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Speckle noise is one of the major artefacts in ultrasound images. The denoising faces the trade-off between noise suppression and structural preservation. In this study, multiscale adaptive regularisation Savitzky-Golay (MARSG) method, the new filter for removing speckle noise, is proposed. The proposed method combines the benefit of the multiscale analysis and the outstanding noise removing capability of Savitzky-Golay (SG) filter. The Laplacian pyramid is employed to separate an image into the noise, texture and object layers. Adaptive regularisation Savitzky-Golay (ARSG) filter is developed as the denoising filter in the noise and the texture layers. The denoising of the ARSG filter is adaptively adjusted in order to preserve the edges of objects in the image. The experiments on the synthetic and ultrasound images demonstrated that MARSG method offered better balance between noise removal and structural preservation than non-linear multiscale wavelet diffusion, feature-enhanced speckle reduction and regularised SG filter.

Background. In Thailand, Japanese encephalitis appears with a higher incidence of the Northern region (0.0/100,000 cases/year) as compared to the central southern region of the country (0.01/100,000/year). Also, all Japanese encephalitis virus (JEV) recently isolated strains in country belong to the genotype I and III.Methods. In order to evaluate the long-term efficacy of the newly developed live attenuated SA 14-14-2 Japanese encephalitis vaccine in Thailand, the immune response in children was studied after a second immunization campaign. Following 2012 immunization campaign, a second JEV immunization vaccine was delivered to children among four provinces of the Northern Region of Thailand. For each province, the recipients were identified accordingly to the two vaccine campaigns time including the children who received the second dose of vaccine: 1/ less than one year before the present study (N=30 by province); 2/ more than one year before the present study (N=70 by province). Altogether, a total of 400 children were enrolled in this project. After receiving the second vaccine dose, blood samples were collected and tested for JEV (Genotypes I and III) neutralizing antibodies following a standard procedure of LLC-MK2 infected cells.Results. All recipient presented neutralizing antibodies cross reacting against the prevalent JEV genotypes I (SM1 JEV strain) and genotype III (SA 14-14-2 and Beijing JEV strains). Most of the children from the first group had a seroconversion rate of 94.2% against homologous (i.e. GIII). Among them, an optimal rate of 100% seroconversion was find at the Phayao Province against both heterologous (GI) and homologous (GIII) JEV strains. Geometric Mean Titer (GMT) of neutralizing antibody against SM1, SA 14-14-2 JEV, and Beijing JEV strains were consistently high, respectively as 239±50, 323±201.2 and 177±36.7. For the second group, seroconversion rate was lower against SM1, SA 14-14-2 and Beijing JEV strains with a rate of 94.3, 90% and 86.8% with respectively a GMT of 154±32, 90±23 and 82±13.6.Conclusion. Although, the immunity appears to decrease within the period of 2.5 years, neutralizing antibody rate are consistent to protect against the JEV infection. Nevertheless, a long-term follow up is suitable to evaluate an eventual third dose opportunity.

The demand of the fast acquisition in 3D ultrasound (3D-US) imaging leads to the dense population of speckle noise which causes the difficulty in medical diagnosis. In this Letter, 3Dadaptive regularisation Savitzky–Golay (3D ARSG) filter is proposed as the real-time filter for removing speckle noise in 3D-US imaging. The 3D ARSG filter uses the local intensity homogeneity to classify between noisy and texture regions. The intensity in the noisy regions is heavily smoothed, while the resolvable edges remain sharp. The experiment on the denoising of the 3D simulated models and the 3D-US images indicated that 3D ARSG filter provided the image with far less noise and crisper objects’ boundary than conventional methods. In addition, the proposed filter preserves the structural details with regard to the dimension and can denoise images in real time.

Region growing is a widely used image-segmentation techniques. However, oversegmentation and dependency on the starting point (seed) pose difficulties. We propose au algorithm using the robust homogeneous criteria consisting of (1) intensity and (2) no edge in-between criterion. The region is grown into pixels that satisfy both criteria. The intensity criterion ensures that no abrupt. change occurs in the image intensity inside a region. It is calculated based on the local average image intensity after rejecting outliers. The no edge in-between criterion prevents the growing across the boundaries. The segmentation algorithm consists of a (1) coarse and a (2) fine scaled segmentation. In the first stage, large regions are segmented, which include both uniform regions and regions containing a small area of intensity fluctuation. In the second stage, the remaining regions are segmented. Our experiments showed good segmentation results for both gray-scale and color images.

The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.