Analysis of low-order autoregressive models for ultrasonic grain signal characterization

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k -nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Blind detection of nonlinearities in multiple-echo ultrasonic signals

In this paper, we propose and analyze by means of simulations the use of surrogate data algorithms for blind detection of nonlinearities in multiple-echo ultrasonic signals. We assume a blind scheme so that no information about the input (emitted ultrasonic pulse) can be used. The metrics and equations that model some nonlinear situations are carefully reviewed. Also, closed form equations of the third-order metrics from a simplified second-order Volterra kernel are derived. Computer simulations show that the surrogate data technique is a potentially powerful tool for blind detection of nonlinearities in multiple-echo ultrasonic signals if adequate metrics are chosen. They also reveal interesting trade-offs among parameters that model ultrasonic systems and detection percentages.

Scatterer size estimation in pulse-echo ultrasound using focused sources: calibration measurements and phantom experiments

In a companion paper [T. A. Bigelow and W. D. O'Brien Jr., J. Acoust. Soc. Am. 116, 578 (2004)], theory, supported by simulations, showed that accurate scatterer size estimates could be obtained using highly focused sources provided that the derived generalized attenuation-compensation function was used and the velocity potential field near the focus could be approximated as a three-dimensional Gaussian. Herein, the theory is further evaluated via experimental studies. A calibration technique is developed to find the necessary equivalent Gaussian dimensions for a focused source using reflections obtained from a rigid plane scanned through the focus. Then, the theoretical analysis of focused sources is validated experimentally using three spherically focused ultrasound transducers to estimate the radius of glass beads imbedded in tissue mimicking phantoms. Both the impact of focusing (f/1, f/2, and f/4) and the effect of scatterer type (comparing glass bead results to simulation results that used scatterers with Gaussian impedance distributions) were tested. The simulated differences agree with the measured differences to within 2.5% provided that the comparison is made between the same scatterer type and sources with the same equivalent Gaussian dimensions. The improvement provided by the generalized attenuation-compensation function is greatly influenced by the type of scatterer whose size is being estimated and decreases as the wavelength dependence of the Gaussian depth of focus is reduced.

On estimating the center frequency of ultrasonic pulses

In this paper we propose a new technique for estimating the center frequency of the ultrasound pulse from records of backscattering noise. We start by considering that the conventional maximum frequency method can be seen as a filtering (differentiator) of the pulse spectrum magnitude followed by a searching for the zero-crossing value. The new approach replaces the differentiator by a Hilbert transformer. We show in the paper that the proposed method has less variance than the maximum frequency method. In particular, we analyse the performance assuming that the real cepstrum method is used for extracting pulse spectrum magnitude. We give an upper bound for the variance reduction when practical criteria are applied for fitting the cepstrum cut-off frequency. The analytical work is verified by real and simulated data.

Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization

In the field of biological tissue characterization, fundamental acoustic attenuation properties have been demonstrated to have diagnostic importance. Attenuation caused by scattering and absorption shifts the instantaneous spectrum to the lower frequencies. Due to the time-dependence of the spectrum, the attenuation phenomenon is a time-variant process. This downward shift may be evaluated either by the maximum energy frequency of the spectrum or by the center frequency. In order to improve, in strongly attenuating media, the results given by the short-time Fourier analysis and the short-time parametric analysis, we propose two approaches adapted to this time-variant process: an adaptive method and a time-varying method. Signals backscattered by an homogeneous medium of scatterers are modeled by a computer algorithm with attenuation values ranging from 1 to 5 dB/cm MHz and a 45 MHz transducer center frequency. Under these conditions, the preliminary results obtained with the proposed time-variant methods, compared with the classical short-time Fourier analysis and the short-time auto-regressive (AR) analysis, are superior in terms of standard deviation (SD) of the attenuation coefficient estimate. This study, based on nonstationary AR spectral estimation, promises encouraging perspectives for in vitro and in vivo applications both in weakly and highly attenuating media.

Recursive autoregressive spectral maps for ocular pathology detection

This article presents a new approach to the problem of obtaining topological maps for tissue characterization, based on spectral parameters extracted from radio frequency (RF) backscattered ultrasonic signals. The spectral parameter we deal with is the power spectral density centroid, since it is an efficient indicator of the tissue microstructure characteristics as far as the particle dimensions are concerned. The spectral analysis of RF ultrasonic echoes is performed using a recursive least-squares scheme with a variable forgetting factor, based on low-order autoregressive models. The proposed technique is particularly tailored to the differentiation of ocular pathologies; moreover, it is capable of tracking the spatial high-varying signal characteristics. The proposed approach was tested on simulated signals and on a gel suspension of calibrated latex spheres; finally, it was applied to signals scattered by in vitro eye specimens, giving satisfactory results in terms of frequency resolution and computational efficiency. The reduced computational burden allows an on-line implementation of the procedure. Topological spectral maps, combined with the conventional B-mode display, may offer a complete and integrated diagnostic tool, able to locally characterize the investigated tissue region in terms of amplitude and frequency shift of the corresponding echoes.

Non-conventional ultrasonography (power spectrum analysis) in the management of retinoblastoma

The differential diagnosis among the diseases that may simulate retinoblastoma is supported by several techniques; however, none of them can give an undeniable answer. In the authors' opinion the tissue characterization by power spectrum analysis of the radiofrequency ultrasound data may play an important role in the backscattered signal spectrum while being sensitive to the spectral shift trend. In order to evaluate the patterns of regression of quiescence of retinoblastoma foci after conservative treatment the authors try to create a model of tissue characterization that provides information not available in conventional A & B scan ultrasonography about cell type, vascularization and necrosis.

Analysis of order-statistic CFAR threshold estimators for improved ultrasonic flaw detection

In the pulse-echo method using broadband transducers, flaw detection can be improved by using optimal bandpass filtering to resolve flaw echoes surrounded by grain scatterers. Optimal bandpass filtering is achieved by examining spectral information of the flaw and grain echoes where frequency differences have been experimentally shown to be predictable in the Rayleigh scattering region. Using optimal frequency band information, flaw echoes can then be discriminated by applying adaptive thresholding techniques based on surrounding range cells. The authors present order-statistic (OS) processors, ranked and trimmed mean (TM), to robustly estimate the threshold while censoring outliers. The design of these OS processors is accomplished analytically based on constant false-alarm rate (CFAR) detection. It is shown that OS-CFAR and TM-CFAR processors can detect flaw echoes robustly with the CFAR of 10 (-4) where the range cell used for the threshold estimate contains outliers.