Image Width Research Articles

Damage detection in structures using modal curvatures gapped smoothing method and deep learning

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

Validation of the influence of CT slice thickness on the quantitative accuracy and image quality of single photon emission computed tomography.

Computed tomography (CT) images are used for precise anatomical location of lesions and for accurate attenuation correction in single-photon emission computed tomography (SPECT) image reconstruction in SPECT/CT examination. The aim of this study was to verify the effects of varying CT collimation width and slice thickness on CT images and on CT attenuation corrected SPECT images. We acquired SPECT/CT images of a micro-coin phantom and the National Electrical Manufacturers Association body phantom filled with 99mTc-pertechnetate while varying the abovementioned CT parameters. The full width at half maximum of the slice sensitivity profile, the standard deviation of CT image background noise, and the radiation dose from CT scans were evaluated. Subsequently, the percentage contrast, background variability, and absolute recovery coefficient of the SPECT image were measured. Furthermore, we retrospectively reviewed the clinical bone SPECT images of 23 patients, and statistical testing of differences was performed. As the collimation width and reconstruction slice thickness of the CT image increased, z-axis resolution deteriorated, and background noise decreased. In addition, CT radiation dose decreased with increasing collimation width. Meanwhile, SPECT image quality and quantitative accuracy were unchanged with varying CT collimation width and slice thickness. There were no notable variations in clinical SPECT images and no statistically significant differences. When high-resolution CT slices on the z-axis are not required for clinical diagnosis, increasing collimation width or slice thickness can reduce the radiation dose and image noise with no influence on the quality of SPECT images .

Flattening in the Anteroposterior Direction of the Terminal Ileum or Sigmoid Colon Lying Across the Psoas Muscle on Magnetic Resonance Enterography in Patients with Crohn's Disease.

ObjectiveFlattening in the anteroposterior direction (AP flattening) of the terminal ileum (TI) or sigmoid colon (SC) lying across the psoas muscle, on magnetic resonance enterography (MRE), might mimic bowel inflammation in the coronal view. This study investigated the prevalence of AP flattening and the factors associated with its development.Materials and MethodsA total of 364 surgery-naïve patients with Crohn's disease (CD) who had undergone MRE were retrospectively reviewed. AP flattening was defined as a luminal collapse in the anteroposterior direction, with a bowel width in the axial plane < 1/4 of the normal diameter without reduction of bowel width in coronal images. The prevalence of AP flattening of the TI and SC on MRE in patients with bowel segments lying across the psoas muscle was determined. We further compared the rate of AP flattening between MRE and computed tomography enterography (CTE) in a subcohort of patients with prior CTE. The factors associated with AP flattening were analyzed using multivariable logistic regression in a subcohort of patients with endoscopic findings of TI.ResultsThree hundred and twenty-two and 363 patients, respectively, had TI and SC lying across the psoas muscle. The prevalence of AP flattening on MRE was 7.5% (24/322) in TI and 5.2% (19/363) in SC. The prevalences were significantly higher on MRE than on CTE in both the TI (7.3% [12/164] vs. 0.6% [1/164]; p = 0.003) and SC (5.8% [11/190] vs. 1.6% [3/190]; p = 0.039). AP flattening of the TI was independently and strongly associated with the absence of CD inflammation on endoscopy, with an adjusted odds ratio of 0.066 (p = 0.003) for the presence versus the absence (reference) of inflammation.ConclusionAP flattening of the TI or SC lying across the psoas muscle was uncommon and predominantly observed on MRE of the bowel without CD inflammation.

Hierarchically Structured Network With Attention Convolution and Embedding Propagation for Imbalanced Few-Shot Learning

Generally, a few-shot distribution shift will lead to a poor generalization. Furthermore, while the number of instances of each class in the real world may significantly different, the existing few-shot classification methods are based on the assumption that the number of samples in each class is equal, which causes the trained classifier invalid. Moreover, through ResNet and WRN (Wide Residual Network) have achieved great success in the image processing field, the depth and width of CNNs constrain the conventional convolution layer performance. Thus, to overcome the above problems, the model of this paper proposes a novel few-shot classification model that uses learning balance variables to decide how much to learn from the imbalance dataset, which dynamically generates the convolution kernel based on each input. In our model, to extend the decision boundaries and enhance the class representations, this paper uses embedding propagation as a regularizer for manifold smoothing. Manifold smoothing can effectively solve the above problems of transductive learning. The interpolations between neural network features based on similarity graphs are used by embedding propagation. Experiments show that embedding propagation can produce a better embedding manifold and our model in standard few-shot datasets, such as miniImagenet, tieredImagenet, CUB has state-of-the-art results. It significantly outperforms the existing few-shot approaches, which consistently improves the accuracy of the models by about 11%.

Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations

In recent years, Unmanned Aerial Vehicles (UAVs) are widely utilized in precision agriculture, such as tree plantations. Due to limited intelligence, these UAVs can only operate at high altitudes, leading to the use of expensive and heavy sensors for obtaining important health information of the plants. To fly at low altitudes, these UAVs must possess the capability of obstacle avoidance to prevent crashes. However, most current obstacle avoidance systems with active sensors are not applicable to small aerial vehicles due to the cost, weight, and power consumption constraints. To this end, this paper presents a novel approach to the autonomous navigation of a small UAV in tree plantations only using a single camera. As the monocular vision does not provide depth information, a machine learning model, Faster Region-based Convolutional Neural Network (Faster R-CNN), was trained for the tree trunk detection. A control strategy was implemented to avoid the collision with trees. The detection model uses image heights of detected trees to indicate their distances from the UAV and image widths between trees to find the widest obstacle-free space. The control strategy allows the UAV to navigate until any approaching obstacle is detected and to turn to the safest area before continuing its flight. This paper demonstrates the feasibility and performance of the proposed algorithms by carrying out 11 flight tests in real tree plantation environments at two different locations, one of which is a new place. All the successful results indicate that the proposed method is accurate and robust for autonomous navigation in tree plantations.

Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures

The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis.

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient's lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient's lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm (p = 0.92) and 0.29 mm (p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm (p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

Application Test and Analysis of 2m/8m optical satellite in coastal zone survey

The 2-meter/8meter optical satellite (GF-1 B, C, D) constellation was successfully launched on March 31, 2018. The constellation consists of three operational satellites with the same performance and status. The design life of the satellite is 6 years;The panchromatic spatial resolution is 2m and the multispectral resolution was better than 8 m; single star imaging width is greater than 60 km The three satellites will have a global coverage of 15 days and a revisit capacity of two days. In this paper, GF-1C satellite was used to investigate the ecological and geological environmental problems in the silty coastal zone in northern Jiangsu province, and we contrast and analysis the ability of ecological and geological environmental investigations of GF1C satellite in the coastal water boundary line, coastal type, human engineering activities and so on[1] [3].

Two-Year Outcomes After Meniscoplasty by Capsular Advancement With the Application of Arthroscopic Centralization Technique for Lateral Compartment Knee Osteoarthritis

Background: A high prevalence of osteoarthritis (OA) progression in patients with lateral meniscal defects has been reported. However, optimal management techniques for active patients remain ill-defined. Hypothesis: Meniscoplasty by capsular advancement with the application of the centralization technique would improve clinical and radiological outcomes in patients with lateral compartment OA attributed to lateral meniscal defects. Study Design: Case series; Level of evidence, 4. Methods: A total of 27 patients were enrolled who had undergone meniscoplasty by capsular advancement for lateral compartment OA attributed to lateral meniscal defects. In these patients, the meniscotibial capsule was released from the tibia and advanced with the remaining meniscus onto the rim of the tibial plateau to reform a meniscus-like configuration. Measures of clinical outcomes included clinical examination, Lysholm score, International Knee Documentation Committee (IKDC) subjective score, Knee injury and Osteoarthritis Outcome Score (KOOS), subjective rating scales regarding recovery of the operated knee, and sports performance level. Measures of radiographic outcomes included meniscal extrusion width or regeneration of the meniscus-like tissue on magnetic resonance imaging and lateral joint space width on standing extension anteroposterior and Rosenberg views. All clinical and radiographic outcomes were reported preoperatively and 2 years postoperatively, except magnetic resonance imaging findings, which were reported preoperatively and 1 year postoperatively. Results: The clinical outcomes were significantly improved 2 years postoperatively as compared with baseline: Lysholm score, IKDC subjective score, and KOOS subscores (all P < .0001). The patients’ subjective recovery (P < .0001) and sports performance level (P < .0001) were also improved. One year postoperatively, 10 of 11 patients who had no meniscus remaining at the middle segment showed more than one-third the volume of meniscal tissue–like regeneration, and meniscal extrusion width was significantly reduced as compared with baseline in the remaining 16 patients (P = .0006). Joint space width increased at 2 years on the standing anteroposterior view (P < .0001) and the Rosenberg view (P = .0001). Conclusion: Meniscoplasty of the lateral meniscus by capsular advancement improved clinical and radiographic outcomes at 2-year follow-up in patients with lateral compartment OA attributed to lateral meniscal defects.

Development of Efficient CNN model for Tomato crop disease identification

Tomato is an important vegetable crop cultivated worldwide coming next only to potato. However, the crop can be damaged due to various diseases. It is important for the farmer to know the type of disease for timely treatment of the crop. It has been observed that leaves are clear indicator of specific diseases. A number of Machine Learning (ML) algorithms and Convolution Neural Network (CNN) models have been proposed in literature for identification of tomato crop diseases. CNN models are based on Deep Learning Neural Networks and differ inherently from traditional Machine Learning algorithms like k-NN, Decision-Trees etc. While pretrained CNN models perform fairly well, they tend to be computationally heavy due to large number of parameters involved. In this paper a simplified CNN model is proposed comprising of 8 hidden layers. Using the publicly available dataset PlantVillage, proposed light weight model performs better than the traditional machine learning approaches as well as pretrained models and achieves an accuracy of 98.4%. PlantVillage dataset comprises of 39 classes of different crops like apple, potato, corn, grapes etc. of which 10 classes are of tomato diseases. While traditional ML methods gives best accuracy of 94.9% with k-NN, best accuracy of 93.5% is obtained with VGG16 in pretrained models. To increase performance of proposed CNN, image pre-processing has been used by changing image brightness by a random value of a random width of image after image augmentation. The proposed model also performs extremely well on dataset other than PlantVillage with accuracy of 98.7%.

Artifacts in Diagnostic Ultrasonography

In this review, the ultrasound (US) artifacts features such as propagation path, velocity, attenuation, multiple echoes, and resolution are explained. The main restrictions of an axial and lateral resolution are artifacts. However, failure to fix them lead to the lack of details, and adjacent neighboring objects may be observed as one image. This article describes an essential law to control wave movement, and the US artifacts are formed. Thus, it is substantial for the radiologist and sonographer to differentiate between the artifacts and abnormal signs during the diagnostic procedure. Furthermore, the use of enhancing and shadowing artifacts in determining the nature of masses was explained. The comet tail, attenuation, mirror image, refraction, speed displacement, side lobe, and beam width artifacts happened typically in clinical (in-vivo) practice were also discussed. Finally, an estimation and evaluation of these artifacts are necessary to increase the accuracy in diagnosis.

Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.

Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5×10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5×10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2mm and 5mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

Pulse sequences as tissue property filters (TP-filters): a way of understanding the signal, contrast and weighting of magnetic resonance images.

This paper describes a quantitative approach to understanding the signal, contrast and weighting of magnetic resonance (MR) images. It uses the concept of pulse sequences as tissue property (TP) filters and models the signal, contrast and weighting of sequences using either a single TP-filter (univariate model) or several TP-filters (the multivariate model). For the spin echo (SE) sequence using the Bloch equations, voxel signal intensity is plotted against the logarithm of the value of the TPs contributing to the sequence signal to produce three TP-filters, an exponential ρm-filter, a low pass T1-filter and a high pass T2-filter. Using the univariate model which considers signal changes in only one of ρm, T1, or T2 at a time, the first partial derivative of signal with respect to the natural logarithm of ρm, T1 or T2 is the sequence weighting for each filter (for small changes in each TP). Absolute contrast is then the sequence weighting multiplied by the fractional change in TP for each filter. For large changes in TPs, the same approach is followed, but using the mean slope of the filter as the sequence weighting. These approaches can also be used for fractional contrast. The univariate TP-filter model provides a mathematical framework for converting conventional qualitative univariate weighting as used in everyday clinical practice into quantitative univariate weighting. Using the multivariate model which considers several TP-filters together, the relative contributions of each TP to overall sequence and image weighting are expressed as sequence and imaging weighting ratios respectively. This is not possible with conventional qualitative weighting which is univariate. The same approaches are used for inversion recovery (IR), pulsed gradient SE, spoiled gradient echo (SGE), balanced steady state free precession, ultrashort echo time and other pulse sequences. Other TPs such as susceptibility, chemical shift and flow can be included with phase along the Y axis of the TP-filter. Contrast agent effects are also included. In the text TP-filters are distinguished from k-space filters, signal filters (S-filters) which are used in imaging processing as well as to describe windowing the signal width and level of images, and spatial filters. The TP-filters approach resolves many of the ambiguities and inconsistencies associated with conventional qualitative weighting and provides a variety of new insights into the signal, contrast and weighting of MR images which are not apparent using qualitative weighting. The TP-filter approach relates the preparation component of pulse sequences to voxel signal, and contrast between two voxels. This is complementary to k-space which relates the acquisition component of pulse sequences to the spatial properties of MR images and their global contrast.

Relative radiometric calibration of yaw data without calibration field of hyperspectral images based on harmonic analysis

ABSTRACT Radiometric calibration of satellite sensor could be divided into absolute radiometric calibration and relative radiometric calibration. Relative radiometric calibration can greatly reduce the impact of the sensor’s instability due to many factors like failure to perform calibration experiments or satellite post-operative attenuation. One of the methods is to use the 90° yaw data for relative radiation calibration, where the camera or satellite rotates by 90° so that the linear charge-coupled device (CCD) detector is parallel to the flight direction and sequentially passes through the ground uniform field to obtain the same radiance to achieve the satellite in order to achieve relative radiometric calibration of satellite sensor and eliminate stripe phenomenon in image. The orbita hyperspectral satellites (OHS) are commercial hyperspectral remote sensing satellites with the largest image width and the highest spatial resolution in China. It has realized the operation of multiple hyperspectral satellites network for the first time. However, there are still problems such as inaccurate calculation of radiation calibration parameters, difficulty in realizing uniform field ground, and difficulty in achieving full dynamic calibration of the sensor. In this study, the hyperspectral images acquired by OHS satellites were taken as the research object to propose a method to achieve field-free relative radiometric calibration based on harmonic analysis of yaw data. This method combines the advantages of harmonic analysis technology and the least squares theory and is applied to the relative radiation calibration of hyperspectral yaw data. It is called the harmonic analysis radiometric calibration (HARC) algorithm. First, HARC algorithm performed the first-order forward and reverses harmonic analysis on the 86° yaw data of OHS satellites which were pre-processed into the data of the conventional image mode to obtain the theoretical value of the pre-processed yaw data. Then, the actual radiance value of yaw data in column direction would be fitted to the theoretical value using the least squares theory to calculate the offset and gain of radiometric calibration parameter after eliminating the high and low radiance values in yaw data. Finally, the offset and gain parameters were used to carry out relative radiometric calibration on conventional push-broom satellite image. The comparative analysis of the test showed that relative radiometric calibration of all band images of six pieces of CCD arrays in the hyperspectral sensor of OHS satellites was realized based on yaw data without uniform ground calibration field. The HARC algorithm proposed in this paper can overcome the inaccurate calculation of calibration parameters caused by the yaw angle less than 90°. It not only retains the image detail information but also effectively removes various types of thick and thin stripe noise in the sweep direction, and is not limited by the different bands. The algorithm has good universality in applications on various bands. Qualitative and quantitative comparison analyses were conducted between HARC algorithm and classical relative radiometric calibration methods, and the corrected image has a great improvement in image quality and sharpness compared with the original image. The effect of stripe removal and the accuracy of radiation correction of the HARC algorithm are superior to the classical relative radiation calibration method.

Application of a Deep Machine Learning Model for Automatic Measurement of EZ Width in SD-OCT Images of RP.

PurposeWe applied a deep convolutional neural network model for automatic identification of ellipsoid zone (EZ) in spectral domain optical coherence tomography B-scans of retinitis pigmentosa (RP).MethodsMidline B-scans having visible EZ from 220 patients with RP and 20 normal subjects were manually segmented for inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane. A total of 2.87 million labeled image patches (33 × 33 pixels) extracted from 480 B-scans were used for training a convolutional neural network model implemented in MATLAB. B-scans from a separate group of 80 patients with RP were used for testing the model. A local connected area searching algorithm was developed to process the model output for reconstructing layer boundaries. Correlation and Bland-Altman analyses were conducted to compare EZ width measured by the model to those by manual segmentation.ResultsThe accuracy of the trained model to identify inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane patches in the test dataset was 98%, 89%, 91%, 94%, and 96%, respectively. The EZ width measured by the model was highly correlated with that by two graders (r = 0.97; P < 0.0001). Bland-Altman analysis revealed a mean EZ width difference of 0.30 mm (coefficient of repeatability = 0.9 mm) between the model and the graders, comparable to the mean difference of 0.34mm (coefficient of repeatability = 0.8 mm) between two graders.ConclusionsThe results demonstrated the capability of a deep machine learning-based method for automatic identification of EZ in RP, suggesting that the method can be used to quantify structural deficits in RP for detecting disease progression and for evaluating treatment effect.Translational RelevanceA deep machine learning model has the potential to replace humans for grading spectral domain optical coherence tomography images in RP.

Virtual binocular vision sensing and control of molten pool width for gas metal arc additive manufactured thin-walled components

Gas metal arc (GMA) additive manufacturing (AM) is one of the significant wire and arc AM processes with the ability to produce large-scale metal parts in a layer by layer fashion. Despite this fact, techniques to realize process sensing and geometry control have not been perfectly developed. This study aims at molten pool width control in GMA AM using a passive vision sensing technique. A virtual binocular vision sensing system consisting of a biprism and a camera is designed to monitor the molten pool geometry. The molten pool width in a captured image pair is extracted by a series of procedures, such as sensor calibration, image pair rectification, disparity calculation, and width reconstruction. A verification test is conducted and reveals that the detection error of the sensing system is less than 3%. To keep consistent layer width in each layer, the deviation of the molten pool width is compensated by designing a fuzzy intelligent controller to adjust the arc current in real time. The effectiveness of the controller is evaluated via the deposition of thin-walled parts. The results indicate that the consistency of the molten pool width in GMA AM can be improved when employing the fuzzy controller.

Image recognition method of building wall cracks based on feature distribution

In order to solve the building damage caused by cracks in the wall surface, based on feature distribution and SAR image segmentation technology, the cracks in the building wall were identified by extracting feature data images, de-noising and enhancing image edges, and segmenting target images. The validity and feasibility of the method are verified by the actual concrete wall image. The results show that the recognition accuracy of cracks in non-cracked walls is 100%, and that of longitudinal cracks is 78.2%. Compared with the color feature discrimination, this method has a good processing effect on the image, clear crack line, good coincidence degree with the original image, and crack width is close to the width of the original image. After processing the image with rough set, the recognition rate of the image is 98.1%, the false reject rate is 1.9%, the recognition time is 12 min, and the execution time of the algorithm is 126 s. After the processing of gray histogram, the feature distribution of image set has a certain distribution transfer, but the transfer effect is not particularly obvious. It can be found that this method has advantages of high recognition accuracy, short time, and practical application value, significantly enhancing pretreatment effect.

Correlation between magnetic resonance imaging and cone-beam computed tomography for maxillary sinus graft assessment

PurposeLittle is known regarding the accuracy of clinical magnetic resonance imaging (MRI) protocols with acceptable scan times in sinus graft assessment. The aim of this study was to evaluate the correlations between MRI and cone-beam computed tomographic (CBCT) measurements of maxillary sinus grafts using 2 different clinical MRI imaging protocols.Materials and MethodsA total of 15 patients who underwent unilateral sinus lift surgery with biphasic calcium phosphate were included in this study. CBCT, T1-weighted MRI, and T2-weighted MRI scans were taken 6 months after sinus lift surgery. Linear measurements of the maximum height and buccolingual width in coronal images, as well as the maximum anteroposterior depth in sagittal images, were performed by 2 trained observers using CBCT and MRI Digital Imaging and Communication in Medicine files. Microcomputed tomography (micro-CT) was also performed to confirm the presence of bone tissue in the grafted area. Correlations between MRI and CBCT measurements were assessed with the Pearson test.ResultsSignificant correlations between CBCT and MRI were found for sinus graft height (T1-weighted, r=0.711 and P<0.05; T2-weighted, r=0.713 and P<0.05), buccolingual width (T1-weighted, r=0.892 and P<0.05; T2-weighted, r=0.956 and P<0.05), and anteroposterior depth (T1-weighted, r=0.731 and P<0.05; T2-weighted, r=0.873 and P<0.05). The presence of bone tissue in the grafted areas was confirmed via micro-CT.ConclusionBoth MRI pulse sequences tested can be used for sinus graft measurements, as strong correlations with CBCT were found. However, correlations between T2-weighted MRI and CBCT were slightly higher than those between T1-weighted MRI and CBCT.

Shennan Road and the Modernization of Shenzhen Architecture

Shenzhensetsanexampleforrapiddevelopmentofurbanplanningandconstruction.It was the starting point of the most massive city-construction movement in contemporary China. In less than 40 years, many representative urban space and buildings on the mainmast-west highway—-ShennanRoad,have witnessed the for mation of the banded multi-center structural layout and the miraculous expansion of the city. Many of those iconic buildings are designed by Hong Kong or foreign architects. With the continuous development of the length and width of Shennan road, its broad and prosperous image is not only a symbol of the fruits of reform and opening up in Shenzhen or even China, but also contains the growth history of Shenzhen’s architectural modernization. This paper reviews and summarizes the changes of the urban fabric and the design trend of representative buildings along with the Shennan Road in different periods by the historical research methods. Combined with the transfer path of the city center, this study analyzes what kind of unique role the street and buildings act as in the developmentofurbanstructureinShenzhen,and expound what other urban functions and symbolic meaning they have. In the context of globalization, this article discusses how do the buildings designed by foreign architects change our city,thedrivenfactors behind the phenomenon of the design trend change. This research can make a supplement to the history and theory of the modernization of contemporary Chinese architecture.

Principles of High-Resolution Dopant Profiling in the Scanning Helium Ion Microscope, Image Widths, and Surface Band Bending

Dopant mapping using secondary electrons (SEs) in the scanning helium ion microscope is correlated with the surface charge distributions and electron inelastic mean free path. Numerical results show that the doping contrast is mainly a function of bulk built-in voltages modified by surface band-bending effects within a 3-nm-thick surface layer. The strong surface work function sensitivity of the SE yield and highly suppressed SE2/SE1 ratio militate the higher specificity to acceptors (donors), with surface charging constraining the threshold sensitivity to around 1017 cm−3 in thicker p-layers, compared to ~1015 cm−3 commonly attainable in the scanning electron microscope.