Zero-watermarking Algorithm for Medical Volume Data Based on Difference Hashing

In order to protect the copyright of medical volume data, a new zerowatermarking algorithm for medical volume data is presented based on Legendre chaotic neural network and difference hashing in three-dimensional discrete cosine transform domain. It organically combines the Legendre chaotic neural network, three-dimensional discrete cosine transform and difference hashing, and becomes a kind of robust zero-watermarking algorithm. Firstly, a new kind of Legendre chaotic neural network is used to generate chaotic sequences, which causes the original watermarking image scrambling. Secondly, it uses three-dimensional discrete cosine transform to the original medical volume data, and the perception of the low frequency coefficient invariance in the three-dimensional discrete cosine transform domain is utilized to extract the first 4*5*4 coefficient in order to form characteristic matrix (16*5). Then, the difference hashing algorithm is used to extract a robust perceptual hashing value which is a binary sequence, with the length being 64-bit. Finally, the hashing value serves as the image features to construct the robust zero-watermarking. The results show that the algorithm can resist the attack, with good robustness and high security

MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different sizes of infection sites, some researchers have improved the segmentation accuracy by adding model complexity. However, this approach has severe limitations. Increasing the computational complexity and the number of parameters is unfavorable for model transfer from laboratory to clinic. Meanwhile, the current COVID-19 infections segmentation DCNN-based methods only apply to a single modality. To solve the above issues, this paper proposes a symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism similar to the Transformer to acquire self-attention and achieve local-to-global semantic dependency. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to expand the receptive field and extract multi-scale features. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other U-shape models. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results

Comparison of Polysaccharides Extracted from Cultivated Mycelium of Inonotus obliquus with Polysaccharide Fractions Obtained from Sterile Conk (Chaga) and Birch Heart Rot

The polysaccharides of the sterile conk of Inonotus obliquus (Chaga) have demonstrated multiple bioactivities. The mycelium of this basidiomycete, obtained after submerged cultivation, has been considered a feasible alternative to the sterile conk for the production of polysaccharides. However, previous research has paid little attention to the differences in the structures of polymers obtained from the different resources. Moreover, the birch wood colonized by I. obliquus has never been investigated as a source of bioactive polysaccharides. In the present study, polysaccharide fractions produced from cultivated mycelium, sterile conks of different geographical origins, and birch heart rot were investigated. High amounts of phenolic compounds, possibly lignans, were bound to the sterile conk polysaccharides. Mycelial polysaccharides were rich in alpha- and beta-glucans and had high (10(5) Da) and low (10(4) Da) molecular weight populations. On the other hand, sterile conk polysaccharides were mainly beta-glucan of lower and monodispersed molecular weight (10(3) Da). Heart rot polysaccharides were comprised mainly of low molecular weight (10(3) Da) hemicelluloses. Nevertheless, fungal polysaccharides were identified in the extracts. The differences in structure and molecular properties among the polysaccharide fractions of mycelium, heart rot, and sterile conk are likely associated with differences in bioactivities and, therefore, in nutraceutical potential

Study of the sterile conk of Inonotus obliquus using 13C CPMAS NMR and FTIR spectroscopies coupled with multivariate analysis

The sterile conk caused by the infection of the basidiomycete Inonotus obliquus (Chaga) is an important source of bioactive compounds. However, its structure and biochemistry are only generally understood. Solid state 13C NMR and FTIR spectroscopy have been utilized for the first time to investigate the sterile conk with non-invasive methods. The application of multivariate data analysis techniques and spectral distance algorithm to the obtained datasets showed clear distinction between the outer and inner layers of the sterile conk. Moreover, the sterile conk bark, compared to the inner layers, was spectroscopically more similar to wood tissues. The fungal tissue was proven to be concentrated below the bark. The similarity of the sterile conk inner layers to both decayed wood and hyphae of I. obliquus was shown by the multivariate data analysis of both spectra datasets. The spectroscopic data indicated lack of lignin degradation in the heart rot, except for demethoxylation, and a slight preference for hemicellulose degradation. Therefore, the results obtained suggest that the classification of I. obliquus as preferential lignin degrader (white-rot fungus) should be revised and clarified by further studies.</p

A novel robust watermarking algorithm for encrypted medical image based on DTCWT-DCT and chaotic map

In order to solve the problem of patient information security protection in medical images, whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for medical images themselves to be protected, a novel robust watermarking algorithm for encrypted medical images based on dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) and chaotic map is proposed in this paper. First, DTCWT-DCT transformation was performed on medical images, and dot product was per-formed in relation to the transformation matrix and logistic map. Inverse transformation was undertaken to obtain encrypted medical images. Then, in the low-frequency part of the DTCWT-DCT transformation coefficient of the encrypted medical image, a set of 32 bits visual feature vectors that can effectively resist geometric attacks are found to be the feature vector of the encrypted medical image by using perceptual hashing. After that, different logistic initial values and growth parameters were set to encrypt the watermark, and zero-watermark technology was used to embed and extract the encrypted medical images by combining cryptography and third-party concepts. The proposed watermarking algorithm does not change the region of interest of medical images thus it does not affect the judgment of doctors. Additionally, the security of the algorithm is enhanced by using chaotic mapping, which is sensitive to the initial value in order to encrypt the medical image and the watermark. The simulation results show that the pro-posed algorithm has good homomorphism, which can not only protect the original medical image and the watermark information, but can also embed and extract the watermark directly in the encrypted image, eliminating the potential risk of decrypting the embedded watermark and extracting watermark. Compared with the recent related research, the proposed algorithm solves the contradiction between robustness and invisibility of the watermarking algorithm for encrypted medical images, and it has good results against both conventional attacks and geometric attacks. Under geometric attacks in particular, the proposed algorithm performs much better than existing algorithms

Medical Image Watermarking in Sub-block Three-dimensional Discrete Cosine Transform Domain

Digital watermarking can be applied to protection of medical images privacy, hiding of patient's diagnosis information and so on. In order to improve the ability of resisting geometric attacks, a new watermarking algorithm for medical volume data in sub-block three-dimensional discrete cosine transform domain is presented. The original watermarking image is scrambled by a Chebyshev chaotic neural network so as to improve watermarking security. Sub-block three-dimensional discrete cosine transform and perceptual hashing are used to construct zero-watermarking. In this way it does not produce medical image distortion and gives the algorithm the ability to resist geometric attacks. Experimental results show that the algorithm has good security, and it has good robustness to various geometric attacks

A Novel Medical Image Watermarking in Three-dimensional Fourier Compressed Domain

Digital watermarking is a research hotspot in the field of image security, which is protected digital image copyright. In order to ensure medical image information security, a novel medical image digital watermarking algorithm in three-dimensional Fourier compressed domain is proposed. The novel medical image digital watermarking algorithm takes advantage of three-dimensional Fourier compressed domain characteristics, Legendre chaotic neural network encryption features and robust characteristics of differences hashing, which is a robust zero-watermarking algorithm. On one hand, the original watermarking image is encrypted in order to enhance security. It makes use of Legendre chaotic neural network implementation. On the other hand, the construction of zero-watermarking adopts differences hashing in three-dimensional Fourier compressed domain. The novel watermarking algorithm does not need to select a region of interest, can solve the problem of medical image content affected. The specific implementation of the algorithm and the experimental results are given in the paper. The simulation results testify that the novel algorithm possesses a desirable robustness to common attack and geometric attack

Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 &micro;M to 3.9 &micro;M based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk

A meta-analysis of the relationship between circulating microRNA-155 and coronary artery disease

Objective Coronary artery disease (CAD) is a leading cause of death worldwide. Many studies in China and abroad have reported an association between the expression level of microRNA-155 and CAD; however, the results remain controversial. We aimed to comprehensively investigate this association based on a meta-analysis. Methods We first systematically searched eight Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database, PubMed, Web of Science, Embase, Google Scholar, and Cochrane Library, to identify studies concerning the relationship between microRNA-155 levels and CAD published before February 7, 2021. The quality of the literature was assessed by the Newcastle–Ottawa Scale (NOS). Meta-analysis was performed using a random-effects model to calculate the standard mean difference with a 95% confidence interval (CI). Results Sixteen articles with a total of 2069 patients with CAD and 1338 controls were included. All the articles were of high quality according to the NOS. The meta-analysis showed that the mean level of microRNA-155 was significantly lower in patients with CAD than in controls. Based on subgroup analyses, the level of microRNA-155 in the plasma of CAD patients and in acute myocardial infarction (AMI) patients was significantly lower than that in controls, whereas this level in CAD patients with mild stenosis was significantly higher than that in controls. Conclusion Our study indicates that the expression level of circulating microRNA-155 in patients with CAD is lower than that in a non-CAD group, suggesting a new possible reference index for the diagnosis and monitoring of patients with CAD

A meta-analysis of the relationship between circulating microRNA-155 and coronary artery disease.

ObjectiveCoronary artery disease (CAD) is a leading cause of death worldwide. Many studies in China and abroad have reported an association between the expression level of microRNA-155 and CAD; however, the results remain controversial. We aimed to comprehensively investigate this association based on a meta-analysis.MethodsWe first systematically searched eight Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database, PubMed, Web of Science, Embase, Google Scholar, and Cochrane Library, to identify studies concerning the relationship between microRNA-155 levels and CAD published before February 7, 2021. The quality of the literature was assessed by the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using a random-effects model to calculate the standard mean difference with a 95% confidence interval (CI).ResultsSixteen articles with a total of 2069 patients with CAD and 1338 controls were included. All the articles were of high quality according to the NOS. The meta-analysis showed that the mean level of microRNA-155 was significantly lower in patients with CAD than in controls. Based on subgroup analyses, the level of microRNA-155 in the plasma of CAD patients and in acute myocardial infarction (AMI) patients was significantly lower than that in controls, whereas this level in CAD patients with mild stenosis was significantly higher than that in controls.ConclusionOur study indicates that the expression level of circulating microRNA-155 in patients with CAD is lower than that in a non-CAD group, suggesting a new possible reference index for the diagnosis and monitoring of patients with CAD