Standard Image Processing Algorithms Research Articles

CNN-based Image Processing algorithm for autonomous optical navigation of Hera mission to the binary asteroid Didymos

Hera mission is the European Space Agency’s contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation.

Fully adaptive Bayesian algorithm for data analysis: FABADA

Abstract The discovery potential from astronomical and other data is limited by their noise. We introduce a novel non-parametric noise reduction technique based on Bayesian inference techniques, fully adaptive Bayesian algorithm for data analysis (FABADA) that automatically improves the signal-to-noise ratio of one- and two-dimensional data, such as astronomical images and spectra. The algorithm iteratively evaluates possible smoothed versions of the data, the smooth models, estimating the underlying signal that is statistically compatible with the noisy measurements. Iterations stop based on the evidence and the χ2 statistic of the last smooth model. We then compute the expected value of the signal as a weighted average of the whole set of smooth models. We explain the mathematical formalism and numerical implementation of the algorithm, and evaluate its performance in terms of the peak signal-to-noise ratio, the structural similarity index, and the time payload, using a battery of real astronomical observations. Our FABADA yields results that, without any parameter tuning, are comparable with standard image processing algorithms whose parameters have been optimized based on the true signal to be recovered, something that is impossible in a real application. On the other hand, state-of-the-art non-parametric methods, such as block-matching and three-dimensional filtering, offer slightly better performance at high signal-to-noise ratio, while our algorithm is significantly more accurate for extremely noisy data, a situation usually encountered in astronomy.

Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines

In this work, we present a vision system particularly suited to the automatic recognition of reels in the field of automatic packaging machines. The output of the vision system is used to guide the autonomous grasping of the reels by a robot for a subsequent manipulation task. The proposed solution is built around three different methods to solve the ellipse-detection problem in an image. Such methods leverage standard image processing and mathematical algorithms, which are tailored to the targeted application. An experimental campaign demonstrates the efficacy of the proposed approach, even in the presence of low computational power and limited hardware resources, as in the use-case at hand.

On-ground validation of a CNN-based monocular pose estimation system for uncooperative spacecraft: Bridging domain shift in rendezvous scenarios

The estimation of the relative pose of an inactive spacecraft by an active servicer spacecraft is a critical task for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. Among all the challenges, the lack of available space images of the inactive satellite makes the on-ground validation of current monocular camera-based navigation systems a challenging task, mostly due to the fact that standard Image Processing (IP) algorithms, which are usually tested on synthetic images, tend to fail when implemented in orbit. In response to this need to guarantee a reliable validation of pose estimation systems, this paper presents the most recent advances of ESA’s GNC Rendezvous, Approach and Landing Simulator (GRALS) testbed for close-proximity operations around uncooperative spacecraft. The proposed testbed is used to validate a Convolutional Neural Network (CNN)-based monocular pose estimation system on representative rendezvous scenarios with special focus on solving the domain shift problem which characterizes CNNs trained on synthetic datasets when tested on more realistic imagery. The validation of the proposed system is ensured by the introduction of a calibration framework, which returns an accurate reference relative pose between the target spacecraft and the camera for each lab-generated image, allowing a comparative assessment at a pose estimation level. The VICON Tracker System is used together with two KUKA robotic arms to respectively track and control the trajectory of the monocular camera around a scaled 1:25 mockup of the Envisat spacecraft. After an overview of the facility, this work describes a novel data augmentation technique focused on texture randomization, aimed at improving the CNN robustness against previously unseen target textures. Despite the feature detection challenges under extreme brightness and illumination conditions, the results on the high exposure scenario show that the proposed system is capable of bridging the domain shift from synthetic to lab-generated images, returning accurate pose estimates for more than 50% of the rendezvous trajectory images despite the large domain gaps in target textures and illumination conditions.

Optical navigation for Lunar landing based on Convolutional Neural Network crater detector

Traditional vision-based navigation algorithms are highly affected from non-nominal conditions, which comprise illumination conditions and environmental uncertainties. Thanks to the outstanding generalization capability and flexibility, deep neural networks (and AI algorithms in general) are excellent candidates to solve the aforementioned shortcoming of navigation algorithms. The paper presents a vision-based navigation system using a Convolutional Neural Network to solve the task of pinpoint landing on the Moon using absolute navigation, namely with respect to the Mean Earth/Polar Axis reference frame. The Moon landing scenario consists in the spacecraft descent on the South Pole from a parking orbit to the powered descent phase. The architecture features an Object Detection Convolutional Neural Network (ODN) trained with supervised learning approach. The CNN is used to extract features of the observed craters that are then processed by standard image processing algorithms in order to provide pseudo-measurements that can be used by navigation filter. The craters are matched against a database containing the inertial location of the known craters. An Extended Kalman Filter with time-delayed measurements integration is developed to fuse optical and altimeter information.

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

Анализ точности построения 3D координат рабочего пространства подводного робота

Distortion of underwater images can impair both the accuracy and robustness of 3D scene reconstruction algorithms. The problems that arise are related to the lack of robustness of these methods to changes in the underwater environment and features of transmitting and receiving signals under water, including, in particular, uneven illumination of the underwater environment, rapid attenuation, scattering and refraction of light when passing through an inhomogeneous medium of air-water-glass, limiting the frequency spectrum of passing light, which leads to the absorption of low-frequency components to a greater extent than light of higher frequencies. All this seriously complicates the ability to extract information about the scene as a whole and objects of interest located in the underwater environment, limits the use of standard image processing algorithms and requires their significant improvement. This article offers a new approach to analyzing the accuracy of constructing 3D coordinates of the working space of an underwater robot. The approach is based on underwater camera calibration, assessment of camera image centers taking into account the waterproof shell. We use statistical analysis that allows us to evaluate the impact of all sources of disturbances (both hardware and software) based only on experimental data. In particular, it shows how to get the error distribution using the measured values of the calibration sample and obtained by triangulation under underwater conditions. This makes it possible to simultaneously evaluate the systematic error and the distribution characteristics of the random component of the error in restoring 3D coordinates of the workspace. An important feature of the proposed approach is the ability to assess the impact of all sources of disturbances in the aggregate, including the design of a waterproof shell, based only on experimental data obtained in the underwater environment. In addition, the same approach can also provide estimates of the position of camera image centers, allowing for the presence of a waterproof shell to improve the accuracy of image processing algorithms. The proposed approach was tested on real data.

Evaluation of the extreme traffic load effects on the Forth Road Bridge using image analysis of traffic data

This paper proposes a novel technique to estimate site-specific characteristic maximum traffic load effects on the Forth Road suspension Bridge (FRB). Congestion represents a critical loading state for long-span bridges and congested traffic data such as car/truck mix, vehicle weights and inter-vehicle gaps are not generally simultaneously available. Most Weigh-in-Motion (WIM) systems used worldwide to collect traffic data are not reliable in congested conditions. A closed-circuit television camera and image processing techniques were used here to collect five months of bridge traffic data on the FRB. Frames extracted from videos are automatically analysed by applying standard image processing algorithms. Hence vehicle lengths and relative positions on the bridge are extracted. An on-site WIM database with free-flowing traffic data is used to establish a statistical correlation between vehicle lengths and their weights. This correlation is applied to infer weights from the vehicle lengths recorded on the bridge. Congestion is modelled for northbound traffic by collapsing the inter-vehicle gaps to minimum values. The axial force in the main cable of the bridge is calculated using the corresponding influence line. The characteristic maximum values for axial force are evaluated for 75-year and 1000-year return periods.

Object-Based Analog Forecasts for Surface Wind Speed

Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method’s ability (to find good analogs) from the training set’s ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.

Automatic localization of endoscope in intraoperative CT image: A simple approach to augmented reality guidance in laparoscopic surgery.

The use of augmented reality in minimally invasive surgery has been the subject of much research for more than a decade. The endoscopic view of the surgical scene is typically augmented with a 3D model extracted from a preoperative acquisition. However, the organs of interest often present major changes in shape and location because of the pneumoperitoneum and patient displacement. There have been numerous attempts to compensate for this distortion between the pre- and intraoperative states. Some have attempted to recover the visible surface of the organ through image analysis and register it to the preoperative data, but this has proven insufficiently robust and may be problematic with large organs. A second approach is to introduce an intraoperative 3D imaging system as a transition. Hybrid operating rooms are becoming more and more popular, so this seems to be a viable solution, but current techniques require yet another external and constraining piece of apparatus such as an optical tracking system to determine the relationship between the intraoperative images and the endoscopic view. In this article, we propose a new approach to automatically register the reconstruction from an intraoperative CT acquisition with the static endoscopic view, by locating the endoscope tip in the volume data. We first describe our method to localize the endoscope orientation in the intraoperative image using standard image processing algorithms. Secondly, we highlight that the axis of the endoscope needs a specific calibration process to ensure proper registration accuracy. In the last section, we present quantitative and qualitative results proving the feasibility and the clinical potential of our approach.

ISpectra: An Open Source Toolbox For The Analysis of Spectral Images Recorded on Scanning Electron Microscopes.

iSpectra is an open source and system-independent toolbox for the analysis of spectral images (SIs) recorded on energy-dispersive spectroscopy (EDS) systems attached to scanning electron microscopes (SEMs). The aim of iSpectra is to assign pixels with similar spectral content to phases, accompanied by cumulative phase spectra with superior counting statistics for quantification. Pixel-to-phase assignment starts with a threshold-based pre-sorting of spectra to create groups of pixels with identical elemental budgets, similar to a method described by van Hoek (2014). Subsequent merging of groups and re-assignments of pixels using elemental or principle component histogram plots enables the user to generate chemically and texturally plausible phase maps. A variety of standard image processing algorithms can be applied to groups of pixels to optimize pixel-to-phase assignments, such as morphology operations to account for overlapping excitation volumes over pixels located at phase boundaries. iSpectra supports batch processing and allows pixel-to-phase assignments to be applied to an unlimited amount of SIs, thus enabling phase mapping of large area samples like petrographic thin sections.

Evaluation of a Noise Reduction Imaging Technology in Iliac Digital Subtraction Angiography: Noninferior Clinical Image Quality with Lower Patient and Scatter Dose

PurposeTo determine whether equivalent-quality images can be obtained from digital subtraction angiography (DSA) of the iliac artery after implementation of a novel imaging technology that reduces patient and scatter x-ray dose. Materials and MethodsImaging using two randomly ordered DSA runs was performed in 51 adults scheduled for iliac artery angiography or intervention or both. One DSA run used standard acquisition chain and image processing algorithms (referred to as “ reference DSA”), and the other DSA run used dose-reduction and real-time advanced image noise reduction technology (referred to as “study DSA”). The quality of each pair of runs, consecutively performed without changes in working projection or injection parameters, was independently rated by five radiologists blinded to the imaging technology used. Patient radiation dose was evaluated using air kerma and dose area product, and scatter dose was evaluated using three dosimeters (DoseAware, Philips Healthcare, Best, The Netherlands), located at fixed positions. ResultsComparable image pairs were available in 48 patients. There were 44 patients undergoing treatment involving the common (n = 33) or external (n = 29) iliac arteries. Study DSA images were rated as equal to or better than reference DSA images for 96% of comparisons, with an average overall agreement among raters of 0.93 (95% confidence interval, 0.65–0.96). Mean patient radiation dose (n = 48) and scatter dose rate for the three dosimeters (n = 50) was 83% ± 5 and 69% ± 10 lower, respectively, using the study technology (P < .001). ConclusionsIliac artery DSA performed using a dose-reduction and real-time advanced image noise reduction technology results in image quality that is noninferior to conventional DSA but with significantly lower patient and scatter radiation exposure (P < .001).

An Improved JPEG Image Compression Technique based on Selective Quantization

In today’s communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2:15% and 14:10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively.

Digital detection and analysis of branching and cell contacts in neural cell cultures

Changes in human/animal behaviour and the involved neural functions are characterized by structural alterations in the brain circuitry. These changes comprise the formation of new synapses and the elimination of existing synapses aside from the modulation of connecting properties within other ones. The mechanisms of neuronal branching and cell contacting regulate and prepare for the processes of synaptic formation. In this study, we present a set of methods to detect, describe and analyse the dynamics attributed to the process of cell contacting in cell cultures in vitro. This involves the dynamics of branching and seeking for synaptic partners. The proposed technique formally distinguishes between the actual formed synapses and the potential synaptic sites, i.e. where cell contacts are likely. The study investigates the dynamic behaviour of these potential synaptic sites within the process of seeking for contacts. The introduced tools use morphological image processing algorithms to automatically detect the sites of interest. Results indicate that the introduced tools can reliably describe experimentally observed branching and seeking for contacts dynamics. Being straightforward in terms of implementation and analysis, our framework represents a solid method for studying the neural preparation phases of synaptic formation via cell contacting in random networks using standard phase contrast microscopy.

Preliminary analysis of the connection between ocean dynamics and the noise of gravity tide observed at the Sopronbánfalva Geodynamical Observatory, Hungary

An experimental development of a computer controlled photoelectric ocular system applied for the LaCoste and Romberg G949 gravimeter made the continuous observation of time variation of gravity possible. The system was operated for half a year in the Sopronbánfalva Geodynamical Observatory to test its capabilities. The primary aim of this development was to provide an alternative and self-manageable solution instead of the standard electronic (Capacitive Position Indicator) reading of this type of gravimeter and use it for the monitoring of Earth tide. It, however, turned out that this system is sensitive enough to observe the effect of variable seismic noise (microseisms) due to the changes of ocean weather in the North Atlantic and North Sea regions at microGal level (1μGal=10−8m/s2). Up to now not much attention was paid to its influence on the quality and accuracy of gravity observations because of the large distance (>1000km) between the observation place (generally the Carpathian–Pannonian basin) and the locations (centres of storm zones of the northern hydrosphere) of triggering events. Based on an elementary harmonic surface deformation model the noise level of gravity observations was compared to the spectral characteristics of seismic time series recorded at the same time in the observatory. Although the sampling rate of gravity records was 120s the daily variation of gravity noise level showed significant correlation with the variation of spectral amplitude distribution of the analysed high pass filtered (cut-off frequency=0.005Hz) seismograms up to 10Hz. Also available daily maps of ocean weather parameters were used to support both the correlation analysis and the parameterization of the triggering events of microseisms for further statistical investigations. These maps, which were processed by standard image processing algorithms, provide numerical data about geometrical (distance and azimuth of the storm centres relative to the observation point) and physical (mass of swelling water) quantities. The information can be applied for characterizing the state of ocean weather at a given day which may help the prediction of its influence on gravity measurements in the future. Probably it is the first attempt to analyse quantitatively the effect of ocean weather on gravity observations in this specific area of the Carpathian–Pannonian region.

A Fisher-Rao Metric for Paracatadioptric Images of Lines

In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it difficult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied. The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and based on the Hough transform.

γH2AX foci as a measure of DNA damage: A computational approach to automatic analysis

The γH2AX focus assay represents a fast and sensitive approach for the detection of one of the critical types of DNA damage – double-strand breaks (DSB) induced by various cytotoxic agents including ionising radiation. Apart from research applications, the assay has a potential in clinical medicine/pathology, such as assessment of individual radiosensitivity, response to cancer therapies, as well as in biodosimetry. Given that generally there is a direct relationship between numbers of microscopically visualised γH2AX foci and DNA DSB in a cell, the number of foci per nucleus represents the most efficient and informative parameter of the assay. Although computational approaches have been developed for automatic focus counting, the tedious and time consuming manual focus counting still remains the most reliable way due to limitations of computational approaches.We suggest a computational approach and associated software for automatic focus counting that minimises these limitations. Our approach, while using standard image processing algorithms, maximises the automation of identification of nuclei/cells in complex images, offers an efficient way to optimise parameters used in the image analysis and counting procedures, optionally invokes additional procedures to deal with variations in intensity of the signal and background in individual images, and provides automatic batch processing of a series of images. We report results of validation studies that demonstrated correlation of manual focus counting with results obtained using our computational algorithm for mouse jejunum touch prints, mouse tongue sections and human blood lymphocytes as well as radiation dose response of γH2AX focus induction for these biological specimens.

A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images

Omnidirectional cameras are useful in applications requiring rapid capture of image data representing the complete local environment. Feature detection from such image data is thus a prominent research issue. Transforming an omnidirectional image to a panoramic image may result in a sparse panoramic image with missing image data. Whilst image reconstruction techniques have been developed that enable the subsequent use of standard image processing algorithms, the development of image processing algorithms that can be applied directly to sparse image data has received less attention. We address the problem of corner point detection for sparse panoramic images by developing an algorithmic approach that can be applied directly to sparse unwarped omnidirectional images without the requirement of image reconstruction, and we illustrate the accurate performance of the algorithm through visual results and receiver operating characteristic curves.

A neural model of selective attention and object segmentation in the visual scene: An approach based on partial synchronization and star-like architecture of connections

A brain-inspired computational system is presented that allows sequential selection and processing of objects from a visual scene. The system is comprised of three modules. The selective attention module is designed as a network of spiking neurons of the Hodgkin-Huxley type with star-like connections between the central unit and peripheral elements. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics corresponds to the partial synchronization mode. It is shown that peripheral neurons with higher firing rates are preferentially drawn into partial synchronization. We show that local excitatory connections facilitate synchronization, while local inhibitory connections help distinguishing between two groups of peripheral neurons with similar intrinsic frequencies. The module automatically scans a visual scene and sequentially selects regions of interest for detailed processing and object segmentation. The contour extraction module implements standard image processing algorithms for contour extraction. The module computes raw contours of objects accompanied by noise and some spurious inclusions. At the next stage, the object segmentation module designed as a network of phase oscillators is used for precise determination of object boundaries and noise suppression. This module has a star-like architecture of connections. The segmented object is represented by a group of peripheral oscillators working in the regime of partial synchronization with the central oscillator. The functioning of each module is illustrated by an example of processing of the visual scene taken from a visual stream of a robot camera.

Alignment of angiogenic microvessels and the surrounding collagen matrix under anchored boundary conditions

Previously we have observed an alignment of newly formed microvessels when 3D collagen gel cultures were axially anchored to their culture chamber . In contrast, microvessels in free‐floating gels exhibit a random orientation. The objective of this study was to examine changes in collagen alignment that occur during microvessel outgrowth over time in free‐floating and anchored 3D cultures. Microvessel cultures were fixed after 1 to 6 days in culture and endothelial cells were stained with Isolectin IB4‐Alexa 488. 3D image stacks of microvessels and collagen fibrils were obtained using confocal and confocal reflection microscopy, respectively. Microvessel and collagen fibril organization were quantified using standard image processing algorithms. There was significant alignment of vessels in anchored cultures, starting at 3‐4 days in culture. Similarly, the organization of collagen went from a random organization (anisotropy &lt; 0.1) to a progressively more aligned orientation (anisotropy &gt; 0.25) in a direction consistent with the microvessels. These results suggest that the orientation of microvessel outgrowth and matrix realignment follow similar patterns during angiogenesis. Although these events were highly correlated, it is unclear which event leads in the process of alignment.