Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning.

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients' condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering.

In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.

Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals.

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.

A uniform 2,5-dihydroxybenzoic acid layer as a matrix for MALDI-FTICR MS-based lipidomics.

A very uniform 2,5-dihydroxybenzoic acid (DHB) layer was for the first time constructed and used as a matrix for matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR MS) for quickly exploring the changes in lipids within biological systems. Lipid extracts from biological samples were dissolved in chloroform and deposited onto the DHB layer. Benefiting from the insolubility of DHB in chloroform, the uniform matrix crystals were still maintained, and more importantly, the lipid analytes were distributed homogeneously on the layer, which significantly increased the reproducibility of analysis using MALDI-FTICR MS. Taking advantage of the benefit of high resolution of FTICR MS and the fragment ions obtained by MS/MS, lots of lipids were identified. This method was used for exploring the changes of lipids in drug-resistant tumor cells compared with paired drug-sensitive tumor cells. The principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were employed for discovery of the changed lipids. This method, characterized by the simplicity and the speediness, demonstrated a new and promising approach for lipidomics study.

Ultra-high-performance liquid chromatography electrospray ionization tandem mass spectrometry for accurate analysis of glycerophospholipids and sphingolipids in drug resistance tumor cells.

Glycerophospholipids and sphingolipids are important signaling molecules which are involved in many physiological and pathological processes. Here we reported an effective method for accurate analysis of these lipids by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). The methanol method was adopted for extraction of lipids due to its simplicity and high efficiency. It was found that two subclasses of sphingolipids, sulfatide (ST) and cerebroside (CB), were heat labile, so a decreased temperature in the ion source of MS might be necessary for these compounds analysis. In addition, it was found that the isobaric interferences were commonly existent, for example, the m/z of 16:0/18:1 PC containing two (13)C isotope being identical to that of 16:0/18:0 PC determined by a unit mass resolution mass spectrometer; therefore, a baseline separation of interferential species was required to maintain selectivity and accuracy of analysis. In this work, an ultra-high-performance liquid chromatography (UHPLC)-based method was developed for separation of interferential species. Moreover, in order to deal with the characteristics of different polarity and wide dynamic range of glycerophospholipids and sphingolipids in biological systems, three detecting conditions were combined together for comprehensive and rational analysis of glycerophospholipids and sphingolipids. The method was utilized to profile glycerophospholipids and sphingolipids in drug resistant tumor cells. Our results showed that many lipids were significantly changed in drug resistant tumor cells compared to paired drug sensitive tumor cells. This is a systematic report about the isobaric interferences and heat labile compounds interferences when analyzing glycerophospholipids and sphingolipids by ESI-MS/MS, which aids in ruling out one potential source of systematic error to ensure the accuracy of analysis.

Polystyrene spheres-assisted matrix-assisted laser desorption ionization mass spectrometry for quantitative analysis of plasma lysophosphatidylcholines.

The quantitative analysis by matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) is a challenge due to the poor reproducibility originating from the heterogeneity of the matrix-analyte crystals. Polystyrene (PS) colloidal spheres have superior monodispersed property and can self-assemble to form photonic crystals. With the assistance of PS spheres, a uniform matrix-analyte cocrystal was constructed for the quantitative analysis of plasma lysophosphatidylcholines (LPCs). The matrix and the solvent in MALDI MS analysis were optimized, and the reproducibility and the accuracy were investigated in detail. This is the first report about the very simple method of PS spheres-assisted MALDI MS for quantitative analysis, where it is believed that this approach will greatly expand the applications of MALDI MS.

Spatial characteristics of tree diameter distributions in a temperate old-growth forest.

This contribution identifies spatial characteristics of tree diameter in a temperate forest in north-eastern China, based on a fully censused observational study area covering 500×600 m. Mark correlation analysis with three null hypothesis models was used to determine departure from expectations at different neighborhood distances. Tree positions are clumped at all investigated scales in all 37 studied species, while the diameters of most species are spatially negatively correlated, especially at short distances. Interestingly, all three cases showing short-distance attraction of dbh marks are associated with light-demanding shrub species. The short-distance attraction of dbh marks indicates spatially aggregated cohorts of stems of similar size. The percentage of species showing significant dbh suppression peaked at a 4 m distance under the heterogeneous Poisson model. At scales exceeding the peak distance, the percentage of species showing significant dbh suppression decreases sharply with increasing distances. The evidence from this large observational study shows that some of the variation of the spatial characteristics of tree diameters is related variations of topography and soil chemistry. However, an obvious interpretation of this result is still lacking. Thus, removing competitors surrounding the target trees is an effective way to avoid neighboring competition effects reducing the growth of valuable target trees in forest management practice.