Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation, was proposed and tested on the numerically simulated homogeneous speckle textures. The ICA filters were learned directly from the training images. Histogram regularization was conducted to make the speckle images quasi-stationary in the wide sense so as to be adaptive to an ICA algorithm. Both Principal Component Analysis (PCA) and a greedy algorithm were used to reduce the dimension of feature space. Finally, Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel were chosen as the classifier for achieving best classification accuracy. Several representative conventional methods, including both low and high order statistics based methods, and both filtering and non-filtering methods, have been chosen for comparison study. The numerical experiments have shown that the proposed ICA-based algorithm in many cases outperforms other algorithms for comparison. Two-component texture segmentation experiments were conducted and the proposed algorithm showed strong capability of segmenting two visually very similar yet different texture regions with rather fuzzy boundaries and almost the same mean and variance. Through simulating speckle with first order statistics approaching gradually to the Rayleigh model from different non-Rayleigh models, the experiments to some extent reveal how the behavior of higher order statistics changes with the underlying property of tissues. It has been demonstrated that when the speckle approaches the Rayleigh model, both the second and higher order statistics lose the texture differentiation capability. However, when the speckles tend to some non-Rayleigh models, methods based on higher order statistics show strong advantage over those solely based on first or second order statistics. The proposed algorithm may potentially find clinical application in the early detection of soft tissue disease, and also be helpful for better understanding ultrasound speckle phenomenon in the perspective of higher order statistics. For the despeckling problem, an algorithm was proposed which adapted the ICA Sparse Code Shrinkage (ICA-SCS) method for the ultrasound B-mode image despeckling problem by applying an appropriate preprocessing step proposed by other researchers. The preprocessing step makes the speckle noise much closer to the real white Gaussian noise (WGN) hence more amenable to a denoising algorithm such as ICS-SCS that has been strictly designed for additive WGN. A discussion is given on how to obtain the noise-free training image samples in various ways. The experimental results have shown that the proposed method outperforms several classical methods chosen for comparison, including first or second order statistics based methods (such as Wiener filter) and multichannel filtering methods (such as wavelet shrinkage), in the capability of both speckle reduction and edge preservation

Model based effective cell volume calculation in ultrasound tissue characterization

In reflective ultrasonic imaging, some statistical parameters can be extracted from the backscattered RF signal. Applying Rayleigh statistics or non-Rayleigh statistics to these parameters, the underlying tissue parameters such as effective scatterer number density that characterize the tissues can be further estimated. However, if the statistical processing is applied to the time domain signal, narrow band excitation pulse needs to be used when we consider the frequency dependency of the backscattered signal. The scattering resolution volume is also difficult to estimate. Chen, et al proposed a different method to determine the frequency-dependent effective scatterer number density, which is the actual number density multiplied by a frequency-dependent factor that depends on the differential scattering cross sections of all the scatterers. This method accounts for the possibility that different sets of scatterers may dominate the echo signal at different frequencies. It also avoids the difficulties for estimating the scattering volume. The frequency-dependent effective volume confined by some depth from z1 to z2 need to be evaluated. To evaluate this effective volume, the factors such as the input pulse function, the transducer transfer function, as well as the two-way diffraction effect need to be taken into consideration. The one way diffraction filter is approximated by the closed form Lommel diffraction formulations since the Lommel diffraction formulations and the exact time domain solutions known as \u27arccos\u27 form an approximate Fourier transform pair. The advantage of this approximation is that it is valid not only in the focal zone but also in the near field of transducer. In this thesis, an algorithm was designed to numerically calculate the effective volume. We demonstrated how the value of effective volume changes with different time gate weighting functions, different time gate widths, different input pulse bandwidths, as well as the different center locations of the time gate. When the input pulse bandwidth is narrow enough so that the width of pulse can\u27t be ignored when it is compared with the width of time gate, a modified method was proposed to compensate the edge effect. For two types of transducers, the calculation results were compared with the results obtained experimentally by Chen, et al under same conditions. The numerical results prove to be very close to the experimental results. The numerical calculation results obtained were further applied to the post-processing of the experimental echo signal data obtained from fresh pig liver, sponge in water and excised breast tissue samples. The echo signal data was acquired with different bandwidths and 3.5MHz center frequency. For 1.0MHz input pulse bandwidth, the frequency dependent effective scatterer number densities with different analysis time gate lengths were estimated. This analysis often reveals the presence of a coherent phasor sum in the random walk problem, also known as Rician behavior. The frequency dependent behavior was shown for different samples even at a narrow range of frequency around the center frequency. Different methods were used to estimate the statistical parameters by curve fitting either the histogram of the spectrum or the 2nd and higher order spectral moments, including fractional moments. The spectral moments fitting parameters obtained from the Generalized-K distribution model were used in a scatter plot for tissue differentiation

Growing Story Forest Online from Massive Breaking News

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page

Tunable current circulation in triangular quantum-dot metastructures

Advances in fabrication and control of quantum dots allow the realization of metastructures that may exhibit novel electrical transport phenomena. Here, we investigate the electrical current passing through one such metastructure, a system composed of quantum dots placed at the vertices of a triangle. The wave natural of quantum particles leads to internal current circulation within the metastructure in the absence of any external magnetic field. We uncover the relation between its steady-state total current and the internal circulation. By calculating the electronic correlations in quantum transport exactly, we present phase diagrams showing where different types of current circulation can be found as a function of the correlation strength and the coupling between the quantum dots. Finally, we show that the regimes of current circulation can be further enhanced or reduced depending on the local spatial distribution of the interactions, suggesting a single-particle scattering mechanism is at play even in the strongly-correlated regime. We suggest experimental realizations of actual quantum-dot metastructures where our predictions can be directly tested.Comment: 5 pages, 4 figures, the Supplemental Information is attached at the en

Reconstructing propagation networks with natural diversity and identifying hidden sources

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.Comment: 20 pages and 5 figures. For Supplementary information, please see http://www.nature.com/ncomms/2014/140711/ncomms5323/full/ncomms5323.html#

Frequency-Dependent Effective Cell Volume Estimation for Circular Disk Transducers Using Lommel Diffraction Formulation

In medical ultrasonic imaging the signal reflected from the tissue often has a random character to it. It is believed that the random nature of the tissue scattering microstructure is responsible for the stochastic nature of the echo signal. Chen, et. al. Have proposed a signal processing scheme that is based on the statistical moments calculated on the Fourier transform of the time gated echo signal. The theory requires the knowledge of a frequency- dependent effective cell volume term. This paper describes the use of a closed form expression (Lommel diffraction formulation) for this purpose. Our simulation results suggest that reliable estimation of the cell volume is possible only when the time duration of the excitation pulse is small compared to the time gate length

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page