Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

An Approach to Underwater Image Enhancement Based on Image Structural Decomposition

Underwater imaging posts a challenge due to the degradation by the absorption and scattering occurred during light propagation as well as poor lighting conditions in water medium Although image filtering techniques are utilized to improve image quality effectively, problems of the distortion of image details and the bias of color correction still exist in output images due to the complexity of image texture distribution. This paper proposes a new underwater image enhancement method based on image struc- tural decomposition. By introducing a curvature factor into the Mumford_Shah_G decomposition algorithm, image details and struc- ture components are better preserved without the gradient effect. Thus, histogram equalization and Retinex algorithms are applied in the decomposed structure component for global image enhancement and non-uniform brightness correction for gray level and the color images, then the optical absorption spectrum in water medium is incorporate to improve the color correction. Finally, the en- hauced structure and preserved detail component are re.composed to generate the output. Experiments with real underwater images verify the image improvement by the proposed method in image contrast, brightness and color fidelity.

SEGMENTATION ALGORITHM BASED ON EDGE-SEARCHING FOR MULTI-LINEAR STRUCTURED LIGHT IMAGES

Aiming at the problem that the existence of disturbances on the edges of light-stripe makes the segmentation of the light-stripes images difficult, a new segmentation algorithm based on edge-searching is presented. It firstly calculates every edge pixel＇s horizontal coordinate grads to produce the corresponding grads-edge, then uses a designed length-variable l D template to scan the light-stripes＇ grads-edges. The template is able to find the disturbances with different width utilizing the distributing character of the edge disturbances. The found disturbances are eliminated finally. The algorithm not only can smoothly segment the light-stripes images, but also eliminate most disturbances on the light-stripes＇ edges without damaging the light-stripes images＇ 3D information. A practical example of using the proposed algorithm is given in the end. It is proved that the efficiency of the algorithm has been improved obviously by comparison.

A study on coherent structures and drag-reduction in the wall turbulence with polymer additives by TRPIV

An experimental measurement was performed us- ing time-resolved particle image velocimetry （TRPIV） to in- vestigate the spatial topological character of coherent struc- tures in wall-bounded turbulence of polymer additive solu- tion. The fully developed near-wall turbulent flow fields with and without polymer additives at the same Reynolds number were measured by TRPIV in a water channel. The compar- isons of turbulent statistics confirm that due to viscoelastic structure of long-chain polymers, the wall-normal velocity fluctuation and Reynolds shear stress in the near-wall region are suppressed significantly. Furthermore, it is noted that such a behavior of polymers is closely related to the decease of the motion of the second and forth quadrants, i.e., the ejection and sweep events, in the near-wall region. The spa- tial topological mode of coherent structures during bursts has been extracted by the new mu-level criteria based on locally averaged velocity structure function. Although the general shapes of coherent structures are unchanged by polymer additives, the fluctuating velocity, velocity gradient, velocity strain rate and vorticity of coherent structures during burst events are suppressed in the polymer additive solution com- pared with that in water. The results show that due to the polymer additives the occurrence and intensity of coherent structures are suppressed, leading to drag reduction.

Tomographic PIV investigation on coherent vortex structures over shark-skin-inspired drag-reducing riblets

Nature has shown us that the microstructure of the skin of fast-swimming sharks in the ocean can reduce the skin friction drag due to the well-known shark-skin effect.In the present study,the effect of shark-skin-inspired riblets on coherent vortex structures in a turbulent boundary layer（TBL） is investigated.This is done by means of tomographic particle image velocimetry（TPIV） measurements in channel fl ws over an acrylic plate of drag-reducing riblets at a friction Reynolds number of 190.The turbulent fl ws over drag-reducing riblets are verifie by a planar time-resolved particle image velocimetry（TRPIV） system initially,and then the TPIV measurements are performed.Two-dimensional（2D） experimental results with a dragreduction rate of around 4.81% are clearly visible over triangle riblets with a peak-to-peak spacing s＋of 14,indicating from the drag-reducing performance that the buffer layer within the TBL has thickened;the logarithmic law region has shifted upward and the Reynolds shear stress decreased.A comparison of the spatial topological distributions of the spanwise vorticity of coherent vortex structures extracted at different wall-normal heights through the improved quadrant splitting method shows that riblets weaken the amplitudesof the spanwise vorticity when ejection（Q2） and sweep（Q4） events occur at the near wall,having the greatest effect on Q4 events in particular.The so-called quadrupole statistical model for coherent structures in the whole TBL is verified Meanwhile,their spatial conditional-averaged topological shapes and the spatial scales of quadrupole coherent vortex structures as a whole in the overlying turbulent fl w over riblets are changed,suggesting that the riblets dampen the momentum and energy exchange between the regions of near-wall and outer portion of the TBL by depressing the bursting events（Q2 and Q4）,thereby reducing the skin friction drag.

THE DETECTION OF THE BOUNDARY OF IMAGE OF WOODANATOMICAL STRUCTURE MOLECULAR

Basing on a lot of examinations, according to the fundamental inage processing theories and methods, getting touch with the property of wood anatomical structure image,we put forward the optimum method and theory which are suitable for the binary processing of the wood anatomical structure image. After the wood image has been processed binary, with the help of computer vision technology, the boundary of wood anatomical structure molecular binary image was sought This kind of theory and method lay a solid foundaion on the collection of feature and the pottern recognition and other high level processing of wood anatomical structure molecular image.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

Exploring Brain Age Calculation Models Available for Alzheimer's Disease

The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.

Application of the equivalent offset migration method in acoustic log reflection imaging

Borehole acoustic reflection logging can provide high resolution images of nearborehole geological structure. However, the conventional seismic migration and imaging methods are not effective because the reflected waves are interfered with the dominant borehole-guided modes and there are only eight receiving channels per shot available for stacking. In this paper, we apply an equivalent offset migration method based on wave scattering theory to process the acoustic reflection imaging log data from both numerical modeling and recorded field data. The result shows that, compared with the routine post-stack depth migration method, the equivalent offset migration method results in higher stack fold and is more effective for near-borehole structural imaging with low SNR acoustic reflection log data.

Application of four-component dipole shear reflection imaging to interpret the geological structure around a deviated well

Acoustic reflection imaging in deep water wells is a new application scope for offshore hydrocarbon exploration.Two-dimensional(2 D)geological structure images can be obtained away from a one-dimensional(1 D)borehole using single-well acoustic reflection imaging.Based on the directivity of dipole source and four-component dipole data,one can achieve the azimuth detection and the three-dimensional(3 D)structural information around the wellbore can be obtained.We first perform matrix rotation on the field fourcomponent data.Then,a series of processing steps are applied to the rotated dipole data to obtain the reflector image.According to the above dipole shear-wave imaging principle,we used four-component cross-dipole logging data from a deviated well in the South China Sea to image geological structures within 50 m of a deviated well,which can delineate the structural configuration and determine its orientation.The configuration of near-borehole bedding boundaries and fault structures from shear-wave imaging results agrees with those from the Inline and Xline seismic profiles of the study area.In addition,the configuration and orientation of the fault structure images are consistent with regional stress maps and the results of the borehole stress anisotropy analysis.Furthermore,the dip azimuth of the bedding boundary images was determined using borehole wall resistivity data.Results of this study indicate that integrating borehole acoustic reflection with seismic imaging not only fills the gap between the two measurement scales but also accurately delineates geological structures in the borehole vicinity.

Gut bless you:The microbiota-gut-brain axis in irritable bowel syndrome

Irritable bowel syndrome(IBS)is a common clinical label for medically unexplained gastrointestinal symptoms,recently described as a disturbance of the microbiota-gut-brain axis.Despite decades of research,the pathophysiology of this highly heterogeneous disorder remains elusive.However,a dramatic change in the understanding of the underlying pathophysiological mechanisms surfaced when the importance of gut microbiota protruded the scientific picture.Are we getting any closer to understanding IBS’etiology,or are we drowning in unspecific,conflicting data because we possess limited tools to unravel the cluster of secrets our gut microbiota is concealing?In this comprehensive review we are discussing some of the major important features of IBS and their interaction with gut microbiota,clinical microbiota-altering treatment such as the low FODMAP diet and fecal microbiota transplantation,neuroimaging and methods in microbiota analyses,and current and future challenges with big data analysis in IBS.

Application of modern neuroimaging technology in the diagnosis and study of Alzheimer’s disease

Neurological abnormalities identified via neuroimaging are common in patients with Alzheimer’s disease.However,it is not yet possible to easily detect these abnormalities using head computed tomography in the early stages of the disease.In this review,we evaluated the ways in which modern imaging techniques such as positron emission computed tomography,single photon emission tomography,magnetic resonance spectrum imaging,structural magnetic resonance imaging,magnetic resonance diffusion tensor imaging,magnetic resonance perfusion weighted imaging,magnetic resonance sensitive weighted imaging,and functional magnetic resonance imaging have revealed specific changes not only in brain structure,but also in brain function in Alzheimer’s disease patients.The reviewed literature indicated that decreased fluorodeoxyglucose metabolism in the temporal and parietal lobes of Alzheimer’s disease patients is frequently observed via positron emission computed tomography.Furthermore,patients with Alzheimer’s disease often show a decreased N-acetylaspartic acid/creatine ratio and an increased myoinositol/creatine ratio revealed via magnetic resonance imaging.Atrophy of the entorhinal cortex,hippocampus,and posterior cingulate gyrus can be detected early using structural magnetic resonance imaging.Magnetic resonance sensitive weighted imaging can show small bleeds and abnormal iron metabolism.Task-related functional magnetic resonance imaging can display brain function activity through cerebral blood oxygenation.Resting functional magnetic resonance imaging can display the functional connection between brain neural networks.These are helpful for the differential diagnosis and experimental study of Alzheimer’s disease,and are valuable for exploring the pathogenesis of Alzheimer’s disease.

Diagnostic value of amygdala volume on structural magnetic resonance imaging in Alzheimer’s disease

BACKGROUND The main clinical manifestation of Alzheimer’s disease(AD)is memory loss,which can be accompanied by neuropsychiatric symptoms at different stages of the disease.Amygdala is closely related to emotion and memory.AIM To evaluate the diagnostic value of amygdala on structural magnetic resonance imaging(sMRI)for AD.METHODS In this study,22 patients with AD and 26 controls were enrolled.Their amygdala volumes were measured by sMRI and analyzed using an automatic analysis software.RESULTS The bilateral amygdala volumes of AD patients were significantly lower than those of the controls and were positively correlated with the hippocampal volumes.Receiver operating characteristic curve analyses showed that the sensitivity of the left and right amygdala volumes in diagnosing AD was 80.8%and 88.5%,respectively.Subgroup analyses showed that amygdala atrophy was more serious in AD patients with neuropsychiatric symptoms,which mainly included irritability(22.73%),sleep difficulties(22.73%),apathy(18.18%),and hallucination(13.64%).CONCLUSION Amygdala volumes measured by sMRI can be used to diagnose AD,and amygdala atrophy is more serious in patients with neuropsychiatric symptoms.

Synthesis, Crystal Structure, Two-photon Absorption and Biological Imaging Application of a Water Soluble Carbazole Quaternary Ammonium Compound

A novel carbazole quaternary ammonium compound（abbreviated as T_2） had been synthesized and characterized by ~1H NMR, ^（13）C NMR and Mass spectrometry. The single-crystal structure has been determined by X-ray single-crystal diffraction. The electrochemical and two-photon absorption properties of T_2 were systematically studied by cyclic voltammetry and Z-scan determination methods, respectively. The results suggested that T_2 had a good oxidation-reduction and excellent nonlinear optical property. The two-photon absorption（TPA） value has a maximum corresponding to cross section σ = 7963.3 GM（Goeppert-Mayer units） at 700 nm, indicating potential applications in nonlinear optical materials. Furthermore, attributing to the excellent water solubility and low cytotoxicity, the compound was explored on its primary application in biological imaging.

From structural to functional imaging:the developments of clinical ophthalmology optical coherence tomography

Starting with introduction of basic concept of optical coherence tomography(OCT) techniques,this paper focuses on a detailed review of ophthalmic OCT instruments and their clinical applications. As one of the most important inventions of ophthalmology instruments,OCT has become a standard imaging tool for daily ophthalmic diagnosis. The imaging capability has been significantly improved during the past ~ 30 years. In this article,several representing systems which have made significant contributions to OCT developments will be reviewed in details. For each system,the system configuration will be discussed first,follow ed by a brief introduction of their clinical applications. The review concludes with discussions on potential directions of OCT developments and expectations for further improvements of OCT imaging capabilities.

Neuroimage-Based Consciousness Evaluation of Patients with Secondary Doubtful Hydrocephalus Before and After Lumbar Drainage

Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.

Brain structure underlying the empathizing–systemizing difference in children with autism spectrum disorder

Background Behavioral research has shown that children with autism spectrum disorder(ASD)have a higher empathizing–systemizing difference(D score)than normal children.However,there is no research about the neuroanatomical mechanisms of the empathizing–systemizing difference in children with ASD.Methods Participants comprised 41 children with ASD and 39 typically developing(TD)children aged 6‒12 years.Empathizing–systemizing difference was estimated using the D score from the Chinese version of Children’s Empathy Quotient and Systemizing Quotient.We quantified brain morphometry,including global and regional brain volumes and surface-based cortical measures(cortical thickness,surface area,and gyrification)via structural magnetic resonance imaging.Results We found that the D score was significantly negatively associated with amygdala gray matter volume[β=−0.16;95%confidence interval(CI):−0.30,−0.02;P value=0.030]in children with ASD.There was a significantly negative association between D score and gyrification in the left lateral occipital cortex(LOC)in children with ASD(B=−0.10;SE=0.03;cluster-wise P value=0.006)and a significantly positive association between D score and gyrification in the right fusiform in TD children(B=0.10;SE=0.03;cluster-wise P value=0.022).Moderation analyses demonstrated significant interactions between D score and diagnosed group in amygdala gray matter volume(β=0.19;95%CI 0.04,0.35;P value=0.013)and left LOC gyrification(β=0.11;95%CI 0.05,0.17;P value=0.001)but not in right fusiform gyrification(β=0.08;95%CI−0.02,0.17;P value=0.105).Conclusions Neuroanatomical variation in amygdala volume and gyrification of LOC could be potential biomarkers for the empathizing–systemizing difference in children with ASD but not in TD children.Large-scale neuroimaging studies are necessary to test the replicability of our findings.

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Mesial temporal lobe epilepsy(mTLE),the most common type of focal epilepsy,is associated with functional and structural brain alterations.Machine learning(ML)techniques have been successfully used in discriminating mTLE from healthy controls.However,either functional or structural neuroimaging data are mostly used separately as input,and the opportunity to combine both has not been exploited yet.We conducted a multimodal ML study based on functional and structural neuroimaging measures.We enrolled 37 patients with left mTLE,37 patients with right mTLE,and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures.For each single measure,we obtained a mean accuracy of 74%and 69%for discriminating left mTLE and right mTLE from controls,respectively,and 64%when all patients were combined.We achieved an accuracy of 78%by integrating functional data and 79%by integrating structural data for left mTLE,and the highest accuracy of 84%was obtained when all functional and structural measures were combined.These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.