Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation

Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries / veins classi cation are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. Methods: We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A non-local total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. Results: The proposed segmentation method yields competitive results on three pub- lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com- pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to ve public databases 1 (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. Conclusions: The experimental results show that the proposed framework has e ectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon- struction. The vascular topology information signi cantly improves the accuracy on arteries / veins classi cation

The Study of Microwave and Electric Hybrid Sintering Process of AZO Target

We simulated the microwave sintering of ZnO by 3D modelling. A large-size Al-doped ZnO (AZO) green ceramic compact was prepared by slurry casting. Through studying the microwave and electric hybrid sintering of the green compact, a relative density of up to 98.1% could be obtained by starting microwave heating at 1200°C and increasing the power 20 min later to 4 kW for an AZO ceramic target measuring 120 × 240 × 12 mm. The resistivity of AZO targets sintered with microwave assistance was investigated. The energy consumption of sintering could be greatly reduced by this heating method. Until now, few studies have been reported on the microwave and electric hybrid sintering of large-size AZO ceramic targets. This research can aid in developing sintering technology for large-size high-quality oxide ceramic targets

The Current Progress in Research about the Effect of Phenolics from Brown Rice on the Digestibility of Starch

Chronic diseases inclding diabetes have been important public health problems worldwide. Starch intake is one of the main causes of postprandial blood glucose elevation. Recent studies have demonstrated that polyphenols can slow down the rate of starch digestion. Brown rice is rich in phenolics, and its nutritional health benefits are widely recognized around the world as an essential source of whole grains. The unique functional groups of phenolic substances in brown rice, such as phenolic hydroxyl, have a certain inhibitory effect on digestive enzymes. Changes in the structure of starch during processing also decrease the effect of digestive enzymes on it. This not only affects the digestion rate and digestibility of starch in an effective way, but also improves food quality. This paper reviews several aspects of phenolics in brown rice and their antioxidant activities, the process of starch digestion, the effects of brown rice polyphenols on starch digestive properties and their mechanisms of action. The aim of this review is to elucidate the scientific basis of whole grain brown rice polyphenols to retard starch digestion, and provide theoretical references for the development of whole grain brown rice-based and starch-based foods which are beneficial for populations of chronic disease, obesity, overweight, elderly, etc

AGT on the S-duality Wall

Three-dimensional gauge theory T[G] arises on a domain wall between four-dimensional N=4 SYM theories with the gauge groups G and its S-dual G^L. We argue that the N=2^* mass deformation of the bulk theory induces a mass-deformation of the theory T[G] on the wall. The partition functions of the theory T[SU(2)] and its mass-deformation on the three-sphere are shown to coincide with the transformation coefficient of Liouville one-point conformal block on torus under the S-duality.Comment: 14 pages, 3 figures. v2: Revised the analysis in sections 3.3 and 4. Notes and references added. Version to appear in JHE

Formation of octapod MnO nanoparticles with enhanced magnetic properties through kinetically-controlled thermal decomposition of polynuclear manganese complexes

Polynuclear manganese complexes are used as precursors for the synthesis of manganese oxide nanoparticles (MnO NPs). Altering the thermal decomposition conditions can shift the nanoparticle product from spherical, thermodynamically-driven NPs to unusual, kinetically-controlled octapod structures. The resulting increased surface area profoundly alters the NP's surface-dependent magnetism and may have applications in nanomedicine

Polynomial Normal Transform Based on L-moments and Its Application to Structural Reliability

Information on the distribution of the basic random variable is essential for the accurate analysis of structural reliability. The usual method for determining the distributions is to fit a candidate distribution to the histogram of available statistical data of the variable. Generally, such candidate distribution would have parameters that may be evaluated from the statistical moments of the statistical data. Probability distributions are usually determined using one or two parameters evaluated from the mean and standard deviation of statistical data. However, these distributions are not flexible enough to represent the skewness and the kurtosis of statistical data. Normal transformation is often used in probabilistic analysis especially when multivariate non-normal random variables are involved. This study proposes a probability distribution based on polynomial normal transform, of which parameters are determined using the first four L-moments (L-mean, L-standard deviation, L-skewness and L-kurtosis) of the available data. The simplicity, generality, flexibility and advantages of this distribution in statistical data analysis are discussed. The results are found to better than two- and three-parameter distributions, and similar to cubic normal distribution based on central moments (C-moments). With the aiming at illustrate the stability of polynomial normal transform based on L-moments, several extreme values are added to data. The proposed distribution is demonstrated to provide significant stability and flexibility. Then this method is applied to reliability index calculation, and its significance in structural reliability evaluation is discussed. The calculation results are compared with Monte Carlo calculations. Several numerical examples are further presented to demonstrate the accuracy and efficacy of the distribution for conducting reliability analyses.The study is partially supported by the National Natural Science Foundation of China (Grant No.: 51820105014, 51738001). The support is gratefully acknowledged

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on 8 publicly available datasets (six 2D datasets, one 3D dataset and one 3D synthetic dataset) demonstrate its superior performance to other state-ofthe- art methods