This paper proposes the use of independent component analysis (ICA) method for learning features from radio frequency (RF) ultrasonic signals. Conventional feature extractors usually suffer from limitations caused by some of their assumptions about the structure of imaged organ and the interaction between ultrasonic signal and tissue. ICA, on the other hand, is a data-driven approach which learns efficient representation of data by maximizing independence of some basis functions that describe the important structures of the data. It has less restrictive considerations about formation of data and as a consequence, could adapt itself to the characteristics of data with different natures. These features of ICA make it a proper candidate for dealing with the problem of feature extraction from medical ultrasound signals where variations in tissue structures and data acquisition conditions are considerable. Experimental results on raw backscattered RF signals of prostate gland show favorable performance of ICA as compared with the conventional methods designed for extracting feature from RF signals.

Level-set methods have proven to be powerful and flexible tools in computer vision and medical imaging. Unfortunately, the flexibility of such models has historically resulted in long computational times and therefore limited clinical utility. In this context, we propose the first rigorous GPU implementation of the sparse field algorithm. We show that this model is able to reach high computational efficiency with no reduction in segmentation accuracy compared to its sequential counter-part.

In this work we introduce the dual frame operator relative to the aliasing compensated frequency warping operator for non-smooth warping functions. Except for approximation errors, the resulting operators pair allows perfect reconstruction. Unlike previously introduced frequency warping frame and dual frame pair, which was obtained by adding redundancy to the group delay shift covariance analysis space, in this case no modifications are needed to the analysis operator. Anyway, to calculate the dual frame, previously introduced computational models are exploited and forward developed. Basically, the dual frame construction is based on an operator power series which is entirely precomputed, so the complexity of frequency warping inversion is not increased.

In this work we introduce a new frequency warping operator for non-smooth warping function allowing perfect reconstruction. The transformation is based on a previously introduced aliasing compensated frequency warping operator having a residual error because of finite output dimension. By adding some redundancy, the effect of an infinite output dimension can be taken into account in a compressed way, based on an analytical factorization. In the reconstruction process, the additional redundant samples are expanded, making the inverse transform differ from the direct one, but guaranteeing perfect reconstruction.

Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

In this work we provide an accurate analytical estimation of the frame bounds for frequency warping operators of arbitrary shaped non-smooth warping maps. We deal with both the nonuniform Fourier transform approximation and the aliasing suppressed form of the frequency warping operator. The estimation procedure is obtained by using an analytical model of the aliasing operators which has been previously introduced. The provided estimations can be used as design formulas for the parameters, such as degree of smoothness and redundancy, involved in the definition of the frequency warping operators.

In this work we introduce an analytical characterization of the frequency warping operator of arbitrary shaped non-smooth warping maps. The transformation matrix is decomposed in two additive terms: the first term represents its nonuniform Fourier transform approximation while the second term is imposed for aliasing suppression. The first transformation is known to be analytically characterized and fast computable by an interpolation approach. For the second transformation an analytical representation is introduced which allows a fast computation and a simple design. Finally, an example of a potential application is shown.

Biomedical ultrasound (US) image quality is limited due to the blurring of tissue reflectivity introduced by the transducer point spread function (PSF). Deconvolution techniques can be used to eliminate this effect and to obtain the pure tissue response. In this paper we propose a new enhancement method based on recursive least squares (RLS) adaptive filtering. The method handles the spatial variability of the PSF by means of blind homomorphic characterization of the transducer. The results show that the RLS adaptation increases contrast and resolution by eliminating the spectral shaping of the backscattered echo and thus enhancing the diagnostic capability of US images.

In this work we describe a mathematical operator for the fast calculation of frequency warping. Such invertible transformation can be used to reshape the tiling of time- frequency analysis in a flexible way. In our approach the warping is computed by splitting the transformation operator in two additive terms: the first term represents its Nonuniform Fourier Transform approximation while the second term is imposed for aliasing suppression. The first transformation is known to be analytically characterized and rapidly computable by interpolation approaches. An analytical characterization of the second transformation operator is proposed for arbitrary shaped non-smooth warping maps commonly used for signal processing applications.

In this paper we present a deconvolution technique for ultrasound images based on a Maximum Likelihood (ML) estimation procedure. In our approach the ultrasonic radio-frequency (RF) signal is considered as a sequence affected by intersymbol interference (ISI) and AWGN noise. In order to reduce the computational cost, the estimation is performed with a reduced-state Viterbi algorithm. The channel effect is estimated in two different ways: either measuring the transducer response with an experimental setting or with blind homomorphic techniques. We verify an image quality enhancement with respect to different metrics. Extensive tests are made to estimate the quantization alphabet that gives the best performances.

In this work we present a fast and accurate algorithm to compute frequency warping of arbitrary shaped maps. In contrast to the common Laguerre approach, frequency warping is represented by a matrix of truncated finite dimensions. The transformation matrix is decomposed in two additive terms: the first term represents its nonuniform Fourier transform approximation while the second term is imposed for aliasing suppression. Both matrices are approximated with a least square approach according to a suitable set of vectors. The cardinality of this set is shown to be nearly proportional to the logarithm of the matrix dimension. Finally, trade-off aspects between algorithm complexity and performance are discussed

A new technique has been implemented with the aim of providing an adaptive tool for the generation of computational meshes for 3-D semiconductor device simulation. The core of the proposed wavelet-based adaptive method (WAM) is a refinement algorithm based on the wavelet transform, which gives an estimation of solution regularity and progressively adapts the grid by increasing the resolution only in regions where the important physical phenomena take place. WAM is inserted into a validation tool that provides the interfacing filters with both the solver and a meshing engine. Additional features assure the quality of the generated meshes by increasing the selectivity properties of the refinement tool, preventing numerical instabilities or artifacts, such as abrupt variations of I-V curves between successive bias points during quasi-stationary simulations. Simulation results are provided, which show the effectiveness of the proposed approach as a means to guide the automatic refinement of the discretization grid, preserving accuracy with negligible computational overhead and no skilled control from the user.

In this paper the experimental chaotic behavior of the well known 741 operational amplifier is presented when electromagnetic interference is conveyed to its power supply pins. Such a complex dynamic behavior occurs if the interfering signal is in the 5 MHz-28 MHz range with amplitudes greater than about 100 mV. These frequencies are out of the standard operating conditions, but they easily occur in real applications as a result of interference. The chaotic behavior was measured on 741 amplifiers of three different manufacturers and it was also verified by simulation of an equivalent circuit described in the literature. The strong similarities between simulation and measurements argue that the chaotic behavior is a property of the amplifier circuit architecture and it is not an effect of the package or due to the experimental setup.