Matching Regions Research Articles

Color Image Feature Matching Method Based on the Improved Firework Algorithm

An improved ORB feature point purification method has been proposed to address the problem of large feature point matching error and low image registration rate in the oriented FAST and rotated BRIEF (ORB) feature point color image matching algorithm. Pure virtual quaternions were used to represent the color image pixels in this method, and an improved FAST algorithm was used to detect the color feature points at first. The firework algorithm has been used to divide the detected feature points into key areas, auxiliary areas, and small influence areas, depending on the degree of attachment. The feature points of the key area and the subsidiary area are the required feature points. In order to further improve the efficiency of matching the color image feature points, the firework explosion radius formula and the explosion number formula in the firework algorithm have been improved, and an improved firework algorithm is proposed. This improved algorithm purifies the quaternion-represented color image feature points. For feature matching, the hamming distance was used. Experiments show that, when compared to the traditional ORB algorithm, the improved algorithm retains the key feature points of the color image with high accuracy, removes a large number of irrelevant feature points and noise points, and provides significantly higher accuracy and efficiency of color image matching.

Optimally filtering and matching processing for regional upstrokes to improve ultrasound transit time-based local PWV estimation

Background and objectivePulse wave velocity (PWV) is an important index for quantifying the elasticity of artery. Local PWV estimates based on ultrasonic transit time (TT) methods, however, are affected by the reflected waves and ultrasonic noise, biasing the spatiotemporal propagation of the time fiduciary point (TFP) positioning in the distension waveforms. In this study, an optimally filtering and matching processing for regional upstrokes is proposed to improve the ultrasound TT-based local PWV estimation. Method(i) Smooth the pulse waves (PWs) using the Savitzky–Golay filter with one set of randomly combined parameters. (ii) An arbitrary region at the first beam upstroke of the smoothed PWs is selected as the curve template, and then matched with the upstrokes of other PWs by calculating the sum of square differences (SSD) between the template and matching regions to find its similar regions. (iii) Update the filter parameters and the template using the moth-flame optimization (MFO) feedback for computing the new SSD value. When the new SSD value is smaller than the historical one, the later will be replaced. (iv) Repeat the above steps until the MFO algorithm converges to the minimum SSD value. (v) Output the optimal filter parameters and the locations of regional curves corresponding to the minimum SSD value. Then the time delay of the PWs propagation can be detected by using the starting points of the regional curves as the TFPs. ResultsWe conducted performance comparison with the advanced TT method through both simulation and clinical experiments. The results demonstrate that the proposed work observes considerable reductions on both the normalized root mean square error ± the standard deviation (from 6.73 ± 2.27% to 1.57 ± 0.72%) and the coefficient of variation (from 13.39% to 8.87%). ConclusionsThe results of this study support that the proposed method may facilitate the early diagnosis and prevention of local arterial stiffness .

PointpartNet: 3D point-cloud registration via deep part-based feature extraction

This paper proposes a deep learning model for point cloud registrations of different sizes. 3D point clouds play a very important role in various fields. They have been widely studied, and recently deep learning has also started to deal with point clouds. PointNet was the first deep learning model for point cloud classification and semantic segmentation. Since then, methods based on PointNet for tasks like point cloud registration have also been proposed. However, these methods are only suitable for identical or nearly identical point clouds. However, in practice, the sizes of point clouds vary depending on the capture distance, sensor type, the environment, and many other factors. Therefore, it is often the case that point clouds that need to be registered are of very different sizes. For example, point clouds captured in the same environment by an omnidirectional LiDAR and an RGB-D camera will have very different sizes. Conventional methods cannot cope with such situations. In this paper, we propose ‘PointpartNet', a new deep neural network based on partial feature extraction. This network enables feature extraction of partial point clouds by partitioning the point clouds. It uses the features of partial point clouds to search for matching regions between point clouds of different sizes. This makes it capable of registering point clouds of different sizes. In qualitative experiments, we demonstrate its high robustness and accuracy for point cloud registration of different sizes in comparison to previous research.

Signatures of gluon saturation from structure-function measurements

We study experimentally observable signals for nonlinear QCD dynamics in deep inelastic scattering (DIS) at small Bjorken variable $x$ and moderate virtuality $Q^2$, by quantifying differences between the linear Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution and nonlinear evolution with the Balitsky-Kovchegov (BK) equation. To remove the effect of the parametrization freedom in the initial conditions of both equations, we first match the predictions for the DIS structure functions $F_2$ and $F_{\rm L}$ from both frameworks in a region in $x,Q^2$ where both frameworks should provide an accurate description of the relevant physics. The differences in the dynamics are then quantified by the deviations when one moves away from this matching region. For free protons we find that the differences in $F_2$ remain at a few-percent level, while in $F_{\rm L}$ the deviations are larger, up to $10\,\%$ at the EIC and $40\,\%$ at the LHeC kinematics. With a heavy nucleus the differences are up to $10\,\%$ in $F_2$, and can reach $20\,\%$ and $60\,\%$ in $F_{\rm L}$ for the EIC and the LHeC, respectively.

Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud

PurposeMany metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.Design/methodology/approachDual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.FindingsIn the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.Originality/valueThe contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.

Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping

This paper investigates an ordered partial matching alignment problem, in which the goal is to align two sequences in the presence of potentially non-matching regions. We propose a novel parameter-free dynamic programming alignment method called hidden state time warping that allows an alignment path to switch between two different planes: a “visible” plane corresponding to matching sections and a “hidden” plane corresponding to non-matching sections. By defining two distinct planes, we can allow different types of time warping in each plane (e.g., imposing a maximum warping factor in matching regions while allowing completely unconstrained movements in non-matching regions). The resulting algorithm can determine the optimal continuous alignment path via dynamic programming, and the visible plane induces a (possibly) discontinuous alignment path containing matching regions. We show that this approach outperforms existing parameter-free methods on two different partial matching alignment problems involving speech and music.

A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera

Expansion-based methods are among the fastest algorithms to detect obstacles for safe navigation of Micro-Aerial Vehicles (MAVs). These methods are based on estimating an enlarging rate which is mostly computed using point features. Using points alone may result in situations where obstacles are only partially identified. This paper presents a new technique that uses image regions, instead of points, for estimating the expansion rate. The proposed algorithm utilises a fisheye camera that can be installed in front of a drone to detect obstacles in all directions. The camera takes images on which obstacles are identified. At each point in time, we extract the regions on the latest fisheye image and check to determine whether or not its regions belong to an obstacle. This step is completed by matching region points with those within the previous image. If at least three points are matched, then the convex hulls of the matched points on both images are formed. The expansion ratio of the convex hull areas is then estimated. If this ratio is bigger than a certain threshold, the region on the latest image is determined as an obstacle; otherwise, it is disregarded. This process is repeated until all pixels of the image are labelled either as obstacle or non-obstacle. Experiments were carried out using 50 pairs of fisheye images that covered a variety of obstacles, including people, pillars, trees, walls, and so on. The findings showed between 74%, and 84% of pixels were labelled correctly. By comparing these results with those obtained by the method developed by Al-Kaff et al. (2017), it is clear that the proposed method produces more stable and accurate results.

Association Analysis Between Introns and mRNAs in Caenorhabditis elegans Genes with Different Expression Levels

Background: Introns are ubiquitous in pre-mRNA but are often overlooked. They also play an important role in the regulation of gene expression. Objective and Method: We mainly use the improved Smith-Waterman local alignment approach to compare the optimal matching regions between introns and mRNA sequences in Caenorhabditis elegans (C. elegans) genes with high and low expression. Results: We found that the relative matching frequency distributions of all genes lie exactly between highly and lowly expressed genes, indicating that introns in highly and lowly expressed genes have different biological functions. Highly expressed genes have higher matching strengths on mRNA sequences than genes expressed at lower levels; the remarkably matched regions appear in UTR regions, particularly in the 3'UTR. The optimal matching frequency distributions have obvious differences in functional regions of the translation initiation and termination sites in highly and lowly expressed genes. The mRNA sequences with CpG islands tend to have stronger relative matching frequency distributions, especially in highly expressed genes. Additionally, the sequence characteristics of the optimal matched segments are consistent with those of the miRNAs, and they are considered a type of functional RNA segment. Conclusion: Introns in highly and lowly expressed genes contribute to the recognition translation initiation sites and translation termination sites. Moreover, our results suggest that the potential matching relationships between introns and mRNA sequences in highly and lowly expressed genes are significantly different and indicate that the matching strength correlates with the ability of introns to enhance gene expression.

Zr92(n,γ) and ( n ,tot) measurements at the GELINA and n_TOF facilities

Background: Stellar nucleosynthesis of elements heavier than iron is driven by neutron capture processes. Zr92 is positioned at a strategic point along the slow nucleosynthesis path, given its proximity to the neutron magic number N=50 and its position at the matching region between the weak and main slow processes. Purpose: In parallel with recent improved astronomical data, the extraction of accurate Maxwellian averaged cross sections (MACSs) derived from a more complete and accurate set of resonance parameters should allow for a better understanding of the stellar conditions at which nucleosynthesis takes place. Methods: Transmission and capture cross section measurements using enriched Zr92 metallic samples were performed at the time-of flight facilities GELINA of JRC-Geel (BE) and n_TOF of CERN (CH). The neutron beam passing through the samples was investigated in transmission measurements at GELINA using a Li-glass scintillator. The γ rays emitted during the neutron capture reactions were detected by C6D6 detectors at both GELINA and n_TOF. Results: Resonance parameters of individual resonances up to 81 keV were extracted from a combined resonance shape analysis of experimental transmissions and capture yields. For the majority of the resonances the parity was determined from an analysis of the transmission data obtained with different sample thicknesses. Average resonance parameters were calculated. Conclusions: Maxwellian averaged cross sections were extracted from resonances observed up to 81 keV. The MACS for kT=30keV is fully consistent with experimental data reported in the literature. The MACSs for kT≲15keV are in good agreement with those derived from the ENDF/B-VIII.0 library and recommended in the KADoNiS database. For kT higher than 30 keV differences are observed. A comparison with MACSs obtained with the cross sections recommended in the JEFF-3.3 and JENDL-4.0 libraries shows discrepancies even for kT≲15keV.5 MoreReceived 21 February 2020Revised 7 January 2022Accepted 7 February 2022DOI:https://doi.org/10.1103/PhysRevC.105.025805Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasHydrostatic stellar nucleosynthesisNeutron physicsNuclear astrophysicsNuclear reactionsRadiative captureResonance reactionsProperties90 ≤ A ≤ 149TechniquesNuclear data analysis & compilationNuclear Physics

Multiplex immunohistochemical phenotyping of T cells in primary prostate cancer.

Most prostate cancers are "immune cold" and poorly responsive to immune checkpoint inhibitors. However, the mechanisms responsible for the lack of a robust antitumor adaptive immune response in the prostate are poorly understood, which hinders the development of novel immunotherapeutic approaches. Most inflammatory infiltrates in the prostate are centered around benign glands and stroma, which can confound the molecular characterization of the antitumor immune response. We sought to analytically validate a chromogenic-based multiplex immunohistochemistry (IHC) approach applicable to whole slide digital image analysis to quantify T cell subsets from the tumor microenvironment of primary prostatic adenocarcinomas. As an initial application, we tested the hypothesis that PTEN loss leads to an altered antitumor immune response by comparing matched regions of tumors within the same individual with and without PTEN loss. Using the HALO Image Analysis Platform (Indica Labs), we trained a classifier to quantify the densities of eight T cell phenotypes separately in the tumor epithelial and stromal subcompartments. The iterative chromogenic approach using 7 different antibodies on the same slide provides highly similar findings to results using individually stained slides with single antibodies. Our main findings in carcinomas (benign removed) include the following: i) CD4+ T cells are present at higher density than CD8+ T cells; ii) all T cell subsets are present at higher densities in the stromal compartment compared to the epithelial tumor compartment; iii) most CD4+ and CD8+ T cells are PD1+; iv) cancer foci with PTEN loss harbored increased numbers of T cells compared to regions without PTEN loss, in both stromal and epithelial compartments; and v) the increases in T cells in PTEN loss regions were associated with ERG gene fusion status. This modular approach can apply to any IHC-validated antibody combination and sets the groundwork for more detailed spatial analyses. Iterative chromogenic IHC can be used for whole slide analysis of prostate tissue samples and can complement transcriptomic results including those using single cell and spatial genomic approaches.

Two-stage fusion localization based on UWB/PDR/Geomagnetism in underground space

A two-stage fusion positioning model based on UWB/PDR/Geomagnetism is proposed for pedestrian positioning in underground space. Firstly, an improved particle filter (PF) based on regional constraint is applied to PDR/Geomagnetism combination positioning, where the PDR positioning results are used to constrain the geomagnetic matching region. The proposed algorithm improves the inherent blindness of scattered particles of traditional PF, thus to enhance the positioning accuracy. Furtherly, Factor graph (FG) is used to fuse the output of PF above with the UWB positioning results, which effectively overcomes the serious problem of positioning hopping points caused by signal occlusion in some areas of UWB system. Experimental results show that the improved PF can outperform extended Kalman filter (EKF) for PDR/Geomagnetism combination positioning and FG algorithm can provide a higher positioning accuracy for UWB/PDR/Geomagnetism fusion positioning.

Global Color Consistency Correction for Large-Scale Images in 3-D Reconstruction

Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.

Cartilage Matrix Degeneration Occurs within the First Year after ACLR and Is Associated with Impaired Clinical Outcome.

Anterior cruciate ligament reconstruction (ACLR) has not been shown to decrease the risk for development of post-traumatic osteoarthritis. Magnetic resonance imaging (MRI) T2 mapping can be used to assess cartilage compositional changes. This study tests whether (1) worse cartilage arthroscopic status at ACLR is reflected by higher cartilage T2 values in matched study regions 6 weeks and 1 year after ACLR, and (2) increasing cartilage T2 values between 6 weeks and 1 year after ACLR are associated with worsening patient-reported outcomes. Twenty-two participants with ACLR and 26 controls underwent 3T MRI. T2 values in medial and lateral femoral and tibial cartilage were measured at 6 weeks and 1 year after ACLR and compared with arthroscopic grades, Knee injury and Osteoarthritis Outcome Scores (KOOS), and control T2 values. Most (59%-86%) cartilage study regions examined by arthroscopy demonstrated intact articular surfaces. Average T2 value increased in 3 of 4 study regions between 6 weeks and 1 year after ACLR (P = .001-.011). T2 value increased (P < .013) even for participants whose cartilage had intact articular surfaces at ACLR. Participants with ACLR who showed greater increases in cartilage T2 values had less improvement to KOOS Quality of Life (P = .009, ρ = -0.62). Cartilage status assessed arthroscopically at ACLR and by MRI T2 maps 6 weeks later was healthier than cartilage status assessed by MRI T2 maps at 1-year follow-up. Progressive T2 elevations were observed over the first year after ACLR even in patients with arthroscopically intact cartilage at the time of surgery and were associated with reduced improvement in knee quality of life suggesting preosteoarthritis.

Amino acid sequence homology between thyroid autoantigens and central nervous system proteins: Implications for the steroid-responsive encephalopathy associated with autoimmune thyroiditis

A few patients with Hashimoto’s thyroiditis or Graves’ disease develop a multiform syndrome of the central nervous system (CNS) termed Hashimoto’s encephalopathy or steroid-responsive encephalopathy associated with autoimmune thyroid disease (HE/SREAT). They have high levels of thyroid autoantibodies (TgAb, TPOAb and/or TSH-R-Ab) in blood and cerebrospinal fluid. Autoantibodies against alpha-enolase, aldehyde reductase-I (AKRIA) and/or dimethylargininase-I (DDAHI), proteins expressed in the CNS among other tissues, were detected in the blood and, when searched, in the cerebrospinal fluid of HE/SREAT patients. Recently, we reported that alpha-enolase, AKRIA and DDAHI share local sequence homology with each of the three autoantigens (TgAb, TPOAb, TSH-R-Ab), often in epitope-containing segments of the thyroid autoantigens. We hypothesized that there might be additional CNS-expressed proteins homologous to thyroid autoantigens, possibly overlapping known epitopes of the thyroid autoantigens. We used bioinformatic methods to address this hypothesis.Six, 27 and 47 of 46,809 CNS-expressed proteins share homology with TSH-R, Tg and TPO, respectively. The homologous regions often contain epitopes, and some match regions of thyroid autoantigens which have homology with alpha-enolase, AKRIA and/or DDAHI. Several of the aforementioned proteins are present in CNS areas that show abnormalities at neuroimaging in HE/SREAT patients. Furthermore, autoantibodies against some of the said six, 27 and 47 proteins were reported to be associated with a number of autoimmune diseases.Not only we validated our hypothesis, but we think that such a variety of potential CNS targets for thyroid Ab against epitopes contained in regions that have local homology with CNS proteins may explain the polymorphic phenotypes of HE/SREAT. Only when elevated amounts of these Ab are synthesized and trespass the blood-brain barrier, HE/SREAT appears. This might explain why HE/SREAT is so relatively rare.

A self-powered hydrogen leakage sensor based on impedance adjustable windmill-like triboelectric nanogenerator

With the development of fuel cell vehicles, the hydrogen leakage detector is urgent to satisfy the safety requirements of the vehicles with low power consumption. In this work, a self-powered hydrogen leakage detector based on impedance adjustable windmill-like triboelectric nanogenerator (WL-TENG) has been demonstrated. It could harvest wind energy during the driving of hydrogen fuel vehicle to power the whole sensing system. The inherent impedance and matching region of WL-TENG can be tuned effectively by adjusting the center angle of the windmill plate. The resistance of the Pd/ZnO nanorods based hydrogen sensor changes from 300 kΩ to 60 kΩ under pure air atmosphere to 1000 ppm H 2 . Then, a matching WL-TENG with 3° center angle was series connected to H 2 sensor to construct the self-powered H 2 sensing system. Finally, by introducing 150 kΩ divider constant resistor, the voltage across it increases from ~15 V to ~60 V with the H 2 concentration increasing from 0 to 1000 ppm. The output voltage of WL-TENG varies with the variable working states of the gas sensor and then directly reflects on the on/off status of the LEDs parallel connected to the divider resistor. This work contributes to the development of impedance matching theory of TENG and further expand its application on self-powered sensing system. A self-powered hydrogen leakage detector based on impedance adjustable windmill-like triboelectric nanogenerator (WL-TENG) has been demonstrated. It could harvest wind or movement etc. mechanical energy during the driving of hydrogen fuel vehicle to drive the whole sensing system. The inherent impedance and matching region of WL-TENG can be tuned effectively by adjusting the center angle of the windmill plate. • A windmill-like TENG harvests the wind energy from hydrogen energy vehicle and the Pd/ZnO nanorods sensor acts as hydrogen monitor. • The inherent impedance and matching region of WL-TENG can be adjusted effectively by changing the center angle. • By introducing divider constant resistor, the voltage across the TENG increases with the the increase of H 2 concentration 1000 ppm. • The output voltage of the TENG varies with the working states of the gas sensor and then directly reflects on the status of the LEDs in the circuit. • This work promotes the impedance matching theory of TENG and further expands its application in self-powered sensing system.

Learning complementary Siamese networks for real-time high-performance visual tracking

Recently, Siamese based methods have made a breakthrough in the visual tracking field. However, the existing trackers still cannot take full advantage of the deep features. In this work, we improve the performances of Siamese trackers by complementary learning with different types of matching features. Specifically, a Matching Activation Network (MAN) is firstly designed to highlight the matching regions of the search image given a template. Since only sparse parts of feature maps contribute to the matching result, an important design choice is to emphasize the weak-matching features by erasing the strong-matching ones and learn complementary classifiers from both types of features. Then we propose a novel complementary region proposal network (CoRPN) to take complementary features as inputs and their outputs complement to each other, which are fused to improve the performance. Experiments show that our proposed tracker achieves leading performances on five tracking datasets while retaining real-time speed.

Face feature matching based on semantic Information

Face feature matching problems have always been a very important research topic in the field of image processing. Face feature matching is always the most important part of common face recognition algorithms, but how to match features quickly and accurately is a quite essential and urgent problem to be solved. Therefore, we propose a face feature matching method which can effectively improve the matching efficiency and accuracy. For each feature point in the input image, face feature matching based on nearest neighbor ratio method requires global search for the best matching in the reference image. However, we find that such matching method can achieve better matching results, but it costs a lot of computing resources and time. Therefore, we use the feature vectors constructed by CNN convolution feature map to provide the local matching region based on semantic information for handcrafted descriptors after feature matching. Although two feature matching processes are used in this paper, the matching efficiency is improved several times compared with the traditional global search scheme. Experiments show that our proposed can be used as an important module of face recognition system to improve the accuracy of face recognition.

Protocol for Retrieving Three-Dimensional Biological Shapes for a Few XFEL Single-Particle Diffraction Patterns.

X-ray free-electron laser (XFEL) scattering promises to probe single biomolecular complexes without crystallization, enabling the study of biomolecular structures under near-physiological conditions at room temperature. However, such structural determination of biomolecules is extremely challenging thus far. In addition to the large numbers of diffraction patterns required, the orientation of each diffraction pattern needs to be accurately estimated and the missing phase information needs to be recovered for three-dimensional (3D) structure reconstruction. Given the current limitations to the amount and resolution of the data available from single-particle XFEL scattering experiments, we propose an alternative approach to find plausible 3D biological shapes from a limited number of diffraction patterns to serve as a starting point for further analyses. In our proposed strategy, small sets of input (e.g., five) XFEL diffraction patterns were matched against a library of diffraction patterns simulated from 1628 electron microscopy (EM) models to find potential matching 3D models that are consistent with the input diffraction patterns. This approach was tested for three example cases: EMD-3457 (Thermoplasma acidophilum 20S proteasome), EMD-5141 (Escherichia coli 70S ribosome complex), and EMD-5152 (budding yeast Nup84 complex). We observed that choosing the best strategy to define matching regions on the diffraction patterns is critical for identifying correctly matching diffraction patterns. While increasing the number of input diffraction patterns improved the matches in some cases, we found that the resulting matches are more dependent on the uniqueness or complexity of the shape as captured in the individual input diffraction patterns and the availability of a similar 3D biological shape in the search library. The protocol could be useful for finding candidate models for a limited amount of low-resolution data, even when insufficient for reconstruction, performing a quick exploration of new data upon collection, and the analysis of the conformational heterogeneity of the particle of interest as captured within the diffraction patterns.

Ground Visibility in Satellite Optical Time Series Based on A Contrario Local Image Matching

Assessing ground visibility is a crucial step in automatic satellite image analysis. Some Earth observation satellites are provided with spectral bands specially designed for cloud detection. An alternative approach is to detect ground visibility by comparing locally the images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series, while clouds change shape constantly. We describe here a ground visibility detection algorithm based on an a contrario local image matching method, coupled with an efficient greedy algorithm.

Newly Identified Hemodynamic Parameter to Predict Thin-Walled Regions of Unruptured Cerebral Aneurysms Using Computational Fluid Dynamics Analysis

The thin-walled regions (TIWRs) of intracranial aneurysms have a high risk of rupture during surgical manipulation. They have been reported to be predicted by wall shear stress and pressure (PS) based on computational fluid dynamics analysis, although this remains controversial. In this study, we investigated whether the oscillatory shear index (OSI) can predict TIWRs. Twenty-five unruptured aneurysms were retrospectively analyzed; the position and orientation of the computational fluid dynamics color maps were adjusted to match the intraoperative micrographs. The red area on the aneurysm wall was defined as TIWR, and if most of the regions on the color map corresponding to TIWR were OSI low (lower quartile range), time-averaged wall shear stress (TAWSS) high, or PS high (upper quartile range), each region was defined as a matched region and divided by the total number of TIWRs to calculate the match rate. In addition, the mean values of OSI, TAWSS, and PS corresponding to TIWRs were quantitatively compared with those in adjacent thick-walled regions. Among 27 TIWRs of 25 aneurysms, 23, 10, and 14 regions had low OSI, high TAWSS, and high PS regions (match rate: 85.2%, 37.0%, and 51.9%), respectively. Receiver operating characteristic curve analysis demonstrated that OSI was the most effective hemodynamic parameter (area under the curve, 0.881), followed by TAWSS (0.798). Multivariate analysis showed that OSI was a significant independent predictor of TIWRs (odds ratio, 18.30 [95% CI, 3.2800-102.00], P < 0.001). OSI may be a unique predictor for TIWRs. Low OSI strongly corresponds with TIWRs of intracranial aneurysms.