Set Of High-resolution Images Research Articles

Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy

The use of multiparametric magnetic resonance imaging (mpMRI)-derived radiomics has the potential to offer noninvasive, imaging-based biomarkers for the identification of subvisual characteristics indicative of a poor oncologic outcome. The present study, therefore, seeks to develop, validate, and assess the performance of an MRI-derived radiomic model for the prediction of prostate cancer (PC) recurrence following radical prostatectomy (RP) with curative intent. mpMRI imaging was obtained from 251 patients who had undergone an RP for the treatment of localized prostate cancer across two institutions and three surgeons. All patients had a minimum of 2 years follow-up via prostate-specific antigen serum testing. Each prostate mpMRI was individually reviewed, and the prostate was delineated as a single slice (ROI) on axial T2 high-resolution image sets. A total of 924 radiomic features were extracted and tested for stability via intraclass correlation coefficient (ICC) following image normalization via histogram matching. Fourteen important and nonredundant features were found to be predictors of PC recurrence at a mean ± SD of 3.2 ± 2.2 years post-RP. Five-fold, ten-run cross-validation of the model containing these fourteen features yielded an area under the curve (AUC) of 0.89 ± 0.04 in the training set (n = 225). In comparison, the University of California San Fransisco Cancer of the Prostate Risk Assessment score (UCSF-CAPRA) and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical prostatectomy nomograms yielded AUC of 0.66 ± 0.05 and 0.67 ± 0.05, respectively (p < 0.01). When the radiomic model was applied to the test set (n = 26), AUC was 0.78; sensitivity, specificity, positive predictive value, and negative predictive value were 60%, 86%, 52%, and 89%, respectively. Accuracy in predicting PC recurrence was 81%.

Oral CT Contrast Agents: What's New and Why, From the AJR Special Series on Contrast Media.

Current CT oral contrast agents improve the conspicuity and confidence for bowel and peritoneal findings in many clinical scenarios, particularly for outpatient and oncologic abdominopelvic imaging. Yet, existing positive and neutral oral contrast agents may diminish the detectability of certain radiologic findings, frequently in the same scans in which the oral contrast agent improves the detectability of other findings. With ongoing improvements in CT technology, particularly multi-energy CT, opportunities are opening for new types of oral contrast agents to further improve anatomic delineation and disease detection using CT. The CT signal of new dark oral contrast agents and of new high-Z oral contrast agents promise to combine the strengths of both positive and neutral oral CT contrast agents by providing distinct CT appearances in comparison with bodily tissues, iodinated IV contrast agents, and other classes of new CT contrast agents. High-Z oral contrast agents will unlock previously inaccessible capabilities of multi-energy CT, particularly photon-counting detector CT, for differentiating simultaneously administered IV and oral contrast agents; this technique will allow generation of rich 3D, intuitive, perfectly co-registered, high-resolution image sets with individual contrast-agent "colors" that provide compelling clarity for intertwined intraabdominal anatomy and disease processes.

Land Classification on Satellite Images Using CNN

Abstract: Land cover classification from these images is a difficult task be- cause of very large sized data and high variation types. Satellite based information is presently available as huge sets of high resolution images from a large number of satellites like Sentinel, Landsat8, etc. Land cover classification from these images is a difficult task be- cause of very large sized data and high variation types. The purpose of land use and land cover classification is that monitoring and identifying the various land cover classes exactly. Land-use is the rational and judicious approach of allocating available land resources for different activities (such as settlements, arable fields, pastures, and managed woods) within a city. The use of a particular patch of land and its physical character are linked. However, research that establishes this link is lacking despite the proliferation of geo spatial data.

3D Anatomy of the Developing Heart: Understanding Ventricular Septation.

Understanding how the four-chambered mammalian heart is formed from a simple, looped tube remains challenging, notwithstanding the descriptive accounts left by generations of cardiac anatomists. Much of the difficulty lies in attempting to visualize an intricate series of morphological transformations through the restrictive lens of two-dimensional images derived from histology. Modern imaging methods offer a way to overcome this limitation by providing comprehensive and high-resolution image sets of the developing heart. We have used one such method, high-resolution episcopic microscopy (HREM), to obtain virtual three-dimensional (3D) models of successive stages in mouse heart development. Taking advantage of the ability afforded by 3D modeling to view each heart in any orientation or erosion plane, we provide an illustrated account of how the mouse heart divides into left and right ventricular chambers, and how each acquires its own distinct outflow vessel.

Asymmetric Craters on the Dwarf Planet Ceres—Results of Second Extended Mission Data Analysis

After almost three years of successful operation on Ceres, the Dawn spacecraft entered its last orbits around the dwarf planet and obtained a set of high-resolution images of 3 to 5 m/pixel. These images reveal a variety of morphologic features, including a set of asymmetric crater morphologies as observed earlier in the mission on the asteroid Vesta. We identified 269 craters, which are located between 60° N to 60° S latitude and 197° E to 265° E longitude, and investigated their morphological characteristics using a digital terrain model (DTM). These craters range in diameter from 0.30 to 4.2 km, and exhibit a sharp crater rim on the uphill side and a smooth one on the downhill side. We found that all asymmetric craters are formed on a sloping surface with the majority appearing at slope angles between 5 and 20 degrees. This implies that, as observed on Vesta, the topography is the main cause for these asymmetries.

Spectral Energy Distribution of Blue Stragglers in the Core of 47 Tucanae

Abstract We have constructed the spectral energy distributions (SEDs) of a sample of blue straggler stars (BSSs) in the core of the globular cluster 47 Tucanae, taking advantage of the large set of high resolution images, ranging from the ultraviolet to the near-infrared, obtained with the Advanced Camera for Surveys/High Resolution Channel of the Hubble Space Telescope. Our final BSS sample consists of 22 objects, spanning the whole color and magnitude extension of the BSS sequence in 47 Tucanae. We fitted the BSS broadband SEDs with models to derive temperature, surface gravity, radius, luminosity, and mass. We show that BSSs indeed define a mass sequence, where the mass increases for increasing luminosity. Interestingly, the BSS mass estimates from the SED fitting turn out to be comparable to those derived from the projection of the stellar position in the color–magnitude diagram on standard star evolutionary tracks. We compare our results with previous, direct mass estimates of a few BSSs in 47 Tucanae. We also find a couple of supermassive BSS candidates, i.e., BSSs with masses larger than twice the turn-off mass, the formation of which must have involved more than two progenitors.

Convolutional neural network-based inspection of metal additive manufacturing parts

PurposePart quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts.Design/methodology/approachThe proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part.FindingsThe classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.Originality/valueA real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

Integration of point cloud data and hyperspectral imaging as a data gathering methodology for refurbishment projects using building information modelling (BIM)

PurposeBuilding information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of benefits in terms of achieving the efficient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and significant benefits in terms of efficiency gains and cost savings should be achieved.Design/methodology/approachLaser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building specification and construction materials, to be in used in BIM.FindingsHyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identification and classification of surface materials and would also convert the 3D representation to BIM.Originality/valueThis integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the efficiency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the first time in the context of buildings.

PROPER MOTIONS IN TERZAN 5: MEMBERSHIP OF THE MULTI-IRON SUBPOPULATIONS AND FIRST CONSTRAINT ON THE ORBIT

By exploiting two sets of high-resolution images obtained with HST ACS/WFC over a baseline of ~10 years we have measured relative proper motions of ~70,000 stars in the stellar system Terzan 5. The results confirm the membership of the three sub-populations with different iron abudances discovered in the system. The orbit of the system has been derived from a first estimate of its absolute proper motion, obtained by using bulge stars as reference. The results of the integration of this orbit within an axisymmetric Galactic model exclude any external accretion origin for this cluster. Terzan 5 is known to have chemistry similar to the Galactic bulge; our findings support a kinematic link between the cluster and the bulge, further strengthening the possibility that Terzan 5 is the fossil remnant of one of the pristine clumps that originated the bulge.

Multi-scale Segmentation of High-Spatial Resolution Remote Sensing Images Using Adaptively Increased Scale Parameter

AbstractThe adaptively increased scale parameter (AISP) strategy is proposed to control multi-scale segmentation based on region growing methods. AISP strategy contains a set of gradually increased scale parameters to produce nested multi-scale segments. Instead of independently assigning the set of scale parameters ahead of segmentation, the contribution of this study is to dynamically determine scale parameters during segmentation procedure, making scale parameters adaptive to specific images and cover meaningful segmentation scales. Furthermore, the effectiveness of gradually increased scale parameters on segmentation accuracy is analyzed, which gives a thorough understanding to local-oriented region growing methods. The experimental results on a set of high-resolution images proved the effectiveness of AISP on controlling multi-scale segmentation. AISP holds the application potential for object-based analysis of high-resolution images.

Rapid Multiclass Traffic Sign Detection in High-Resolution Images

This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.

The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operator-dependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the “ground truth”). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%±0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%±0.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.

Comparative effectiveness of immune-cell depletion and a targeted therapy against LTβR-signaling in the treatment of autoimmune pancreatitis

s / Pancreatology 14 (2014) S1eS129 S18 Aims: In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. Materials&methods: HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the “ground truth”). After the expert defined the ground truth, the computer was able to develop a prediction rule which was then applied to a unique set of high resolution images in order to validate the process. Results: For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue areawith only a difference of 1% ± 0.05% (p1⁄40.21). Within regions of injured tissue, the software reported a difference of 2.5% ± 0.04% in acinar area compared with the pathologist (p1⁄40.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. Conclusion: The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.

Traitement d’images sur architectures parallèles et hétérogènes

ABSTRACT. Image processing algorithms present a necessary tool for various domains relatedto computer vision. These algorithms are hampered by their high consumption of computingtimes when processing large sets of high resolution images. In this work, we propose a deve-lopment scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous (Multi-CPU/Multi-GPU) platforms, in order to improve performance of image processing algorithms.The proposed scheme enables an efficient scheduling of hybrid tasks and an effective manage-ment of heterogeneous memories. We present also parallel and hybrid implementations of edgeand corner detection methods. Experimental results showed a global speedup ranging from 5to 25, when processing different sets of images, by comparison with CPU implementations. MOTS-CLES : calcul heterogene, GPU, traitement d’images, detection des coins et contours. KEYWORDS: heterogeneous computing, GPU, image processing, corner and edge detection. DOI:10.3166/TSI.31.1183-1203 c 2012 Lavoisier

Patient-Specific 3D Simulation of Cyclic CSF Flow at the Craniocervical Region

Flow simulations in patient-specific models of the subarachnoid space characterize CSF flow in more detail than MR flow imaging. We extended previous simulation studies by including cyclic CSF flow and patient-specific models in multiple patients with Chiari I. We compared simulation results with MR flow measurements. Volumetric high resolution image sets acquired in 7 patients with Chiari I, 3 patients who had previous craniovertebral decompression, and 3 controls were segmented and converted to mathematical models of the subarachnoid space. CSF flow velocities and pressures were calculated with high spatial and temporal resolution during simulated oscillatory flow in each model with the Navier-Stokes equations. Pressures, velocities, and bidirectional flow were compared in the groups (with Student t test). Peak velocities in the simulations were compared with peak velocities measured in vivo with PCMR. Flow visualization for patients and volunteers demonstrated nonuniform reversing patterns resembling those observed with PCMR. Velocities in the 13 subjects were greater between C2 and C5 than in the foramen magnum. Chiari patients had significantly greater peak systolic and diastolic velocities, synchronous bidirectional flow, and pressure gradients than controls. Peak velocities measured in PCMR correlated significantly (P = .003; regression analysis) despite differences between them. In simulations of CSF, patients with Chiari I had significantly greater peak systolic and diastolic velocities, synchronous bidirectional flow, and pressure gradients than controls.

A Variable-Resolution Probabilistic Three-Dimensional Model for Change Detection

Given a set of high-resolution images of a scene, it is often desirable to predict the scene's appearance from viewpoints not present in the original data for purposes of change detection. When significant 3-D relief is present, a model of the scene geometry is necessary for accurate prediction to determine surface visibility relationships. In the absence of an a priori high-resolution model (such as those provided by LIDAR), scene geometry can be estimated from the imagery itself. These estimates, however, cannot, in general, be exact due to uncertainties and ambiguities present in image data. For this reason, probabilistic scene models and reconstruction algorithms are ideal due to their inherent ability to predict scene appearance while taking into account such uncertainties and ambiguities. Unfortunately, existing data structures used for probabilistic reconstruction do not scale well to large and complex scenes, primarily due to their dependence on large 3-D voxel arrays. The work presented in this paper generalizes previous probabilistic 3-D models in such a way that multiple orders of magnitude savings in storage are possible, making high-resolution change detection of large-scale scenes from high-resolution aerial and satellite imagery possible. Specifically, the inherent dependence on a discrete array of uniformly sized voxels is removed through the derivation of a probabilistic model which represents uncertain geometry as a density field, allowing implementations to efficiently sample the volume in a nonuniform fashion.

Efficient large-scale multi-view stereo for ultra high-resolution image sets

We present a new approach for large-scale multi-view stereo matching, which is designed to operate on ultra high-resolution image sets and efficiently compute dense 3D point clouds. We show that, using a robust descriptor for matching purposes and high-resolution images, we can skip the computationally expensive steps that other algorithms require. As a result, our method has low memory requirements and low computational complexity while producing 3D point clouds containing virtually no outliers. This makes it exceedingly suitable for large-scale reconstruction. The core of our algorithm is the dense matching of image pairs using DAISY descriptors, implemented so as to eliminate redundancies and optimize memory access. We use a variety of challenging data sets to validate and compare our results against other algorithms.

A Review of Super-Resolution Restoration from Image Sequences

Super-resolution (SR) restoration produces one or a set of high resolution images from low-resolution observations. In particular, SR restoration involves many multidisciplinary studies. A review on recent SR restoration approaches was given in this paper. First, we introduced the characteristics and framework of SR restoration. The state of the art in SR restoration was surveyed by taxonomy. Then we summarized and analyzed the existing algorithms of registration and reconstruction. A comparison of performing differences between these methods would only be valid given. After that we discussed the SR problems of color images and compressed videos. At last, we concluded with some thoughts about future directions.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

BackgroundMassive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used.MethodWe deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas.ResultsPivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome)ConclusionsOur proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.

Image sub-pixel registration based on SVM with application to supper-resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images.In the process of reconstruction methods,sub-pixel registration among the set of low-resolution images is a very important step.A sub-pixel registration method based on Support Vector Machine(SVM)was proposed:the related motion parameters were considered as the target set of SVM.After the mapping between the image features and the target set was built through SVM training,the motion parameters could be calculated using SVM regression.The experimental results confirm the effectiveness of the proposed method compared to other sub-pixel registration methods.