effects from daylight during sample removal, X-ray and scanning was conducted under very low ambient light in a dark room with unshielded image plates.
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Automatic non-destructive quality inspection system for oil palm fruits

Automatic non-destructive quality inspection system for oil palm fruits

In the tests, the placement of FFBs on the conveyor belt was not regulated, resulting in different bunch orientations. However, this showed no influence on the accuracy. The FFBs were fed by the operator onto machine, with its handle facing toward the inspection chamber. Motor and transmis- sion combination arrangement produced a constant belt speed of 110 mm s -1 , with the software processing the image less than 5 s for examination of each FFB. The machine examination capacity is more than 12 t of FFBs per hour; which fairly satisfies mill grading capacity requirement. All FFB samples used in the test showed no major bruise, and hence the grading process can be considered safe in hand- ling the bunch without damaging it. This work opens an op- portunity to further develop the automatic grading systems to be applied in agricultural sectors as well as in other sectors. More methods can be developed to increase the efficiency and effectiveness of this kind of system.
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Implementation of an X-ray image-plate camera in characterisation and crystallisation studies of iron-based alloys

Implementation of an X-ray image-plate camera in characterisation and crystallisation studies of iron-based alloys

Particular thanks are also due to Dr Mark Roberts, formerly a postdoctorate researcher at UCL and now station scientist on beamline 9.1 at Daresbuiy, who aquainted me with the practicalities of the CIP system at the start of this project, and provided invaluable help at Daresbury Laboratory. Thanks also to Dr Graham Bushnell-Wye, former station scientist on 9.1 and co-founder member o f the CIP project, for his assistance at Daresbuiy. Thanks to Dr Scott Belmonte of Edinburgh University for his willing advice and insider knowledge on image-plate issues, and all the others who gave professional or moral support at Daresbury and helped make the long days and nights just a little more bearable. Special thanks are due to Dr Luis Femândez Barquin and Dr Glyn Forster, predecessors on the chemical-reduction project, who both willingly participated in discussions and offered advice despite having moved on to pastures new. The assistance of Kevin Reeves, with atomic absorption, SEM and EDAX measurements within UCL Archaeology, and Dr Neil Cohen and Dr Marianne Odlyha o f the ULIRS Thermal Methods service with the DSC characterisation is gratefully acknowledged. Thank you to Dr Ian Harvey and Dr Fred Mosselmans for practical help on the EXAFS beamline, and Dr Dave Gleeson of the Royal Institution of Great Britain for subsequent help with the data analysis.
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Non destructive determination of floral staging in cereals using X ray micro computed tomography (µCT)

Non destructive determination of floral staging in cereals using X ray micro computed tomography (µCT)

In this article we demonstrate the feasibility of using X-ray µCT for non-destructively visualising and quantifying the internal organ (spike) size of Barley to allow for accurate staging of floral tissue. The rapid 1 min scans require minimal preparation and computational analysis to enable accurate development staging. This has a major advantage over current staging methods that are destructive resulting in the death of the tiller for spike measurement and cytological analysis. Cytological analysis is also time consuming and requires a number of different steps, such as fixation, sectioning and staining before eventual microscope analysis of the stages. This means that accurate staging often occurs post-sample collection. Both of these current methods provide no opportunity for on-going developmental analysis, or subsequent sample collection if staging has been premature. The added benefit of this method is that the stage of all the spikes within the barley tillers can be established and therefore multiple stages can be observed in the same plant under particular treatments/ mutants/cultivars. This is more consistent with what would happen in the field with environmental stresses affecting tillers differently depending on the current developmental stage of the flower.
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Determining The Detective Quantum Efficiency (DQE) Of X-Ray Detectors In Clinical Environments

Determining The Detective Quantum Efficiency (DQE) Of X-Ray Detectors In Clinical Environments

According to Health Canada, dental and medical radiography accounts for more than 90% of total man-made radiation dose to the general population. Ensuring pa- tients receive the health benefits of diagnostic x-ray imaging without use of higher radiation exposures requires knowledge and understanding of the detective quantum efficiency (DQE). Currently, the DQE is not measured in clinics because it requires specialized instrumentation and specific DQE-expertise to perform an accurate anal- ysis. In this regard, the goals of this thesis were to: 1) address the limitations of measuring the DQE in clinical environments that affects the accuracy of the mea- surement; 2) develop and validate an automated method of measuring the DQE that is compliant with current regulatory standards to relieve experimental burden on the end-user. It is shown that the DQE can be measured with confidence using the au- tomated method despite the limitations present in clinical environments. This work provides the oppurtunity for the clinical end-user who may not be familiar with the DQE-measurement process to accurately measure the DQE of clinical x-ray detec- tors, and provides the opportunity for the DQE to be a primary metric for quality assurance and control practices in the clinical environment.
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Experimental Study for Soil Modification Cement Grouting

Experimental Study for Soil Modification Cement Grouting

• Unexpected results from the permeability and sedimentation tests and uncertainties about the fineness of the consumed cements led to the use of specialized Laser-ray technique by in order to determine the grading and fineness of cement particles. The grading curves of all blain cements were determined accurately In Abiek Research and Development Center of Cement Plant. The device output determined the accumulating curves of percentage of pass- ing and remaining of particles with a range of particle size between 0.5 to 1000 microns. • Sedimentation tests carried out on the slurry
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Inconsistency in 9 mm bullets : correlation of jacket thickness to post impact geometry measured with non destructive X ray computed tomography

Inconsistency in 9 mm bullets : correlation of jacket thickness to post impact geometry measured with non destructive X ray computed tomography

This paper presents the application of X-ray CT [14] for the extraction of bullet geometry both before and after impact. Following the method prescribed by Kumar et al. [15] (summarised in Section 2), a set of 20, nominally identical, bullets were CT scanned prior to being subjected to ballistic performance tests under controlled conditions. The deformed projectiles were then re-scanned in order to observe and quantify the degree of deformation resulting from the impacts, with a view to correlating these ballistic performance indicators with geometric features of the un-deformed bullets prior to impact. This data will be used going forward to assist in the creation and validation of finite element models to more accurately describe ballistic impacts and help quantify the variability of threat level of ammunition of this kind.
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Imaging air volume fraction in sea ice using non-destructive X-ray tomography

Imaging air volume fraction in sea ice using non-destructive X-ray tomography

We propose a methodological advancement employing computed tomography (CT) X-ray imaging for measurement of air inclusions within sea ice. For many years CT X-ray has been widely used as a medical diagnostic tool. This non- invasive technique has largely contributed to the study of rock fractures and rock porosity, and has recently been ap- plied to the sea ice field, advancing percolation theory for the brine system (Golden et al., 2007; Pringle et al., 2009, Obbard et al., 2009). Here we present high-resolution pro- files of the distribution of air inclusions in sea ice, which are derived from CT X-ray images of whole ice cores at the sub-millimeter scale. A detailed statistical analysis of the air volume fraction in experimental sea ice is presented, as well as comparisons to the air volume equations of Cox and Weeks (1983) and measurement of total gas content. Throughout this work, we highlight the parameters and pro- cesses influencing the air porosity (air volume fraction, V a ).
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FORMATION AND CHARACTERISATION OF NANOCRYSTALLINE COPPER IN AMORPHOUS SILICA

FORMATION AND CHARACTERISATION OF NANOCRYSTALLINE COPPER IN AMORPHOUS SILICA

Only minor temperature dependent variations are observed in the Debye-Waller (DW) factor within each shell of each sample (Appendix I). In general, the DW factor is comprised of both thermal and static disorder components. The definition of the DW factor thus includes displacements arising from any source. Thermal and structural sources dominate, and by performing low temperature (liquid He) EXAFS, the structural disorder is singled out. Sources of structural disorder include lattice defects within the nc and defects and strain at the nc surface associated with bonding to the amorphous matrix. The influence of the latter increases as the surface to volume ratio increases. An increase in DW factor diminishes the scattering amplitude. In general the nc DW factor tends to be slightly increased with respect to the bulk value for all non-oxidised samples, which is characteristic for an enhanced surface to volume ratio [52].
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Rapid Sonochemical Synthesis and Characterisation of Copper Oxide Nanoparticles from Schweizer's Reagent

Rapid Sonochemical Synthesis and Characterisation of Copper Oxide Nanoparticles from Schweizer's Reagent

During the last decade there has been significant progress in synthesizing a wide variety of nanometre scale materials via sonochemical processes using a variety of ultrasound based techniques [23]. The sonochemical approach has a number of features that clearly distinguish it from conventional non- ultrasound based techniques. For most materials these features include: (1) faster primary nucleation; (2) relatively easy nucleation in materials that are usually difficult to nucleate otherwise; (3) the initiation of secondary nucleation; and (4) the production of smaller, purer crystals that are more uniform in size. It is due to these advantageous features that have made sonochemical synthesis an attractive technique for producing nanometre scale materials.
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GPU Accelerated X Ray Image Enhancement

GPU Accelerated X Ray Image Enhancement

Like most image processing techniques there are a number of methods available for detecting and extracting features from images, ranging from fairly simple contour tracking to complex algorithms that analyse not only contours but texture. Active Contours (“Snakes”) (Kass et al., 1988) are a method of feature extraction which attempts to extract a shape by altering the location of a set of points so that they enclose the target feature. “Snakes” are the predominant method of feature extraction in medical imagery (McInerney and Terzopolous, 1996). Active Shape Models (ASMs) (Cootes et al., 1995) are an improvement of the “Snakes” feature extraction technique, being capable of locating objects whose appearance may vary. ASMs locate objects by being trained using a number of images with marked landmark points covering the possible shape variations. Active Appearance Models (AAMs) are gaining popularity because whilst ASMs are faster to implement than AAMs, an AMM requires fewer landmark points and typically converges to a better result, especially in terms of textures (Cootes et al., 1999). AAMs differ from ASMs mainly due to the fact that they also analyse the texture of areas surrounding the landmarks.
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Analysis of Image Processing for Digital X Ray

Analysis of Image Processing for Digital X Ray

reduction techniques applied on the image and improve quality the image so that it is easy for doctor to diagnosis the diseases efficiently and immediately. Digital x-ray is a used to capture the images, scanned the image or stores the image in digital format like jpeg, png etc. so it is useful to diagnosis the disease of the patients. But digital x-ray images contains noise like gaussian noise or salt and paper noise so that sometimes it’s not gives the clear view of the x-ray image. Therefore for improving the quality of images, this is important to remove the noise from the image so that image gives the clear view of bone structure.
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Biometric Template Feature Extraction And Matching Using Canny Edge Detection And Sift Based Algorithm

Biometric Template Feature Extraction And Matching Using Canny Edge Detection And Sift Based Algorithm

After removing the noise the edge of the image are detected using canny edge detection. The Canny edge detection algorithm is known to many as the optimal edge detector. The purpose of edge detection algorithm is to expressively reduce the amount of data in an image, while protecting the structural properties of the data for further image processing. In the feature extraction module the biometric image feature are extracted from the X- ray image during user enrolment and compare with the authenticated X-ray image.The SIFT algorithm is used for skull feature extraction.
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Non-destructive 3D geometric modeling of maize root-stubble in-situ via X-ray computed tomography

Non-destructive 3D geometric modeling of maize root-stubble in-situ via X-ray computed tomography

Figure 8 shows the 3D reconstructed model of the maize root-stubble in-situ segmented from the soil and other growing media via CT by the following procedures: CT scanning of the maize root-stubble-soil composite, image reconstruction via FBP with the Hanning filter, segmentation of root-stubble via the above variational level set method, and post-processing via morphological operations. The model can be interactively operated, such as rotation and cut. Figure 8a shows the complex complete architecture of maize root-stubble with numerous lateral roots which play a crucial role in the root-stubble architecture.
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Fully automatic image analysis framework for cervical vertebra in X-ray images

Fully automatic image analysis framework for cervical vertebra in X-ray images

size depends on the magnitude of the vector and the orientation is given by its direction. This process works because when the manually annotated centers are available, the topological information about the identity of the vertebra is also known. Unfortunately, the vertebra identity is not available for the centers localized using the center localization algorithm. To solve the problem, based on our assumption that image is upright, we can arrange the predicted centers sequentially from top to bottom as C3 to C7 and proceed with the extraction process. This way we compute the orientation vector (F F F). Although the direction of the vector is relevant for test patch extraction, the magnitude of this vector cannot be used as a measure of patch size because of the possibility of missing center in between two predicted centers. To solve this issue, a common patch size of 32 mm is chosen based on the distribution of training vertebral sizes. The vertebral size is defined as the distance from the vertebral center to the farthest point in the manually annotated vertebral boundary, similar to the base vertebral size discussed in Sec. 6.3. About 90% of the training vertebrae have a size smaller than 32 mm. A bigger patch size can increase this percentage. However, it would make a significant amount of the test vertebrae to appear smaller inside the extracted patch. This will adversely affect the following methods, as they are not trained on examples where vertebral size is smaller compared to the patch size. Fig. 8.1 shows the distribution of the vertebral sizes in our training dataset.
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Structural and optical studies of zno nanorods prepared of nanorods prepared by hydrothermal method

Structural and optical studies of zno nanorods prepared of nanorods prepared by hydrothermal method

biased diode and is converted to voltage pulse by a charge sensing amplifier. The pulse is then analyzed through a multichannel analyzer and is sent to the computer. The software then displays the peak after identification and quantifications. The spectrum consists of energy along the x- axis and the counts/intensity along the y-axis, which gives rise to peaks where each peak corresponds to a specific element. Quantitative information about an element in the specimen could also be obtained, which show what weight per cent and what atomic per cent of an individual element does exist in the sample.
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Rapid phenotyping of crop root systems in undisturbed field soils using X-ray computed tomography

Rapid phenotyping of crop root systems in undisturbed field soils using X-ray computed tomography

Yet, using CT, the throughput suffers from the bottle- neck of rapid and standardized segmentation methods to extract root structures [4, 9]. Indeed, unaltered RSA can be analyzed by CT, but the segmentation of the root (optical separation of root and soil) is done to a large extent manually and therefore requires a lot of time. Segmentation is usually performed by defining a local threshold for gray values of the CT voxels, classifying them either as root or non-root. These local thresholds often vary throughout a sample due to heterogeneities in the substrate and CT-artefacts. The complete reconstruc- tion of a root system therefore requires a lot of interactive parameter optimization and interpretation, which is sup- ported by software tools that allow identification of con- nected root systems (e.g. by region growing algorithms as used in [10–12]). Yet, the premise of a completely con- nected root system can also lead to difficulties. Algo- rithms that follow root voxels in CT image stacks slice by slice [13, 14] can be confused, if a root seems to be interrupted due to surrounding soil heterogeneities or if roots are only a few voxels in diameter or if multiple root systems contained in soil cores from a field experiment are investigated. These root systems typically consist of cut segments of roots from multiple plants contained in the cylinder of a soil core. Besides this difficulty of seg- menting unconnected roots, root segmentation is far more difficult in undisturbed field soils compared to sieved soil filled in pots due to further obstacles. Undis- turbed soils frequently contain much higher amounts of organic particles, which are commonly removed in pot experiments by means of sieving the soil. Those organic particles typically have gray values similar to those of roots. Moreover, undisturbed soil samples often show more inhomogeneous moisture distribution compared to sieved and homogenized soils.
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Non Destructive Porosity Calculation of an Indian Fermented Food “Khaman” using X Ray Microtomography and Image Processing

Non Destructive Porosity Calculation of an Indian Fermented Food “Khaman” using X Ray Microtomography and Image Processing

For image analysis, a set of 600 cross sectional µCT images for each sample of Indian food known as “KHAMAN” were used. The input images were converted to the binary images by using global thresholding technique suggested by Otsu (1979). The volume fraction of air bubbles was determined from the total stack of binary images by dividing the number of pixels identified as air bubbles by the number of pixels inside the sample holder. The size of the air bubbles were measured using a 3D measurement function (eq. 3).The results of the analysis were presented in Table I.
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