Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version

NEW ALGORITHMS FOR COMPRESSED SENSING OF MRI: WTWTS, DWTS, WDWTS

Magnetic resonance imaging (MRI) is one of the most accurate imaging techniques that can be used to detect several diseases, where other imaging methodologies fail. MRI data takes a longer time to capture. This is a pain taking process for the patients to remain still while the data is being captured. This is also hard for the doctor as well because if the images are not captured correctly then it will lead to wrong diagnoses of illness that might put the patients lives in danger. Since long scanning time is one of most serious drawback of the MRI modality, reducing acquisition time for MRI acquisition is a crucial challenge for many imaging techniques. Compressed Sensing (CS) theory is an appealing framework to address this issue since it provides theoretical guarantees on the reconstruction of sparse signals while projection on a low dimensional linear subspace. Further enhancements have extended the CS framework by performing Variable Density Sampling (VDS) or using wavelet domain as sparsity basis generator. Recent work in this approach considers parent-child relations in the wavelet levels. This paper further extends the prior approach by utilizing the entire wavelet tree structure as an argument for coefficient correlation and also considers the directionality of wavelet coefficients using Hybrid Directional Wavelets (HDW). Incorporating coefficient thresholding in both wavelet tree structure as well as directional wavelet tree structure, the experiments reveal higher Signal to Noise ratio (SNR), Peak Signal to Noise ratio (PSNR) and lower Mean Square Error (MSE) for the CS based image reconstruction approach. Exploiting the sparsity of wavelet tree using the above-mentioned techniques achieves further lessening for data needed for the reconstruction, while improving the reconstruction result. These techniques are applied on a variety of images including both MRI and non-MRI data. The results show the efficacy of our techniques

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Hybrid non-destructive technique for volumetric defect analysis and reconstruction by remote laser induced ultrasound

This PhD thesis is devoted to the design, development and implementation of a non-contact hybrid non-destructive testing (NDT) method applied to the analysis of metallic objects that contain embedded defects or fractures. We propose a hybrid opto-acoustic technique that combines laser generated ultrasound as exciter and ultrasound transducers as receivers. This work envisages a detailed study of the detection and one, two or three-dimensional reconstruction of defects, using the proposed hybrid technique and its application as a remotely controlled non-contact NDT. Our device combines several advantages of both photonic and ultrasonic techniques, while reduces some of the drawbacks of both individual methods. Our method relay on the combination of experimental results with high-resolution signal processing procedures based on different mathematical algorithms. Our basic experimental setup uses a nanosecond pulsed laser at 532nm wavelength that impacts onto the surface of the object under study. The laser pulse is rapidly absorbed into a shallow volume of material and creates a localized thermo-elastic expansion inducing a broadband ultrasound pulse that propagate inside the material. The laser beam scans a selected area of the object surface, being remotely controlled by means of a programmable XY scanner. For each excitation point, the ultrasound waves propagate through the object are reflected or scattered by material 3D defects. They are detected by ultrasound transducers and recorded with a PC data-acquisition system for a further process and analysis. As a first step, the time of flight analysis provides enough data for the location and size of the defect in 1D view. The detection capabilities of internal defects in a metallic sample are studied by means of wavelet transform, chosen due to its multi-resolution time-frequency characteristics. A novel algorithm using a density-based spatial clustering is applied to the resulting time frequency maps to estimate the defect’s position. For the 2D visualization and reconstruction of the defects we extended the signal analysis using the synthetic aperture focusing technique (SAFT). We implement a novel 2D apodization window filtering applied along with the SAFT, and we show it removes undesired effects of the side lobes and wide-angle reflections of ultrasound waves, enhancing the reconstructed image of the defect. We move then towards the 3D analysis and reconstruction of defects and in this case we achieve and implement a fully non-contact and automatized experimental configuration allowing the scan areas on different object’s faces. The defect details are recorded from different angles/perspectives and a complete 3D reconstruction is achieved. Finally, we show our results on a complementary topic related to a particular case of the ultrasound propagation in solids. We were concerned on the physical understanding of the propagation and diffraction of ultrasound waves in solid materials from the first moment. The control of the diffraction pattern in solids, using an ultrasonic lens, would help focus/collimate the ultrasound reducing echoes and boundary reflections, resulting in a further improve NDT process. Phononic crystals have been used to regulate the diffraction and frequency response of ultrasonic waves traveling in fluids. However, they were much less studied in solid materials due to the difficulty of building the crystal and to high coupling losses. We perform detailed numerical simulations of the ultrasound propagation in a solid phononic crystal and we show focusing and the self-collimation effects. We further extend our analysis and couple our phononic crystal lens to a solid under study, showing that the diffraction control is preserved inside the target solid object trough the coupling material.Esta tesis doctoral versa sobre el diseño, estudio e implementación de un método híbrido, sin contacto, de ensayos no destructivos (NDT, non-destructive testing) para el análisis de objetos metálicos que contienen defectos o fracturas internas. Proponemos una técnica híbrida opto-acústica que combina ultrasonidos generados por impacto láser como excitador y transductores de ultrasonidos como receptores. El trabajo plantea un estudio detallado de la detección y reconstrucción en 1D, 2D y 3D de defectos presentes en un objeto metálico, usando la técnica híbrida de NDT sin contacto y controlado remotamente. Nuestro dispositivo presenta varias ventajas de las técnicas fotónicas y de ultrasonidos, reduciendo al mismo tiempo algunos inconvenientes de dichos métodos tomados por separado. Nuestro método combina resultados experimentales con simulaciones numéricas basadas en el procesado de señal de alta resolución. El montaje experimental consiste en un láser pulsado de ns a una longitud de onda de 532 nm, que impacta sobre la superficie del objeto. El pulso láser se absorbe, creando una expansión termoelástica localizada que induce un pulso de ultrasonidos de banda ancha que se propaga en el material. El láser, controlado remotamente, realiza un barrido sobre un área seleccionada de la superficie del objeto. Por cada punto de excitación, el ultrasonido se propaga a través del objeto y se refleja o dispersa en los defectos del material. Dichas ondas se detectan mediante transductores y se registran en un sistema de adquisición de datos para su ulterior procesado. En un primer paso, mediante el análisis del tiempo de vuelo, podemos localizar y determinar el tamaño del defecto en una vista 1D. Las capacidades de detección de defectos internos en una muestra metálica se estudian también mediante transformación wavelet debido a sus características de multi-resolución en tiempo y frecuencia. Se aplica un algoritmo novedoso de agrupamiento (clustering) espacial y se usan los mapas resultantes de tiempo y frecuencia para estimar la posición del defecto. Para la visualización 2D de los defectos ampliamos el análisis de la señal utilizando la técnica de focalización por apertura sintética (SAFT, synthetic aperture focusing technique). Implementamos un novedoso filtro de apodización 2D, juntamente con la técnica SAFT, y demostramos que elimina efectos no deseados, mejorando la resolución de la imagen reconstruida del defecto. El siguiente paso es un análisis y reconstrucción 3D. En este caso conseguimos una configuración experimental totalmente automatizada y sin contacto, permitiendo áreas de barrido sobre diferentes caras de un objeto. Los detalles de los defectos se registran desde diferentes ángulos, consiguiéndose una completa reconstrucción 3D. Finalmente, mostramos nuestros resultados en un tema complementario, relacionado con un caso particular de propagación de ultrasonidos en sólidos. Desde un primer momento, quisimos tener una comprensión física de la propagación y difracción de ondas de ultrasonidos en materiales sólidos. El control de los patrones de difracción en sólidos, mediante el uso de lentes ultrasónicas, ayudaría a la focalización/colimación del ultrasonido, reduciendo ecos y reflexiones en la superficie de contorno, mejorando del proceso de análisis NDT. Los cristales fonónicos se usan para regular la difracción y la respuesta en frecuencia de ondas de ultrasonido que se propagan en fluidos. No obstante, dichas estructuras se han estudiado mucho menos en materiales sólidos. Hemos realizado detalladas simulaciones numéricas de la propagación de ultrasonidos en un cristal fonónico sólido y hemos demostrado efectos de focalización y autocolimación. Finalmente hemos acoplado nuestra lente de cristal fonónico al sólido objeto de estudio, demostrando que el control de la difracción se conserva en el interior de dicho objeto a través del material de acoplamiento. Finalmente, proporcionamos una conclusión general sobre el trabajo declarado en esta tesis y un plan de trabajo futuro donde esta investigación puede extenderse y expandirse aún más a aplicaciones industriales en colaboración con el mercado de producciónPostprint (published version

Novel Method for Denoising Medical Image Using 2nd Level Discrete Wavelet Transform and Bilinear Filter

Medical imaging is one of the crucial subfields in the world of science and technology. There must be genuine image quality of medical images as it is used to diagnose diverse types of illness and in medical image we cannot settlement or compromise in quality because it provide us important data of patients and if quality compromised then severe effects may come into existence which definitely harmful for patients. Developing a significant denoising method plays a crucial role in image processing. In this research paper, image is first decomposed using filter into eight subbands using 3D DWT and bilateral filter technique and in this process we got approximation coefficient using DWT and once again DWT technique used on approximation coefficient image then we applied bilateral filter and the detail coefficients are subjected to Wavelet Thresholding. After that there is requirement of image reconstructed and this process is executed by inverse wavelet transform (IDWT) of the resultant coefficients and then it is filtered using bilateral filter. In our research work two types of images are considered which are MRI images and Ultrasound images. In this work IDWT process carried out two times that is why our research work known as 2 level DWT process. In this process finally two parameters are calculated PSNR and MSE

Novel entropy coding and its application of the compression of 3D image and video signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe broadcast industry is moving future Digital Television towards Super high resolution TV (4k or 8k) and/or 3D TV. This ultimately will increase the demand on data rate and subsequently the demand for highly efficient codecs. One of the techniques that researchers found it one of the promising technologies in the industry in the next few years is 3D Integral Image and Video due to its simplicity and mimics the reality, independently on viewer aid, one of the challenges of the 3D Integral technology is to improve the compression algorithms to adequate the high resolution and exploit the advantages of the characteristics of this technology. The research scope of this thesis includes designing a novel coding for the 3D Integral image and video compression. Firstly to address the compression of 3D Integral imaging the research proposes novel entropy coding which will be implemented first on 2D traditional images content in order to compare it with the other traditional common standards then will be applied on 3D Integra image and video. This approach seeks to achieve high performance represented by high image quality and low bit rate in association with low computational complexity. Secondly, new algorithm will be proposed in an attempt to improve and develop the transform techniques performance, initially by using a new adaptive 3D-DCT algorithm then by proposing a new hybrid 3D DWT-DCT algorithm via exploiting the advantages of each technique and get rid of the artifact that each technique of them suffers from. Finally, the proposed entropy coding will be further implemented to the 3D integral video in association with another proposed algorithm that based on calculating the motion vector on the average viewpoint for each frame. This approach seeks to minimize the complexity and reduce the speed without affecting the Human Visual System (HVS) performance. Number of block matching techniques will be used to investigate the best block matching technique that is adequate for the new proposed 3D integral video algorithm