Predicting pregnancy rate following multiple embryo transfers using algorithms developed through static image analysis.

Single-embryo image assessment involves a high degree of inaccuracy because of the imprecise labelling of the transferred embryo images. In this study, we considered the entire transfer cycle to predict the implantation potential of embryos, and propose a novel algorithm based on a combination of local binary pattern texture feature and Adaboost classifiers to predict pregnancy rate. The first step of the proposed method was to extract the features of the embryo images using the local binary pattern operator. After this, multiple embryo images in a transfer cycle were considered as one entity, and the pregnancy rate was predicted using three classifiers: the Real Adaboost, Gentle Adaboost, and Modest Adaboost. Finally, the pregnancy rate was determined via the majority vote rule based on classification results of the three Adaboost classifiers. The proposed algorithm was verified to have a good predictive performance and may assist the embryologist and clinician to select embryos to transfer and in turn improve pregnancy rate.

[The meteorological satellite spectral image registration based on Fourier-Mellin transform].

The meteorological satellite spectral image is an effective tool for researches on meteorological science and environmental remote sensing science. Image registration is the basis for the application of the meteorological satellite spectral image data. In order to realize the registration of the satellite image and the template image, a new registration method based on the Fourier-Mellin transform is presented in this paper. Firstly, we use the global coastline vector map data to build a landmark template, which is a reference for the meteorological satellite spectral image registration. Secondly, we choose infrared sub-image of no cloud according to the cloud channel data, and extract the edges of the infrared image by Sobel operator. Finally, the affine transform model parameters between the landmark template and the satellite image are determined by the Fourier-Mellin transform, and thus the registration is realized. The proposed method is based on the curve matching in essence. It needs no feature point extraction, and can greatly simplify the process of registration. The experimental results using the infrared spectral data of the FY-2D meteorological satellite show that the method is robust and can reach a high speed and high accuracy.

[A coarse-to-fine registration method for satellite infrared image and visual image].

In the present paper, in order to resolve the registration of the multi-mode satellite images with different signal properties and features, a two-phase coarse-to-fine registration method is presented and is applied to the registration of satellite infrared images and visual images. In the coarse registration phase of this method, the edge of infrared and visual images is firstly detected. Then the Fourier-Mellin transform is adopted to process the edge images. Finally, the affine transformation parameters of the registration are computed rapidly by the transformation relation between the registering images in frequency domain. In the fine registration phase of the proposed method, the feature points of infrared and visual images are firstly detected by Harris operator. Then the matched feature points of infrared and visual images are determined by the cross-correlation similarity of their local neighborhoods. The fine registration is finally realized according to the spatial correspondent relation of the matched feature points in infrared and visual images. The proposed coarse-to-fine registration method derives both the advantages of two methods, the high efficiency of Fourier-Mellin transform based registration method and the accuracy of Harris operator based registration method, which is considered the novelty and merit of the proposed method. To evaluate the performance of the proposed registration method, the coarse-to-fine registration method is implemented on the infrared and visual images captured by the FY-2D meteorological satellite. The experimental results show that the presented registration method is robust and has acceptable registration accuracy.

Efficient modulation of thermal transport in two-dimensional materials for thermal management in device applications.

With the development of chip technology, the density of transistors on integrated circuits is increasing and the size is gradually shrinking to the micro-/nanoscale, with the consequent problem of heat dissipation on chips becoming increasingly serious. For device applications, efficient heat dissipation and thermal management play a key role in ensuring device operation reliability. In this review, we summarize the thermal management applications based on 2D materials from both theoretical and experimental perspectives. The regulation approaches of thermal transport can be divided into two main types: intrinsic structure engineering (acting on the intrinsic structure) and non-structure engineering (applying external fields). On one hand, the thermal transport properties of 2D materials can be modulated by defects and disorders, size effect (including length, width, and the number of layers), heterostructures, structure regulation, doping, alloy, functionalizing, and isotope purity. On the other hand, strain engineering, electric field, and substrate can also modulate thermal transport efficiently without changing the intrinsic structure of the materials. Furthermore, we propose a perspective on the topic of using magnetism and light field to modulate the thermal transport properties of 2D materials. In short, we comprehensively review the existing thermal management modulation applications as well as the latest research progress, and conclude with a discussion and perspective on the applications of 2D materials in thermal management, which will be of great significance to the development of next-generation nanoelectronic devices.

Customizable Metal Micromesh Electrode Enabling Flexible Transparent Zn-Ion Hybrid Supercapacitors with High Energy Density.

Emerging flexible and wearable electronic products are placing a compelling demand on lightweight transparent energy storage devices. Owing to their distinguishing features of safety, high specific energy, cycling stability, and rapid charge/discharge advantages, Zn-ion hybrid supercapacitors are a current topic of discussion. However, the trade-off for optical transmittance and energy density remains a great challenge. Here, a high-performance Zn-ion hybrid supercapacitor based on the customizable ultrathin (5 µm), ultralight (0.45 mg cm-2 ), and ultra-transparent (87.6%) Ni micromesh based cathode and Zn micromesh anode with the highest figure of merit (84 843) is proposed. The developed flexible transparent Zn-ion hybrid supercapacitors reveal excellent cycle stability (no decline after 20 000 cycles), high areal energy density (31.69 µWh cm-2 ), and high power density (512 µW cm-2 ). In addition, the assembled solid flexible and transparent Zn-ion hybrid supercapacitor with polyacrylamide gel electrolyte shows extraordinary mechanical properties even under extreme bending and twisting operation. Furthermore, the full device displays a high optical transmittance over 55.04% and can be conformally integrated with diverse devices as a flexible transparent power supply. The fabrication technology offers seamless compatibility with industrial manufacturing, making it an ideal model for the advancement of portable and wearable devices.

Unique Arrangement of Atoms Leads to Low Thermal Conductivity: A Comparative Study of Monolayer Mg2C.

Two-dimensional Mg2C, one of the typical representative MXene materials, is attracting lots of attention due to its outstanding properties. In this study, we find the thermal conductivity of monolayer Mg2C is more than 2 orders of magnitude lower than graphene and is even lower than MoS2 despite the relatively lighter atoms of Mg and C. Based on the comparative analysis with graphene, silicene, and MoS2, the underlying mechanism is found lying in the unique arrangement of atoms (lighter atoms in the middle plane) and large electronegativity difference in Mg2C. The phonon anharmonicity is strong due to the resonant bonding. In addition, dual band gaps emerge in the phonon dispersion of Mg2C, which limit the phonon-phonon scattering and reduce the phonon relaxation time. This study reveals a new mechanism responsible for low thermal conductivity, which would be helpful for designing thermal functional materials and pave the way for applications in thermoelectrics.

[Spectral classification based on Bayes decision].

The rapid development of astronomical observation has led to many large sky surveys such as SDSS (Sloan digital sky survey) and LAMOST (large sky area multi-object spectroscopic telescope). Since these surveys have produced very large numbers of spectra, automated spectral analysis becomes desirable and necessary. The present paper studies the spectral classification method based on Bayes decision theory, which divides spectra into three types: star, galaxy and quasar. Firstly, principal component analysis (PCA) is used in feature extraction, and spectra are projected into the 3D PCA feature space; secondly, the class conditional probability density functions are estimated using the non-parametric density estimation technique, Parzen window approach; finally, the minimum error Bayes decision rule is used for classification. In Parzen window approach, the kernel width affects the density estimation, and then affects the classification effect. Extensive experiments have been performed to analyze the relationship between the kernel widths and the correct classification rates. The authors found that the correct rate increases with the kernel width being close to some threshold, while it decreases with the kernel width being less than this threshold.

[Redshift estimation of galaxy spectra based on similarity measure].

Automated spectra analysis is desirable and necessary for efficiency of large sky surveys such as SDSS (Sloan digital sky survey), 2DF (2 degree fields) and LAMOST (large sky area multi-object spectroscopic telescope). In the present paper, we present a method for redshift estimation of galaxy spectra based on similarity measure. Firstly, we extract the spectral lines of the observed spectrum using the feature constrains of spectral lines; secondly, the authors determine the redshift candidates of the observed spectrum by spectral line features; then, the similarity between the observed spectrum and the template spectra shifted by each redshift candidate is measured; finally, the candidate of the highest similarity is chosen as the estimated redshift. PCA (principal component analysis) is used to build the static galaxy template spectra. The authors perform PCA for the four template spectra E, S0, Sa and Sb of the normal galaxy and the seven template spectra Sc, Sb1, Sb2, Sb3, Sb4, Sb5 and Sb6 of the starburst galaxy respectively, where the eleven template spectra are presented by Kinney & Calzetti et al. Two eigen-spectra are produced with the variance contribution rate of 99%. The authors choose the two eigen-spectra as the galaxy templates. The similarity measure proposed, which is similar to the evidence accumulation, is defined as the weighted sum of several similarity evidences. It can reduce the influence caused by some error matching. The authors divide the observed spectrum and the template spectrum respectively into several parts, and measure the correlations of the corresponding parts of them, which is chosen as the similarity evidences in the proposed similarity measure. The principle of setting the weights is that the higher the correlation, the higher the corresponding weight. The proposed approach is compared with the method based on spectral line matching and the traditional cross correlation technique by experiments, the results show that the proposed method has a higher correct rate.

3D craniofacial registration using thin-plate spline transform and cylindrical surface projection.

Craniofacial registration is used to establish the point-to-point correspondence in a unified coordinate system among human craniofacial models. It is the foundation of craniofacial reconstruction and other craniofacial statistical analysis research. In this paper, a non-rigid 3D craniofacial registration method using thin-plate spline transform and cylindrical surface projection is proposed. First, the gradient descent optimization is utilized to improve a cylindrical surface fitting (CSF) for the reference craniofacial model. Second, the thin-plate spline transform (TPST) is applied to deform a target craniofacial model to the reference model. Finally, the cylindrical surface projection (CSP) is used to derive the point correspondence between the reference and deformed target models. To accelerate the procedure, the iterative closest point ICP algorithm is used to obtain a rough correspondence, which can provide a possible intersection area of the CSP. Finally, the inverse TPST is used to map the obtained corresponding points from the deformed target craniofacial model to the original model, and it can be realized directly by the correspondence between the original target model and the deformed target model. Three types of registration, namely, reflexive, involutive and transitive registration, are carried out to verify the effectiveness of the proposed craniofacial registration algorithm. Comparison with the methods in the literature shows that the proposed method is more accurate.

Craniofacial similarity analysis through sparse principal component analysis.

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.

Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method.

The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristic decision. First, the heart region is extracted using a multi-atlas-based approach. Second, the vessel structures are enhanced via a 3D multiscale line filter. Next, seed points are detected automatically through a threshold preprocessing and a subsequent morphological operation. Based on the set of detected seed points, a statistics-based region growing is applied. Finally, results are obtained by setting conservative parameters. A heuristic decision method is then used to obtain the desired result automatically because parameters in region growing vary in different patients, and the segmentation requires full automation. The experiments are carried out on a dataset that includes eight-patient multivendor cardiac computed tomography angiography (CTA) volume data. The DICE similarity index, mean distance, and Hausdorff distance metrics are employed to compare the proposed algorithm with two state-of-the-art methods. Experimental results indicate that the proposed algorithm is capable of performing complete, robust, and accurate extraction of coronary arteries.

An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

[A method for redshift determination of quasars based on cross correlation].

This paper presents a novel method for redshift determination of quasars. Firstly, a group of redshifts were determined using the emission line info extracted from the observed spectrum; Secondly, the template was redshifted according to the candidates, and the correlation between the observed spectrum and the redshifted template was measured. Finally, the redshift candidate corresponding to the highest correlation was chosen as the redshift. Compared with the existing methods based on spectral line matching, the proposed method has a lower dependence on the quality of spectral line extraction. Experiments show that this method is robust and superior to the methods based on spectral linematching.

[Mean shift based auto-extraction of spectral lines for non-emission-line objects].

The mean shift algorithm is used. At first, the property that mean shift vectors always point toward local maxima of the density is used to get the pseudo continuum; secondly, mean shift filtering is a goodedge preserving smoothing, which canadaptively reduce the amount of smoothing near feature spectral lines, so the authors use mean shift filtering in noise reduction after the noramalization of continuum spectra; finally, the authors extract feature spectral lines by setting local thresholds. The experiments on both stars and normal galaxies show that our method can extract spectral lines accurately, which is helpful to the parameter measure and the automatic classification of spectra based on spectral lines.

[Density estimation based model matching method for redshift determination].

The present paper proposes a model matching method based on density estimation for redshift determination, in whichthe problem of redshift determination is translated into the problem of searching for the point of maximum density within a data set. At first, the mean shift-based method for auto-extraction of spectral lines is used to get feature spectrallines. Secondly, according tothe redshift formula, the authors use the feature wavelength array and the spectral template to get a data set. Finally, the authors findthe point of maximum density within the data set, then the average of the data in epsilon-neighbor of the point is regarded as the redshift estimation. The information of feature wavelength and spectral line type is used in this method so that it can deal with every kind of spectra. Experiments show that our method is stable and the correct identification rate is high.

A vessel active contour model for vascular segmentation.

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

Automatic blastomere recognition from a single embryo image.

The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

Craniofacial reconstruction evaluation by geodesic network.

Craniofacial reconstruction is to estimate an individual's face model from its skull. It has a widespread application in forensic medicine, archeology, medical cosmetic surgery, and so forth. However, little attention is paid to the evaluation of craniofacial reconstruction. This paper proposes an objective method to evaluate globally and locally the reconstructed craniofacial faces based on the geodesic network. Firstly, the geodesic networks of the reconstructed craniofacial face and the original face are built, respectively, by geodesics and isogeodesics, whose intersections are network vertices. Then, the absolute value of the correlation coefficient of the features of all corresponding geodesic network vertices between two models is taken as the holistic similarity, where the weighted average of the shape index values in a neighborhood is defined as the feature of each network vertex. Moreover, the geodesic network vertices of each model are divided into six subareas, that is, forehead, eyes, nose, mouth, cheeks, and chin, and the local similarity is measured for each subarea. Experiments using 100 pairs of reconstructed craniofacial faces and their corresponding original faces show that the evaluation by our method is roughly consistent with the subjective evaluation derived from thirty-five persons in five groups.

Automatic sex determination of skulls based on a statistical shape model.

Sex determination from skeletons is an important research subject in forensic medicine. Previous skeletal sex assessments are through subjective visual analysis by anthropologists or metric analysis of sexually dimorphic features. In this work, we present an automatic sex determination method for 3D digital skulls, in which a statistical shape model for skulls is constructed, which projects the high-dimensional skull data into a low-dimensional shape space, and Fisher discriminant analysis is used to classify skulls in the shape space. This method combines the advantages of metrical and morphological methods. It is easy to use without professional qualification and tedious manual measurement. With a group of Chinese skulls including 127 males and 81 females, we choose 92 males and 58 females to establish the discriminant model and validate the model with the other skulls. The correct rate is 95.7% and 91.4% for females and males, respectively. Leave-one-out test also shows that the method has a high accuracy.