Evaluation of Digital Speckle Filters for Ultrasound Images

Ultrasound (US) images are inherently corrupted by speckle noise causing inaccuracy of medical diagnosis using this technique. Hence, numerous despeckling filters are used to denoise US images. However most of the despeckling techniques cause blurring to the US images. In this work, four filters namely Lee, Wavelet Linear Minimum Mean Square Error (LMMSE), Speckle-reduction Anisotropic Diffusion (SRAD) and Non-local-means (NLM) filters are evaluated in terms of their ability in noise removal and capability to preserve the image contrast. This is done through calculating four performance metrics Peak Signal to Noise Ratio (PSNR), Ultrasound Despeckling Assessment Index (USDSAI), Normalized Variance and Mean Preservation. The experiments were conducted on three different types of images which is simulated noise images, computer generated image and real US images. The evaluation in terms of PSNR, USDSAI, Normalized Variance and Mean Preservation shows that NLM filter is the best filter in all scenarios considering both speckle noise suppression and image restoration however with quite slow processing time. It may not be the best option of filter if speed is the priority during the image processing. Wavelet LMMSE filter is the next best performing filter after NLM filter with faster speed

Evaluation of Digital Speckle Filters for Ultrasound Images

Ultrasound (US) images are inherently corrupted by speckle noise causing inaccuracy of medical diagnosis using this technique. Hence, numerous despeckling filters are used to denoise US images. However most of the despeckling techniques cause blurring to the US images. In this work, four filters namely Lee, Wavelet Linear Minimum Mean Square Error (LMMSE), Speckle-reduction Anisotropic Diffusion (SRAD) and Non-local-means (NLM) filters are evaluated in terms of their ability in noise removal and capability to preserve the image contrast. This is done through calculating four performance metrics Peak Signal to Noise Ratio (PSNR), Ultrasound Despeckling Assessment Index (USDSAI), Normalized Variance and Mean Preservation. The experiments were conducted on three different types of images which is simulated noise images, computer generated image and real US images. The evaluation in terms of PSNR, USDSAI, Normalized Variance and Mean Preservation shows that NLM filter is the best filter in all scenarios considering both speckle noise suppression and image restoration however with quite slow processing time. It may not be the best option of filter if speed is the priority during the image processing. Wavelet LMMSE filter is the next best performing filter after NLM filter with faster speed

Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture Detection

Noise reduction in medical image analysis is still an interesting hot topic, especially in the field of ultrasonic images. Actually, a big concern has been given to automatically reducing noise in human-bone ultrasonic computed tomography (USCT) images. In this chapter, a new hardware prototype, called USCT, is used but images given by this device are noisy and difficult to interpret. Our approach aims to reinforce the peak signal-to-noise ratio (PSNR) in these images to perform an automatic segmentation for bone structures and pathology detection. First, we propose to improve USCT image quality by implementing the discrete wavelet transform algorithm. Second, we focus on a hybrid algorithm combining the k-means with the Otsu method, hence improving the PSNR. Our assessment of the performance shows that the algorithmic approach is comparable with recent methods. It outperforms most of them with its ability to enhance the PSNR to detect edges and pathologies in the USCT images. Our proposed algorithm can be generalized to any medical image to carry out automatic image diagnosis due to noise reduction, and then we have to overcome classical medical image analysis by achieving a short-time process

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Real-time Ultrasound Signals Processing: Denoising and Super-resolution

Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies

3D Characterisation of microcracks in concrete

The nature of microcracks that developed in concrete is not well understood. One reason for this is the lack of suitable techniques to detect and characterise the microcracks. Conventional methods include imaging polished cross sections with scanning electron microscopy and optical microscopy. However, these techniques only provide a two-dimensional representation of a three-dimensional structure, which significantly reduces the insights from such analysis. Another reason is that the development of microcracks may be associated with various complex forms of concrete deterioration during service life, e.g. due to mechanical loading, drying, thermal effects and chemical reactions. This complicates laboratory scale experiments and inducing “realistic” microcracks in concrete samples becomes very difficult. The aim of this study is to develop new techniques for three-dimensional quantitative characterisation of microcracks and to apply these to understand the properties of microcracks in concrete. A thorough literature review was conducted to identify the causes of microcracking in concrete, mechanisms of microcrack initiation and propagation, transport properties of micro-cracked concrete and methods to characterise microcracks in two dimensions (2D) and three dimensions (3D). Materials and experimental procedures for inducing different types of microcracks, sample preparation for imaging and image analysis of microcracks are discussed. The feasibility of three-dimensional techniques such as focused ion beam nanotomography (FIB-nt), broad ion beam combines with serial sectioning (BIB), X-ray microtomography (μ-CT) and laser scanning confocal microscopy (LSCM) for imaging microcracks were investigated. A new approach that combines LSCM with serial sectioning was proposed to enhance the capability of LSCM for imaging microcracks in 3D. A major focus of this thesis was dedicated to microcracks induced by autogenous shrinkage because this has been previously neglected due to the dominant role of drying shrinkage. Nonetheless, the increasing use of high strength concretes containing low water/binder ratio, complex binder systems and multiple chemical admixtures in recent years has highlighted the problem of autogenous shrinkage in these concretes. This study presents a first attempt on direct characterisation and understanding of the microcracks caused by autogenous shrinkage in 3D. Various concrete samples were produced and sealed cured to induce autogenous shrinkage. The water/binder ratio, cement type and content, and aggregate particle size distribution were varied to vary the magnitude of autogenous shrinkage and degree of microcracking. Linear deformation measurement was performed to correlate autogenous shrinkage with degree of microcracking. Samples were imaged in 2D using laser scanning confocal microscope (LSCM) and in 3D with X-ray microtomography (μ-CT). Subsequently, 2D and 3D image analysis was employed to quantify microcracks > 1 μm in width. A major challenge was to isolate the microcracks that are inherently connected to pores and air voids. Therefore, an algorithm was developed to separate microcracks from pores, and to extract quantitative data such as crack density, orientation degree, distribution of width and length, as well as connectivity and tortuosity. The results show that use of supplementary cementitious materials and low water/binder ratio can increase linear deformation and the amount of the microcracks. The thesis discusses the effect of autogenous shrinkage on the characteristics of the induced microcracking, which is critical to understanding the transport properties and long-term durability of concretes containing supplementary cementitious materials.Open Acces

Amélioration de la résolution en imagerie ultrasonore

L'imagerie ultrasonore est une modalité d'imagerie médicale couramment utilisée pour l'établissement de diagnostics de thérapie ou de suivi divers (croissance du fœtus, détection de certains cancers, assistance à la réalisation d'actes thérapeutiques). Si cette modalité dispose de nombreux avantages comme son innocuité, sa facilité d'utilisation et son faible coût, elle souffre cependant d'une résolution spatiale limitée quand on la compare à d'autres modalités comme l'imagerie par résonance magnétique. L'amélioration de la résolution des images ultrasonores est un défi de taille et de très nombreux travaux ont depuis longtemps exploré des approches instrumentales portant sur l'optimisation du dispositif d'acquisition. L'imagerie échographique haute résolution permet ainsi d'atteindre cet objectif à l'aide de sondes particulières mais se trouve aujourd'hui confrontée à des limitations d'ordre physique et technologique. L'objet de cette thèse est d'adopter une stratégie de post-traitement afin de contourner ces contraintes inhérentes aux approches instrumentales. Dans ce contexte, nous présentons deux approches pour l'amélioration de la résolution des images ultrasonores, selon que les données disponibles prennent la forme d'une séquence d'images ou d'une image unique. Dans le premier cas, l'adaptation d'une technique d'estimation du mouvement originellement proposée pour l'élastographie nous permet d'établir un cadre de reconstruction haute résolution efficace dédié à la modalité qui nous intéresse. Cette approche est évaluée à l'aide d'une simulation réaliste d'images ultrasonores avant d'être appliquée à des données in vivo. Nous proposons ensuite, dans le cadre du traitement d'une seule image, deux méthodes de déconvolution rapide pour l'amélioration de la résolution. Ces approches prennent en compte, suivant leur disponibilité, certaines informations a priori sur les conditions d'acquisition comme la réponse impulsionnelle spatiale du système. Les résultats sont caractérisés dans un premier temps à l'aide de données synthétiques et sont ensuite validés sur des images in vivoUltrasound imaging is a medical imaging modality commonly involved in various therapeutic and monitoring diagnoses such as fetal growth, cancer detection or image-guided intervention. Despite its harmless, easy-to-use and cost-effective features, ultrasound imaging has some intrinsic limitations regarding its spatial resolution, especially compared to other modalities such as magnetic resonance imaging. Improving the spatial resolution of ultrasound images is an up-to-date challenge and many works have long studied instrumentation approaches dealing with the optimisation of the acquisition device. High resolution ultrasound imaging achieves this goal through the use of specific probes but is now facing physical and technological limitations. The goal of this thesis is to make use of post-processing techniques in order to circumvent the inherent constraints of instrumental approaches. In this framework, we present two approaches for the resolution enhancement of ultrasound images, depending on whether the available data is composed of an image sequence or a single image. In the former case, we show that the adaptation of a motion estimation technique originally proposed for elastography makes it possible to design an effective high-resolution reconstruction framework dedicated to ultrasound imaging. This approach is first assessed using a realistic simulation of ultrasound images and then used for the processing of in vivo data. In the latter case, dealing with the restoration of a single image, we develop two fast deconvolution methods for the resolution enhancement task. These approaches take into account, according to their availability, specific a priori information about the image acquisition process such as the system spatial impulse response. Results are performed with synthetic data and extended to in vivo ultrasound image