Search

The adaptive block size processing method in different image areas makes block-matching and 3D-filtering (BM3D) have a very good image denoising effect. Based on these observation, in this paper, we improve BM3D in three aspects: adaptive noise variance estimation, domain transformation filtering and nonlinear filtering. First, we improve the noise-variance estimation method of principle component analysis using multilayer wavelet decomposition. Second, we propose compressive sensing based Gaussian sequence Hartley domain transform filtering to reduce noise. Finally, we perform edge-preserving smoothing on the preprocessed image using the guided filtering based on total variation. Experimental results show that the proposed denoising method can be competitive with many representative denoising methods on the evaluation criteria of PSNR. However, it is worth further research on the visual quality of denoised images. [ABSTRACT FROM AUTHOR]

Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728–0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03–172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42–39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation. [ABSTRACT FROM AUTHOR]

The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. [ABSTRACT FROM AUTHOR]

In many application domains, there is an urgent need for data owners to mine attribute associations hidden in linguistic conceptual knowledge. Numerous linguistically valued facts from the actual world have been modeled using the fuzzy linguistic approach. To solve the problem of association rule mining with fuzzy linguistic information, this paper proposes an association rule mining approach based on fuzzy linguistic attribute partial ordered structure diagram (FL-APOSD). First, complex relationships between linguistic values in association rule mining are represented by fuzzy linguistic association nodes and association paths via FL-APOSD. On this basis, the maximum frequent attribute set is mined from the FL-APOSD, and then the non-redundancy association rules are extracted. Second, to show the information hidden in the rules and help users to deeply understand the mining results, a fuzzy linguistic association rule visualization approach is proposed to convert the association rules into the FL-APOSD-based knowledge representation. Finally, experimental results on real-world datasets show the proposed approach's high efficiency, outperforming two relevant state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A prompt gamma neutron activation analysis logging method for formation water salinity determination is proposed. The response of formation water salinity for capture gamma ray spectrum and thermal neutron counts ratio are studied by Monte Carlo simulation method. Formation porosity and borehole fluid salinity influence on formation water salinity is corrected. A synthetical logging case is simulated to indicate the effectiveness of this logging method. The new logging method is also applied in oil field, and the formation water salinity of different depth is calculated. [ABSTRACT FROM AUTHOR]