SAR Amplitude Probability Density Function Estimation Based on a Generalized Gaussian Model

International audienceIn the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena

Modeling the statistics of high resolution SAR images

In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images

Deep Learning Methods for Estimation of Elasticity and Backscatter Quantitative Ultrasound

Ultrasound (US) imaging is increasingly attracting the attention of both academic and industrial researchers due to being a real-time and nonionizing imaging modality. It is also less expensive and more portable compared to other medical imaging techniques. However, the granular appearance hinders the interpretation of US images, hindering its wider adoption. This granular appearance (also referred to as speckles) arises from the backscattered echo from microstructural components smaller than the ultrasound wavelength, which are called scatterers. While significant effort has been undertaken to reduce the appearance of speckles, they contain scatterer properties that are highly correlated with the microstructure of the tissue that can be employed to diagnose different types of disease. There are many properties that can be extracted from speckles that are clinically valuable, such as the elasticity and organization of scatterers. Analyzing the motion of scatterers in the presence of an internal or external force can be used to obtain the elastic properties of the tissue. The technique is called elastography and has been widely used to characterize the tissue. Estimating the scatterer organization (scatterer number density and coherent to diffuse scattering power) is also crucial as it provides information about tissue microstructure and potentially aids in disease diagnosis and treatment monitoring. This thesis proposes several deep learning-based methods to facilitate and improve the estimation of speckle motion and scatterer properties, potentially simplifying the interpretation of US images. In particular, we propose new methods for displacement estimation in Chapters 2 to 6 and introduce novel techniques in Chapters 7 to 11 to quantify scatterers’ number density and organization

High frequency ultrasonic characterization of human skin In vivo

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 144-161).High frequency (>20 MHz) ultrasound has numerous potential applications in dermatology because of its ability to penetrate several millimeters into the skin and provide information at a spatial resolution of tens of microns. However, conventional B-scan images of skin tissues often lack the capability to characterize and differentiate various skin tissues. In this work, quantitative ultrasonic methods using the attenuation coefficient, backscatter coefficient, and echo envelope statistics were studied for their potential to characterize human skin tissues in vivo. A high frequency ultrasound system was developed using polymer transducers, a pulser/receiver, high-speed digitizer, 3-axis scanning system, and a PC. Data collected using three different transducers with center frequencies of 28, 30 and 44 MHz were processed to determine the characteristics of normal human dermis and subcutaneous fat. Attenuation coefficients were obtained by computing spectral slopes vs. depth, with the transducers axially translated to minimize diffraction effects. Backscatter coefficients were obtained by compensating recorded backscatter spectra for system-dependent effects, and additionally for one transducer, using the reference phantom technique. Good agreement was seen between the results from the different transducers/methods. The attenuation coefficients were well described by a linear frequency dependence whose slope showed significant differences between the forearm and fingertip dermis, but not between the forearm dermis and fat. The backscatter coefficient of the dermis showed an increasing trend with frequency and was significantly higher than that of fat.(cont.) A maximum likelihood fit of six probability distributions (Rayleigh, Rician, K, Nakagami, Weibull, and Generalized Gamma) to fluctuations in echo envelope data showed that the Generalized Gamma distribution modeled the envelope better than the other distributions. Fat was seen to exhibit significantly more pre-Rayleigh behavior than the dermis. Data were also obtained from the skin of patients patch-tested for contact dermatitis. A significant increase in skin thickness, decrease in mean backscatter of the upper dermis, and decrease in attenuation coefficient slope was found at the affected sites compared to normal skin. However, no differences in terms of echo statistics were found in the mid-dermis. These results indicate that a combination of ultrasonic parameters have the potential to non-invasively characterize skin tissues.by Balasundara I. Raju.Ph.D

Modeling the statistics of high resolution SAR images

In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images

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

Ultrasonic differentiation of healthy and cancerous neural tissue

It is well documented that intraoperative ultrasound offers improvements to the extent of tumour resected in neurosurgery but currently fails to depict the boundaries of more invasive tumours. Quantitative ultrasound (QUS) is a technique that models ultrasound scattering in tissue mathematically. It can act as a quantitative tool to identify cancerous regions and be used to define features which can train a machine learning (ML) classifier. The use of QUS to differentiate healthy and malignant brain tissue is the objective of this thesis. This work began with a proof of concept study which saw the effective implementation of QUS with a linear array transducer, at conventional frequencies, on phantom materials. The results were then used to train a K-nearest neighbours (KNN) binary classifier to differentiate between two soft tissues. Insight into the most practical parameters for near real time tissue identification was achieved, as well as the opportunity to produce parametric images for various QUS parameters. The effects of freezing and fixation of tissue on QUS results were also considered. The experimental design was developed to obtain a higher lateral spatial resolution before applying it to ex vivo human samples of ten healthy and eight high-grade glioma (HGG) tissues. This was accomplished with both a linear array and a single element scanning system, at centre frequencies of 25 and 74 MHz, respectively. The SoS and attenuation were found to be higher, on average, in the tumour samples than in the healthy tissue. The homodyned K-distribution (HK) parameters alone could distinguish between healthy and HGG tissue to 96% accuracy at 74 MHz, suggesting this is a viable solution for residual HGG detection. To explore the potential of ML with a larger data set, and to extend the study to low grade glioma (LGG) tissue, acoustic impedance maps based on 300 previously recorded microscope histology images of each tissue type were created. The interaction with high frequency (HF) ultrasound was explored using finite element analysis and QUS parameters were obtained. A classification algorithm was able to differentiate healthy and HGG to near perfect accuracy, but a significantly lower accuracy of 79% was found when distinguishing LGG from healthy tissue maps. This research represents a step forward in the otherwise unexplored landscape of HF QUS in brain tissue which necessitates further work to transition from laboratory based experiments to in vivo QUS to aid intraoperative glioma detection