Science.gov

Sample records for automated change detection

  1. Automated change detection for synthetic aperture sonar

    NASA Astrophysics Data System (ADS)

    G-Michael, Tesfaye; Marchand, Bradley; Tucker, J. D.; Sternlicht, Daniel D.; Marston, Timothy M.; Azimi-Sadjadi, Mahmood R.

    2014-05-01

    In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four stage process: a coarse navigational alignment; fine-scale co-registration using the scale invariant feature transform (SIFT) algorithm to match features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation analysis (CCA). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co-registration tools and change detection algorithms, it is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days.

  2. Automated Change Detection for Synthetic Aperture Sonar

    DTIC Science & Technology

    2014-01-01

    analysis ( CCA ). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co...coherent-based change detection results using canonical correlation analysis ( CCA ) described by Azimi-Sadjadi and Srinivasan,18 G-Michael and Tucker15 and...Sternlicht and G-Michael,19 where the preliminary studies were performed on simulated SAR and SAS imagery. The motivation behind CCA comes from recent

  3. Automated baseline change detection phase I. Final report

    SciTech Connect

    1995-12-01

    The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER&WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements.

  4. Automated baseline change detection -- Phases 1 and 2. Final report

    SciTech Connect

    Byler, E.

    1997-10-31

    The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. The ABCD image processing software was installed on a robotic vehicle developed under a related DOE/FETC contract DE-AC21-92MC29112 Intelligent Mobile Sensor System (IMSS) and integrated with the electronics and software. This vehicle was designed especially to navigate in DOE Waste Storage Facilities. Initial system testing was performed at Fernald in June 1996. After some further development and more extensive integration the prototype integrated system was installed and tested at the Radioactive Waste Management Facility (RWMC) at INEEL beginning in April 1997 through the present (November 1997). The integrated system, composed of ABCD imaging software and IMSS mobility base, is called MISS EVE (Mobile Intelligent Sensor System--Environmental Validation Expert). Evaluation of the integrated system in RWMC Building 628, containing approximately 10,000 drums, demonstrated an easy to use system with the ability to properly navigate through the facility, image all the defined drums, and process the results into a report delivered to the operator on a GUI interface and on hard copy. Further work is needed to make the brassboard system more operationally robust.

  5. Early detection of health and welfare compromises through automated detection of behavioural changes in pigs.

    PubMed

    Matthews, Stephen G; Miller, Amy L; Clapp, James; Plötz, Thomas; Kyriazakis, Ilias

    2016-11-01

    Early detection of health and welfare compromises in commercial piggeries is essential for timely intervention to enhance treatment success, reduce impact on welfare, and promote sustainable pig production. Behavioural changes that precede or accompany subclinical and clinical signs may have diagnostic value. Often referred to as sickness behaviour, this encompasses changes in feeding, drinking, and elimination behaviours, social behaviours, and locomotion and posture. Such subtle changes in behaviour are not easy to quantify and require lengthy observation input by staff, which is impractical on a commercial scale. Automated early-warning systems may provide an alternative by objectively measuring behaviour with sensors to automatically monitor and detect behavioural changes. This paper aims to: (1) review the quantifiable changes in behaviours with potential diagnostic value; (2) subsequently identify available sensors for measuring behaviours; and (3) describe the progress towards automating monitoring and detection, which may allow such behavioural changes to be captured, measured, and interpreted and thus lead to automation in commercial, housed piggeries. Multiple sensor modalities are available for automatic measurement and monitoring of behaviour, which require humans to actively identify behavioural changes. This has been demonstrated for the detection of small deviations in diurnal drinking, deviations in feeding behaviour, monitoring coughs and vocalisation, and monitoring thermal comfort, but not social behaviour. However, current progress is in the early stages of developing fully automated detection systems that do not require humans to identify behavioural changes; e.g., through automated alerts sent to mobile phones. Challenges for achieving automation are multifaceted and trade-offs are considered between health, welfare, and costs, between analysis of individuals and groups, and between generic and compromise-specific behaviours. Copyright © 2016

  6. Automated Change Detection Using Synthetic Aperture Sonar Imagery

    DTIC Science & Technology

    2010-06-01

    using shadow outlining, scene matching using control-point matching, and visualization capabilities. This system was developed for sidescan sonar ...surveys using instrumentation such as the high-frequency Marine Sonic Technology sidescan sonar . In this paper, the authors describe modifications to...the sidescan -based system required to perform change detection using Synthetic Aperture Sonar (SAS) bottom imagery. Index Terms—Acoustic signal

  7. Automated detection of bone metastatic changes using serial CT scans.

    PubMed

    Oh, Jihun; Kim, Gyehyun; Lee, Jaesung; Cheon, Minsu; Park, Yongsup; Kim, Sewon; Yi, Jonghyon; Lee, Ho Yun

    2017-06-01

    Bone metastases resulting from a primary tumor invasion to the bone are common and cause significant morbidity in advanced cancer patients. Although the detection of bone metastases is often straightforward, it is difficult to identify their spread and track their changes, particularly in early stages. This paper presents a novel method that automatically finds the changes in appearance and the progress of bone metastases using longitudinal CT images. In contrast to previous methods based on nodule detection within a specific bone site in an individual CT scan, the approach in the present study is based on the subtraction between two registered CT volumes. The volumes registered using the proposed weighted-Demons registration and symmetric warping were subtracted with minimizing noise, and the Jacobian and false positive suppressions were performed to reduce false alarms. The proposed method detects the changes in bone metastases within 3min for entire chest bone structures covering the spine, ribs, and sternum. The method was validated based on 3-fold cross validation using the radiologists' markings of 459 lesions in 24 subjects and was performed with a sensitivity of 92.59%, a false positive volume of 2.58%, and 9.71 false positives per patient. Note that 113 lesions (24%) missed by the radiologists were identified by the present system and confirmed to be true metastases. Indeed, three patients diagnosed initially as normal, having no metastatic difference, by radiologists were found to be abnormal using the proposed system. Automatic detection method of bone metastatic changes in the entire chest bone was developed. Weighted Demons, symmetric warping, following false positive suppressions, and their parallel computing implementation enabled precise and fast computation of delicate changes in serial CT scans. The cross validation proved that this method can be quite useful for assisting radiologists in sensing minute metastatic changes from early stage

  8. Automated detection of changes in sequential color ocular fundus images

    NASA Astrophysics Data System (ADS)

    Sakuma, Satoshi; Nakanishi, Tadashi; Takahashi, Yasuko; Fujino, Yuichi; Tsubouchi, Tetsuro; Nakanishi, Norimasa

    1998-06-01

    A recent trend is the automatic screening of color ocular fundus images. The examination of such images is used in the early detection of several adult diseases such as hypertension and diabetes. Since this type of examination is easier than CT, costs less, and has no harmful side effects, it will become a routine medical examination. Normal ocular fundus images are found in more than 90% of all people. To deal with the increasing number of such images, this paper proposes a new approach to process them automatically and accurately. Our approach, based on individual comparison, identifies changes in sequential images: a previously diagnosed normal reference image is compared to a non- diagnosed image.

  9. Information Foraging and Change Detection for Automated Science Exploration

    NASA Technical Reports Server (NTRS)

    Furlong, P. Michael; Dille, Michael

    2016-01-01

    This paper presents a new algorithm for autonomous on-line exploration in unknown environments. The objective is to free remote scientists from possibly-infeasible extensive preliminary site investigation prior to sending robotic agents. We simulate a common exploration task for an autonomous robot sampling the environment at various locations and compare performance against simpler control strategies. An extension is proposed and evaluated that further permits operation in the presence of environmental variability in which the robot encounters a change in the distribution underlying sampling targets. Experimental results indicate a strong improvement in performance across varied parameter choices for the scenario.

  10. SU-E-J-191: Automated Detection of Anatomic Changes in H'N Patients

    SciTech Connect

    Usynin, A; Ramsey, C

    2014-06-01

    Purpose: To develop a novel statistics-based method for automated detection of anatomical changes using cone-beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just-in-time” adaptation of the treatment plan. Methods: Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold-based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two-sample Kolmogorov-Smirnov test, which is a non-parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H'N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H'N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection. Results: The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy. Conclusion: The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co-registration facilities.

  11. Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

    NASA Astrophysics Data System (ADS)

    Kit, Oleksandr; Lüdeke, Matthias

    2013-09-01

    This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

  12. Automated detection of sperm whale sounds as a function of abrupt changes in sound intensity

    NASA Astrophysics Data System (ADS)

    Walker, Christopher D.; Rayborn, Grayson H.; Brack, Benjamin A.; Kuczaj, Stan A.; Paulos, Robin L.

    2003-04-01

    An algorithm designed to detect abrupt changes in sound intensity was developed and used to identify and count sperm whale vocalizations and to measure boat noise. The algorithm is a MATLAB routine that counts the number of occurrences for which the change in intensity level exceeds a threshold. The algorithm also permits the setting of a ``dead time'' interval to prevent the counting of multiple pulses within a single sperm whale click. This algorithm was used to analyze digitally sampled recordings of ambient noise obtained from the Gulf of Mexico using near bottom mounted EARS buoys deployed as part of the Littoral Acoustic Demonstration Center experiment. Because the background in these data varied slowly, the result of the application of the algorithm was automated detection of sperm whale clicks and creaks with results that agreed well with those obtained by trained human listeners. [Research supported by ONR.

  13. Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

    DTIC Science & Technology

    2009-01-01

    http://www.informaworld.com/smpp/title~content=t775653681 Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change...Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload’,Military Psychology,21:2,270 — 297 To link to this...Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload 5a. CONTRACT NUMBER 5b. GRANT

  14. Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection

    NASA Astrophysics Data System (ADS)

    Maurer, Joshua; Rupper, Summer

    2015-10-01

    Declassified historical imagery from the Hexagon spy satellite database has near-global coverage, yet remains a largely untapped resource for geomorphic change studies. Unavailable satellite ephemeris data make DEM (digital elevation model) extraction difficult in terms of time and accuracy. A new fully-automated pipeline for DEM extraction and image orthorectification is presented which yields accurate results and greatly increases efficiency over traditional photogrammetric methods, making the Hexagon image database much more appealing and accessible. A 1980 Hexagon DEM is extracted and geomorphic change computed for the Thistle Creek Landslide region in the Wasatch Range of North America to demonstrate an application of the new method. Surface elevation changes resulting from the landslide show an average elevation decrease of 14.4 ± 4.3 m in the source area, an increase of 17.6 ± 4.7 m in the deposition area, and a decrease of 30.2 ± 5.1 m resulting from a new roadcut. Two additional applications of the method include volume estimates of material excavated during the Mount St. Helens volcanic eruption and the volume of net ice loss over a 34-year period for glaciers in the Bhutanese Himalayas. These results show the value of Hexagon imagery in detecting and quantifying historical geomorphic change, especially in regions where other data sources are limited.

  15. Point Cloud Based Change Detection - an Automated Approach for Cloud-based Services

    NASA Astrophysics Data System (ADS)

    Collins, Patrick; Bahr, Thomas

    2016-04-01

    The fusion of stereo photogrammetric point clouds with LiDAR data or terrain information derived from SAR interferometry has a significant potential for 3D topographic change detection. In the present case study latest point cloud generation and analysis capabilities are used to examine a landslide that occurred in the village of Malin in Maharashtra, India, on 30 July 2014, and affected an area of ca. 44.000 m2. It focuses on Pléiades high resolution satellite imagery and the Airbus DS WorldDEMTM as a product of the TanDEM-X mission. This case study was performed using the COTS software package ENVI 5.3. Integration of custom processes and automation is supported by IDL (Interactive Data Language). Thus, ENVI analytics is running via the object-oriented and IDL-based ENVITask API. The pre-event topography is represented by the WorldDEMTM product, delivered with a raster of 12 m x 12 m and based on the EGM2008 geoid (called pre-DEM). For the post-event situation a Pléiades 1B stereo image pair of the AOI affected was obtained. The ENVITask "GeneratePointCloudsByDenseImageMatching" was implemented to extract passive point clouds in LAS format from the panchromatic stereo datasets: • A dense image-matching algorithm is used to identify corresponding points in the two images. • A block adjustment is applied to refine the 3D coordinates that describe the scene geometry. • Additionally, the WorldDEMTM was input to constrain the range of heights in the matching area, and subsequently the length of the epipolar line. The "PointCloudFeatureExtraction" task was executed to generate the post-event digital surface model from the photogrammetric point clouds (called post-DEM). Post-processing consisted of the following steps: • Adding the geoid component (EGM 2008) to the post-DEM. • Pre-DEM reprojection to the UTM Zone 43N (WGS-84) coordinate system and resizing. • Subtraction of the pre-DEM from the post-DEM. • Filtering and threshold based classification of

  16. Comparing automated classification and digitization approaches to detect change in eelgrass bed extent during restoration of a large river delta

    USGS Publications Warehouse

    Davenport, Anna Elizabeth; Davis, Jerry D.; Woo, Isa; Grossman, Eric; Barham, Jesse B.; Ellings, Christopher S.; Takekawa, John

    2017-01-01

    Native eelgrass (Zostera marina) is an important contributor to ecosystem services that supplies cover for juvenile fish, supports a variety of invertebrate prey resources for fish and waterbirds, provides substrate for herring roe consumed by numerous fish and birds, helps stabilize sediment, and sequesters organic carbon. Seagrasses are in decline globally, and monitoring changes in their growth and extent is increasingly valuable to determine impacts from large-scale estuarine restoration and inform blue carbon mapping initiatives. Thus, we examined the efficacy of two remote sensing mapping methods with high-resolution (0.5 m pixel size) color near infrared imagery with ground validation to assess change following major tidal marsh restoration. Automated classification of false color aerial imagery and digitized polygons documented a slight decline in eelgrass area directly after restoration followed by an increase two years later. Classification of sparse and low to medium density eelgrass was confounded in areas with algal cover, however large dense patches of eelgrass were well delineated. Automated classification of aerial imagery from unsupervised and supervised methods provided reasonable accuracies of 73% and hand-digitizing polygons from the same imagery yielded similar results. Visual clues for hand digitizing from the high-resolution imagery provided as reliable a map of dense eelgrass extent as automated image classification. We found that automated classification had no advantages over manual digitization particularly because of the limitations of detecting eelgrass with only three bands of imagery and near infrared.

  17. Trajectory-based change detection for automated characterization of forest disturbance dynamics

    Treesearch

    Robert E. Kennedy; Warren B. Cohen; Todd A. Schroeder

    2007-01-01

    Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (...

  18. Automated anomaly detection processor

    NASA Astrophysics Data System (ADS)

    Kraiman, James B.; Arouh, Scott L.; Webb, Michael L.

    2002-07-01

    Robust exploitation of tracking and surveillance data will provide an early warning and cueing capability for military and civilian Law Enforcement Agency operations. This will improve dynamic tasking of limited resources and hence operational efficiency. The challenge is to rapidly identify threat activity within a huge background of noncombatant traffic. We discuss development of an Automated Anomaly Detection Processor (AADP) that exploits multi-INT, multi-sensor tracking and surveillance data to rapidly identify and characterize events and/or objects of military interest, without requiring operators to specify threat behaviors or templates. The AADP has successfully detected an anomaly in traffic patterns in Los Angeles, analyzed ship track data collected during a Fleet Battle Experiment to detect simulated mine laying behavior amongst maritime noncombatants, and is currently under development for surface vessel tracking within the Coast Guard's Vessel Traffic Service to support port security, ship inspection, and harbor traffic control missions, and to monitor medical surveillance databases for early alert of a bioterrorist attack. The AADP can also be integrated into combat simulations to enhance model fidelity of multi-sensor fusion effects in military operations.

  19. Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Chitchian, Shahab; Vincent, Kathleen L.; Vargas, Gracie; Motamedi, Massoud

    2012-11-01

    We have explored the use of optical coherence tomography (OCT) as a noninvasive tool for assessing the toxicity of topical microbicides, products used to prevent HIV, by monitoring the integrity of the vaginal epithelium. A novel feature-based segmentation algorithm using a nearest-neighbor classifier was developed to monitor changes in the morphology of vaginal epithelium. The two-step automated algorithm yielded OCT images with a clearly defined epithelial layer, enabling differentiation of normal and damaged tissue. The algorithm was robust in that it was able to discriminate the epithelial layer from underlying stroma as well as residual microbicide product on the surface. This segmentation technique for OCT images has the potential to be readily adaptable to the clinical setting for noninvasively defining the boundaries of the epithelium, enabling quantifiable assessment of microbicide-induced damage in vaginal tissue.

  20. Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation.

    PubMed

    Batt, Ryan D; Carpenter, Stephen R; Cole, Jonathan J; Pace, Michael L; Johnson, Robert A

    2013-10-22

    Environmental sensor networks are developing rapidly to assess changes in ecosystems and their services. Some ecosystem changes involve thresholds, and theory suggests that statistical indicators of changing resilience can be detected near thresholds. We examined the capacity of environmental sensors to assess resilience during an experimentally induced transition in a whole-lake manipulation. A trophic cascade was induced in a planktivore-dominated lake by slowly adding piscivorous bass, whereas a nearby bass-dominated lake remained unmanipulated and served as a reference ecosystem during the 4-y experiment. In both the manipulated and reference lakes, automated sensors were used to measure variables related to ecosystem metabolism (dissolved oxygen, pH, and chlorophyll-a concentration) and to estimate gross primary production, respiration, and net ecosystem production. Thresholds were detected in some automated measurements more than a year before the completion of the transition to piscivore dominance. Directly measured variables (dissolved oxygen, pH, and chlorophyll-a concentration) related to ecosystem metabolism were better indicators of the approaching threshold than were the estimates of rates (gross primary production, respiration, and net ecosystem production); this difference was likely a result of the larger uncertainties in the derived rate estimates. Thus, relatively simple characteristics of ecosystems that were observed directly by the sensors were superior indicators of changing resilience. Models linked to thresholds in variables that are directly observed by sensor networks may provide unique opportunities for evaluating resilience in complex ecosystems.

  1. Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation

    PubMed Central

    Batt, Ryan D.; Carpenter, Stephen R.; Cole, Jonathan J.; Pace, Michael L.; Johnson, Robert A.

    2013-01-01

    Environmental sensor networks are developing rapidly to assess changes in ecosystems and their services. Some ecosystem changes involve thresholds, and theory suggests that statistical indicators of changing resilience can be detected near thresholds. We examined the capacity of environmental sensors to assess resilience during an experimentally induced transition in a whole-lake manipulation. A trophic cascade was induced in a planktivore-dominated lake by slowly adding piscivorous bass, whereas a nearby bass-dominated lake remained unmanipulated and served as a reference ecosystem during the 4-y experiment. In both the manipulated and reference lakes, automated sensors were used to measure variables related to ecosystem metabolism (dissolved oxygen, pH, and chlorophyll-a concentration) and to estimate gross primary production, respiration, and net ecosystem production. Thresholds were detected in some automated measurements more than a year before the completion of the transition to piscivore dominance. Directly measured variables (dissolved oxygen, pH, and chlorophyll-a concentration) related to ecosystem metabolism were better indicators of the approaching threshold than were the estimates of rates (gross primary production, respiration, and net ecosystem production); this difference was likely a result of the larger uncertainties in the derived rate estimates. Thus, relatively simple characteristics of ecosystems that were observed directly by the sensors were superior indicators of changing resilience. Models linked to thresholds in variables that are directly observed by sensor networks may provide unique opportunities for evaluating resilience in complex ecosystems. PMID:24101479

  2. Automated terrestrial laser scanning with near-real-time change detection - monitoring of the Séchilienne landslide

    NASA Astrophysics Data System (ADS)

    Kromer, Ryan A.; Abellán, Antonio; Hutchinson, D. Jean; Lato, Matt; Chanut, Marie-Aurelie; Dubois, Laurent; Jaboyedoff, Michel

    2017-05-01

    We present an automated terrestrial laser scanning (ATLS) system with automatic near-real-time change detection processing. The ATLS system was tested on the Séchilienne landslide in France for a 6-week period with data collected at 30 min intervals. The purpose of developing the system was to fill the gap of high-temporal-resolution TLS monitoring studies of earth surface processes and to offer a cost-effective, light, portable alternative to ground-based interferometric synthetic aperture radar (GB-InSAR) deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. Additionally, we found the ATLS system to be an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.

  3. Detecting Glaucoma Using Automated Pupillography

    PubMed Central

    Tatham, Andrew J.; Meira-Freitas, Daniel; Weinreb, Robert N.; Zangwill, Linda M.; Medeiros, Felipe A.

    2014-01-01

    Objective To evaluate the ability of a binocular automated pupillograph to discriminate healthy subjects from those with glaucoma. Design Cross-sectional observational study. Participants Both eyes of 116 subjects, including 66 patients with glaucoma in at least 1 eye and 50 healthy subjects from the Diagnostic Innovations in Glaucoma Study. Eyes were classified as glaucomatous by repeatable abnormal standard automated perimetry (SAP) or progressive glaucomatous changes on stereophotographs. Methods All subjects underwent automated pupillography using the RAPDx pupillograph (Konan Medical USA, Inc., Irvine, CA). Main Outcome Measures Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic ability of pupil response parameters to white, red, green, yellow, and blue full-field and regional stimuli. A ROC regression model was used to investigate the influence of disease severity and asymmetry on diagnostic ability. Results The largest area under the ROC curve (AUC) for any single parameter was 0.75. Disease asymmetry (P < 0.001), but not disease severity (P = 0.058), had a significant effect on diagnostic ability. At the sample mean age (60.9 years), AUCs for arbitrary values of intereye difference in SAP mean deviation (MD) of 0, 5, 10, and 15 dB were 0.58, 0.71, 0.82, and 0.90, respectively. The mean intereye difference in MD was 2.2±3.1 dB. The best combination of parameters had an AUC of 0.85; however, the cross-validated bias-corrected AUC for these parameters was only 0.74. Conclusions Although the pupillograph had a good ability to detect glaucoma in the presence of asymmetric disease, it performed poorly in those with symmetric disease. PMID:24485921

  4. Automated Corrosion Detection Program

    DTIC Science & Technology

    2001-10-01

    color. 14. ABSTRACT An evaluation of several hidden corrosion-detection technologies was performed using a probability of detection ( POD ) method for...for improved corrosion management maintenance philosophies. 15. SUBJECT TERMS Corrosion, NDE, probability of detection ( POD ), KC-135, material loss...Size ...............................30 11 POD Curve

  5. LANDSAT image differencing as an automated land cover change detection technique

    NASA Technical Reports Server (NTRS)

    Stauffer, M. L.; Mckinney, R. L.

    1978-01-01

    Image differencing was investigated as a technique for use with LANDSAT digital data to delineate areas of land cover change in an urban environment. LANDSAT data collected in April 1973 and April 1975 for Austin, Texas, were geometrically corrected and precisely registered to United States Geological Survey 7.5-minute quadrangle maps. At each pixel location reflectance values for the corresponding bands were subtracted to produce four difference images. Areas of major reflectance differences are isolated by thresholding each of the difference images. The resulting images are combined to obtain an image data set to total change. These areas of reflectance differences were found, in general, to correspond to areas of land cover change. Information on areas of land cover change was incorporated into a procedure to mask out all nonchange areas and perform an unsupervised classification only for data in the change areas. This procedure identified three broad categories: (1) areas of high reflectance (construction or extractive), (2) changes in agricultural areas, and (3) areas of confusion between agricultural and other areas.

  6. Use of an automated digital images system for detecting plant status changes in response to climate change manipulations

    NASA Astrophysics Data System (ADS)

    Cesaraccio, Carla; Piga, Alessandra; Ventura, Andrea; Arca, Angelo; Duce, Pierpaolo

    2014-05-01

    The importance of phenological research for understanding the consequences of global environmental change on vegetation is highlighted in the most recent IPCC reports. Collecting time series of phenological events appears to be of crucial importance to better understand how vegetation systems respond to climatic regime fluctuations, and, consequently, to develop effective management and adaptation strategies. However, traditional monitoring of phenology is labor intensive and costly and affected to a certain degree of subjective inaccuracy. Other methods used to quantify the seasonal patterns of vegetation development are based on satellite remote sensing (land surface phenology) but they operate at coarse spatial and temporal resolution. To overcome the issues of these methodologies different approaches for vegetation monitoring based on "near-surface" remote sensing have been proposed in recent researches. In particular, the use of digital cameras has become more common for phenological monitoring. Digital images provide spectral information in the red, green, and blue (RGB) wavelengths. Inflection points in seasonal variations of intensities of each color channel can be used to identify phenological events. Canopy green-up phenology can be quantified from the greenness indices. Species-specific dates of leaf emergence can be estimated by RGB image analyses. In this research, an Automated Phenological Observation System (APOS), based on digital image sensors, was used for monitoring the phenological behavior of shrubland species in a Mediterranean site. The system was developed under the INCREASE (an Integrated Network on Climate Change Research) EU-funded research infrastructure project, which is based upon large scale field experiments with non-intrusive climatic manipulations. Monitoring of phenological behavior was conducted continuously since October 2012. The system was set to acquire one panorama per day at noon which included three experimental plots for

  7. Noninvasive Measurement of Transient Change in Viscoelasticity Due to Flow-Mediated Dilation Using Automated Detection of Arterial Wall Boundaries

    NASA Astrophysics Data System (ADS)

    Ikeshita, Kazuki; Hasegawa, Hideyuki; Kanai, Hiroshi

    2011-07-01

    We measured the stress-strain relationship of the radial arterial wall during a heartbeat noninvasively. In our previous study, the viscoelasticity of the intima-media region was estimated from the stress-strain relationship, and the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated. In this estimation, it is necessary to detect the lumen-intima boundary (LIB) and the media-adventitia boundary (MAB). To decrease the operator dependence, in the present study, a method is proposed for automatic and objective boundary detection based on template matching between the measured and adaptive model ultrasonic signals. Using this method, arterial wall boundaries were appropriately detected in in vivo experiments. Furthermore, the transient change in viscoelasticity estimated from the stress-strain relationship was similar to that obtained manually. These results show the feasibility of the proposed method for automatic boundary detection enabling an objective and appropriate analysis of the transient change in viscoelasticity due to FMD.

  8. An automated process for deceit detection

    NASA Astrophysics Data System (ADS)

    Nwogu, Ifeoma; Frank, Mark; Govindaraju, Venu

    2010-04-01

    In this paper we present a prototype for an automated deception detection system. Similar to polygraph examinations, we attempt to take advantage of the theory that false answers will produce distinctive measurements in certain physiological manifestations. We investigate the role of dynamic eye-based features such as eye closure/blinking and lateral movements of the iris in detecting deceit. The features are recorded both when the test subjects are having non-threatening conversations as well as when they are being interrogated about a crime they might have committed. The rates of the behavioral changes are blindly clustered into two groups. Examining the clusters and their characteristics, we observe that the dynamic features selected for deception detection show promising results with an overall deceptive/non-deceptive prediction rate of 71.43% from a study consisting of 28 subjects.

  9. Automated Content Detection for Cassini Images

    NASA Astrophysics Data System (ADS)

    Stanboli, A.; Bue, B.; Wagstaff, K.; Altinok, A.

    2017-06-01

    NASA missions generate numerous images ever organized in increasingly large archives. Image archives are currently not searchable by image content. We present an automated content detection prototype that can enable content search.

  10. Satellite mapping and automated feature extraction: Geographic information system-based change detection of the Antarctic coast

    NASA Astrophysics Data System (ADS)

    Kim, Kee-Tae

    Declassified Intelligence Satellite Photograph (DISP) data are important resources for measuring the geometry of the coastline of Antarctica. By using the state-of-art digital imaging technology, bundle block triangulation based on tie points and control points derived from a RADARSAT-1 Synthetic Aperture Radar (SAR) image mosaic and Ohio State University (OSU) Antarctic digital elevation model (DEM), the individual DISP images were accurately assembled into a map quality mosaic of Antarctica as it appeared in 1963. The new map is one of important benchmarks for gauging the response of the Antarctic coastline to changing climate. Automated coastline extraction algorithm design is the second theme of this dissertation. At the pre-processing stage, an adaptive neighborhood filtering was used to remove the film-grain noise while preserving edge features. At the segmentation stage, an adaptive Bayesian approach to image segmentation was used to split the DISP imagery into its homogenous regions, in which the fuzzy c-means clustering (FCM) technique and Gibbs random field (GRF) model were introduced to estimate the conditional and prior probability density functions. A Gaussian mixture model was used to estimate the reliable initial values for the FCM technique. At the post-processing stage, image object formation and labeling, removal of noisy image objects, and vectorization algorithms were sequentially applied to segmented images for extracting a vector representation of coastlines. Results were presented that demonstrate the effectiveness of the algorithm in segmenting the DISP data. In the cases of cloud cover and little contrast scenes, manual editing was carried out based on intermediate image processing and visual inspection in comparison of old paper maps. Through a geographic information system (GIS), the derived DISP coastline data were integrated with earlier and later data to assess continental scale changes in the Antarctic coast. Computing the area of

  11. Toward automated detection of malignant melanoma

    NASA Astrophysics Data System (ADS)

    Huang, Billy; Gareau, Daniel S.

    2009-02-01

    In vivo reflectance confocal microscopy shows promise for the early detection of malignant melanoma (MM). Two hallmarks of MM have been identified: the presence of pagetoid melanocytes in the epidermis and the breakdown of the dermal papillae. For detection of MM, these features must be identified qualitatively by the clinician and qualitatively through automated pattern recognition. A machine vision algorithm was developed for automated detection. The algorithm detected pagetoid melanocytes and breakdown of the dermal/epidermal junction in a pre-selected set of five MMs and five benign nevi for correct diagnosis.

  12. Automated Detection of Events of Scientific Interest

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

    A report presents a slightly different perspective of the subject matter of Fusing Symbolic and Numerical Diagnostic Computations (NPO-42512), which appears elsewhere in this issue of NASA Tech Briefs. Briefly, the subject matter is the X-2000 Anomaly Detection Language, which is a developmental computing language for fusing two diagnostic computer programs one implementing a numerical analysis method, the other implementing a symbolic analysis method into a unified event-based decision analysis software system for real-time detection of events. In the case of the cited companion NASA Tech Briefs article, the contemplated events that one seeks to detect would be primarily failures or other changes that could adversely affect the safety or success of a spacecraft mission. In the case of the instant report, the events to be detected could also include natural phenomena that could be of scientific interest. Hence, the use of X- 2000 Anomaly Detection Language could contribute to a capability for automated, coordinated use of multiple sensors and sensor-output-data-processing hardware and software to effect opportunistic collection and analysis of scientific data.

  13. Automated detection of bacteria in urine

    NASA Technical Reports Server (NTRS)

    Fleig, A. J.; Picciolo, G. L.; Chappelle, E. W.; Kelbaugh, B. N.

    1972-01-01

    A method for detecting the presence of bacteria in urine was developed which utilizes the bioluminescent reaction of adenosine triphosphate with luciferin and luciferase derived from the tails of fireflies. The method was derived from work on extraterrestrial life detection. A device was developed which completely automates the assay process.

  14. Robust statistical methods for automated outlier detection

    NASA Technical Reports Server (NTRS)

    Jee, J. R.

    1987-01-01

    The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.

  15. Automated image based prominent nucleoli detection

    PubMed Central

    Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

    2015-01-01

    Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

  16. An Automated Flying-Insect-Detection System

    NASA Technical Reports Server (NTRS)

    Vann, Timi; Andrews, Jane C.; Howell, Dane; Ryan, Robert

    2005-01-01

    An automated flying-insect-detection system (AFIDS) was developed as a proof-of-concept instrument for real-time detection and identification of flying insects. This type of system has use in public health and homeland security decision support, agriculture and military pest management, and/or entomological research. Insects are first lured into the AFIDS integrated sphere by insect attractants. Once inside the sphere, the insect's wing beats cause alterations in light intensity that is detected by a photoelectric sensor. Following detection, the insects are encouraged (with the use of a small fan) to move out of the sphere and into a designated insect trap where they are held for taxonomic identification or serological testing. The acquired electronic wing beat signatures are preprocessed (Fourier transformed) in real-time to display a periodic signal. These signals are sent to the end user where they are graphically displayed. All AFIDS data are pre-processed in the field with the use of a laptop computer equipped with LABVIEW. The AFIDS software can be programmed to run continuously or at specific time intervals when insects are prevalent. A special DC-restored transimpedance amplifier reduces the contributions of low-frequency background light signals, and affords approximately two orders of magnitude greater AC gain than conventional amplifiers. This greatly increases the signal-to-noise ratio and enables the detection of small changes in light intensity. The AFIDS light source consists of high-intensity Al GaInP light-emitting diodes (LEDs). The AFIDS circuitry minimizes brightness fluctuations in the LEDs and when integrated with an integrating sphere, creates a diffuse uniform light field. The insect wing beats isotropically scatter the diffuse light in the sphere and create wing beat signatures that are detected by the sensor. This configuration minimizes variations in signal associated with insect flight orientation.

  17. Automated Methods for Multiplexed Pathogen Detection

    SciTech Connect

    Straub, Tim M.; Dockendorff, Brian P.; Quinonez-Diaz, Maria D.; Valdez, Catherine O.; Shutthanandan, Janani I.; Tarasevich, Barbara J.; Grate, Jay W.; Bruckner-Lea, Cindy J.

    2005-09-01

    Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cycler where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides ''live vs. dead'' capabilities. However

  18. Automated methods for multiplexed pathogen detection.

    PubMed

    Straub, Timothy M; Dockendorff, Brian P; Quiñonez-Díaz, Maria D; Valdez, Catherine O; Shutthanandan, Janani I; Tarasevich, Barbara J; Grate, Jay W; Bruckner-Lea, Cynthia J

    2005-09-01

    Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cycler where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides "live vs. dead" capabilities. However

  19. Automated detection of solar eruptions

    NASA Astrophysics Data System (ADS)

    Hurlburt, N.

    2015-12-01

    Observation of the solar atmosphere reveals a wide range of motions, from small scale jets and spicules to global-scale coronal mass ejections (CMEs). Identifying and characterizing these motions are essential to advancing our understanding of the drivers of space weather. Both automated and visual identifications are currently used in identifying Coronal Mass Ejections. To date, eruptions near the solar surface, which may be precursors to CMEs, have been identified primarily by visual inspection. Here we report on Eruption Patrol (EP): a software module that is designed to automatically identify eruptions from data collected by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory (SDO/AIA). We describe the method underlying the module and compare its results to previous identifications found in the Heliophysics Event Knowledgebase. EP identifies eruptions events that are consistent with those found by human annotations, but in a significantly more consistent and quantitative manner. Eruptions are found to be distributed within 15 Mm of the solar surface. They possess peak speeds ranging from 4 to 100 km/s and display a power-law probability distribution over that range. These characteristics are consistent with previous observations of prominences.

  20. Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease.

    PubMed

    van Beers, Eduard J; Samsel, Leigh; Mendelsohn, Laurel; Saiyed, Rehan; Fertrin, Kleber Y; Brantner, Christine A; Daniels, Mathew P; Nichols, James; McCoy, J Philip; Kato, Gregory J

    2014-06-01

    In preclinical and early phase pharmacologic trials in sickle cell disease, the percentage of sickled erythrocytes after deoxygenation, an ex vivo functional sickling assay, has been used as a measure of a patient's disease outcome. We developed a new sickle imaging flow cytometry assay (SIFCA) and investigated its application. To perform the SIFCA, peripheral blood was diluted, deoxygenated (2% oxygen) for 2 hr, fixed, and analyzed using imaging flow cytometry. We developed a software algorithm that correctly classified investigator tagged "sickled" and "normal" erythrocyte morphology with a sensitivity of 100% and a specificity of 99.1%. The percentage of sickled cells as measured by SIFCA correlated strongly with the percentage of sickle cell anemia blood in experimentally admixed samples (R = 0.98, P ≤ 0.001), negatively with fetal hemoglobin (HbF) levels (R = -0.558, P = 0.027), negatively with pH (R = -0.688, P = 0.026), negatively with pretreatment with the antisickling agent, Aes-103 (5-hydroxymethyl-2-furfural) (R = -0.766, P = 0.002), and positively with the presence of long intracellular fibers as visualized by transmission electron microscopy (R = 0.799, P = 0.002). This study shows proof of principle that the automated, operator-independent SIFCA is associated with predictable physiologic and clinical parameters and is altered by the putative antisickling agent, Aes-103. SIFCA is a new method that may be useful in sickle cell drug development.

  1. Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease

    PubMed Central

    van Beers, Eduard J.; Samsel, Leigh; Mendelsohn, Laurel; Saiyed, Rehan; Fertrin, Kleber Y.; Brantner, Christine A.; Daniels, Mathew P.; Nichols, James; McCoy, J. Philip; Kato, Gregory J.

    2014-01-01

    In preclinical and early phase pharmacologic trials in sickle cell disease, the percentage of sickled erythrocytes after deoxygenation, an ex vivo functional sickling assay, has been used as a measure of a patient’s disease outcome. We developed a new sickle imaging flow cytometry assay (SIFCA) and investigated its application. To perform the SIFCA, peripheral blood was diluted, deoxygenated (2% oxygen) for 2 hr, fixed, and analyzed using imaging flow cytometry. We developed a software algorithm that correctly classified investigator tagged “sickled” and “normal” erythrocyte morphology with a sensitivity of 100% and a specificity of 99.1%. The percentage of sickled cells as measured by SIFCA correlated strongly with the percentage of sickle cell anemia blood in experimentally admixed samples (R = 0.98, P ≤ 0.001), negatively with fetal hemoglobin (HbF) levels (R = −0.558, P = 0.027), negatively with pH (R = −0.688, P = 0.026), negatively with pretreatment with the antisickling agent, Aes-103 (5-hydroxymethyl-2-furfural) (R = −0.766, P = 0.002), and positively with the presence of long intracellular fibers as visualized by transmission electron microscopy (R = 0.799, P = 0.002). This study shows proof of principle that the automated, operator-independent SIFCA is associated with predictable physiologic and clinical parameters and is altered by the putative antisickling agent, Aes-103. SIFCA is a new method that may be useful in sickle cell drug development. PMID:24585634

  2. Towards automated ingestion detection: swallow sounds.

    PubMed

    Walker, William P; Bhatia, Dinesh

    2011-01-01

    Obesity is a worldwide epidemic and is a cause of many major chronic diseases. In most cases, obesity is a result of an imbalance between food intake and calories burned. Steps toward automated ingestion detection are being made. In order to automate the process of capturing ingestion, a method for detecting, analyzing, and recording sounds related to ingestion is being developed. In this paper, preliminary swallow sound analysis is presented and compared with various other noises captured from a throat mounted microphone. Initial frequency analysis indicates a stronger presence at high frequency intervals for swallow sounds in relation to other captured sounds such as voice. Comparisons show that a single high-pass filter can offer similar results as wavelet decomposition. Two simple methods for event detection are given.

  3. An Automated Flying-Insect Detection System

    NASA Technical Reports Server (NTRS)

    Vann, Timi; Andrews, Jane C.; Howell, Dane; Ryan, Robert

    2007-01-01

    An automated flying-insect detection system (AFIDS) was developed as a proof-of-concept instrument for real-time detection and identification of flying insects. This type of system has use in public health and homeland-security decision support, agriculture and military pest management, and/or entomological research. Insects are first lured into the AFIDS integrated sphere by insect attractants. Once inside the sphere, the insect s wing beats cause alterations in light intensity that is detected by a photoelectric sensor. Following detection, the insects are encouraged (with the use of a small fan) to move out of the sphere and into a designated insect trap where they are held for taxonomic identification or serological testing. The acquired electronic wing-beat signatures are preprocessed (Fourier transformed) in real time to display a periodic signal. These signals are sent to the end user where they are graphically. All AFIDS data are preprocessed in the field with the use of a laptop computer equipped with LabVIEW. The AFIDS software can be programmed to run continuously or at specific time intervals when insects are prevalent. A special DC-restored transimpedance amplifier reduces the contributions of low-frequency background light signals, and affords approximately two orders of magnitude greater AC gain than conventional amplifiers. This greatly increases the signal-to-noise ratio and enables the detection of small changes in light intensity. The AFIDS light source consists of high-intensity Al-GaInP light-emitting diodes (LEDs). The AFIDS circuitry minimizes brightness fluctuations in the LEDs and when integrated with an integrating sphere, creates a diffuse uniform light field. The insect wing beats isotropically scatter the diffuse light in the sphere and create wing-beat signatures that are detected by the sensor. This configuration minimizes variations in signal associated with insect flight orientation. Preliminary data indicate that AFIDS has

  4. Automated detection of ocular focus.

    PubMed

    Hunter, David G; Nusz, Kevin J; Gandhi, Nainesh K; Quraishi, Imran H; Gramatikov, Boris I; Guyton, David L

    2004-01-01

    We characterize objectively the state of focus of the human eye, utilizing a bull's eye photodetector to detect the double-pass blur produced from a point source of light. A point fixation source of light illuminates the eye. Fundus-reflected light is focused by the optical system of the eye onto a bull's eye photodetector [consisting of an annulus (A) and a center (C) of approximately equal active area]. To generate focus curves, C/A is measured with a range of trial lenses in the light path. Three human eyes and a model eye are studied. In the model eye, the focus curve showed a sharp peak with a full width at half maximum (FWHM) of +/-0.25 D. In human eyes, the ratio C/A was >4 at best focus in all cases, with a FWHM of +/-1 D. The optical apparatus detects ocular focus (as opposed to refractive error) in real time. A device that can assess focus rapidly and objectively will make it possible to perform low-cost, mass screening for focusing problems such as may exist in children at risk for amblyopia.

  5. Toward Automated Feature Detection in UAVSAR Images

    NASA Astrophysics Data System (ADS)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  6. Photoelectric detection system. [manufacturing automation

    NASA Technical Reports Server (NTRS)

    Currie, J. R.; Schansman, R. R. (Inventor)

    1982-01-01

    A photoelectric beam system for the detection of the arrival of an object at a discrete station wherein artificial light, natural light, or no light may be present is described. A signal generator turns on and off a signal light at a selected frequency. When the object in question arrives on station, ambient light is blocked by the object, and the light from the signal light is reflected onto a photoelectric sensor which has a delayed electrical output but is of the frequency of the signal light. Outputs from both the signal source and the photoelectric sensor are fed to inputs of an exclusively OR detector which provides as an output the difference between them. The difference signal is a small width pulse occurring at the frequency of the signal source. By filter means, this signal is distinguished from those responsive to sunlight, darkness, or 120 Hz artificial light. In this fashion, the presence of an object is positively established.

  7. Automated Monitoring with a BSP Fault-Detection Test

    NASA Technical Reports Server (NTRS)

    Bickford, Randall L.; Herzog, James P.

    2003-01-01

    The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.

  8. Automated macromolecular crystal detection system and method

    DOEpatents

    Christian, Allen T.; Segelke, Brent; Rupp, Bernard; Toppani, Dominique

    2007-06-05

    An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image.

  9. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  10. Automated assistance for detecting malicious code

    SciTech Connect

    Crawford, R.; Kerchen, P.; Levitt, K.; Olsson, R.; Archer, M.; Casillas, M.

    1993-06-18

    This paper gives an update on the continuing work on the Malicious Code Testbed (MCT). The MCT is a semi-automated tool, operating in a simulated, cleanroom environment, that is capable of detecting many types of malicious code, such as viruses, Trojan horses, and time/logic bombs. The MCT allows security analysts to check a program before installation, thereby avoiding any damage a malicious program might inflict.

  11. Ultrasonic Imaging and Automated Flaw Detection System

    DTIC Science & Technology

    1986-03-01

    imager sold by Searle Ultrasound. An LSI-11 microcomputer is interfaced to the imager with custom designed modules. Ultrasonic image data is loaded...phased array ultrasonic imager, an LSI-11 microcomputer , and an assortment of custom-designed electronic modules. There is also a CRT display terminal...AD CONTRACTOR REPORT ARCCB-CR-86011 ULTRASONIC IMAGING AND AUTOMATED FLAW DETECTION SYSTEM L. JONES DTIC3ZLECTE J. F. MC DONALD JUNCTE G.P

  12. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

  13. Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation.

    PubMed

    Boyer, Célia; Dolamic, Ljiljana

    2015-06-02

    than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the "document" definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed.

  14. Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation

    PubMed Central

    2015-01-01

    of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the “document” definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Conclusions Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed. PMID:26036669

  15. Computing and Office Automation: Changing Variables.

    ERIC Educational Resources Information Center

    Staman, E. Michael

    1981-01-01

    Trends in computing and office automation and their applications, including planning, institutional research, and general administrative support in higher education, are discussed. Changing aspects of information processing and an increasingly larger user community are considered. The computing literacy cycle may involve programming, analysis, use…

  16. Automated detection of microcalcification clusters in mammograms

    NASA Astrophysics Data System (ADS)

    Karale, Vikrant A.; Mukhopadhyay, Sudipta; Singh, Tulika; Khandelwal, Niranjan; Sadhu, Anup

    2017-03-01

    Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

  17. Sunglint Detection for Unmanned and Automated Platforms

    PubMed Central

    Garaba, Shungudzemwoyo Pascal; Schulz, Jan; Wernand, Marcel Robert; Zielinski, Oliver

    2012-01-01

    We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture sea surface and sky images of the investigated points. The quality control consists of meteorological flags, to mask dusk, dawn, precipitation and low light conditions, utilizing incoming solar irradiance (ES) spectra. Using 629 from a total of 3,121 spectral measurements that passed the test conditions of the meteorological flagging, a new sunglint flag was developed. To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented. Applying this algorithm, two sets of data, one with (having too much or detectable white pixels or sunglint) and one without sunglint (having least visible/detectable white pixel or sunglint), were derived. To identify the most effective sunglint flagging criteria we evaluated the spectral characteristics of these two data sets using water leaving radiance (LW) and remote sensing reflectance (RRS). Spectral conditions satisfying ‘mean LW (700–950 nm) < 2 mW·m−2·nm−1·Sr−1’ or alternatively ‘minimum RRS (700–950 nm) < 0.010 Sr−1’, mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control.

  18. Automated oil spill detection with multispectral imagery

    NASA Astrophysics Data System (ADS)

    Bradford, Brian N.; Sanchez-Reyes, Pedro J.

    2011-06-01

    In this publication we present an automated detection method for ocean surface oil, like that which existed in the Gulf of Mexico as a result of the April 20, 2010 Deepwater Horizon drilling rig explosion. Regions of surface oil in airborne imagery are isolated using red, green, and blue bands from multispectral data sets. The oil shape isolation procedure involves a series of image processing functions to draw out the visual phenomenological features of the surface oil. These functions include selective color band combinations, contrast enhancement and histogram warping. An image segmentation process then separates out contiguous regions of oil to provide a raster mask to an analyst. We automate the detection algorithm to allow large volumes of data to be processed in a short time period, which can provide timely oil coverage statistics to response crews. Geo-referenced and mosaicked data sets enable the largest identified oil regions to be mapped to exact geographic coordinates. In our simulation, multispectral imagery came from multiple sources including first-hand data collected from the Gulf. Results of the simulation show the oil spill coverage area as a raster mask, along with histogram statistics of the oil pixels. A rough square footage estimate of the coverage is reported if the image ground sample distance is available.

  19. Automated detection of Antarctic blue whale calls.

    PubMed

    Socheleau, Francois-Xavier; Leroy, Emmanuelle; Pecci, Andres Carvallo; Samaran, Flore; Bonnel, Julien; Royer, Jean-Yves

    2015-11-01

    This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate.

  20. Automated Detection of Clouds in Satellite Imagery

    NASA Technical Reports Server (NTRS)

    Jedlovec, Gary

    2010-01-01

    Many different approaches have been used to automatically detect clouds in satellite imagery. Most approaches are deterministic and provide a binary cloud - no cloud product used in a variety of applications. Some of these applications require the identification of cloudy pixels for cloud parameter retrieval, while others require only an ability to mask out clouds for the retrieval of surface or atmospheric parameters in the absence of clouds. A few approaches estimate a probability of the presence of a cloud at each point in an image. These probabilities allow a user to select cloud information based on the tolerance of the application to uncertainty in the estimate. Many automated cloud detection techniques develop sophisticated tests using a combination of visible and infrared channels to determine the presence of clouds in both day and night imagery. Visible channels are quite effective in detecting clouds during the day, as long as test thresholds properly account for variations in surface features and atmospheric scattering. Cloud detection at night is more challenging, since only courser resolution infrared measurements are available. A few schemes use just two infrared channels for day and night cloud detection. The most influential factor in the success of a particular technique is the determination of the thresholds for each cloud test. The techniques which perform the best usually have thresholds that are varied based on the geographic region, time of year, time of day and solar angle.

  1. Automated detection of elephants in wildlife video.

    PubMed

    Zeppelzauer, Matthias

    2013-08-01

    Biologists often have to investigate large amounts of video in behavioral studies of animals. These videos are usually not sufficiently indexed which makes the finding of objects of interest a time-consuming task. We propose a fully automated method for the detection and tracking of elephants in wildlife video which has been collected by biologists in the field. The method dynamically learns a color model of elephants from a few training images. Based on the color model, we localize elephants in video sequences with different backgrounds and lighting conditions. We exploit temporal clues from the video to improve the robustness of the approach and to obtain spatial and temporal consistent detections. The proposed method detects elephants (and groups of elephants) of different sizes and poses performing different activities. The method is robust to occlusions (e.g., by vegetation) and correctly handles camera motion and different lighting conditions. Experiments show that both near- and far-distant elephants can be detected and tracked reliably. The proposed method enables biologists efficient and direct access to their video collections which facilitates further behavioral and ecological studies. The method does not make hard constraints on the species of elephants themselves and is thus easily adaptable to other animal species.

  2. Automated Hydrogen Gas Leak Detection System

    NASA Technical Reports Server (NTRS)

    1995-01-01

    The Gencorp Aerojet Automated Hydrogen Gas Leak Detection System was developed through the cooperation of industry, academia, and the Government. Although the original purpose of the system was to detect leaks in the main engine of the space shuttle while on the launch pad, it also has significant commercial potential in applications for which there are no existing commercial systems. With high sensitivity, the system can detect hydrogen leaks at low concentrations in inert environments. The sensors are integrated with hardware and software to form a complete system. Several of these systems have already been purchased for use on the Ford Motor Company assembly line for natural gas vehicles. This system to detect trace hydrogen gas leaks from pressurized systems consists of a microprocessor-based control unit that operates a network of sensors. The sensors can be deployed around pipes, connectors, flanges, and tanks of pressurized systems where leaks may occur. The control unit monitors the sensors and provides the operator with a visual representation of the magnitude and locations of the leak as a function of time. The system can be customized to fit the user's needs; for example, it can monitor and display the condition of the flanges and fittings associated with the tank of a natural gas vehicle.

  3. Automated detection of elephants in wildlife video

    PubMed Central

    Zeppelzauer, Matthias

    2015-01-01

    Biologists often have to investigate large amounts of video in behavioral studies of animals. These videos are usually not sufficiently indexed which makes the finding of objects of interest a time-consuming task. We propose a fully automated method for the detection and tracking of elephants in wildlife video which has been collected by biologists in the field. The method dynamically learns a color model of elephants from a few training images. Based on the color model, we localize elephants in video sequences with different backgrounds and lighting conditions. We exploit temporal clues from the video to improve the robustness of the approach and to obtain spatial and temporal consistent detections. The proposed method detects elephants (and groups of elephants) of different sizes and poses performing different activities. The method is robust to occlusions (e.g., by vegetation) and correctly handles camera motion and different lighting conditions. Experiments show that both near- and far-distant elephants can be detected and tracked reliably. The proposed method enables biologists efficient and direct access to their video collections which facilitates further behavioral and ecological studies. The method does not make hard constraints on the species of elephants themselves and is thus easily adaptable to other animal species. PMID:25902006

  4. Sensitivity Analysis of Automated Ice Edge Detection

    NASA Astrophysics Data System (ADS)

    Moen, Mari-Ann N.; Isaksem, Hugo; Debien, Annekatrien

    2016-08-01

    The importance of highly detailed and time sensitive ice charts has increased with the increasing interest in the Arctic for oil and gas, tourism, and shipping. Manual ice charts are prepared by national ice services of several Arctic countries. Methods are also being developed to automate this task. Kongsberg Satellite Services uses a method that detects ice edges within 15 minutes after image acquisition. This paper describes a sensitivity analysis of the ice edge, assessing to which ice concentration class from the manual ice charts it can be compared to. The ice edge is derived using the Ice Tracking from SAR Images (ITSARI) algorithm. RADARSAT-2 images of February 2011 are used, both for the manual ice charts and the automatic ice edges. The results show that the KSAT ice edge lies within ice concentration classes with very low ice concentration or open water.

  5. Automated System Marketplace 1995: The Changing Face of Automation.

    ERIC Educational Resources Information Center

    Barry, Jeff; And Others

    1995-01-01

    Discusses trends in the automated system marketplace with specific attention to online vendors and their customers: academic, public, school, and special libraries. Presents vendor profiles; tables and charts on computer systems and sales; and sidebars that include a vendor source list and the differing views on procuring an automated library…

  6. Automated detection of Karnal bunt teliospores

    NASA Astrophysics Data System (ADS)

    Linder, Kim D.; Baumgart, Chris W.; Creager, Jim; Heinen, Bob; Troupe, Tim; Meyer, Dick; Carr, Janie; Quint, Jack

    1998-02-01

    Karnal bunt is a fungal disease which infects wheat and, when present in wheat crops, yields it unsatisfactory for human consumption. Due to the fact that Karnal bunt (KB) is difficult to detect in the field, samples are taken to laboratories where technicians use microscopes and methodically search for KB teliospores. AlliedSignal Federal Manufacturing & Technologies, working with the Kansas Department of Agriculture, created a system which utilizes pattern recognition, feature extraction, and neural networks to prototype an automated detection system for identifying KB teliospores. System hardware consists of a biological compound microscope, motorized stage, CCD camera, frame grabber, and a PC. Integration of the system hardware with custom software comprises the machine vision system. Fundamental processing steps involve capturing an image from the slide, while concurrently processing the previous image. Features extracted from the acquired imagery are then processed by a neural network classifier which has been trained to recognize `spore-like' objects. Images with `spore-like' objects are reviewed by trained technicians. Benefits of this system include: (1) reduction of the overall cycle-time; (2) utilization of technicians for intelligent decision making (vs. manual searching); (3) a regulatory standard which is quantifiable and repeatable; (4) guaranteed 100% coverage of the cover slip; and (5) significantly enhanced detection accuracy.

  7. Automated Detection of Activity Transitions for Prompting

    PubMed Central

    Feuz, Kyle D.; Cook, Diane J.; Rosasco, Cody; Robertson, Kayela; Schmitter-Edgecombe, Maureen

    2016-01-01

    Individuals with cognitive impairment can benefit from intervention strategies like recording important information in a memory notebook. However, training individuals to use the notebook on a regular basis requires a constant delivery of reminders. In this work, we design and evaluate machine learning-based methods for providing automated reminders using a digital memory notebook interface. Specifically, we identify transition periods between activities as times to issue prompts. We consider the problem of detecting activity transitions using supervised and unsupervised machine learning techniques, and find that both techniques show promising results for detecting transition periods. We test the techniques in a scripted setting with 15 individuals. Motion sensors data is recorded and annotated as participants perform a fixed set of activities. We also test the techniques in an unscripted setting with 8 individuals. Motion sensor data is recorded as participants go about their normal daily routine. In both the scripted and unscripted settings a true positive rate of greater than 80% can be achieved while maintaining a false positive rate of less than 15%. On average, this leads to transitions being detected within 1 minute of a true transition for the scripted data and within 2 minutes of a true transition on the unscripted data. PMID:27019791

  8. Automated Detection of Activity Transitions for Prompting.

    PubMed

    Feuz, Kyle D; Cook, Diane J; Rosasco, Cody; Robertson, Kayela; Schmitter-Edgecombe, Maureen

    2015-10-01

    Individuals with cognitive impairment can benefit from intervention strategies like recording important information in a memory notebook. However, training individuals to use the notebook on a regular basis requires a constant delivery of reminders. In this work, we design and evaluate machine learning-based methods for providing automated reminders using a digital memory notebook interface. Specifically, we identify transition periods between activities as times to issue prompts. We consider the problem of detecting activity transitions using supervised and unsupervised machine learning techniques, and find that both techniques show promising results for detecting transition periods. We test the techniques in a scripted setting with 15 individuals. Motion sensors data is recorded and annotated as participants perform a fixed set of activities. We also test the techniques in an unscripted setting with 8 individuals. Motion sensor data is recorded as participants go about their normal daily routine. In both the scripted and unscripted settings a true positive rate of greater than 80% can be achieved while maintaining a false positive rate of less than 15%. On average, this leads to transitions being detected within 1 minute of a true transition for the scripted data and within 2 minutes of a true transition on the unscripted data.

  9. Automated detection of Karnal bunt teliospores

    SciTech Connect

    Linder, K.D.; Baumgart, C.; Creager, J.; Heinen, B.; Troupe, T.; Meyer, D.; Carr, J.; Quint, J.

    1998-02-01

    Karnal bunt is a fungal disease which infects wheat and, when present in wheat crops, yields it unsatisfactory for human consumption. Due to the fact that Karnal bunt (KB) is difficult to detect in the field, samples are taken to laboratories where technicians use microscopes and methodically search for KB teliospores. AlliedSignal Federal Manufacturing and Technologies (FM and T), working with the Kansas Department of Agriculture, created a system which utilizes pattern recognition, feature extraction, and neural networks to prototype an automated detection system for identifying KB teliospores. System hardware consists of a biological compound microscope, motorized stage, CCD camera, frame grabber, and a PC. Integration of the system hardware with custom software comprises the machine vision system. Fundamental processing steps involve capturing an image from the slide, while concurrently processing the previous image. Features extracted from the acquired imagery are then processed by a neural network classifier which has been trained to recognize spore-like objects. Images with spore-like objects are reviewed by trained technicians. Benefits of this system include: (1) reduction of the overall cycle-time; (2) utilization of technicians for intelligent decision making (vs. manual searching); (3) a regulatory standard which is quantifiable and repeatable; (4) guaranteed 100% coverage of the cover slip; and (5) significantly enhanced detection accuracy.

  10. Automated Early Detection of Diabetic Retinopathy

    PubMed Central

    Abràmoff, Michael D.; Reinhardt, Joseph M.; Russell, Stephen R.; Folk, James C.; Mahajan, Vinit B.; Niemeijer, Meindert; Quellec, Gwénolé

    2010-01-01

    Purpose To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, (‘Challenge2009’) against that of the one currently used in EyeCheck, a large computer-aided early DR detection project. Design Evaluation of diagnostic test or technology. Participants Fundus photographic sets, consisting of two fundus images from each eye, were evaluated from 16,670 patient visits of 16,670 people with diabetes who had not previously been diagnosed with DR. Methods The fundus photographic set from each visit was analyzed by a single retinal expert; 793 of the 16,770 sets were classified as containing more than minimal DR (threshold for referral). The outcomes of the two algorithmic detectors were applied separately to the dataset and compared by standard statistical measures. Main Outcome Measures The area under the Receiver Operating Characteristic curve (AUC), a measure of the sensitivity and specificity of DR detection. Results Agreement was high, and exams containing more than minimal DR were detected with an AUC of 0.839 by the ‘Eyecheck’ algorithm and an AUC of 0.821 for ‘Challenge2009’, a statistically non-significant difference (z-score 1.91). If either of the algorithms detected DR in combination, AUC for detection was 0.86, the same as the theoretically expected maximum. At 90% sensitivity, the specificity of the ‘EyeCheck’ algorithm was 47.7% and the ‘Challenge2009’ algorithm, 43.6%. Conclusions DR detection algorithms appear to be maturing, and further improvements in detection performance cannot be differentiated from best clinical practices, because the performance of competitive algorithm development has now reached the human intra-reader variability limit. Additional validation studies on larger, well-defined, but more diverse populations of patients with diabetes are urgently needed, anticipating cost-effective early detection of DR in

  11. Automated System for Early Breast Cancer Detection in Mammograms

    NASA Technical Reports Server (NTRS)

    Bankman, Isaac N.; Kim, Dong W.; Christens-Barry, William A.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

    1993-01-01

    The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed.

  12. Detect and Avoid (DAA) Automation Maneuver Study

    DTIC Science & Technology

    2017-02-01

    7 Figure 5. Dual Perspective Display... Dual Perspective) across two automation thresholds (Well Clear & Near Mid-Air Collision (NMAC)). The advanced SAA algorithm maneuvered significantly...Several hypotheses were formulated based on past research: (1) those SAA displays designed to provide advanced algorithm transparency ( Dual

  13. TimeSync: Synching Human and Automated Interpretations of Landsat Time-Series to Support a New Paradigm in Land Cover Change Detection

    NASA Astrophysics Data System (ADS)

    Cohen, W. B.; Yang, Z.; Kennedy, R. E.

    2008-12-01

    By early next year, the Landsat archive will be freely available via the web in a format that facilitates its easy use by a variety of algorithms that take advantage of dense time series of observations since 1972. Landsat has always been well-suited for change detection, but with the opening of the archive will come a revolution in how the Landsat user community approaches the problem of mapping land cover change. Several techniques have recently been developed that exploit the temporally dense time series that will soon be in common usage. However, few algorithms currently exist that are designed to effectively and efficiently mine the 36-year archive. Moreover, approaches for calibrating and validating such algorithms are essentially non- existent. In addition to briefly reviewing a new algorithm for detecting change in dense Landsat time series (LandTrendr), we will describe a tool that has been developed specifically for the purpose of calibrating and validation any algorithm that exploits dense Landsat time series. The tool (TimeSync) uses geographic coordinates for an area of interest to extract image chips from a time series stack for that area and its neighborhood. The times series of chips is displayed for easy viewing, along with a spectral plot of raw bands and indices over the time series for the area of interest. Using these two data visualization windows, one identifies changes that have occurred, if any, within the area of interest and uses a series of pick-lists associated with a relational database to label segments in the spectral profiles that are associated with cover changes in the area of interest. The process involves selection of time-series vertices that identify dates associated with start and end points of change segments, and the segments are labeled according to the cause of the observed change. The tool is linked to Google Earth which displays a recent high resolution image for detailed spatial reference (commonly a georeferenced

  14. Automated Detection of Opaque Volcanic Plumes in Polar Satellite Data

    NASA Astrophysics Data System (ADS)

    Dehn, J.; Webley, P.

    2013-12-01

    Response to an explosive volcanic eruption is time sensitive, so automated eruption detection techniques are essential to minimize alert times after an event. Automated detection of volcanic ash plumes in satellite imagery is usually done using a variant of the split-window or reverse-absorption method. This method is often effective but requires among other things that an ash plume be translucent to allow thermal radiation to pass through it. In the critical first hour or two of an eruption, plumes are most often opaque, and therefore cannot be detected by this method. It has been shown that an emergent plume appears as a sudden cold cloud over a volcano where a weather system should not appear, and this has been applied to geostationary data that is acquired every 15 to 30 minutes and will be an integral part of the upcoming geostationary mission, GOES-R. In this study this concept is used on time sequential polar orbiting satellite data to detect emergent plumes. This augments geostationary data, and may detect smaller plumes at higher latitudes where geostationary data suffers from poorer spatial resolution. A series of weighted credits and demerits are used to determine the presence of an anomalously cold cloud over a volcano in time sequential advanced very high resolution radiometer (AVHRR) data. Parameters such as coldest thermal infrared temperature, time between images, ratio of cold to background temperature, and temperature trend are assigned a weighted value and a threshold used to determine the presence of an anomalous cloud. The weighting and threshold is unique for each volcano due to weather conditions and satellite coverage. Using the 20 year archive of eruptions in the North Pacific at the Geophysical Institute of the University of Alaska Fairbanks, explosive eruptions were evaluated at Karmsky Volcano (1996), Pavlof volcano (1996, 2007, 2013), Cleveland Volcano (1994, 2001, 2008), Shishaldin Volcano (1999), Augustine Volcano (2006), Fourpeaked

  15. Detecting Unidentified Changes

    PubMed Central

    Howe, Piers D. L.; Webb, Margaret E.

    2014-01-01

    Does becoming aware of a change to a purely visual stimulus necessarily cause the observer to be able to identify or localise the change or can change detection occur in the absence of identification or localisation? Several theories of visual awareness stress that we are aware of more than just the few objects to which we attend. In particular, it is clear that to some extent we are also aware of the global properties of the scene, such as the mean luminance or the distribution of spatial frequencies. It follows that we may be able to detect a change to a visual scene by detecting a change to one or more of these global properties. However, detecting a change to global property may not supply us with enough information to accurately identify or localise which object in the scene has been changed. Thus, it may be possible to reliably detect the occurrence of changes without being able to identify or localise what has changed. Previous attempts to show that this can occur with natural images have produced mixed results. Here we use a novel analysis technique to provide additional evidence that changes can be detected in natural images without also being identified or localised. It is likely that this occurs by the observers monitoring the global properties of the scene. PMID:24454727

  16. Laboratory detection of respiratory viruses by automated techniques.

    PubMed

    Pérez-Ruiz, Mercedes; Pedrosa-Corral, Irene; Sanbonmatsu-Gámez, Sara; Navarro-Marí, María

    2012-01-01

    Advances in clinical virology for detecting respiratory viruses have been focused on nucleic acids amplification techniques, which have converted in the reference method for the diagnosis of acute respiratory infections of viral aetiology. Improvements of current commercial molecular assays to reduce hands-on-time rely on two strategies, a stepwise automation (semi-automation) and the complete automation of the whole procedure. Contributions to the former strategy have been the use of automated nucleic acids extractors, multiplex PCR, real-time PCR and/or DNA arrays for detection of amplicons. Commercial fully-automated molecular systems are now available for the detection of respiratory viruses. Some of them could convert in point-of-care methods substituting antigen tests for detection of respiratory syncytial virus and influenza A and B viruses. This article describes laboratory methods for detection of respiratory viruses. A cost-effective and rational diagnostic algorithm is proposed, considering technical aspects of the available assays, infrastructure possibilities of each laboratory and clinic-epidemiologic factors of the infection.

  17. Laboratory Detection of Respiratory Viruses by Automated Techniques

    PubMed Central

    Pérez-Ruiz, Mercedes; Pedrosa-Corral, Irene; Sanbonmatsu-Gámez, Sara; Navarro-Marí, José-María

    2012-01-01

    Advances in clinical virology for detecting respiratory viruses have been focused on nucleic acids amplification techniques, which have converted in the reference method for the diagnosis of acute respiratory infections of viral aetiology. Improvements of current commercial molecular assays to reduce hands-on-time rely on two strategies, a stepwise automation (semi-automation) and the complete automation of the whole procedure. Contributions to the former strategy have been the use of automated nucleic acids extractors, multiplex PCR, real-time PCR and/or DNA arrays for detection of amplicons. Commercial fully-automated molecular systems are now available for the detection of respiratory viruses. Some of them could convert in point-of-care methods substituting antigen tests for detection of respiratory syncytial virus and influenza A and B viruses. This article describes laboratory methods for detection of respiratory viruses. A cost-effective and rational diagnostic algorithm is proposed, considering technical aspects of the available assays, infrastructure possibilities of each laboratory and clinic-epidemiologic factors of the infection PMID:23248735

  18. Automated interplanetary shock detection and its application to Wind observations

    NASA Astrophysics Data System (ADS)

    Kruparova, O.; Maksimovic, M.; Å AfráNková, J.; NěMečEk, Z.; Santolik, O.; Krupar, V.

    2013-08-01

    We present an automated two-step detection algorithm for identification of interplanetary (IP) shocks regardless their type in a real-time data stream. This algorithm is aimed for implementation on board the future Solar Orbiter mission for triggering the transmission of the high-resolution data to the Earth. The first step of the algorithm is based on a determination of a quality factor, Q indicating abrupt changes of plasma parameters (proton density and bulk velocity) and magnetic field strength. We test two sets of weighting coefficients for Q determination and propose the second step consisting of three additional constraints that increase the effectiveness of the algorithm. We checked the algorithm using Wind (at 1 AU) and Helios (at distances from 0.29 to 1 AU) data and compared obtained results with already existing lists of IP shocks. The efficiency of the presented algorithm for the Wind shock lists varies from 60% to 84% for two Q thresholds. The final shock candidate list provided by the presented algorithm contains the real IP shocks, as well as different discontinuities. The detection rate of the IP shocks equals to 64% and 29% for two Q thresholds. The algorithm detected all IP shocks associated with the solar wind transient structures triggering intense (Dst<-100 nT) geomagnetic storms.

  19. Acute kidney injury-how does automated detection perform?

    PubMed

    Sawhney, Simon; Fluck, Nick; Marks, Angharad; Prescott, Gordon; Simpson, William; Tomlinson, Laurie; Black, Corri

    2015-11-01

    Early detection of acute kidney injury (AKI) is important for safe clinical practice. NHS England is implementing a nationwide automated AKI detection system based on changes in blood creatinine. Little has been reported on the similarities and differences of AKI patients detected by this algorithm and other definitions of AKI in the literature. We assessed the NHS England AKI algorithm and other definitions using routine biochemistry in our own health authority in Scotland in 2003 (adult population 438 332). Linked hospital episode codes (ICD-10) were used to identify patients where AKI was a major clinical diagnosis. We compared how well the algorithm detected this subset of AKI patients in comparison to other definitions of AKI. We also evaluated the potential 'alert burden' from using the NHS England algorithm in comparison to other AKI definitions. Of 127 851 patients with at least one blood test in 2003, the NHS England AKI algorithm identified 5565 patients. The combined NHS England algorithm criteria detected 91.2% (87.6-94.0) of patients who had an ICD-10 AKI code and this was better than any individual AKI definition. Some of those not captured could be identified by algorithm modifications to identify AKI in retrospect after recovery, but this would not be practical in real-time. Any modifications also increased the number of alerted patients (2-fold in the most sensitive model). The NHS England AKI algorithm performs well as a diagnostic adjunct in clinical practice. In those without baseline data, AKI may only be seen in biochemistry in retrospect, therefore proactive clinical care remains essential. An alternative algorithm could increase the diagnostic sensitivity, but this would also produce a much greater burden of patient alerts. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA.

  20. Automation, the Impact of Technological Change.

    ERIC Educational Resources Information Center

    Brozen, Yale

    The scale of educational activities is increasing because mechanization, automation, cybernation, or whatever new technology is called, makes it possible to do more than could formerly be done. If a man helped by an automatic machine can turn out twice as much per hour, then, presumably, only half as many hours of work will be available for each…

  1. Automation: An Illustration of Social Change.

    ERIC Educational Resources Information Center

    Warnat, Winifred I.

    Advanced automation is significantly affecting American society and the individual. To understand the extent of this impact, an understanding of the country's service economy is necessary. The United States made the transition from a goods- to service-based economy shortly after World War II. In 1982, services generated 67% of the Gross National…

  2. Systems and Methods for Automated Water Detection Using Visible Sensors

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L. (Inventor); Matthies, Larry H. (Inventor); Bellutta, Paolo (Inventor)

    2016-01-01

    Systems and methods are disclosed that include automated machine vision that can utilize images of scenes captured by a 3D imaging system configured to image light within the visible light spectrum to detect water. One embodiment includes autonomously detecting water bodies within a scene including capturing at least one 3D image of a scene using a sensor system configured to detect visible light and to measure distance from points within the scene to the sensor system, and detecting water within the scene using a processor configured to detect regions within each of the at least one 3D images that possess at least one characteristic indicative of the presence of water.

  3. Automated RNA Extraction and Purification for Multiplexed Pathogen Detection

    SciTech Connect

    Bruzek, Amy K.; Bruckner-Lea, Cindy J.

    2005-01-01

    Pathogen detection has become an extremely important part of our nation?s defense in this post 9/11 world where the threat of bioterrorist attacks are a grim reality. When a biological attack takes place, response time is critical. The faster the biothreat is assessed, the faster countermeasures can be put in place to protect the health of the general public. Today some of the most widely used methods for detecting pathogens are either time consuming or not reliable [1]. Therefore, a method that can detect multiple pathogens that is inherently reliable, rapid, automated and field portable is needed. To that end, we are developing automated fluidics systems for the recovery, cleanup, and direct labeling of community RNA from suspect environmental samples. The advantage of using RNA for detection is that there are multiple copies of mRNA in a cell, whereas there are normally only one or two copies of DNA [2]. Because there are multiple copies of mRNA in a cell for highly expressed genes, no amplification of the genetic material may be necessary, and thus rapid and direct detection of only a few cells may be possible [3]. This report outlines the development of both manual and automated methods for the extraction and purification of mRNA. The methods were evaluated using cell lysates from Escherichia coli 25922 (nonpathogenic), Salmonella typhimurium (pathogenic), and Shigella spp (pathogenic). Automated RNA purification was achieved using a custom sequential injection fluidics system consisting of a syringe pump, a multi-port valve and a magnetic capture cell. mRNA was captured using silica coated superparamagnetic beads that were trapped in the tubing by a rare earth magnet. RNA was detected by gel electrophoresis and/or by hybridization of the RNA to microarrays. The versatility of the fluidics systems and the ability to automate these systems allows for quick and easy processing of samples and eliminates the need for an experienced operator.

  4. Automated Detection of Solar Loops by the Oriented Connectivity Method

    NASA Technical Reports Server (NTRS)

    Lee, Jong Kwan; Newman, Timothy S.; Gary, G. Allen

    2004-01-01

    An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

  5. Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

    PubMed

    Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

    2017-09-19

    We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  6. Defect Prevention and Detection in Software for Automated Test Equipment

    SciTech Connect

    E. Bean

    2006-11-30

    Software for automated test equipment can be tedious and monotonous making it just as error-prone as other software. Active defect prevention and detection are also important for test applications. Incomplete or unclear requirements, a cryptic syntax used for some test applications—especially script-based test sets, variability in syntax or structure, and changing requirements are among the problems encountered in one tester. Such problems are common to all software but can be particularly problematic in test equipment software intended to test another product. Each of these issues increases the probability of error injection during test application development. This report describes a test application development tool designed to address these issues and others for a particular piece of test equipment. By addressing these problems in the development environment, the tool has powerful built-in defect prevention and detection capabilities. Regular expressions are widely used in the development tool as a means of formally defining test equipment requirements for the test application and verifying conformance to those requirements. A novel means of using regular expressions to perform range checking was developed. A reduction in rework and increased productivity are the results. These capabilities are described along with lessons learned and their applicability to other test equipment software. The test application development tool, or “application builder”, is known as the PT3800 AM Creation, Revision and Archiving Tool (PACRAT).

  7. Automated fetal spine detection in ultrasound images

    NASA Astrophysics Data System (ADS)

    Tolay, Paresh; Vajinepalli, Pallavi; Bhattacharya, Puranjoy; Firtion, Celine; Sisodia, Rajendra Singh

    2009-02-01

    A novel method is proposed for the automatic detection of fetal spine in ultrasound images along with its orientation in this paper. This problem presents a variety of challenges, including robustness to speckle noise, variations in the visible shape of the spine due to orientation of the ultrasound probe with respect to the fetus and the lack of a proper edge enclosing the entire spine on account of its composition out of distinct vertebra. The proposed method improves robustness and accuracy by making use of two independent techniques to estimate the spine, and then detects the exact location using a cross-correlation approach. Experimental results show that the proposed method is promising for fetal spine detection.

  8. Automated detection of dilated capillaries on optical coherence tomography angiography

    PubMed Central

    Dongye, Changlei; Zhang, Miao; Hwang, Thomas S.; Wang, Jie; Gao, Simon S.; Liu, Liang; Huang, David; Wilson, David J.; Jia, Yali

    2017-01-01

    Automated detection and grading of angiographic high-risk features in diabetic retinopathy can potentially enhance screening and clinical care. We have previously identified capillary dilation in angiograms of the deep plexus in optical coherence tomography angiography as a feature associated with severe diabetic retinopathy. In this study, we present an automated algorithm that uses hybrid contrast to distinguish angiograms with dilated capillaries from healthy controls and then applies saliency measurement to map the extent of the dilated capillary networks. The proposed algorithm agreed well with human grading. PMID:28271005

  9. Automated Human Screening for Detecting Concealed Knowledge

    ERIC Educational Resources Information Center

    Twyman, Nathan W.

    2012-01-01

    Screening individuals for concealed knowledge has traditionally been the purview of professional interrogators investigating a crime. But the ability to detect when a person is hiding important information would be of high value to many other fields and functions. This dissertation proposes design principles for and reports on an implementation…

  10. Automated Detection of Stereotypical Motor Movements

    ERIC Educational Resources Information Center

    Goodwin, Matthew S.; Intille, Stephen S.; Albinali, Fahd; Velicer, Wayne F.

    2011-01-01

    To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average,…

  11. Automated Detection of Stereotypical Motor Movements

    ERIC Educational Resources Information Center

    Goodwin, Matthew S.; Intille, Stephen S.; Albinali, Fahd; Velicer, Wayne F.

    2011-01-01

    To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average,…

  12. Automated Human Screening for Detecting Concealed Knowledge

    ERIC Educational Resources Information Center

    Twyman, Nathan W.

    2012-01-01

    Screening individuals for concealed knowledge has traditionally been the purview of professional interrogators investigating a crime. But the ability to detect when a person is hiding important information would be of high value to many other fields and functions. This dissertation proposes design principles for and reports on an implementation…

  13. Automated detection of masses and clustered microcalcifications on mammograms

    NASA Astrophysics Data System (ADS)

    Fujita, Hiroshi; Endo, Tokiko; Matsubara, Tomoko; Hirako, Kenichi; Hara, Takeshi; Ueda, Hitoshi; Torisu, Yasuhiro; Riyahi-Alam, Nader; Horita, Katsuhei; Kido, Choichiro; Ishigaki, Takeo

    1995-05-01

    We are developing automated-detection schemes for the masses and clustered microcalcifications on laser-digitized mammograms (0.1 mm, 10-bit resolution, 2000 X 2510) by using a conventional workstation. The purpose of this paper is to provide an overview of our recent schemes and to evaluate the current performance of the schemes. The fully automated computer system consists of several parts such as the extraction of breast region, detection of masses, detection of clustered microcalcifications, classification of the candidates, and the display of the detected results. Our schemes tested with more than 200 cases of Japanese women achieved an about 95% (86%) true-positive rate with 0.61 (0.55) false-positive masses (clusters) per image. It was found that the automated method has the potential to aid physicians in screening mammograms for breast tumors. Initial results for the mammograms digitized with the pixel sizes of 25, 50, and 100 micrometers are also discussed, in which a genetic algorithm (GA) technique was applied to the detection filter for the microcalcifications. It was indicated from the experiment with a breast phantom that 100- micrometers pixel size is not enough for the computer detection of microcalcifications, and it seems that at least 50-micrometers pixel size is required.

  14. Automated Volcanic Eruption Detection Using MODIS

    NASA Astrophysics Data System (ADS)

    Wright, R.; Wright, R.; Flynn, L. P.; Garbeil, H.; Harris, A. J.; Pilger, E.

    2001-12-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) flown on-board NASA's first EOS platform, Terra, offers complete global data coverage every 1-2 days at spatial resolutions of 250, 500, and 1000-m. Its ability to detect emitted radiation in the short (4 micron) and long-wave (12 micron) infrared regions of the electromagnetic spectrum, combined with the excellent geolocation of the image pixels (~200-m), make it an ideal source of data for automatically detecting and monitoring high-temperature volcanic thermal anomalies. This presentation will describe the underlying principles of, and results obtained from, just such a system, developed at the Hawaii Institute of Geophysics and Planetology. Our algorithm interrogates the MODIS Level 1B stream for evidence of high-temperature volcanic features. Once a hot-spot has been identified its details (location, emitted spectral radiance, satellite observational parameters) are written to an ASCII text file and transferred via FTP to HIGP, where the results are posted on the internet (http://modis.higp.hawaii.edu). The global distribution of volcanic hot-spots can be examined visually at a variety of scales using this web-site, which also allows easy access to the quantitative data contained in the ASCII files themselves. We outline how the algorithm has proven robust as a hot-spot detection tool for a wide range of eruptive styles at both permanently and sporadically active volcanoes including Soufriere Hills (Montserrat), Popocatepetl (Mexico), Bezymianny (Russia), and Merapi (Java), amongst others. We also present case studies of how the system has allowed the onset, development and cessation of discrete eruptive events to be monitored at Nyamuragira (Congo), Piton de la Fournaise (Reunion Island), Shiveluch (Russia), Kilauea (Hawaii) and Etna (Sicily).

  15. Automated Sargassum Detection for Landsat Imagery

    NASA Astrophysics Data System (ADS)

    McCarthy, S.; Gallegos, S. C.; Armstrong, D.

    2016-02-01

    We implemented a system to automatically detect Sargassum, a floating seaweed, in 30-meter LANDSAT-8 Operational Land Imager (OLI) imagery. Our algorithm for Sargassum detection is an extended form of Hu's approach to derive a floating algae index (FAI) [1]. Hu's algorithm was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data, but we extended it for use with the OLI bands centered at 655, 865, and 1609 nm, which are comparable to the MODIS bands located at 645, 859, and 1640 nm. We also developed a high resolution true color product to mask cloud pixels in the OLI scene by applying a threshold to top of the atmosphere (TOA) radiances in the red (655 nm), green (561 nm), and blue (443 nm) wavelengths, as well as a method for removing false positive identifications of Sargassum in the imagery. Hu's algorithm derives a FAI for each Sargassum identified pixel. Our algorithm is currently set to only flag the presence of Sargassum in an OLI pixel by classifying any pixel with a FAI > 0.0 as Sargassum. Additionally, our system geo-locates the flagged Sargassum pixels identified in the OLI imagery into the U.S. Navy Global HYCOM model grid. One element of the model grid covers an area 0.125 degrees of latitude by 0.125 degrees of longitude. To resolve the differences in spatial coverage between Landsat and HYCOM, a scheme was developed to calculate the percentage of pixels flagged within the grid element and if above a threshold, it will be flagged as Sargassum. This work is a part of a larger system, sponsored by NASA/Applied Science and Technology Project at J.C. Stennis Space Center, to forecast when and where Sargassum will land on shore. The focus area of this work is currently the Texas coast. Plans call for extending our efforts into the Caribbean. References: [1] Hu, Chuanmin. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment 113 (2009) 2118-2129.

  16. Automated detection of stereotypical motor movements.

    PubMed

    Goodwin, Matthew S; Intille, Stephen S; Albinali, Fahd; Velicer, Wayne F

    2011-06-01

    To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average, pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Precise and efficient recording of stereotypical motor movements could enable researchers and clinicians to systematically study what functional relations exist between these behaviors and specific antecedents and consequences. These measures could also facilitate efficacy studies of behavioral and pharmacologic interventions intended to replace or decrease the incidence or severity of stereotypical motor movements.

  17. Automated image analysis of microstructure changes in metal alloys

    NASA Astrophysics Data System (ADS)

    Hoque, Mohammed E.; Ford, Ralph M.; Roth, John T.

    2005-02-01

    The ability to identify and quantify changes in the microstructure of metal alloys is valuable in metal cutting and shaping applications. For example, certain metals, after being cryogenically and electrically treated, have shown large increases in their tool life when used in manufacturing cutting and shaping processes. However, the mechanisms of microstructure changes in alloys under various treatments, which cause them to behave differently, are not yet fully understood. The changes are currently evaluated in a semi-quantitative manner by visual inspection of images of the microstructure. This research applies pattern recognition technology to quantitatively measure the changes in microstructure and to validate the initial assertion of increased tool life under certain treatments. Heterogeneous images of aluminum and tungsten carbide of various categories were analyzed using a process including background correction, adaptive thresholding, edge detection and other algorithms for automated analysis of microstructures. The algorithms are robust across a variety of operating conditions. This research not only facilitates better understanding of the effects of electric and cryogenic treatment of these materials, but also their impact on tooling and metal-cutting processes.

  18. BacT/Alert: an automated colorimetric microbial detection system.

    PubMed Central

    Thorpe, T C; Wilson, M L; Turner, J E; DiGuiseppi, J L; Willert, M; Mirrett, S; Reller, L B

    1990-01-01

    BacT/Alert (Organon Teknika Corp., Durham, N.C.) is an automated microbial detection system based on the colorimetric detection of CO2 produced by growing microorganisms. Results of an evaluation of the media, sensor, detection system, and detection algorithm indicate that the system reliably grows and detects a wide variety of bacteria and fungi. Results of a limited pilot clinical trial with a prototype research instrument indicate that the system is comparable to the radiometric BACTEC 460 system in its ability to grow and detect microorganisms in blood. On the basis of these initial findings, large-scale clinical trials comparing BacT/Alert with other commercial microbial detection systems appear warranted. PMID:2116451

  19. Method and automated apparatus for detecting coliform organisms

    NASA Technical Reports Server (NTRS)

    Dill, W. P.; Taylor, R. E.; Jeffers, E. L. (Inventor)

    1980-01-01

    Method and automated apparatus are disclosed for determining the time of detection of metabolically produced hydrogen by coliform bacteria cultured in an electroanalytical cell from the time the cell is inoculated with the bacteria. The detection time data provides bacteria concentration values. The apparatus is sequenced and controlled by a digital computer to discharge a spent sample, clean and sterilize the culture cell, provide a bacteria nutrient into the cell, control the temperature of the nutrient, inoculate the nutrient with a bacteria sample, measures the electrical potential difference produced by the cell, and measures the time of detection from inoculation.

  20. Automated Detection Method of Slow Slip Events in Southwest Japan

    NASA Astrophysics Data System (ADS)

    Kimura, T.; Hirose, H.; Obara, K.; Kimura, H.

    2010-12-01

    In the Nankai subduction zone, southwest Japan, various types of slow earthquakes have been detected using dense seismic and geodetic observation networks such as Hi-net operated by the National Research Institute for Earth Science and Disaster Prevention. Short-term slow slip events (SSEs) which last for several days are detected as crustal deformation by using borehole tiltmeters and strainmeters, and usually accompanied by seismic slow earthquakes such as nonvolcanic deep low-frequency tremor. These coupled phenomena are called episodic tremor and slip (ETS). In previous studies on ETS events in southwest Japan, short-term SSEs have been identified manually consulting with the seismic tremor data. However, in order to clarify the relationship between geodetic SSEs and seismic tremor objectively, an SSE detection method independent of the tremor data is necessary. In this study, we develop a new automated method that identifies signals caused by SSEs and estimates the source model using ground tilt data. Our method is composed of two phases, estimation of the SSE model and identification of SSE. In the model estimation phase, we assume that an SSE must occur in the analyzed time period, and observed ground tilt contains a response to an SSE, background linear trend, random-walk noise, and white noise. An SSE is modeled as a uniform slip on a rectangular fault with a time-invariant slip-rate. We estimate an optimum source model of the possible SSE using the Kalman filter for linear parameters such as total slip and grid-search method for nonlinear parameters such as fault location, origin time and duration. In the identification phase, another model is estimated from the same tilt data with an assumption that no SSE occurs. The tilt changes modeled as an SSE in the estimation phase is evaluated by comparison between the models with and without SSE on the basis of AIC. Then a robustness test is carried and the model is identified as an SSE. We applied the automated

  1. Automated gravity gradient tensor inversion for underwater object detection

    NASA Astrophysics Data System (ADS)

    Wu, Lin; Tian, Jinwen

    2010-12-01

    Underwater abnormal object detection is a current need for the navigation security of autonomous underwater vehicles (AUVs). In this paper, an automated gravity gradient tensor inversion algorithm is proposed for the purpose of passive underwater object detection. Full-tensor gravity gradient anomalies induced by an object in the partial area can be measured with the technique of gravity gradiometry on an AUV. Then the automated algorithm utilizes the anomalies, using the inverse method to estimate the mass and barycentre location of the arbitrary-shaped object. A few tests on simple synthetic models will be illustrated, in order to evaluate the feasibility and accuracy of the new algorithm. Moreover, the method is applied to a complicated model of an abnormal object with gradiometer and AUV noise, and interference from a neighbouring illusive smaller object. In all cases tested, the estimated mass and barycentre location parameters are found to be in good agreement with the actual values.

  2. One step automated unpatterned wafer defect detection and classification

    NASA Astrophysics Data System (ADS)

    Dou, Lie; Kesler, Daniel; Bruno, William; Monjak, Charles; Hunt, Jim

    1998-11-01

    Automated detection and classification of crystalline defects on micro-grade silicon wafers is extremely important for integrated circuit (IC) device yield. High training cost, limited capability of classifying defects, increasing possibility of contamination, and unexpected human mistakes necessitate the need to replace the human visual inspection with automated defect inspection. The Laser Scanning Surface Inspection Systems (SSISs) equipped with the Reconvergent Specular Detection (RSD) apparatus are widely used for final wafer inspection. RSD, more commonly known as light channel detection (LC), is capable of detecting and classifying material defects by analyzing information from two independent phenomena, light scattering and reflecting. This paper presents a new technique including a new type of light channel detector to detect and classify wafer surface defects such as slipline dislocation, Epi spikes, Pits, and dimples. The optical system to study this technique consists of a particle scanner to detect and quantify light scattering events from contaminants on the wafer surface and a RSD apparatus (silicon photo detector). Compared with the light channel detector presently used in the wafer fabs, this new light channel technique provides higher sensitivity for small defect detection and more defect scattering signatures for defect classification. Epi protrusions (mounds and spikes), slip dislocations, voids, dimples, and some other common defect features and contamination on silicon wafers are studied using this equipment. The results are compared quantitatively with that of human visual inspection and confirmed by microscope or AFM. This new light channel technology could provide the real future solution to the wafer manufacturing industry for fully automated wafer inspection and defect characterization.

  3. Automated synthesis, insertion and detection of polyps for CT colonography

    NASA Astrophysics Data System (ADS)

    Sezille, Nicolas; Sadleir, Robert J. T.; Whelan, Paul F.

    2003-03-01

    CT Colonography (CTC) is a new non-invasive colon imaging technique which has the potential to replace conventional colonoscopy for colorectal cancer screening. A novel system which facilitates automated detection of colorectal polyps at CTC is introduced. As exhaustive testing of such a system using real patient data is not feasible, more complete testing is achieved through synthesis of artificial polyps and insertion into real datasets. The polyp insertion is semi-automatic: candidate points are manually selected using a custom GUI, suitable points are determined automatically from an analysis of the local neighborhood surrounding each of the candidate points. Local density and orientation information are used to generate polyps based on an elliptical model. Anomalies are identified from the modified dataset by analyzing the axial images. Detected anomalies are classified as potential polyps or natural features using 3D morphological techniques. The final results are flagged for review. The system was evaluated using 15 scenarios. The sensitivity of the system was found to be 65% with 34% false positive detections. Automated diagnosis at CTC is possible and thorough testing is facilitated by augmenting real patient data with computer generated polyps. Ultimately, automated diagnosis will enhance standard CTC and increase performance.

  4. Automated ingestion detection for a health monitoring system.

    PubMed

    Walker, William P; Bhatia, Dinesh K

    2014-03-01

    Obesity is a global epidemic that imposes a financial burden and increased risk for a myriad of chronic diseases. Presented here is an overview of a prototype automated ingestion detection (AID) process implemented in a health monitoring system (HMS). The automated detection of ingestion supports personal record keeping which is essential during obesity management. Personal record keeping allows the care provider to monitor the therapeutic progress of a patient. The AID-HMS determines the levels of ingestion activity from sounds captured by an external throat microphone. Features are extracted from the sound recording and presented to machine learning classifiers, where a simple voting procedure is employed to determine instances of ingestion. Using a dataset acquired from seven individuals consisting of consumption of liquid and solid, speech, and miscellaneous sounds, > 94% of ingestion sounds are correctly identified with false positive rates around 9% based on 10-fold cross validation. The detected levels of ingestion activity are transmitted and stored on a remote web server, where information is displayed through a web application operating in a web browser. This information allows remote users (health provider) determine meal lengths and levels of ingestion activity during the meal. The AID-HMS also provides a basis for automated reinforcement for the patient.

  5. An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

    2003-01-01

    An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

  6. Hough transform for robust regression and automated detection

    NASA Astrophysics Data System (ADS)

    Ballester, P.

    1994-06-01

    The Hough transform is a robust algorithm for detecting multi-dimensional features in images and estimating their parameters. It is widely used in the domains of remote sensing and machine vision and could find number of applications in astrophysics. A general introduction to the Hough transform, its main variations and implementation techniques is provided. A Hough transform based robust regression method is discussed and analyzed. Also auto-adaptive, fast pattern recognition algorithms for the detection of echelle orders and automated arc line identification are presented.

  7. SAR change detection MTI

    NASA Astrophysics Data System (ADS)

    Scarborough, Steven; Lemanski, Christopher; Nichols, Howard; Owirka, Gregory; Minardi, Michael; Hale, Todd

    2006-05-01

    This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.

  8. An Automated Motion Detection and Reward System for Animal Training

    PubMed Central

    Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F

    2015-01-01

    A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an “off-the-shelf” automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use. PMID:26798573

  9. Cholangiocarcinoma--an automated preliminary detection system using MLP.

    PubMed

    Logeswaran, Rajasvaran

    2009-12-01

    Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases.

  10. AUTOMATION AND TECHNOLOGICAL CHANGE IN BANKING.

    ERIC Educational Resources Information Center

    STEINER, CARL L.

    THE PURPOSES OF THIS STUDY WERE TO DETERMINE THE PERSONNEL CHANGE DIRECTLY RESULTING FROM THE INSTALLATION OF ELECTRONIC DATA PROCESSING IN ONE OF THE LARGE COMMERCIAL BANKS IN BALTIMORE, TO DESCRIBE THE PROCESSES AND JOB DUTIES INVOLVED, AND TO INDICATE HOW CHANGES HAVE AFFECTED EMPLOYMENT AND WHAT MAY BE EXPECTED IN THE FUTURE. THE USE OF THE…

  11. Automated J wave detection from digital 12-lead electrocardiogram.

    PubMed

    Wang, Yi Grace; Wu, Hau-Tieng; Daubechies, Ingrid; Li, Yabing; Estes, E Harvey; Soliman, Elsayed Z

    2015-01-01

    In this report we provide a method for automated detection of J wave, defined as a notch or slur in the descending slope of the terminal positive wave of the QRS complex, using signal processing and functional data analysis techniques. Two different sets of ECG tracings were selected from the EPICARE ECG core laboratory, Wake Forest School of Medicine, Winston Salem, NC. The first set was a training set comprised of 100 ECGs of which 50 ECGs had J-wave and the other 50 did not. The second set was a test set (n=116 ECGs) in which the J-wave status (present/absent) was only known by the ECG Center staff. All ECGs were recorded using GE MAC 1200 (GE Marquette, Milwaukee, Wisconsin) at 10mm/mV calibration, speed of 25mm/s and 500HZ sampling rate. All ECGs were initially inspected visually for technical errors and inadequate quality, and then automatically processed with the GE Marquette 12-SL program 2001 version (GE Marquette, Milwaukee, WI). We excluded ECG tracings with major abnormalities or rhythm disorder. Confirmation of the presence or absence of a J wave was done visually by the ECG Center staff and verified once again by three of the coauthors. There was no disagreement in the identification of the J wave state. The signal processing and functional data analysis techniques applied to the ECGs were conducted at Duke University and the University of Toronto. In the training set, the automated detection had sensitivity of 100% and specificity of 94%. For the test set, sensitivity was 89% and specificity was 86%. In conclusion, test results of the automated method we developed show a good J wave detection accuracy, suggesting possible utility of this approach for defining and detection of other complex ECG waveforms.

  12. Glaucoma risk index: automated glaucoma detection from color fundus images.

    PubMed

    Bock, Rüdiger; Meier, Jörg; Nyúl, László G; Hornegger, Joachim; Michelson, Georg

    2010-06-01

    Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  13. Fully Automated Lipid Pool Detection Using Near Infrared Spectroscopy

    PubMed Central

    Wojakowski, Wojciech

    2016-01-01

    Background. Detecting and identifying vulnerable plaque, which is prone to rupture, is still a challenge for cardiologist. Such lipid core-containing plaque is still not identifiable by everyday angiography, thus triggering the need to develop a new tool where NIRS-IVUS can visualize plaque characterization in terms of its chemical and morphologic characteristic. The new tool can lead to the development of new methods of interpreting the newly obtained data. In this study, the algorithm to fully automated lipid pool detection on NIRS images is proposed. Method. Designed algorithm is divided into four stages: preprocessing (image enhancement), segmentation of artifacts, detection of lipid areas, and calculation of Lipid Core Burden Index. Results. A total of 31 NIRS chemograms were analyzed by two methods. The metrics, total LCBI, maximal LCBI in 4 mm blocks, and maximal LCBI in 2 mm blocks, were calculated to compare presented algorithm with commercial available system. Both intraclass correlation (ICC) and Bland-Altman plots showed good agreement and correlation between used methods. Conclusions. Proposed algorithm is fully automated lipid pool detection on near infrared spectroscopy images. It is a tool developed for offline data analysis, which could be easily augmented for newer functions and projects. PMID:27610191

  14. An Automated Directed Spectral Search Methodology for Small Target Detection

    NASA Astrophysics Data System (ADS)

    Grossman, Stanley I.

    Much of the current efforts in remote sensing tackle macro-level problems such as determining the extent of wheat in a field, the general health of vegetation or the extent of mineral deposits in an area. However, for many of the remaining remote sensing challenges being studied currently, such as border protection, drug smuggling, treaty verification, and the war on terror, most targets are very small in nature - a vehicle or even a person. While in typical macro-level problems the objective vegetation is in the scene, for small target detection problems it is not usually known if the desired small target even exists in the scene, never mind finding it in abundance. The ability to find specific small targets, such as vehicles, typifies this problem. Complicating the analyst's life, the growing number of available sensors is generating mountains of imagery outstripping the analysts' ability to visually peruse them. This work presents the important factors influencing spectral exploitation using multispectral data and suggests a different approach to small target detection. The methodology of directed search is presented, including the use of scene-modeled spectral libraries, various search algorithms, and traditional statistical and ROC curve analysis. The work suggests a new metric to calibrate analysis labeled the analytic sweet spot as well as an estimation method for identifying the sweet spot threshold for an image. It also suggests a new visualization aid for highlighting the target in its entirety called nearest neighbor inflation (NNI). It brings these all together to propose that these additions to the target detection arena allow for the construction of a fully automated target detection scheme. This dissertation next details experiments to support the hypothesis that the optimum detection threshold is the analytic sweet spot and that the estimation method adequately predicts it. Experimental results and analysis are presented for the proposed directed

  15. Change Detection: Training and Transfer

    PubMed Central

    Gaspar, John G.; Neider, Mark B.; Simons, Daniel J.; McCarley, Jason S.; Kramer, Arthur F.

    2013-01-01

    Observers often fail to notice even dramatic changes to their environment, a phenomenon known as change blindness. If training could enhance change detection performance in general, then it might help to remedy some real-world consequences of change blindness (e.g. failing to detect hazards while driving). We examined whether adaptive training on a simple change detection task could improve the ability to detect changes in untrained tasks for young and older adults. Consistent with an effective training procedure, both young and older adults were better able to detect changes to trained objects following training. However, neither group showed differential improvement on untrained change detection tasks when compared to active control groups. Change detection training led to improvements on the trained task but did not generalize to other change detection tasks. PMID:23840775

  16. Change detection: training and transfer.

    PubMed

    Gaspar, John G; Neider, Mark B; Simons, Daniel J; McCarley, Jason S; Kramer, Arthur F

    2013-01-01

    Observers often fail to notice even dramatic changes to their environment, a phenomenon known as change blindness. If training could enhance change detection performance in general, then it might help to remedy some real-world consequences of change blindness (e.g. failing to detect hazards while driving). We examined whether adaptive training on a simple change detection task could improve the ability to detect changes in untrained tasks for young and older adults. Consistent with an effective training procedure, both young and older adults were better able to detect changes to trained objects following training. However, neither group showed differential improvement on untrained change detection tasks when compared to active control groups. Change detection training led to improvements on the trained task but did not generalize to other change detection tasks.

  17. Rapid Change Detection Algorithm for Disaster Management

    NASA Astrophysics Data System (ADS)

    Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

    2012-07-01

    This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

  18. An Automated Visual Event Detection System for Cabled Observatory Video

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Cline, D. E.; Mariette, J.

    2007-12-01

    The permanent presence of underwater cameras on oceanic cabled observatories, such as the Victoria Experimental Network Under the Sea (VENUS) and Eye-In-The-Sea (EITS) on Monterey Accelerated Research System (MARS), will generate valuable data that can move forward the boundaries of understanding the underwater world. However, sightings of underwater animal activities are rare, resulting in the recording of many hours of video with relatively few events of interest. The burden of video management and analysis often requires reducing the amount of video recorded and later analyzed. Sometimes enough human resources do not exist to analyze the video; the strains on human attention needed to analyze video demand an automated way to assist in video analysis. Towards this end, an Automated Visual Event Detection System (AVED) is in development at the Monterey Bay Aquarium Research Institute (MBARI) to address the problem of analyzing cabled observatory video. Here we describe the overall design of the system to process video data and enable science users to analyze the results. We present our results analyzing video from the VENUS observatory and test data from EITS deployments. This automated system for detecting visual events includes a collection of custom and open source software that can be run three ways: through a Web Service, through a Condor managed pool of AVED enabled compute servers, or locally on a single computer. The collection of software also includes a graphical user interface to preview or edit detected results and to setup processing options. To optimize the compute-intensive AVED algorithms, a parallel program has been implemented for high-data rate applications like the EITS instrument on MARS.

  19. Automated sleep scoring and sleep apnea detection in children

    NASA Astrophysics Data System (ADS)

    Baraglia, David P.; Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Martin, A. James; Abbott, Derek

    2005-12-01

    This paper investigates the automated detection of a patient's breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women's and Children's Hospital. The sleep scoring and breathing event detection tasks used neural networks to achieve signal classification. The Fourier transform and the Higuchi fractal dimension were used to extract features for input to the neural network. The filtered skin conductivity appeared visually to bear a similarity to the breathing and heart rate signal, but a more detailed evaluation showed the relation was not consistent. Sleep stage classification was achieved with and accuracy of around 65% with some stages being accurately scored and others poorly scored. The two breathing events hypopnea and apnea were scored with varying degrees of accuracy with the highest scores being around 75% and 30%.

  20. Image analysis techniques for automated IVUS contour detection.

    PubMed

    Papadogiorgaki, Maria; Mezaris, Vasileios; Chatzizisis, Yiannis S; Giannoglou, George D; Kompatsiaris, Ioannis

    2008-09-01

    Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images.

  1. Automated Solar Feature Detection for Space Weather Applications

    NASA Astrophysics Data System (ADS)

    Pérez-Suárez, David; Higgins, Paul A.; Bloomfield, D. Shaun; McAteer, R. T. James; Krista, Larisza D.; Byrne, Jason P.; Gallagher, Peter. T.

    2011-03-01

    The solar surface and atmosphere are highly dynamic plasma environments, which evolve over a wide range of temporal and spatial scales. Large-scale eruptions, such as coronal mass ejections, can be accelerated to millions of kilometres per hour in a matter of minutes, making their automated detection and characterisation challenging. Additionally, there are numerous faint solar features, such as coronal holes and coronal dimmings, which are important for space weather monitoring and forecasting, but their low intensity and sometimes transient nature makes them problematic to detect using traditional image processing techniques. These difficulties are compounded by advances in ground- and space- based instrumentation, which have increased the volume of data that solar physicists are confronted with on a minute-by-minute basis; NASA's Solar Dynamics Observatory for example is returning many thousands of images per hour (~1.5 TB/day). This chapter reviews recent advances in the application of images processing techniques to the automated detection of active regions, coronal holes, filaments, CMEs, and coronal dimmings for the purposes of space weather monitoring and prediction.

  2. An Automated System for Detecting and Measuring Nailfold Capillaries

    PubMed Central

    Berks, Michael; Tresadern, Phil; Dinsdale, Graham; Murray, Andrea; Moore, Tonia; Herrick, Ariane; Taylor, Chris

    2016-01-01

    Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynaud’s phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynaud’s phenomenon, and those with potentially life-threatening systemic sclerosis. PMID:25333175

  3. Automated Vulnerability Detection for Compiled Smart Grid Software

    SciTech Connect

    Prowell, Stacy J; Pleszkoch, Mark G; Sayre, Kirk D; Linger, Richard C

    2012-01-01

    While testing performed with proper experimental controls can provide scientifically quantifiable evidence that software does not contain unintentional vulnerabilities (bugs), it is insufficient to show that intentional vulnerabilities exist, and impractical to certify devices for the expected long lifetimes of use. For both of these needs, rigorous analysis of the software itself is essential. Automated software behavior computation applies rigorous static software analysis methods based on function extraction (FX) to compiled software to detect vulnerabilities, intentional or unintentional, and to verify critical functionality. This analysis is based on the compiled firmware, takes into account machine precision, and does not rely on heuristics or approximations early in the analysis.

  4. Automated detection and classification of lunar craters using multiple approaches

    NASA Astrophysics Data System (ADS)

    Sawabe, Y.; Matsunaga, T.; Rokugawa, S.

    Many missions such as Clementine and SELENE (SELenological and Engineering Explorer) take lunar images for examination. A large volume of imagery data has already been archived and much more is on the way. Extracting the necessary information from the already large and ever growing volume of data is the crucial problem that needs to be overcome. Craters are studied extensively since they provide us with the relative age of the surface unit and more information on the lunar surface geology. Manually extracting craters from lunar images is a difficult task because it requires a great deal of man power as well as specific knowledge and skills of extraction. Several automated craters detection algorithms have been developed but none is yet practical or sufficiently tested to be reliable. Our previous algorithm (Sawabe, Y., Matsunaga, T., Rokugawa, S. Automatic crater detection algorithm for the lunar surface using multiple approaches. J. Remote Sens. Soc. Jpn. 25 (2), 157 168, 2005.) was improved to enhance detection of craters in lunar images and automate crater classification. This algorithm was tested using various images for wide range of applicability. Four approaches were used with the crater detecting algorithm to find (1) “shady and sunny” patters in images with low sun angle, (2) circular features in edge images, (3) curves and circles in thinned and connected edge lines, and (4) discrete or broken circular edge lines using fuzzy Hough transform. The algorithm was applied to mare and highland images of the moon captured by Clementine and Apollo under different solar angles and spatial resolution. The new algorithm was able to detect 80% more without parameter tuning. In addition, the detected craters were classified by spectral characteristics derived from Clementine UV Vis multi-spectral images. Finally, the lunar surface GIS was formulated which has the geological and spectral attributes automatically generated by our algorithm. It could be helpful

  5. Automated High-Throughput Fluorescence Lifetime Imaging Microscopy to Detect Protein-Protein Interactions.

    PubMed

    Guzmán, Camilo; Oetken-Lindholm, Christina; Abankwa, Daniel

    2016-04-01

    Fluorescence resonance energy transfer (FRET) is widely used to study conformational changes of macromolecules and protein-protein, protein-nucleic acid, and protein-small molecule interactions. FRET biosensors can serve as valuable secondary assays in drug discovery and for target validation in mammalian cells. Fluorescence lifetime imaging microscopy (FLIM) allows precise quantification of the FRET efficiency in intact cells, as FLIM is independent of fluorophore concentration, detection efficiency, and fluorescence intensity. We have developed an automated FLIM system using a commercial frequency domain FLIM attachment (Lambert Instruments) for wide-field imaging. Our automated FLIM system is capable of imaging and analyzing up to 50 different positions of a slide in less than 4 min, or the inner 60 wells of a 96-well plate in less than 20 min. Automation is achieved using a motorized stage and controller (Prior Scientific) coupled with a Zeiss Axio Observer body and full integration into the Lambert Instruments FLIM acquisition software. As an application example, we analyze the interaction of the oncoprotein Ras and its effector Raf after drug treatment. In conclusion, our automated FLIM imaging system requires only commercial components and may therefore allow for a broader use of this technique in chemogenomics projects. © 2015 Society for Laboratory Automation and Screening.

  6. The Automated Assessment of Postural Stability: Balance Detection Algorithm.

    PubMed

    Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

    2017-08-30

    Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect(®) sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to

  7. Operator adaptation to changes in system reliability under adaptable automation.

    PubMed

    Chavaillaz, Alain; Sauer, Juergen

    2016-11-25

    This experiment examined how operators coped with a change in system reliability between training and testing. Forty participants were trained for 3 h on a complex process control simulation modelling six levels of automation (LOA). In training, participants either experienced a high- (100%) or low-reliability system (50%). The impact of training experience on operator behaviour was examined during a 2.5 h testing session, in which participants either experienced a high- (100%) or low-reliability system (60%). The results showed that most operators did not often switch between LOA. Most chose an LOA that relieved them of most tasks but maintained their decision authority. Training experience did not have a strong impact on the outcome measures (e.g. performance, complacency). Low system reliability led to decreased performance and self-confidence. Furthermore, complacency was observed under high system reliability. Overall, the findings suggest benefits of adaptable automation because it accommodates different operator preferences for LOA. Practitioner Summary: The present research shows that operators can adapt to changes in system reliability between training and testing sessions. Furthermore, it provides evidence that each operator has his/her preferred automation level. Since this preference varies strongly between operators, adaptable automation seems to be suitable to accommodate these large differences.

  8. Observer performance in semi-automated microbleed detection

    NASA Astrophysics Data System (ADS)

    Kuijf, Hugo J.; Brundel, Manon; de Bresser, Jeroen; Viergever, Max A.; Biessels, Geert Jan; Geerlings, Mirjam I.; Vincken, Koen L.

    2013-03-01

    Cerebral microbleeds are small bleedings in the human brain, detectable with MRI. Microbleeds are associated with vascular disease and dementia. The number of studies involving microbleed detection is increasing rapidly. Visual rating is the current standard for detection, but is a time-consuming process, especially at high-resolution 7.0 T MR images, has limited reproducibility and is highly observer dependent. Recently, multiple techniques have been published for the semi-automated detection of microbleeds, attempting to overcome these problems. In the present study, a 7.0 T dual-echo gradient echo MR image was acquired in 18 participants with microbleeds from the SMART study. Two experienced observers identified 54 microbleeds in these participants, using a validated visual rating scale. The radial symmetry transform (RST) can be used for semi-automated detection of microbleeds in 7.0 T MR images. In the present study, the results of the RST were assessed by two observers and 47 microbleeds were identified: 35 true positives and 12 extra positives (microbleeds that were missed during visual rating). Hence, after scoring a total number of 66 microbleeds could be identified in the 18 participants. The use of the RST increased the average sensitivity of observers from 59% to 69%. More importantly, inter-observer agreement (ICC and Dice's coefficient) increased from 0.85 and 0.64 to 0.98 and 0.96, respectively. Furthermore, the required rating time was reduced from 30 to 2 minutes per participant. By fine-tuning the RST, sensitivities up to 90% can be achieved, at the cost of extra false positives.

  9. Electrophysiological correlates of change detection.

    PubMed

    Eimer, Martin; Mazza, Veronica

    2005-05-01

    To identify electrophysiological correlates of change detection, event-related brain potentials (ERPs) were recorded while participants monitored displays containing four faces in order to detect a face identity change across successive displays. Successful change detection was mirrored by an N2pc component at posterior electrodes contralateral to the side of a change, suggesting close links between conscious change detection and attention. ERPs on undetected-change trials differed from detected-change and no-change trials. We suggest that short-latency ERP differences between these trial types reflect trial-by-trial fluctuations in advance task preparation, whereas differences in the P3 time range are due to variations in the duration of perceptual and decision-related processing. Overall, these findings demonstrate that ERPs are a useful tool for dissociating processes underlying change blindness and change detection.

  10. Development of automated detection of radiology reports citing adrenal findings

    NASA Astrophysics Data System (ADS)

    Zopf, Jason; Langer, Jessica; Boonn, William; Kim, Woojin; Zafar, Hanna

    2011-03-01

    Indeterminate incidental findings pose a challenge to both the radiologist and the ordering physician as their imaging appearance is potentially harmful but their clinical significance and optimal management is unknown. We seek to determine if it is possible to automate detection of adrenal nodules, an indeterminate incidental finding, on imaging examinations at our institution. Using PRESTO (Pathology-Radiology Enterprise Search tool), a newly developed search engine at our institution that mines dictated radiology reports, we searched for phrases used by attendings to describe incidental adrenal findings. Using these phrases as a guide, we designed a query that can be used with the PRESTO index. The results were refined using a modified version of NegEx to eliminate query terms that have been negated within the report text. In order to validate these findings we used an online random date generator to select two random weeks. We queried our RIS database for all reports created on those dates and manually reviewed each report to check for adrenal incidental findings. This survey produced a ground- truth dataset of reports citing adrenal incidental findings against which to compare query performance. We further reviewed the false positives and negatives identified by our validation study, in an attempt to improve the performance query. This algorithm is an important step towards automating the detection of incidental adrenal nodules on cross sectional imaging at our institution. Subsequently, this query can be combined with electronic medical record data searches to determine the clinical significance of these findings through resultant follow-up.

  11. Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

    PubMed Central

    Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.

    2009-01-01

    BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728

  12. Enhancing time-series detection algorithms for automated biosurveillance.

    PubMed

    Tokars, Jerome I; Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A

    2009-04-01

    BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14-28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data.

  13. Automated microaneurysm detection in diabetic retinopathy using curvelet transform

    NASA Astrophysics Data System (ADS)

    Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

    2016-10-01

    Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

  14. Automated 10-channel capillary chip immunodetector for biological agents detection.

    PubMed

    Yacoub-George, Erwin; Hell, Waltraud; Meixner, Leonhard; Wenninger, Franz; Bock, Karlheinz; Lindner, Petra; Wolf, Hans; Kloth, Tanja; Feller, Klaus A

    2007-02-15

    The automated 10-channel capillary chip immunodetector (10K-IDWG) is a prototype, which has been developed for automatically operated biological agents (BA) point detection. The current technology uses a chemiluminescence capillary immunoassay (EIA) technique in combination with integrated microfluidics and allows the highly sensitive and rapid detection and preliminary identification of multiple BA in aqueous solutions in the laboratory. The chemiluminescence capillary EIA are performed within a disposable capillary chip containing 10 fused-silica capillaries arranged in parallel coated with selected capture antibodies. A multianode-photomultiplier array is used to detect chemiluminescence intensity in each capillary. Reservoirs for reagents and buffers and a waste disposal reservoir are integrated. This paper describes the technology of the 10K-IDWG and its evaluation with three different BA, the toxin staphylococcal enterotoxin B (SEB), the bacterial analyte Escherichia coli (E. coli) O157:H7 as a model for bacterial pathogens, and the bacteriophage M13 as a model for virus pathogens. The 10K-IDWG is able to detect the above mentioned three BA in an aqueous sample within 29 min (single analyte-detection and multiplexing). Limits of detection (LOD) are 0.1 ng/ml for SEB, 10(4)cfu/ml for E. coli O157:H7, and 5x10(5) pfu/ml for M13. Cross reactivities between the three assays were not observed.

  15. Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

    PubMed

    Kalitzin, Stiliyan N; Bauer, Prisca R; Lamberts, Robert J; Velis, Demetrios N; Thijs, Roland D; Lopes Da Silva, Fernando H

    2016-12-01

    Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

  16. Detecting staphylococcal enterotoxin B using an automated fiber optic biosensor.

    PubMed

    King, K D; Anderson, G P; Bullock, K E; Regina, M J; Saaski, E W; Ligler, F S

    1999-02-01

    The Man-portable Analyte Identification System (MANTIS), the first fully automated, self-contained, portable fiber optic biosensor, was utilized for the detection of Staphylococcal Enterotoxin B (SEB), a bacterial toxin produced by Staphylococcus aureus that commonly causes food poisoning. Because of its remarkable toxicity and stability, SEB is considered a prime threat as a biological weapon of mass destruction. The assay for SEB was used to evaluate the MANTIS' ability to function in the presence of various environmental interferents. The sensor could reliably detect SEB spiked into liquid samples containing a variety of smoke particles. However, substantial interference occurred when SEB was mixed into matrices capable of adsorbing SEB, such as 1% solutions of clay, topsoil, or pollen. Of equal importance, none of the interferents produced false positives in the MANTIS. The MANTIS demonstrated the capability to perform simultaneous immunoassays rapidly in the field with little or no user intervention.

  17. Automated detection of optical counterparts to GRBs with RAPTOR

    NASA Astrophysics Data System (ADS)

    Wozniak, P. R.; Vestrand, W. T.; Evans, S.; White, R.; Wren, J.

    2006-05-01

    The RAPTOR system (RAPid Telescopes for Optical Response) is an array of several distributed robotic telescopes that automatically respond to GCN localization alerts. Raptor-S is a 0.4-m telescope with 24 arc min. field of view employing a 1k × 1k Marconi CCD detector, and has already detected prompt optical emission from several GRBs within the first minute of the explosion. We present a real-time data analysis and alert system for automated identification of optical transients in Raptor-S GRB response data down to the sensitivity limit of ~ 19 mag. Our custom data processing pipeline is designed to minimize the time required to reliably identify transients and extract actionable information. The system utilizes a networked PostgreSQL database server for catalog access and distributes email alerts with successful detections.

  18. Automated detection of rapid eye movements in children.

    PubMed

    Held, Claudio M; Causa, Javier; Causa, Leonardo; Estévez, Pablo A; Perez, Claudio A; Garrido, Marcelo; Chamorro, Rodrigo; Algarin, Cecilia; Peirano, Patricio

    2012-01-01

    We present an automated multiple-step tool to identify Rapid Eye Movements (REMs) in the polysomnogram, based on modeling expert criteria. It begins by identifying the polysomnogram segments compatible with REMs presence. On these segments, high-energy REMs are identified. Then, vicinity zones around those REMs are defined, and lesser-energy REMs are sought in these vicinities. This strategy has the advantage that it can detect lesser-energy REMs without increasing much the false positive detections. Signal processing, feature extraction, and fuzzy logic tools are used to achieve the goal. The tool was trained and validated on a database consisting of 20 all-night polysomnogram recordings (160 hr) of healthy ten-year-old children. Preliminary results on the validation set show 85.5% sensitivity and a false positive rate of 16.2%. Our tool works on complete polysomnogram recordings, without the need of preprocessing, prior knowledge of the hypnogram, or noise-free segments selection.

  19. An automated detection for axonal boutons in vivo two-photon imaging of mouse

    NASA Astrophysics Data System (ADS)

    Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

    2017-02-01

    Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

  20. Fast-time Simulation of an Automated Conflict Detection and Resolution Concept

    NASA Technical Reports Server (NTRS)

    Windhorst, Robert; Erzberger, Heinz

    2006-01-01

    This paper investigates the effect on the National Airspace System of reducing air traffc controller workload by automating conflict detection and resolution. The Airspace Concept Evaluation System is used to perform simulations of the Cleveland Center with conventional and with automated conflict detection and resolution concepts. Results show that the automated conflict detection and resolution concept significantly decreases growth of delay as traffic demand is increased in en-route airspace.

  1. [Automated detection of estrus and mastitis in dairy cows].

    PubMed

    de Mol, R M

    2001-02-15

    The development and test of detection models for oestrus and mastitis in dairy cows is described in a PhD thesis that was defended in Wageningen on June 5, 2000. These models were based on sensors for milk yield, milk temperature, electrical conductivity of milk, and cow activity and concentrate intake, and on combined processing of the sensor data. The models alert farmers to cows that need attention, because of possible oestrus or mastitis. A first detection model for cows, milked twice a day, was based on time series models for the sensor variables. A time series model describes the dependence between successive observations. The parameters of the time series models were fitted on-line for each cow after each milking by means of a Kalman filter, a mathematical method to estimate the state of a system on-line. The Kalman filter gives the best estimate of the current state of a system based on all preceding observations. This model was tested for 2 years on two experimental farms, and under field conditions on four farms over several years. A second detection model, for cow milked in an automatic milking system (AMS), was based on a generalization of the first model. Two data sets (one small, one large) were used for testing. The results for oestrus detection were good for both models. The results for mastitis detection were varying (in some cases good, in other cases moderate). Fuzzy logic was used to classify mastitis and oestrus alerts with both detection models, to reduce the number of false positive alerts. Fuzzy logic makes approximate reasoning possible, where statements can be partly true or false. Input for the fuzzy logic model were alerts from the detection models and additional information. The number of false positive alerts decreased considerably, while the number of detected cases remained at the same level. These models make automated detection possible in practice.

  2. Automation of Cyber Penetration Testing Using the Detect, Identify, Predict, React Intelligence Automation Model

    DTIC Science & Technology

    2013-09-01

    With increased computing power available, intelligent automation is a clear choice for simplifying the lives of both administrators and developers...with manual cyber penetration [1]. With increased computing power available, intelligent automation is a clear choice for simplifying the lives... power intensive, and basic automation has the limitation of only finding the specific vulnerabilities which it is programmed to find. Penetration

  3. Automated Detection of Actinic Keratoses in Clinical Photographs

    PubMed Central

    Hames, Samuel C.; Sinnya, Sudipta; Tan, Jean-Marie; Morze, Conrad; Sahebian, Azadeh; Soyer, H. Peter; Prow, Tarl W.

    2015-01-01

    Background Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. Objective The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. Methods Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist’s assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. Results The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist’s intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001) were detected in the high actinic keratosis group compared to the no actinic keratosis group. Conclusions The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve sensitivity of detection of 53

  4. Infrared Thermal Imaging for Automated Detection of Diabetic Foot Complications

    PubMed Central

    van Netten, Jaap J.; van Baal, Jeff G.; Liu, Chanjuan; van der Heijden, Ferdi; Bus, Sicco A.

    2013-01-01

    Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. PMID:24124937

  5. Automated detection of circulating tumor cells with naive Bayesian classifiers.

    PubMed

    Svensson, Carl-Magnus; Krusekopf, Solveigh; Lücke, Jörg; Thilo Figge, Marc

    2014-06-01

    Personalized medicine is a modern healthcare approach where information on each person's unique clinical constitution is exploited to realize early disease intervention based on more informed medical decisions. The application of diagnostic tools in combination with measurement evaluation that can be performed in a reliable and automated fashion plays a key role in this context. As the progression of various cancer diseases and the effectiveness of their treatments are related to a varying number of tumor cells that circulate in blood, the determination of their extremely low numbers by liquid biopsy is a decisive prognostic marker. To detect and enumerate circulating tumor cells (CTCs) in a reliable and automated fashion, we apply methods from machine learning using a naive Bayesian classifier (NBC) based on a probabilistic generative mixture model. Cells are collected with a functionalized medical wire and are stained for fluorescence microscopy so that their color signature can be used for classification through the construction of Red-Green-Blue (RGB) color histograms. Exploiting the information on the fluorescence signature of CTCs by the NBC does not only allow going beyond previous approaches but also provides a method of unsupervised learning that is required for unlabeled training data. A quantitative comparison with a state-of-the-art support vector machine, which requires labeled data, demonstrates the competitiveness of the NBC method.

  6. Automated transient detection in the STEREO Heliospheric Imagers.

    NASA Astrophysics Data System (ADS)

    Barnard, Luke; Scott, Chris; Owens, Mat; Lockwood, Mike; Tucker-Hood, Kim; Davies, Jackie

    2014-05-01

    Since the launch of the twin STEREO satellites, the heliospheric imagers (HI) have been used, with good results, in tracking transients of solar origin, such as Coronal Mass Ejections (CMEs), out far into the heliosphere. A frequently used approach is to build a "J-map", in which multiple elongation profiles along a constant position angle are stacked in time, building an image in which radially propagating transients form curved tracks in the J-map. From this the time-elongation profile of a solar transient can be manually identified. This is a time consuming and laborious process, and the results are subjective, depending on the skill and expertise of the investigator. Therefore, it is desirable to develop an automated algorithm for the detection and tracking of the transient features observed in HI data. This is to some extent previously covered ground, as similar problems have been encountered in the analysis of coronagraph data and have led to the development of products such as CACtus etc. We present the results of our investigation into the automated detection of solar transients observed in J-maps formed from HI data. We use edge and line detection methods to identify transients in the J-maps, and then use kinematic models of the solar transient propagation (such as the fixed-phi and harmonic mean geometric models) to estimate the solar transients properties, such as transient speed and propagation direction, from the time-elongation profile. The effectiveness of this process is assessed by comparison of our results with a set of manually identified CMEs, extracted and analysed by the Solar Storm Watch Project. Solar Storm Watch is a citizen science project in which solar transients are identified in J-maps formed from HI data and tracked multiple times by different users. This allows the calculation of a consensus time-elongation profile for each event, and therefore does not suffer from the potential subjectivity of an individual researcher tracking an

  7. Digital tripwire: a small automated human detection system

    NASA Astrophysics Data System (ADS)

    Fischer, Amber D.; Redd, Emmett; Younger, A. Steven

    2009-05-01

    A low cost, lightweight, easily deployable imaging sensor that can dependably discriminate threats from other activities within its field of view and, only then, alert the distant duty officer by transmitting a visual confirmation of the threat would provide a valuable asset to modern defense. At present, current solutions suffer from a multitude of deficiencies - size, cost, power endurance, but most notably, an inability to assess an image and conclude that it contains a threat. The human attention span cannot maintain critical surveillance over banks of displays constantly conveying such images from the field. DigitalTripwire is a small, self-contained, automated human-detection system capable of running for 1-5 days on two AA batteries. To achieve such long endurance, the DigitalTripwire system utilizes an FPGA designed with sleep functionality. The system uses robust vision algorithms, such as a partially unsupervised innovative backgroundmodeling algorithm, which employ several data reduction strategies to operate in real-time, and achieve high detection rates. When it detects human activity, either mounted or dismounted, it sends an alert including images to notify the command center. In this paper, we describe the hardware and software design of the DigitalTripwire system. In addition, we provide detection and false alarm rates across several challenging data sets demonstrating the performance of the vision algorithms in autonomously analyzing the video stream and classifying moving objects into four primary categories - dismounted human, vehicle, non-human, or unknown. Performance results across several challenging data sets are provided.

  8. Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy.

    PubMed

    Perperidis, Antonios; Akram, Ahsan; Altmann, Yoann; McCool, Paul; Westerfeld, Jody; Wilson, David; Dhaliwal, Kevin; McLaughlin, Stephen

    2017-01-01

    Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at a microscopic level. The nature of OEM data, as acquired in clinical use, gives rise to the presence of uninformative frames (i.e., pure-noise and motion-artefacts). Uninformative frames can comprise a considerable proportion (up to > 25%) of a dataset, increasing the resources required for analyzing the data (both manually and automatically), as well as diluting the results of any automated quantification analysis. There is, therefore, a need to automatically detect and remove as many of these uninformative frames as possible while keeping frames with structural information intact. This paper employs Gray Level Cooccurrence Matrix texture measures and detection theory to identify and remove such frames. The detection of pure-noise and motion-artefacts frames is treated as two independent problems. Pulmonary OEM frame sequences of the distal lung are employed for the development and assessment of the approach. The proposed approach identifies and removes uninformative frames with a sensitivity of 93% and a specificity of 92.6%. The detection algorithm is accurate and robust in pulmonary OEM frame sequences. Conditional to appropriate model refinement, the algorithms can become applicable in other organs.

  9. An Automated Road Roughness Detection from Mobile Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Kumar, P.; Angelats, E.

    2017-05-01

    Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

  10. [Automated detection of microemboli in transcranial Doppler signals].

    PubMed

    Casty, M

    1994-10-01

    The detection of micro-emboli (ME) in the intracranial brain arteries by means of transcranial Doppler sonography depends on signal quality and on the definition of the detection levels. The qualitative analysis by ear should be replaced by quantitative measurement techniques to provide results that are cross-comparable to the ones of other studies. Algorithms for ME detection have been developed and implemented on a Doppler signal analyzer. Three quantitative criteria were established with test data. Tape recordings of 44 middle cerebral arteries from an ongoing study including patients before and after implantation of prosthetic heart valves were examined. The data were first corrected for frequency response, phase and amplitude and examined by two independent investigators and by the instrument. Within the 44 measurements both examinations by ear found the same 26 samples to contain zero audible ME signals. The instrument detected in 21 cases zero ME signals, in 4 cases 1 signal and in 1 case 4 signals. For 18 tapes the ear-method provided counts of 1 to 160 ME's. In 12 cases the two investigators got the same result, in the remaining 6 cases the higher of both figures was selected for comparison to the automated count. The counting by the instrument was exactly the same for 7 cases, in 7 cases the instrument counted more, in 4 cases it detected fewer ME signals compared to the reference. The method and the proposed detection criteria provided more false positive than false negative results. This appeared to be due to artifact detection and on higher resolution in time leading to better separation of double events compared to the ear method. Assuming an adequate quality of the quadrature signals, the method may lead to automatic screening for ME signals.

  11. Automated detection of irradiated food with the comet assay.

    PubMed

    Verbeek, F; Koppen, G; Schaeken, B; Verschaeve, L

    2008-01-01

    Food irradiation is the process of exposing food to ionising radiation in order to disinfect, sanitise, sterilise and preserve food or to provide insect disinfestation. Irradiated food should be adequately labelled according to international and national guidelines. In many countries, there are furthermore restrictions to the product-specific maximal dose that can be administered. Therefore, there is a need for methods that allow detection of irradiated food, as well as for methods that provide a reliable dose estimate. In recent years, the comet assay was proposed as a simple, rapid and inexpensive method to fulfil these goals, but further research is required to explore the full potential of this method. In this paper we describe the use of an automated image analysing system to measure DNA comets which allow the discrimination between irradiated and non-irradiated food as well as the set-up of standard dose-response curves, and hence a sufficiently accurate dose estimation.

  12. Automated rice leaf disease detection using color image analysis

    NASA Astrophysics Data System (ADS)

    Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

    2011-06-01

    In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

  13. Evaluation of the initial thematic output from a continuous change-detection algorithm for use in automated operational land-change mapping by the U.S. Geological Survey

    USGS Publications Warehouse

    Pengra, Bruce; Gallant, Alisa L.; Zhu, Zhe; Dahal, Devendra

    2016-01-01

    The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps.

  14. Evaluation of object level change detection techniques

    NASA Astrophysics Data System (ADS)

    Irvine, John M.; Bergeron, Stuart; Hugo, Doug; O'Brien, Michael A.

    2007-04-01

    A variety of change detection (CD) methods have been developed and employed to support imagery analysis for applications including environmental monitoring, mapping, and support to military operations. Evaluation of these methods is necessary to assess technology maturity, identify areas for improvement, and support transition to operations. This paper presents a methodology for conducting this type of evaluation, discusses the challenges, and illustrates the techniques. The evaluation of object-level change detection methods is more complicated than for automated techniques for processing a single image. We explore algorithm performance assessments, emphasizing the definition of the operating conditions (sensor, target, and environmental factors) and the development of measures of performance. Specific challenges include image registration; occlusion due to foliage, cultural clutter and terrain masking; diurnal differences; and differences in viewing geometry. Careful planning, sound experimental design, and access to suitable imagery with image truth and metadata are critical.

  15. Solar Physics Automated Feature Detection: Progress and Scientific Return

    NASA Astrophysics Data System (ADS)

    Martens, P. C.; SDO Feature Finding Team

    2011-12-01

    The SDO Feature Finding Team (FFT) has been implementing 16 feature finding modules for the last two and a half years. These modules have been designed to analyze the incoming stream of SDO data in near-real-time. Several modules are in regular operation now, most others are reaching that point. Our modules detect flares, filaments, dimming regions, sigmoids, emerging flux, bright points, jets, oscillations, active regions, coronal holes, and several other solar features. We are also developing a general trainable feature detection module, which can be applied to detect any phenomenon. Automated feature recognition has several advantages over the same by humans: first, and most importantly, much larger amounts of images can be analyzed by machines; second, the codes will apply consistent criteria for the detection of phenomena, much more so than humans. Of course the second point implies that the detection criteria must be carefully calibrated, otherwise the outcome will be consistent, but consistently wrong. Examples of the scientific potential unleashed our project are: i) Draw a butterfly diagram for Active Regions, ii) Find all filaments that coincide with sigmoids, and then correlate sigmoid handedness with filament chirality, iii) Correlate EUV jets with small scale flux emergence in coronal holes, iv) Draw polarity inversion line maps with regions of high shear and large magnetic field gradients overlayed, to pinpoint potential flaring regions. Then correlate with actual flare occurrence. All of these tasks will be accomplished with great ease; the power of this method is limited merely by the imagination of the researcher. In addition our modules provide space-weather alerts for flares, dimmings (proxies for eruptions), and flux emergence. In my presentation I will present an overview of the output from our feature detection codes, as well as first results of scientific analysis from the metadata.

  16. Automated detection of retinal whitening in malarial retinopathy

    NASA Astrophysics Data System (ADS)

    Joshi, V.; Agurto, C.; Barriga, S.; Nemeth, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Lewallen, S.; Harding, S.

    2016-03-01

    Cerebral malaria (CM) is a severe neurological complication associated with malarial infection. Malaria affects approximately 200 million people worldwide, and claims 600,000 lives annually, 75% of whom are African children under five years of age. Because most of these mortalities are caused by the high incidence of CM misdiagnosis, there is a need for an accurate diagnostic to confirm the presence of CM. The retinal lesions associated with malarial retinopathy (MR) such as retinal whitening, vessel discoloration, and hemorrhages, are highly specific to CM, and their detection can improve the accuracy of CM diagnosis. This paper will focus on development of an automated method for the detection of retinal whitening which is a unique sign of MR that manifests due to retinal ischemia resulting from CM. We propose to detect the whitening region in retinal color images based on multiple color and textural features. First, we preprocess the image using color and textural features of the CMYK and CIE-XYZ color spaces to minimize camera reflex. Next, we utilize color features of the HSL, CMYK, and CIE-XYZ channels, along with the structural features of difference of Gaussians. A watershed segmentation algorithm is used to assign each image region a probability of being inside the whitening, based on extracted features. The algorithm was applied to a dataset of 54 images (40 with whitening and 14 controls) that resulted in an image-based (binary) classification with an AUC of 0.80. This provides 88% sensitivity at a specificity of 65%. For a clinical application that requires a high specificity setting, the algorithm can be tuned to a specificity of 89% at a sensitivity of 82%. This is the first published method for retinal whitening detection and combining it with the detection methods for vessel discoloration and hemorrhages can further improve the detection accuracy for malarial retinopathy.

  17. The Automated Planet Finder telescope's automation and first three years of planet detections

    NASA Astrophysics Data System (ADS)

    Burt, Jennifer

    2016-08-01

    The Automated Planet Finder (APF) is a 2.4m, f/15 telescope located at the UCO's Lick Observatory, atop Mt. Hamilton. The telescope has been specifically optimized to detect and characterize extrasolar planets via high precision, radial velocity (RV) observations using the high-resolution Levy echelle spectrograph. The telescope has demonstrated world-class internal precision levels of 1 m/s when observing bright, RV standard stars. Observing time on the telescope is divided such that ˜80% is spent on exoplanet related research and the remaining ˜20% is made available to the University of California consortium for other science goals. The telescope achieved first light in 2013, and this work describes the APF's early science achievements and its transition from a traditional observing approach to a fully autonomous facility. First we provide a characteristic look at the APF telescope and the Levy spectrograph, focusing on the stability of the instrument and its performance on RV standard stars. Second, we describe the design and implementation of the dynamic scheduling software which has been running our team's nightly observations on the APF for the past year. Third, we discuss the detection of a Neptune-mass planet orbiting the nearby, low-mass star GL687 by the APF in collaboration with the HIRES instrument on Keck I. Fourth, we summarize the APF's detection of two multi-planet systems: the four planet system orbiting HD 141399 and the 6 planet system orbiting HD 219134. Fifth, we expand our science focus to assess the impact that the APF - with the addition of a new, time-varying prioritization scheme to the telescope's dynamic scheduling software - can have on filling out the exoplanet Mass-Radius diagram when pursuing RV follow-up of transiting planets detected by NASA's TESS satellite. Finally, we outline some likely next science goals for the telescope.

  18. Automated analysis for detecting beams in laser wakefield simulations

    SciTech Connect

    Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R.; Geddes, Cameron G.R.; Cormier-Michel, Estelle; Hamann, Bernd; Messmer, Peter; Hagen, Hans

    2008-07-03

    Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets.

  19. Automated anomaly detection for Orbiter High Temperature Reusable Surface Insulation

    NASA Astrophysics Data System (ADS)

    Cooper, Eric G.; Jones, Sharon M.; Goode, Plesent W.; Vazquez, Sixto L.

    1992-11-01

    The description, analysis, and experimental results of a method for identifying possible defects on High Temperature Reusable Surface Insulation (HRSI) of the Orbiter Thermal Protection System (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.

  20. Automated Detection of Firearms and Knives in a CCTV Image.

    PubMed

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

  1. Automated detection of microaneurysms using robust blob descriptors

    NASA Astrophysics Data System (ADS)

    Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

    2013-03-01

    Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

  2. Automated anomaly detection for Orbiter High Temperature Reusable Surface Insulation

    NASA Technical Reports Server (NTRS)

    Cooper, Eric G.; Jones, Sharon M.; Goode, Plesent W.; Vazquez, Sixto L.

    1992-01-01

    The description, analysis, and experimental results of a method for identifying possible defects on High Temperature Reusable Surface Insulation (HRSI) of the Orbiter Thermal Protection System (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.

  3. Automated Detection of Firearms and Knives in a CCTV Image

    PubMed Central

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

  4. Geostationary Fire Detection with the Wildfire Automated Biomass Burning Algorithm

    NASA Astrophysics Data System (ADS)

    Hoffman, J.; Schmidt, C. C.; Brunner, J. C.; Prins, E. M.

    2010-12-01

    The Wild Fire Automated Biomass Burning Algorithm (WF_ABBA), developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS), has a long legacy of operational wildfire detection and characterization. In recent years, applications of geostationary fire detection and characterization data have been expanding. Fires are detected with a contextual algorithm and when the fires meet certain conditions the instantaneous fire size, temperature, and radiative power are calculated and provided in user products. The WF_ABBA has been applied to data from Geostationary Operational Environmental Satellite (GOES)-8 through 15, Meteosat-8/-9, and Multifunction Transport Satellite (MTSAT)-1R/-2. WF_ABBA is also being developed for the upcoming platforms like GOES-R Advanced Baseline Imager (ABI) and other geostationary satellites. Development of the WF_ABBA for GOES-R ABI has focused on adapting the legacy algorithm to the new satellite system, enhancing its capabilities to take advantage of the improvements available from ABI, and addressing user needs. By its nature as a subpixel feature, observation of fire is extraordinarily sensitive to the characteristics of the sensor and this has been a fundamental part of the GOES-R WF_ABBA development work.

  5. Change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Thonnessen, U.; Hofele, G.; Middelmann, W.

    2005-05-01

    Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed; nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After pre-processing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.

  6. Automated detection and recognition of wildlife using thermal cameras.

    PubMed

    Christiansen, Peter; Steen, Kim Arild; Jørgensen, Rasmus Nyholm; Karstoft, Henrik

    2014-07-30

    In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3-10 m and an accuracy of 75.2% for an altitude range of 10-20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3-10 of meters and 77.7% in an altitude range of 10-20 m.

  7. Automated Detection and Recognition of Wildlife Using Thermal Cameras

    PubMed Central

    Christiansen, Peter; Steen, Kim Arild; Jørgensen, Rasmus Nyholm; Karstoft, Henrik

    2014-01-01

    In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3–10 m and an accuracy of 75.2% for an altitude range of 10–20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3–10 of meters and 77.7% in an altitude range of 10–20 m PMID:25196105

  8. Automated Terrestrial EMI Emitter Detection, Classification, and Localization

    NASA Astrophysics Data System (ADS)

    Stottler, R.; Bowman, C.; Bhopale, A.

    2016-09-01

    Clear operating spectrum at ground station antenna locations is critically important for communicating with, commanding, controlling, and maintaining the health of satellites. Electro Magnetic Interference (EMI) can interfere with these communications so tracking down the source of EMI is extremely important to prevent it from occurring in the future. The Terrestrial RFI-locating Automation with CasE based Reasoning (TRACER) system is designed to automate terrestrial EMI emitter localization and identification, providing improved space situational awareness, realizing significant manpower savings, dramatically shortening EMI response time, providing capabilities for the system to evolve without programmer involvement, and offering increased support for adversarial scenarios (e.g. jamming). TRACER has been prototyped and tested with real data (amplitudes versus frequency over time) for both satellite communication antennas and sweeping Direction Finding (DF) antennas located near them. TRACER monitors the satellite communication and DF antenna signals to detect and classify EMI using neural network technology trained on past cases of both normal communications and EMI events. Based on details of the signal (its classification, its direction and strength, etc.) one or more cases of EMI investigation methodologies are retrieved, represented as graphical behavior transition networks (BTNs), which very naturally represent the flowchart-like process often followed by experts in time pressured situations, are intuitive to SMEs, and easily edited by them. The appropriate actions, as determined by the BTN are executed and the resulting data processed by Bayesian Networks to update the probabilities of the various possible platforms and source types of the EMI. Bearing sweep of the EMI is used to determine if the EMI's platform is aerial, a ground vehicle or ship, or stationary. If moving, the Friis transmission equation is used to plot the emitter's location and compare it

  9. Automated detection and characterization of harmonic tremor in continuous seismic data

    NASA Astrophysics Data System (ADS)

    Roman, Diana C.

    2017-06-01

    Harmonic tremor is a common feature of volcanic, hydrothermal, and ice sheet seismicity and is thus an important proxy for monitoring changes in these systems. However, no automated methods for detecting harmonic tremor currently exist. Because harmonic tremor shares characteristics with speech and music, digital signal processing techniques for analyzing these signals can be adapted. I develop a novel pitch-detection-based algorithm to automatically identify occurrences of harmonic tremor and characterize their frequency content. The algorithm is applied to seismic data from Popocatepetl Volcano, Mexico, and benchmarked against a monthlong manually detected catalog of harmonic tremor events. During a period of heightened eruptive activity from December 2014 to May 2015, the algorithm detects 1465 min of harmonic tremor, which generally precede periods of heightened explosive activity. These results demonstrate the algorithm's ability to accurately characterize harmonic tremor while highlighting the need for additional work to understand its causes and implications at restless volcanoes.

  10. Detection of Significant Bacteriuria by Automated Urinalysis Using Flow Cytometry

    PubMed Central

    Okada, Hiroshi; Sakai, Yutaka; Miyazaki, Shigenori; Arakawa, Soichi; Hamaguchi, Yukio; Kamidono, Sadao

    2000-01-01

    A new flow cytometry-based automated urine analyzer, the UF-50, was evaluated for its ability to screen urine samples for significant bacteriuria. One hundred eighty-six urine specimens from patients attending an outpatient clinic of a university-based hospital were examined. The results obtained with the UF-50 were compared with those obtained by conventional quantitative urine culture. The UF-50 detected significant bacteriuria with a sensitivity of 83.1%, a specificity of 76.4%, a positive predictive value of 62.0%, a negative predictive value of 90.7%, and an accuracy of 78.5%. These results are comparable to those obtained by previously reported screening procedures. Besides detecting significant bacteriuria, the UF-50 can also perform routine urinalysis, including measurement of concentrations of red blood cells, white blood cells, epithelial cells, and casts, within 70 s. This capability renders this new flow cytometry-based urine analyzer superior to previously reported rapid screening methods. PMID:10921941

  11. Automated detection of malaria with haematology analyzer Sysmex XE-2100.

    PubMed

    Mohapatra, Sarita; Samantaray, Jyotish C; Arulselvi, S; Panda, Jitender; Munot, Khushboo; Saxena, Renu

    2011-01-01

    Diagnosis of malaria is usually made by microscopy [Giemsa, Acridine Orange (AO), and Quantitative Buffy Coat (QBC) assay], which requires expertise. Currently, automated haematology analyzers are being used for complete blood count (CBC), in all acute febrile and non-febrile illnesses which simultaneously detects malaria. The normal scattergram by the analyzer (Sysmex 2100) comprises of five parameters i.e. lymphocytes (pink), monocytes (green), neutrophils (blue), eosinophils (red) with a space between the neutrophil and eosinophil populations. We carried out a prospective study to compare the efficacy of Sysmex XE-2100 (Sysmex Corporation, Kobe) for detection of malaria in comparison to other conventional techniques. 430 cases were analyzed for malaria by microscopy (QBC, AO, Giemsa), ICT (Immunochromatography) and flowcytometric analyzer (Sysmex XE-2100). The abnormal scattergrams were observed as double neutrophil, double eosinophil, grey zone, extended neutrophil zone with a decrease space between eosinophil and neutrophil, and a combination of above patterns. Out of 70 positive cases [49/70 (70%) P. vivax, 18/70 (25.7%) P. falciparum, and 3/70 (4.2%) both P. vivax and P. falciparum], 52 showed abnormal scattergrams by the analyzer. The sensitivity and specificity of hematology analyzer found to be 74.2% and 88%, respectively. Flowcytometric analyzer is a rapid, high throughput device which needs less expertization for the diagnosis of malaria. Hence, it can be used in the diagnostic laboratories as an early modality for diagnosis of malaria in suspected as well as clinically in apparent cases.

  12. Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

    NASA Technical Reports Server (NTRS)

    Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

    2015-01-01

    An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

  13. Detection of Operator Performance Breakdown as an Automation Triggering Mechanism

    NASA Technical Reports Server (NTRS)

    Yoo, Hyo-Sang; Lee, Paul U.; Landry, Steven J.

    2015-01-01

    Performance breakdown (PB) has been anecdotally described as a state where the human operator "loses control of context" and "cannot maintain required task performance." Preventing such a decline in performance is critical to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there is no validated objective way of detecting such a state or the transition to that state. The purpose of this work is: 1) to empirically demonstrate a PB state, and 2) to develop an objective way of detecting such a state. This paper defines PB and proposes an objective method for its detection. A human-in-the-loop study was conducted: 1) to demonstrate PB by increasing workload until the subject reported being in a state of PB, and 2) to identify possible parameters of a detection method for objectively identifying the subjectively-reported PB point, and 3) to determine if the parameters are idiosyncratic to an individual/context or are more generally applicable. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary) for 18 minutes. The difficulty of the primary task was manipulated over time to induce PB while the difficulty of the secondary tasks remained static. The participants' task performance data was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identifies the threshold parameters that best matches the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters is consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable-only 12 out of 15 participants declared PB. The PB detection method based on

  14. Automated detection of Martian water ice clouds: the Valles Marineris

    NASA Astrophysics Data System (ADS)

    Ogohara, Kazunori; Munetomo, Takafumi; Hatanaka, Yuji; Okumura, Susumu

    2016-10-01

    We need to extract water ice clouds from the large number of Mars images in order to reveal spatial and temporal variations of water ice cloud occurrence and to meteorologically understand climatology of water ice clouds. However, visible images observed by Mars orbiters for several years are too many to visually inspect each of them even though the inspection was limited to one region. Therefore, an automated detection algorithm of Martian water ice clouds is necessary for collecting ice cloud images efficiently. In addition, it may visualize new aspects of spatial and temporal variations of water ice clouds that we have never been aware. We present a method for automatically evaluating the presence of Martian water ice clouds using difference images and cross-correlation distributions calculated from blue band images of the Valles Marineris obtained by the Mars Orbiter Camera onboard the Mars Global Surveyor (MGS/MOC). We derived one subtracted image and one cross-correlation distribution from two reflectance images. The difference between the maximum and the average, variance, kurtosis, and skewness of the subtracted image were calculated. Those of the cross-correlation distribution were also calculated. These eight statistics were used as feature vectors for training Support Vector Machine, and its generalization ability was tested using 10-fold cross-validation. F-measure and accuracy tended to be approximately 0.8 if the maximum in the normalized reflectance and the difference of the maximum and the average in the cross-correlation were chosen as features. In the process of the development of the detection algorithm, we found many cases where the Valles Marineris became clearly brighter than adjacent areas in the blue band. It is at present unclear whether the bright Valles Marineris means the occurrence of water ice clouds inside the Valles Marineris or not. Therefore, subtracted images showing the bright Valles Marineris were excluded from the detection of

  15. Automated motion detection from space in sea surveilliance

    NASA Astrophysics Data System (ADS)

    Charalambous, Elisavet; Takaku, Junichi; Michalis, Pantelis; Dowman, Ian; Charalampopoulou, Vasiliki

    2015-06-01

    The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by the Advanced Land-Observing Satellite (ALOS) was designed to generate worldwide topographic data with its high-resolution and stereoscopic observation. PRISM performs along-track (AT) triplet stereo observations using independent forward (FWD), nadir (NDR), and backward (BWD) panchromatic optical line sensors of 2.5m ground resolution in swaths 35 km wide. The FWD and BWD sensors are arranged at an inclination of ±23.8° from NDR. In this paper, PRISM images are used under a new perspective, in security domain for sea surveillance, based on the sequence of the triplet which is acquired in a time interval of 90 sec (45 sec between images). An automated motion detection algorithm is developed allowing the combination of encompassed information at each instant and therefore the identification of patterns and trajectories of moving objects on sea; including the extraction of geometric characteristics along with the speed of movement and direction. The developed methodology combines well established image segmentation and morphological operation techniques for the detection of objects. Each object in the scene is represented by dimensionless measure properties and maintained in a database to allow the generation of trajectories as these arise over time, while the location of moving objects is updated based on the result of neighbourhood calculations. Most importantly, the developed methodology can be deployed in any air borne (optionally piloted) sensor system with along the track stereo capability enabling the provision of near real time automatic detection of targets; a task that cannot be achieved with satellite imagery due to the very intermittent coverage.

  16. Automated optic disk boundary detection by modified active contour model.

    PubMed

    Xu, Juan; Chutatape, Opas; Chew, Paul

    2007-03-01

    This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).

  17. Automated single particle detection and tracking for large microscopy datasets.

    PubMed

    Wilson, Rhodri S; Yang, Lei; Dun, Alison; Smyth, Annya M; Duncan, Rory R; Rickman, Colin; Lu, Weiping

    2016-05-01

    Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.

  18. Automated single particle detection and tracking for large microscopy datasets

    PubMed Central

    Wilson, Rhodri S.; Yang, Lei; Dun, Alison; Smyth, Annya M.; Duncan, Rory R.; Rickman, Colin

    2016-01-01

    Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates. PMID:27293801

  19. Fusion of geometric and thermographic data for automated defect detection

    NASA Astrophysics Data System (ADS)

    Oswald-Tranta, Beata; O'Leary, Paul

    2012-04-01

    Many workpieces produced in large numbers with a large variety of sizes and geometries, e.g. castings and forgings, have to be 100% inspected. In addition to geometric tolerances, material defects, e.g. surface cracks, also have to be detected. We present a fully automated nondestructive testing technique for both types of defects. The workpiece is subject to continuous motion, and during this motion two measurements are performed. In the first step, after applying a short inductive heating, a thermographic measurement is carried out. An infrared camera records the surface temperature of the workpiece enabling the localization of material defects and surface cracks. In the second step, a light sectioning measurement is performed to measure the three-dimensional geometry of the piece. With the help of feature-based registration the data from the two different sources are fused and evaluated together. The advantage of this technique is that a more reliable decision can be made about the nature of the failures and their possible causes. The same registration technique also can be used for the comparison of different pieces and therefore to localize different failure types, via comparison with a ``golden,'' defect-free piece. The registration technique can be applied to any part that has unique geometric features, around which moments can be computed. Consequently, the inspection technique can be applied to many different parts. The efficacy of the method is demonstrated with measurements on three parts having different geometries.

  20. Automated three-dimensional detection and counting of neuron somata.

    PubMed

    Oberlaender, Marcel; Dercksen, Vincent J; Egger, Robert; Gensel, Maria; Sakmann, Bert; Hege, Hans-Christian

    2009-05-30

    We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4%. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.

  1. An automated approach to detecting signals in electroantennogram data

    Treesearch

    D.H. Slone; B.T. Sullivan

    2007-01-01

    Coupled gas chromatography/electroantennographic detection (GC-EAD) is a widely used method for identifying insect olfactory stimulants present in mixtures of volatiles, and it can greatly accelerate the identification of insect semiochemicals. In GC-EAD, voltage changes across an insect's antenna are measured while the antenna is exposed to compounds eluting fi-...

  2. Fully automated nipple detection in digital breast tomosynthesis.

    PubMed

    Chae, Seung-Hoon; Jeong, Ji-Wook; Choi, Jang-Hwan; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook; Lee, Sooyeul

    2017-05-01

    We propose a nipple detection algorithm for use with digital breast tomosynthesis (DBT) images. DBT images have been developed to overcome the weaknesses of 2D mammograms for denser breasts by providing 3D breast images. The nipple location acts as an invaluable landmark in DBT images for aligning the right and left breasts and describing the relative location of any existing lesions. Nipples may be visible or invisible in a breast image, and therefore a nipple detection method must be able to detect the nipples for both cases. The detection method for visible nipples based on their shape is simple and highly efficient. However, it is difficult to detect invisible nipples because they do not have a prominent shape. Fibroglandular tissue in a breast is anatomically connected with the nipple. Thus, the nipple location can be detected by analyzing the location of such tissue. In this paper, we propose a method for detecting the location of both visible and invisible nipples using fibroglandular tissue and changes in the breast area. Our algorithm was applied to 138 DBT images, and its nipple detection accuracy was evaluated based on the mean Euclidean distance. The results indicate that our proposed method achieves a mean Euclidean distance of 3.10±2.58mm. The nipple location can be a very important piece of information in the process of a DBT image registration. This paper presents a method for the automatic nipple detection in a DBT image. The extracted nipple location plays an essential role in classifying any existing lesions and comparing both the right and left breasts. Thus, the proposed method can help with computer-aided detection for a more efficient DBT image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Stage Evolution of Office Automation Technological Change and Organizational Learning.

    ERIC Educational Resources Information Center

    Sumner, Mary

    1985-01-01

    A study was conducted to identify stage characteristics in terms of technology, applications, the role and responsibilities of the office automation organization, and planning and control strategies; and to describe the respective roles of data processing professionals, office automation analysts, and users in office automation systems development…

  4. Automated Ground Penetrating Radar hyperbola detection in complex environment

    NASA Astrophysics Data System (ADS)

    Mertens, Laurence; Lambot, Sébastien

    2015-04-01

    Ground Penetrating Radar (GPR) systems are commonly used in many applications to detect, amongst others, buried targets (various types of pipes, landmines, tree roots ...), which, in a cross-section, present theoretically a particular hyperbolic-shaped signature resulting from the antenna radiation pattern. Considering the large quantity of information we can acquire during a field campaign, a manual detection of these hyperbolas is barely possible, therefore we have a real need to have at our disposal a quick and automated detection of these hyperbolas. However, this task may reveal itself laborious in real field data because these hyperbolas are often ill-shaped due to the heterogeneity of the medium and to instrumentation clutter. We propose a new detection algorithm for well- and ill-shaped GPR reflection hyperbolas especially developed for complex field data. This algorithm is based on human recognition pattern to emulate human expertise to identify the hyperbolas apexes. The main principle relies in a fitting process of the GPR image edge dots detected with Canny filter to analytical hyperbolas, considering the object as a punctual disturbance with a physical constraint of the parameters. A long phase of observation of a large number of ill-shaped hyperbolas in various complex media led to the definition of smart criteria characterizing the hyperbolic shape and to the choice of accepted value ranges acceptable for an edge dot to correspond to the apex of a specific hyperbola. These values were defined to fit the ambiguity zone for the human brain and present the particularity of being functional in most heterogeneous media. Furthermore, the irregularity is particularly taken into account by defining a buffer zone around the theoretical hyperbola in which the edge dots need to be encountered to belong to this specific hyperbola. First, the method was tested in laboratory conditions over tree roots and over PVC pipes with both time- and frequency-domain radars

  5. Rapid toxicity detection in water quality control utilizing automated multispecies biomonitoring for permanent space stations

    NASA Technical Reports Server (NTRS)

    Morgan, E. L.; Young, R. C.; Smith, M. D.; Eagleson, K. W.

    1986-01-01

    The objective of this study was to evaluate proposed design characteristics and applications of automated biomonitoring devices for real-time toxicity detection in water quality control on-board permanent space stations. Simulated tests in downlinking transmissions of automated biomonitoring data to Earth-receiving stations were simulated using satellite data transmissions from remote Earth-based stations.

  6. Fight deck human-automation mode confusion detection using a generalized fuzzy hidden Markov model

    NASA Astrophysics Data System (ADS)

    Lyu, Hao Lyu

    Due to the need for aviation safety, convenience, and efficiency, the autopilot has been introduced into the cockpit. The fast development of the autopilot has brought great benefits to the aviation industry. On the human side, the flight deck has been designed to be a complex, tightly-coupled, and spatially distributed system. The problem of dysfunctional interaction between the pilot and the automation (human-automation interaction issue) has become more and more visible. Thus, detection of a mismatch between the pilot's expectation and automation's behavior in a timely manner is required. In order to solve this challenging problem, separate modeling of the pilot and the automation is necessary. In this thesis, an intent-based framework is introduced to detect the human-automation interaction issue. Under this framework, the pilot's expectation of the aircraft is modeled by pilot intent while the behavior of the automation system is modeled by automation intent. The mode confusion is detected when the automation intent differs from the pilot intent. The pilot intent is inferred by comparing the target value set by the pilot with the aircraft's current state. Meanwhile, the automation intent is inferred through the Generalized Fuzzy Hidden Markov Model (GFHMM), which is an extension of the classical Hidden Markov Model. The stochastic characteristic of the ``hidden'' intents is considered by introducing fuzzy logic. Different from the previous approaches of inferring automation intent, GFHMM does not require a probabilistic model for certain flight modes as prior knowledge. The parameters of GFHMM (initial fuzzy density of the intent, fuzzy transmission density, and fuzzy emission density) are determined through the flight data by using a machine learning technique, the Fuzzy C-Means clustering algorithm (FCM). Lastly, both the pilot's and automation's intent inference algorithms and the mode confusion detection method are validated through flight data.

  7. Quantitative Automated Image Analysis System with Automated Debris Filtering for the Detection of Breast Carcinoma Cells

    PubMed Central

    Martin, David T.; Sandoval, Sergio; Ta, Casey N.; Ruidiaz, Manuel E.; Cortes-Mateos, Maria Jose; Messmer, Davorka; Kummel, Andrew C.; Blair, Sarah L.; Wang-Rodriguez, Jessica

    2011-01-01

    Objective To develop an intraoperative method for margin status evaluation during breast conservation therapy (BCT) using an automated analysis of imprint cytology specimens. Study Design Imprint cytology samples were prospectively taken from 47 patients undergoing either BCT or breast reduction surgery. Touch preparations from BCT patients were taken on cut sections through the tumor to generate positive margin controls. For breast reduction patients, slide imprints were taken at cuts through the center of excised tissue. Analysis results from the presented technique were compared against standard pathologic diagnosis. Slides were stained with cytokeratin and Hoechst, imaged with an automated fluorescent microscope, and analyzed with a fast algorithm to automate discrimination between epithelial cells and noncellular debris. Results The accuracy of the automated analysis was 95% for identifying invasive cancers compared against final pathologic diagnosis. The overall sensitivity was 87% while specificity was 100% (no false positives). This is comparable to the best reported results from manual examination of intraoperative imprint cytology slides while reducing the need for direct input from a cytopathologist. Conclusion This work demonstrates a proof of concept for developing a highly accurate and automated system for the intraoperative evaluation of margin status to guide surgical decisions and lower positive margin rates. PMID:21525740

  8. Demonstration of an Automated Oil Spill Detection System

    DTIC Science & Technology

    2003-04-01

    Spills often occur at unanticipated times or places in which no one is present to see and report the event. The Spill Sentry automated oil spill monitoring...to validate the newly developed automated oil spill sensor technology under real-world conditions and to promote rapid transition to DoD users by...under controlled conditions and to verify performance parameters, wave-tank testing was conducted at the Ohmsett National Oil Spill Response Test

  9. Algorithm for Automated Detection of Edges of Clouds

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.

    2006-01-01

    An algorithm processes cloud-physics data gathered in situ by an aircraft, along with reflectivity data gathered by ground-based radar, to determine whether the aircraft is inside or outside a cloud at a given time. A cloud edge is deemed to be detected when the in/out state changes, subject to a hysteresis constraint. Such determinations are important in continuing research on relationships among lightning, electric charges in clouds, and decay of electric fields with distance from cloud edges.

  10. Automated object detection and tracking with a flash LiDAR system

    NASA Astrophysics Data System (ADS)

    Hammer, Marcus; Hebel, Marcus; Arens, Michael

    2016-10-01

    The detection of objects, or persons, is a common task in the fields of environment surveillance, object observation or danger defense. There are several approaches for automated detection with conventional imaging sensors as well as with LiDAR sensors, but for the latter the real-time detection is hampered by the scanning character and therefore by the data distortion of most LiDAR systems. The paper presents a solution for real-time data acquisition of a flash LiDAR sensor with synchronous raw data analysis, point cloud calculation, object detection, calculation of the next best view and steering of the pan-tilt head of the sensor. As a result the attention is always focused on the object, independent of the behavior of the object. Even for highly volatile and rapid changes in the direction of motion the object is kept in the field of view. The experimental setup used in this paper is realized with an elementary person detection algorithm in medium distances (20 m to 60 m) to show the efficiency of the system for objects with a high angular speed. It is easy to replace the detection part by any other object detection algorithm and thus it is easy to track nearly any object, for example a car or a boat or an UAV in various distances.

  11. Detecting ecological change on coral reefs

    NASA Astrophysics Data System (ADS)

    Dustan, P.

    2011-12-01

    Remote sensing offers the potential to observe the response of coral reef ecosystems to environmental perturbations on a geographical scale not previously accessible. However, coral reef environments are optically, spatially, and temporally complex habitats which all present significant challenges for extracting meaningful information. Virtually every member of the reef community possesses some degree of photosynthetic capability. The community thus generates a matrix of fine scale features with bio-optical signatures that blend as the scale of observation increases. Furthermore, to have any validity, the remotely sensed signal must be "calibrated" to the bio-optics of the reef, a difficult and resource intensive process due to a convergence of photosynthetic light harvesting by green, red, and brown algal pigment systems. To make matters more complex, reefs are overlain by a seawater skin with its own set of hydrological optical challenges. Rather than concentrating on classification, my research has attempted to track change by following the variation in geo-referenced pixel brightness over time with a technique termed temporal texture. Environmental periodicities impart a phenology to the variation in brightness and departures from the norm are easily detected as statistical outliers. This opens the door to using current orbiting technology to efficiently examine large areas of sea for change. If hot spots are detected, higher resolution sensors and field studies can be focused as resources permit. While this technique does not identify the type of change, it is sensitive, simple to compute, easy to automate and grounded in ecological niche theory

  12. A Framework for Automated Marmoset Vocalization Detection And Classification

    DTIC Science & Technology

    2016-09-08

    killer whales [13], and marmosets [8]. Recent work on semi-automated marmoset vocalization classification [10] is primarily based on the use of...Elephant ( Loxodonta africana ) Vocalizations,” vol. 117, no. 2, pp. 956–963, 2005. [13] J. C. Brown, “Automatic classification of killer whale

  13. Assessment of Automated Disease Detection in Diabetic Retinopathy Screening Using Two-Field Photography

    PubMed Central

    Goatman, Keith; Charnley, Amanda; Webster, Laura; Nussey, Stephen

    2011-01-01

    Aim To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography. Methods Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone. Results Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%). Conclusion Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service. PMID:22174741

  14. Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

    PubMed

    Despins, Laurel A

    2016-09-13

    Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

  15. Automated Detection of 50-kHz Ultrasonic Vocalizations Using Template Matching in XBAT

    PubMed Central

    Barker, David J.; Herrera, Christopher; West, Mark O.

    2014-01-01

    Background Ultrasonic vocalizations (USVs) have been utilized to infer animals' affective states in multiple research paradigms including animal models of drug abuse, depression, fear or anxiety disorders, Parkinson's disease, and in studying neural substrates of reward processing. Currently, the analysis of USV data is performed manually, and thus time consuming. New Method The goal of the present study was to develop a method for automated USV recognition using a ‘template detection’ procedure for vocalizations in the 50-kHz range (35-80 kHz). The detector is designed to run within XBAT, a MATLAB graphical user interface and extensible bioacoustics tool developed at Cornell University. Results Results show that this method is capable of detecting >90% of emitted USVs and that time spent collecting data by experimenters is greatly reduced. Comparison with Existing Methods Currently, no viable and publicly available methods exist for the automated detection of USVs. The present method, in combination with the XBAT environment is ideal for the USV community as it allows others to 1) detect USVs within a user-friendly environment, 2) make improvements to the detector and disseminate and 3) develop new tools for analysis within the MATLAB environment. Conclusions The present detector provides an open-source, accurate method for the detection of 50-kHz USVs. Ongoing research will extend the current method for use in the 22-kHz frequency range of ultrasonic vocalizations. Moreover, collaborative efforts among USV researchers might enhance the capabilities of the current detector via changes to the templates and the development of new programs for analysis. PMID:25128724

  16. A Simple Method for Automated Equilibration Detection in Molecular Simulations.

    PubMed

    Chodera, John D

    2016-04-12

    Molecular simulations intended to compute equilibrium properties are often initiated from configurations that are highly atypical of equilibrium samples, a practice which can generate a distinct initial transient in mechanical observables computed from the simulation trajectory. Traditional practice in simulation data analysis recommends this initial portion be discarded to equilibration, but no simple, general, and automated procedure for this process exists. Here, we suggest a conceptually simple automated procedure that does not make strict assumptions about the distribution of the observable of interest in which the equilibration time is chosen to maximize the number of effectively uncorrelated samples in the production timespan used to compute equilibrium averages. We present a simple Python reference implementation of this procedure and demonstrate its utility on typical molecular simulation data.

  17. Optimizing automated gas turbine fault detection using statistical pattern recognition

    NASA Astrophysics Data System (ADS)

    Loukis, E.; Mathioudakis, K.; Papailiou, K.

    1992-06-01

    A method enabling the automated diagnosis of Gas Turbine Compressor blade faults, based on the principles of statistical pattern recognition is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurements data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurements data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an Industrial Gas Turbine while extension to other aspects of Fault Diagnosis is discussed.

  18. Neurodegenerative changes in Alzheimer's disease: a comparative study of manual, semi-automated, and fully automated assessment using MRI

    NASA Astrophysics Data System (ADS)

    Fritzsche, Klaus H.; Giesel, Frederik L.; Heimann, Tobias; Thomann, Philipp A.; Hahn, Horst K.; Pantel, Johannes; Schröder, Johannes; Essig, Marco; Meinzer, Hans-Peter

    2008-03-01

    Objective quantification of disease specific neurodegenerative changes can facilitate diagnosis and therapeutic monitoring in several neuropsychiatric disorders. Reproducibility and easy-to-perform assessment are essential to ensure applicability in clinical environments. Aim of this comparative study is the evaluation of a fully automated approach that assesses atrophic changes in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). 21 healthy volunteers (mean age 66.2), 21 patients with MCI (66.6), and 10 patients with AD (65.1) were enrolled. Subjects underwent extensive neuropsychological testing and MRI was conducted on a 1.5 Tesla clinical scanner. Atrophic changes were measured automatically by a series of image processing steps including state of the art brain mapping techniques. Results were compared with two reference approaches: a manual segmentation of the hippocampal formation and a semi-automated estimation of temporal horn volume, which is based upon interactive selection of two to six landmarks in the ventricular system. All approaches separated controls and AD patients significantly (10 -5 < p < 10 -4) and showed a slight but not significant increase of neurodegeneration for subjects with MCI compared to volunteers. The automated approach correlated significantly with the manual (r = -0.65, p < 10 -6) and semi automated (r = -0.83, p < 10 -13) measurements. It proved high accuracy and at the same time maximized observer independency, time reduction and thus usefulness for clinical routine.

  19. Operations management system advanced automation: Fault detection isolation and recovery prototyping

    NASA Technical Reports Server (NTRS)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  20. Operations management system advanced automation: Fault detection isolation and recovery prototyping

    NASA Technical Reports Server (NTRS)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  1. Land-cover change detection

    USGS Publications Warehouse

    Chen, Xuexia; Giri, Chandra; Vogelmann, James

    2012-01-01

    Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously.  The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001).  Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect.  Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.

  2. SAR Object Change Detection Study.

    DTIC Science & Technology

    1980-03-01

    based techniques when applied to Synthetic Aperature Radar (SAR imagery. DOUGLA 3. PRASKA, 2LT, USAF Project Engineer viii Section 1 INTRODUCTION AND...to assess the applicability of three region-based change-detection methods to synthetic aperture radar imagery. I/ Ac .0ion For K:CTAB [ ft i . i...Section 2, the algorithms developed were applied to synthetic -aperture radar image data furnished by RADC. Some preprocessing of all images was required

  3. Colorimetric sensor for bad odor detection using automated color correction

    NASA Astrophysics Data System (ADS)

    Schmitt, K.; Tarantik, K.; Pannek, C.; Benito-Altamirano, I.; Casals, O.; Fàbrega, C.; Romano-Rodríguez, A.; Wöllenstein, J.; Prades, J. D.

    2017-06-01

    Colorimetric sensors based on color-changing dyes offer a convenient approach for the quantitative measurement of gases. An integrated, mobile colorimetric sensor can be particularly helpful for occasional gas measurements, such as informal air quality checks for bad odors. In these situations, the main requirement is high availability, easy usage, and high specificity towards one single chemical compound, combined with cost-efficient production. In this contribution, we show how a well stablished colorimetric method can be adapted for easy operation and readout, making it suitable for the untrained end user. As an example, we present the use of pH indicators for the selective and reversible detection of NH3 in air (one relevant gas contributing to bad odors) using gas-sensitive layers dip coated on glass substrates. Our results show that the method can be adapted to detect NH3 concentrations lower than 1 ppm, with measure-to-result times in the range of a few minutes. We demonstrate that the color measurements can be carried out with the optical signals of RGB sensors, without losing quantitative performance.

  4. A method for the automated detection phishing websites through both site characteristics and image analysis

    NASA Astrophysics Data System (ADS)

    White, Joshua S.; Matthews, Jeanna N.; Stacy, John L.

    2012-06-01

    Phishing website analysis is largely still a time-consuming manual process of discovering potential phishing sites, verifying if suspicious sites truly are malicious spoofs and if so, distributing their URLs to the appropriate blacklisting services. Attackers increasingly use sophisticated systems for bringing phishing sites up and down rapidly at new locations, making automated response essential. In this paper, we present a method for rapid, automated detection and analysis of phishing websites. Our method relies on near real-time gathering and analysis of URLs posted on social media sites. We fetch the pages pointed to by each URL and characterize each page with a set of easily computed values such as number of images and links. We also capture a screen-shot of the rendered page image, compute a hash of the image and use the Hamming distance between these image hashes as a form of visual comparison. We provide initial results demonstrate the feasibility of our techniques by comparing legitimate sites to known fraudulent versions from Phishtank.com, by actively introducing a series of minor changes to a phishing toolkit captured in a local honeypot and by performing some initial analysis on a set of over 2.8 million URLs posted to Twitter over a 4 days in August 2011. We discuss the issues encountered during our testing such as resolvability and legitimacy of URL's posted on Twitter, the data sets used, the characteristics of the phishing sites we discovered, and our plans for future work.

  5. On the Automated and Objective Detection of Emission Lines in Faint-Object Spectroscopy

    NASA Astrophysics Data System (ADS)

    Hong, Sungryong; Dey, Arjun; Prescott, Moire K. M.

    2014-11-01

    Modern spectroscopic surveys produce large spectroscopic databases, generally with sizes well beyond the scope of manual investigation. The need arises, therefore, for an automated line detection method with objective indicators for detection significance. In this paper, we present an automated and objective method for emission line detection in spectroscopic surveys and apply this technique to 1574 spectra, obtained with the Hectospec spectrograph on the MMT Observatory (MMTO), to detect Lyman alpha emitters near z ~ 2.7. The basic idea is to generate on-source (signal plus noise) and off-source (noise only) mock observations using Monte Carlo simulations, and calculate completeness and reliability values, (C, R), for each simulated signal. By comparing the detections from real data with the Monte Carlo results, we assign the completeness and reliability values to each real detection. From 1574 spectra, we obtain 881 raw detections and, by removing low reliability detections, we finalize 649 detections from an automated pipeline. Most of high completeness and reliability detections, (C, R) ~ (1.0, 1.0), are robust detections when visually inspected; the low C and R detections are also marginal on visual inspection. This method at detecting faint sources is dependent on the accuracy of the sky subtraction.

  6. An automated approach to detecting signals in electroantennogram data

    USGS Publications Warehouse

    Slone, D.H.; Sullivan, B.T.

    2007-01-01

    Coupled gas chromatography/electroantennographic detection (GC-EAD) is a widely used method for identifying insect olfactory stimulants present in mixtures of volatiles, and it can greatly accelerate the identification of insect semiochemicals. In GC-EAD, voltage changes across an insect's antenna are measured while the antenna is exposed to compounds eluting from a gas chromatograph. The antenna thus serves as a selective GC detector whose output can be compared to that of a "general" GC detector, commonly a flame ionization detector. Appropriate interpretation of GC-EAD results requires that olfaction-related voltage changes in the antenna be distinguishable from background noise that arises inevitably from antennal preparations and the GC-EAD-associated hardware. In this paper, we describe and compare mathematical algorithms for discriminating olfaction-generated signals in an EAD trace from background noise. The algorithms amplify signals by recognizing their characteristic shape and wavelength while suppressing unstructured noise. We have found these algorithms to be both powerful and highly discriminatory even when applied to noisy traces where the signals would be difficult to discriminate by eye. This new methodology removes operator bias as a factor in signal identification, can improve realized sensitivity of the EAD system, and reduces the number of runs required to confirm the identity of an olfactory stimulant. ?? 2007 Springer Science+Business Media, LLC.

  7. Automated detection of videotaped neonatal seizures based on motion tracking methods.

    PubMed

    Karayiannis, Nicolaos B; Xiong, Yaohua; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

    2006-12-01

    This study was carried out during the second phase of the project "Video Technologies for Neonatal Seizures" and aimed at the development of a seizure detection system by training neural networks, using quantitative motion information extracted by motion tracking methods from short video segments of infants monitored for seizures. The motion of the infants' body parts was quantified by temporal motion trajectory signals extracted from video recordings by robust motion trackers, based on block motion models. These motion trackers were developed to autonomously adjust to illumination and contrast changes that may occur during the video frame sequence. The computational tools and procedures developed for automated seizure detection were evaluated on short video segments selected and labeled by physicians from a set of 240 video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). This evaluation provided the basis for selecting the most effective strategy for training neural networks to detect neonatal seizures as well as the decision scheme used for interpreting the responses of the trained neural networks. The best neural networks exhibited sensitivity and specificity above 90%. The best among the motion trackers developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of the second phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion strength signals produced by motion segmentation methods.

  8. Strategies for Working with Library Staff Members in Embracing Change Caused by Library Automation.

    ERIC Educational Resources Information Center

    Shepherd, Murray

    This paper begins with a discussion of information management as it pertains to the four operations of automated library systems (i.e., acquisitions, cataloging, circulation, and reference). Library staff reactions to library automation change are summarized, including uncertainty, cynicism, and resignation or hope. Common pitfalls that interfere…

  9. Cell-Detection Technique for Automated Patch Clamping

    NASA Technical Reports Server (NTRS)

    McDowell, Mark; Gray, Elizabeth

    2008-01-01

    A unique and customizable machinevision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify good and bad cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost. The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are "good" or "bad" candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view. The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patchclamping microscope system with an attached CCD camera, a personal computer with an imagedata- processing board, and some experience in utilizing imagedata- processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image-data-processing board, then the image

  10. Improving Endpoint Detection to Support Automated Systematic Reviews

    PubMed Central

    Lucic, Ana; Blake, Catherine L.

    2016-01-01

    Authors of biomedical articles use comparison sentences to communicate the findings of a study, and to compare the results of the current study with earlier studies. The Claim Framework defines a comparison claim as a sentence that includes at least two entities that are being compared, and an endpoint that captures the way in which the entities are compared. Although automated methods have been developed to identify comparison sentences from the text, identifying the role that a specific noun plays (i.e. entity or endpoint) is much more difficult. Automated methods have been successful at identifying the second entity, but classification models were unable to clearly differentiate between the first entity and the endpoint. We show empirically that establishing if head noun is an amount or measure provides a statistically significant improvement that increases the endpoint precision from 0.42 to 0.56 on longer and from 0.51 to 0.58 on shorter sentences and recall from 0.64 to 0.71 on longer and from 0.69 to 0.74 on shorter sentences. The differences were not statistically significant for the second compared entity. PMID:28269949

  11. An Optimal Cell Detection Technique for Automated Patch Clamping

    NASA Technical Reports Server (NTRS)

    McDowell, Mark; Gray, Elizabeth

    2004-01-01

    While there are several hardware techniques for the automated patch clamping of cells that describe the equipment apparatus used for patch clamping, very few explain the science behind the actual technique of locating the ideal cell for a patch clamping procedure. We present a machine vision approach to patch clamping cell selection by developing an intelligent algorithm technique that gives the user the ability to determine the good cell to patch clamp in an image within one second. This technique will aid the user in determining the best candidates for patch clamping and will ultimately save time, increase efficiency and reduce cost. The ultimate goal is to combine intelligent processing with instrumentation and controls in order to produce a complete turnkey automated patch clamping system capable of accurately and reliably patch clamping cells with a minimum amount of human intervention. We present a unique technique that identifies good patch clamping cell candidates based on feature metrics of a cell's (x, y) position, major axis length, minor axis length, area, elongation, roundness, smoothness, angle of orientation, thinness and whether or not the cell is only particularly in the field of view. A patent is pending for this research.

  12. Detecting change as it occurs

    NASA Technical Reports Server (NTRS)

    Radok, Uwe; Brown, Timothy J.

    1992-01-01

    Traditionally climate changes have been detected from long series of observations and long after they have happened. Our 'inverse sequential' procedure, for detecting change as soon as it occurs, describes the existing or most recent data by their frequency distribution. Its parameter(s) are estimated both from the existing set of observations and from the same set augmented by 1,2,....j new observations. Individual-value probability products ('likelihoods') are used to form ratios which yield two probabilities for erroneously accepting the existing parameter(s) as valid for the augmented data set, and vice versa. A genuine parameter change is signaled when these probabilities (or a more stable compound probability) show a progressive decrease. New parameter values can then be estimated from the new observations alone using standard statistical techniques. The inverse sequential procedure will be illustrated for global annual mean temperatures (assumed normally distributed), and for annual numbers of North Atlantic hurricanes (assumed to represent Poisson distributions). The procedure was developed, but not yet tested, for linear or exponential trends, and for chi-squared means or degrees of freedom, a special measure of autocorrelation.

  13. Automated detection of diabetic retinopathy: barriers to translation into clinical practice

    PubMed Central

    Abramoff, Michael D; Niemeijer, Meindert; Russell, Stephen R

    2010-01-01

    Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18–65 years, from color images of the retina has enormous potential to increase the quality, cost–effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed. PMID:20214432

  14. Automated detection of diabetic retinopathy: barriers to translation into clinical practice.

    PubMed

    Abramoff, Michael D; Niemeijer, Meindert; Russell, Stephen R

    2010-03-01

    Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.

  15. Early detection of glaucoma using fully automated disparity analysis of the optic nerve head (ONH) from stereo fundus images

    NASA Astrophysics Data System (ADS)

    Sharma, Archie; Corona, Enrique; Mitra, Sunanda; Nutter, Brian S.

    2006-03-01

    Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. Traditional early detection techniques involve observing changes in the ONH through an ophthalmoscope. Stereo fundus photography is also routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the Heidelberg Retina Tomograph (HRT) has not been found to be sufficiently sensitive for early detection. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. A 3-D visualization of the disc and cup is achieved assuming camera geometry. High correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study involving 159 stereo fundus image pairs has already been demonstrated. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this paper, we present a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.

  16. Unsupervised EEG analysis for automated epileptic seizure detection

    NASA Astrophysics Data System (ADS)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  17. Managing laboratory automation in a changing pharmaceutical industry.

    PubMed

    Rutherford, M L

    1995-01-01

    The health care reform movement in the USA and increased requirements by regulatory agencies continue to have a major impact on the pharmaceutical industry and the laboratory. Laboratory management is expected to improve effciency by providing more analytical results at a lower cost, increasing customer service, reducing cycle time, while ensuring accurate results and more effective use of their staff. To achieve these expectations, many laboratories are using robotics and automated work stations. Establishing automated systems presents many challenges for laboratory management, including project and hardware selection, budget justification, implementation, validation, training, and support. To address these management challenges, the rationale for project selection and implementation, the obstacles encountered, project outcome, and learning points for several automated systems recently implemented in the Quality Control Laboratories at Eli Lilly are presented.

  18. Assessing bat detectability and occupancy with multiple automated echolocation detectors

    Treesearch

    Marcos P. Gorresen; Adam C. Miles; Christopher M. Todd; Frank J. Bonaccorso; Theodore J. Weller

    2008-01-01

    Occupancy analysis and its ability to account for differential detection probabilities is important for studies in which detecting echolocation calls is used as a measure of bat occurrence and activity. We examined the feasibility of remotely acquiring bat encounter histories to estimate detection probability and occupancy. We used echolocation detectors coupled o...

  19. Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

    PubMed

    Campillo-Gimenez, Boris; Garcelon, Nicolas; Jarno, Pascal; Chapplain, Jean Marc; Cuggia, Marc

    2013-01-01

    The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.

  20. Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

    PubMed

    van Zelst, J C M; Tan, T; Platel, B; de Jong, M; Steenbakkers, A; Mourits, M; Grivegnee, A; Borelli, C; Karssemeijer, N; Mann, R M

    2017-04-01

    To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. An Automated Classification Technique for Detecting Defects in Battery Cells

    NASA Technical Reports Server (NTRS)

    McDowell, Mark; Gray, Elizabeth

    2006-01-01

    Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

  2. A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

    NASA Astrophysics Data System (ADS)

    Vickers, H.; Eckerstorfer, M.; Malnes, E.; Larsen, Y.; Hindberg, H.

    2016-11-01

    Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

  3. Applying shot boundary detection for automated crystal growth analysis during in situ transmission electron microscope experiments.

    PubMed

    Moeglein, W A; Griswold, R; Mehdi, B L; Browning, N D; Teuton, J

    2017-01-01

    In situ scanning transmission electron microscopy is being developed for numerous applications in the study of nucleation and growth under electrochemical driving forces. For this type of experiment, one of the key parameters is to identify when nucleation initiates. Typically, the process of identifying the moment that crystals begin to form is a manual process requiring the user to perform an observation and respond accordingly (adjust focus, magnification, translate the stage, etc.). However, as the speed of the cameras being used to perform these observations increases, the ability of a user to "catch" the important initial stage of nucleation decreases (there is more information that is available in the first few milliseconds of the process). Here, we show that video shot boundary detection can automatically detect frames where a change in the image occurs. We show that this method can be applied to quickly and accurately identify points of change during crystal growth. This technique allows for automated segmentation of a digital stream for further analysis and the assignment of arbitrary time stamps for the initiation of processes that are independent of the user's ability to observe and react.

  4. On Radar Resolution in Coherent Change Detection.

    SciTech Connect

    Bickel, Douglas L.

    2015-11-01

    It is commonly observed that resolution plays a role in coherent change detection. Although this is the case, the relationship of the resolution in coherent change detection is not yet defined . In this document, we present an analytical method of evaluating this relationship using detection theory. Specifically we examine the effect of resolution on receiver operating characteristic curves for coherent change detection.

  5. An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups

    PubMed Central

    Wang, Su; Hu, Yin; Da Cruz, Lyndon; Smith, Phil

    2016-01-01

    Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system's performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races. PMID:28074155

  6. A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

    NASA Astrophysics Data System (ADS)

    de Alwis Pitts, Dilkushi; Wieland, Marc; Wang, Shifeng; So, Emily; Pittore, Massimiliano

    2014-05-01

    Following a disaster, change detection via pre- and post-event very high resolution remote sensing images is an essential technique for damage assessment and recovery monitoring over large areas in complex urban environments. Most assessments to date focus on detection, destruction and recovery of man-made objects that facilitate shelter and accessibility, such as buildings, roads, bridges, etc., as indicators for assessment and better decision making. Moreover, many current change-detection mechanisms do not use all the data and knowledge which are often available for the pre-disaster state. Recognizing the continuous rather than dichotomous character of the data-rich/data-poor distinction permits the incorporation of ancillary data and existing knowledge into the processing flow. Such incorporation could improve the reliability of the results and thereby enhance the usability of robust methods for disaster management. This study proposes an application-specific and robust change detection method from multi-temporal very high resolution multi-spectral satellite images. This hybrid indicator-specific method uses readily available pre-disaster GIS data and integrates existing knowledge into the processing flow to optimize the change detection while offering the possibility to target specific types of changes to man-made objects. The indicator-specific information of the GIS objects is used as a series of masks to treat the GIS objects with similar characteristics similarly for better accuracy. The proposed approach is based on a fusion of a multi-index change detection method based on gradient, texture and edge similarity filters. The change detection index is flexible for disaster cases in which the pre-disaster and post-disaster images are not of the same resolution. The proposed automated method is evaluated with QuickBird and Ikonos datasets for abrupt changes soon after disaster. The method could also be extended in a semi-automated way for monitoring

  7. Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

    NASA Technical Reports Server (NTRS)

    Gay, Robert S.

    2011-01-01

    Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of

  8. An automated walk-over weighing system as a tool for measuring liveweight change in lactating dairy cows.

    PubMed

    Dickinson, R A; Morton, J M; Beggs, D S; Anderson, G A; Pyman, M F; Mansell, P D; Blackwood, C B

    2013-07-01

    Automated walk-over weighing systems can be used to monitor liveweights of cattle. Minimal literature exists to describe agreement between automated and static scales, and no known studies describe repeatability when used for daily measurements of dairy cows. This study establishes the repeatability of an automated walk-over cattle-weighing system, and agreement with static electronic scales, when used in a commercial dairy herd to weigh lactating cows. Forty-six lactating dairy cows from a seasonal calving, pasture-based dairy herd in southwest Victoria, Australia, were weighed once using a set of static scales and repeatedly using an automated walk-over weighing system at the exit of a rotary dairy. Substantial agreement was observed between the automated and static scales when assessed using Lin's concordance correlation coefficient. Weights measured by the automated walkover scales were within 5% of those measured by the static scales in 96% of weighings. Bland and Altman's 95% limits of agreement were -23.3 to 43.6 kg, a range of 66.9 kg. The 95% repeatability coefficient for automated weighings was 46.3 kg. Removal of a single outlier from the data set increased Lin's concordance coefficient, narrowed Bland and Altman's 95% limits of agreement to a range of 32.5 kg, and reduced the 95% repeatability coefficient to 18.7 kg. Cow misbehavior during walk-over weighing accounted for many of the larger weight discrepancies. The automated walk-over weighing system showed substantial agreement with the static scales when assessed using Lin's concordance correlation coefficient. This contrasted with limited agreement when assessed using Bland and Altman's method, largely due to poor repeatability. This suggests the automated weighing system is inadequate for detecting small liveweight differences in individual cows based on comparisons of single weights. Misbehaviors and other factors can result in the recording of spurious values on walk-over scales. Excluding

  9. Automated Network Anomaly Detection with Learning, Control and Mitigation

    ERIC Educational Resources Information Center

    Ippoliti, Dennis

    2014-01-01

    Anomaly detection is a challenging problem that has been researched within a variety of application domains. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the specific signatures of every possible attack.…

  10. Automated Network Anomaly Detection with Learning, Control and Mitigation

    ERIC Educational Resources Information Center

    Ippoliti, Dennis

    2014-01-01

    Anomaly detection is a challenging problem that has been researched within a variety of application domains. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the specific signatures of every possible attack.…

  11. Automated Sunspot Detection and Classification Using SOHO/MDI Imagery

    DTIC Science & Technology

    2015-03-01

    18 3.3.1 Center and Radius Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.2 Flat-Field and Window...20 5. Comparison of results for Canny edge detection method (red) and binary thresholding method (green) determining the solar radius ...devices are therefore sensitive to stray radiation, such as cosmic rays, which can produce signals not associated with the object of interest

  12. Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi

    2013-03-01

    Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.

  13. Advanced infrared detection and image processing for automated bat censusing

    NASA Astrophysics Data System (ADS)

    Frank, Jeffery D.; Kunz, Tomas H.; Horn, Jason; Cleveland, Cutler; Petronio, Susan M.

    2003-09-01

    The Brazilian free-tailed bat (Tadarida brasiliensis) forms some of the largest aggregations of mammals known to mankind. However, little is known about population sizes and nightly foraging activities. An advanced infrared (IR) thermal imaging system with a real time imaging and data acquisition system is described for censusing Brazilian free-tailed bats during nightly emergences at selected Texas caves. We developed a statistically-based algorithm suitable for counting emerging bats in columns with relative constant trajectories and velocities. Individual bats are not identified and tracked, but instead column density is calculated at intervals of 1/30th of a second and counts are accumulated based upon column velocity. Preliminary evaluation has shown this method to be far more accurate than those previously used to census large bat populations. This real-time automated censusing system allows us to make accurate and repeatable estimates of the number of bats present independent of colony size, ambient light, or weather conditions, and without causing disturbance to the colony.

  14. Automated prostate tissue referencing for cancer detection and diagnosis.

    PubMed

    Kwak, Jin Tae; Hewitt, Stephen M; Kajdacsy-Balla, André Alexander; Sinha, Saurabh; Bhargava, Rohit

    2016-06-01

    The current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics. The system includes a database of previously evaluated prostate tissue images, clinical information and a tissue retrieval process. In the system, a tissue is characterized by its morphology. The retrieval process seeks to find the closest matching cases with the tissue of interest. Moreover, we define 9 morphologic criteria by which a pathologist arrives at a histomorphologic diagnosis. Based on the 9 criteria, true tissue similarity is determined and serves as the gold standard of tissue retrieval. Here, we found a minimum of 4 and 3 matching cases, out of 5, for ~80 % and ~60 % of the queries when a match was defined as the tissue similarity score ≥5 and ≥6, respectively. We were also able to examine the relationship between tissues beyond the Gleason grading system due to the tissue similarity scoring system. Providing the closest matching cases and their clinical information with pathologists will help to conduct consistent and reliable diagnoses. Thus, we expect the system to facilitate quality maintenance and quality improvement of cancer pathology.

  15. Automated Detection of Eruptive Structures for Solar Eruption Prediction

    NASA Astrophysics Data System (ADS)

    Georgoulis, Manolis K.

    2012-07-01

    The problem of data processing and assimilation for solar eruption prediction is, for contemporary solar physics, more pressing than the problem of data acquisition. Although critical solar data, such as the coronal magnetic field, are still not routinely available, space-based observatories deliver diverse, high-quality information at such a high rate that a manual or semi-manual processing becomes meaningless. We discuss automated data analysis methods and explain, using basic physics, why some of them are unlikely to advance eruption prediction. From this finding we also understand why solar eruption prediction is likely to remain inherently probabilistic. We discuss some promising eruption prediction measures and report on efforts to adapt them for use with high-resolution, high-cadence photospheric and coronal data delivered by the Solar Dynamics Observatory. Concluding, we touch on the problem of physical understanding and synthesis of different results: combining different measures inferred by different data sets is a yet-to-be-done exercise that, however, presents our best opportunity of realizing benefits in solar eruption prediction via a meaningful, targeted assimilation of solar data.

  16. Weld line detection and process control for welding automation

    NASA Astrophysics Data System (ADS)

    Yang, Sang-Min; Cho, Man-Ho; Lee, Ho-Young; Cho, Taik-Dong

    2007-03-01

    Welding has been widely used as a process to join metallic parts. But because of hazardous working conditions, workers tend to avoid this task. Techniques to achieve the automation are the recognition of joint line and process control. A CCD (charge coupled device) camera with a laser stripe was applied to enhance the automatic weld seam tracking in GMAW (gas metal arc welding). The adaptive Hough transformation having an on-line processing ability was used to extract laser stripes and to obtain specific weld points. The three-dimensional information obtained from the vision system made it possible to generate the weld torch path and to obtain information such as the width and depth of the weld line. In this study, a neural network based on the generalized delta rule algorithm was adapted to control the process of GMAW, such as welding speed, arc voltage and wire feeding speed. The width and depth of the weld joint have been selected as neurons in the input layer of the neural-network algorithm. The input variables, the width and depth of the weld joint, are determined by image information. The voltage, weld speed and wire feed rate are represented as the neurons in the output layer. The results of the neural-network learning applied to the welding are as follows: learning ratio 0.5, momentum ratio 0.7, the number of hidden layers 2 and the number of hidden units 8. They have significant influence on the weld quality.

  17. Automated Detection and Location of Indications in Eddy Current Signals

    SciTech Connect

    Brudnoy, David M.; Oppenlander, Jane E.; Levy, Arthur J.

    1998-06-30

    A computer implemented information extraction process that locates and identifies eddy current signal features in digital point-ordered signals, said signals representing data from inspection of test materials, by enhancing the signal features relative to signal noise, detecting features of the signals, verifying the location of the signal features that can be known in advance, and outputting information about the identity and location of all detected signal features.

  18. ASTRiDE: Automated Streak Detection for Astronomical Images

    NASA Astrophysics Data System (ADS)

    Kim, Dae-Won

    2016-05-01

    ASTRiDE detects streaks in astronomical images using a "border" of each object (i.e. "boundary-tracing" or "contour-tracing") and their morphological parameters. Fast moving objects such as meteors, satellites, near-Earth objects (NEOs), or even cosmic rays can leave streak-like traces in the images; ASTRiDE can detect not only long streaks but also relatively short or curved streaks.

  19. Automated detection and location of indications in eddy current signals

    DOEpatents

    Brudnoy, David M.; Oppenlander, Jane E.; Levy, Arthur J.

    2000-01-01

    A computer implemented information extraction process that locates and identifies eddy current signal features in digital point-ordered signals, signals representing data from inspection of test materials, by enhancing the signal features relative to signal noise, detecting features of the signals, verifying the location of the signal features that can be known in advance, and outputting information about the identity and location of all detected signal features.

  20. Fully automated procedure for ship detection using optical satellite imagery

    NASA Astrophysics Data System (ADS)

    Corbane, C.; Pecoul, E.; Demagistri, L.; Petit, M.

    2009-01-01

    Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. In the framework of a European integrated project GMES-Security/LIMES, we developed an operational ship detection algorithm using high spatial resolution optical imagery to complement existing regulations, in particular the fishing control system. The automatic detection model is based on statistical methods, mathematical morphology and other signal processing techniques such as the wavelet analysis and Radon transform. This paper presents current progress made on the detection model and describes the prototype designed to classify small targets. The prototype was tested on panchromatic SPOT 5 imagery taking into account the environmental and fishing context in French Guiana. In terms of automatic detection of small ship targets, the proposed algorithm performs well. Its advantages are manifold: it is simple and robust, but most of all, it is efficient and fast, which is a crucial point in performance evaluation of advanced ship detection strategies.

  1. Automation - Changes in cognitive demands and mental workload

    NASA Technical Reports Server (NTRS)

    Tsang, Pamela S.; Johnson, Walter W.

    1987-01-01

    The effect of partial automation on mental workloads in man/machine tasks is investigated experimentally. Subjective workload measures are obtained from six subjects after performance of a task battery comprising two manual (flight-path control, FC, and target acquisition, TA) tasks and one decisionmaking (engine failure, EF) task; the FC task was performed in both a fully manual (altitude and lateral control) mode and in a semiautomated mode (autmatic latitude control). The performance results and subjective evaluations are presented in graphs and characterized in detail. The automation is shown to improve objective performance and lower subjective workload significantly in the combined FC/TA task, but not in the FC task alone or in the FC/EF task.

  2. Detection of anti-salmonella flgk antibodies in chickens by automated capillary immunoassay

    USDA-ARS?s Scientific Manuscript database

    Western blot is a very useful tool to identify specific protein, but is tedious, labor-intensive and time-consuming. An automated "Simple Western" assay has recently been developed that enables the protein separation, blotting and detection in an automatic manner. However, this technology has not ...

  3. Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

    ERIC Educational Resources Information Center

    Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

    2013-01-01

    The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

  4. Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

    ERIC Educational Resources Information Center

    Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

    2013-01-01

    The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

  5. Automated Detection of Lupus White Matter Lesions in MRI

    PubMed Central

    Roura, Eloy; Sarbu, Nicolae; Oliver, Arnau; Valverde, Sergi; González-Villà, Sandra; Cervera, Ricard; Bargalló, Núria; Lladó, Xavier

    2016-01-01

    Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration. PMID:27570507

  6. Automated detection of periventricular veins on 7 T brain MRI

    NASA Astrophysics Data System (ADS)

    Kuijf, Hugo J.; Bouvy, Willem H.; Zwanenburg, Jaco J. M.; Viergever, Max A.; Biessels, Geert Jan; Vincken, Koen L.

    2015-03-01

    Cerebral small vessel disease is common in elderly persons and a leading cause of cognitive decline, dementia, and acute stroke. With the introduction of ultra-high field strength 7.0T MRI, it is possible to visualize small vessels in the brain. In this work, a proof-of-principle study is conducted to assess the feasibility of automatically detecting periventricular veins. Periventricular veins are organized in a fan-pattern and drain venous blood from the brain towards the caudate vein of Schlesinger, which is situated along the lateral ventricles. Just outside this vein, a region-of- interest (ROI) through which all periventricular veins must cross is defined. Within this ROI, a combination of the vesselness filter, tubular tracking, and hysteresis thresholding is applied to locate periventricular veins. All detected locations were evaluated by an expert human observer. The results showed a positive predictive value of 88% and a sensitivity of 95% for detecting periventricular veins. The proposed method shows good results in detecting periventricular veins in the brain on 7.0T MR images. Compared to previous works, that only use a 1D or 2D ROI and limited image processing, our work presents a more comprehensive definition of the ROI, advanced image processing techniques to detect periventricular veins, and a quantitative analysis of the performance. The results of this proof-of-principle study are promising and will be used to assess periventricular veins on 7.0T brain MRI.

  7. Validation of an automated seizure detection algorithm for term neonates

    PubMed Central

    Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

    2016-01-01

    Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

  8. Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection.

    PubMed

    Ernstsen, Christina L; Login, Frédéric H; Jensen, Helene H; Nørregaard, Rikke; Møller-Jensen, Jakob; Nejsum, Lene N

    2017-10-01

    Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

  9. Automated Detection, Characterization, and Tracking of Sunspots from SoHO/MDI Continuum Images

    NASA Astrophysics Data System (ADS)

    Goel, Suruchi; Mathew, Shibu K.

    2014-04-01

    We describe a procedure for automated detection of sunspots from SoHO/MDI full-disk continuum images. The MDI Level-1.8 continuum images were first corrected for the limb darkening and stray light, and then were flat-fielded. Sunspots were extracted using a newly developed automated sunspot detection procedure, which is based on the level set, namely the selective binary and Gaussian function regularized level set (SBGFRLS) method (Zhang et al., Image Vis. Comput. 28, 668, 2010). In this method we initialize a two-dimensional level-set function and evolve it using a signed pressure force (SPF) function. For sunspot detection, the level-set function was defined twice, first for umbra and then for penumbra extraction. Using this procedure, along with the characterization of detected sunspots we have also generated tracking reports of all sunspots in a fully unsupervised manner.

  10. PCA method for automated detection of mispronounced words

    NASA Astrophysics Data System (ADS)

    Ge, Zhenhao; Sharma, Sudhendu R.; Smith, Mark J. T.

    2011-06-01

    This paper presents a method for detecting mispronunciations with the aim of improving Computer Assisted Language Learning (CALL) tools used by foreign language learners. The algorithm is based on Principle Component Analysis (PCA). It is hierarchical with each successive step refining the estimate to classify the test word as being either mispronounced or correct. Preprocessing before detection, like normalization and time-scale modification, is implemented to guarantee uniformity of the feature vectors input to the detection system. The performance using various features including spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) are compared and evaluated. Best results were obtained using MFCCs, achieving up to 99% accuracy in word verification and 93% in native/non-native classification. Compared with Hidden Markov Models (HMMs) which are used pervasively in recognition application, this particular approach is computational efficient and effective when training data is limited.

  11. An automated computer misuse detection system for UNICOS

    SciTech Connect

    Jackson, K.A.; Neuman, M.C.; Simmonds, D.D.; Stallings, C.A.; Thompson, J.L.; Christoph, G.G.

    1994-09-27

    An effective method for detecting computer misuse is the automatic monitoring and analysis of on-line user activity. This activity is reflected in the system audit record, in the system vulnerability posture, and in other evidence found through active testing of the system. During the last several years we have implemented an automatic misuse detection system at Los Alamos. This is the Network Anomaly Detection and Intrusion Reporter (NADIR). We are currently expanding NADIR to include processing of the Cray UNICOS operating system. This new component is called the UNICOS Realtime NADIR, or UNICORN. UNICORN summarizes user activity and system configuration in statistical profiles. It compares these profiles to expert rules that define security policy and improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations. The first phase of UNICORN development is nearing completion, and will be operational in late 1994.

  12. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology.

    PubMed

    Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

    2016-05-05

    Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

  13. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

    PubMed Central

    Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

    2016-01-01

    Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

  14. Toward automated face detection in thermal and polarimetric thermal imagery

    NASA Astrophysics Data System (ADS)

    Gordon, Christopher; Acosta, Mark; Short, Nathan; Hu, Shuowen; Chan, Alex L.

    2016-05-01

    Visible spectrum face detection algorithms perform pretty reliably under controlled lighting conditions. However, variations in illumination and application of cosmetics can distort the features used by common face detectors, thereby degrade their detection performance. Thermal and polarimetric thermal facial imaging are relatively invariant to illumination and robust to the application of makeup, due to their measurement of emitted radiation instead of reflected light signals. The objective of this work is to evaluate a government off-the-shelf wavelet based naïve-Bayes face detection algorithm and a commercial off-the-shelf Viola-Jones cascade face detection algorithm on face imagery acquired in different spectral bands. New classifiers were trained using the Viola-Jones cascade object detection framework with preprocessed facial imagery. Preprocessing using Difference of Gaussians (DoG) filtering reduces the modality gap between facial signatures across the different spectral bands, thus enabling more correlated histogram of oriented gradients (HOG) features to be extracted from the preprocessed thermal and visible face images. Since the availability of training data is much more limited in the thermal spectrum than in the visible spectrum, it is not feasible to train a robust multi-modal face detector using thermal imagery alone. A large training dataset was constituted with DoG filtered visible and thermal imagery, which was subsequently used to generate a custom trained Viola-Jones detector. A 40% increase in face detection rate was achieved on a testing dataset, as compared to the performance of a pre-trained/baseline face detector. Insights gained in this research are valuable in the development of more robust multi-modal face detectors.

  15. Automated Detection of Anomalous Shipping Manifests to Identify Illicit Trade

    SciTech Connect

    Sanfilippo, Antonio P.; Chikkagoudar, Satish

    2013-11-12

    We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.

  16. System and method for automated object detection in an image

    DOEpatents

    Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

    2015-10-06

    A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

  17. Automated Detection of Ocular Alignment with Binocular Retinal Birefringence Scanning

    NASA Astrophysics Data System (ADS)

    Hunter, David G.; Shah, Ankoor S.; Sau, Soma; Nassif, Deborah; Guyton, David L.

    2003-06-01

    We previously developed a retinal birefringence scanning (RBS) device to detect eye fixation. The purpose of this study was to determine whether a new binocular RBS (BRBS) instrument can detect simultaneous fixation of both eyes. Control (nonmyopic and myopic) and strabismic subjects were studied by use of BRBS at a fixation distance of 45 cm. Binocularity (the percentage of measurements with bilateral fixation) was determined from the BRBS output. All nonstrabismic subjects with good quality signals had binocularity >75%. Binocularity averaged 5% in four subjects with strabismus (range of 0 -20%). BRBS may potentially be used to screen individuals for abnormal eye alignment.

  18. A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation.

    PubMed

    Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-08-01

    Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.

  19. Automated design of image operators that detect interest points.

    PubMed

    Trujillo, Leonardo; Olague, Gustavo

    2008-01-01

    This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

  20. Assessing bat detectability and occupancy with multiple automated echolocation detectors

    USGS Publications Warehouse

    Gorresen, P.M.; Miles, A.C.; Todd, C.M.; Bonaccorso, F.J.; Weller, T.J.

    2008-01-01

    Occupancy analysis and its ability to account for differential detection probabilities is important for studies in which detecting echolocation calls is used as a measure of bat occurrence and activity. We examined the feasibility of remotely acquiring bat encounter histories to estimate detection probability and occupancy. We used echolocation detectors coupled to digital recorders operating at a series of proximate sites on consecutive nights in 2 trial surveys for the Hawaiian hoary bat (Lasiurus cinereus semotus). Our results confirmed that the technique is readily amenable for use in occupancy analysis. We also conducted a simulation exercise to assess the effects of sampling effort on parameter estimation. The results indicated that the precision and bias of parameter estimation were often more influenced by the number of sites sampled than number of visits. Acceptable accuracy often was not attained until at least 15 sites or 15 visits were used to estimate detection probability and occupancy. The method has significant potential for use in monitoring trends in bat activity and in comparative studies of habitat use. ?? 2008 American Society of Mammalogists.

  1. Left-ventricular cavity automated-border detection using an autocovariance technique in echocardiography

    NASA Astrophysics Data System (ADS)

    Morda, Louis S.; Konofagou, Elisa E.

    2005-04-01

    Left-ventricular (LV) segmentation is essential in the early detection of heart disease, where left-ventricular wall motion is being tracked in order to detect ischemia. In this paper, a new method for automated segmentation of the left-ventricular chamber is described. An autocorrelation-based technique isolates the LV cavity from the myocardial wall on 2-D slices of 3D short-axis echocardiograms. A morphological closing function and median filtering are used to generate a uniform border. The proposed segmentation technique is designed to be used in identifying the endocardial border and estimating the motion of the endocardial wall over a cardiac cycle. To this purpose, the proposed technique is particularly successful in border delineation by tracing around structures like papillary muscles and the mitral valve, which constitute the typical obstacle in LV segmentation techniques. The results using this new technique are compared to the manual detection results in short-axis views obtained at the papillary muscle level from 3D datasets in human and canine experiments in vivo. Qualitatively, the automatically-detected borders are highly comparable to the manually-detected borders enclosing regions in the left-ventricular cavity with a relative error within the range of 4.2% - 6%. The new technique constitutes, thus, a robust segmentation method for automated segmentation of endocardial borders and suitable for wall motion tracking for automated detection of ischemia.

  2. Proof of Concept of Automated Collision Detection Technology in Rugby Sevens.

    PubMed

    Clarke, Anthea C; Anson, Judith M; Pyne, David B

    2017-04-01

    Clarke, AC, Anson, JM, and Pyne, DB. Proof of concept of automated collision detection technology in rugby sevens. J Strength Cond Res 31(4): 1116-1120, 2017-Developments in microsensor technology allow for automated detection of collisions in various codes of football, removing the need for time-consuming postprocessing of video footage. However, little research is available on the ability of microsensor technology to be used across various sports or genders. Game video footage was matched with microsensor-detected collisions (GPSports) in one men's (n = 12 players) and one women's (n = 12) rugby sevens match. True-positive, false-positive, and false-negative events between video and microsensor-detected collisions were used to calculate recall (ability to detect a collision) and precision (accurately identify a collision). The precision was similar between the men's and women's rugby sevens game (∼0.72; scale 0.00-1.00); however, the recall in the women's game (0.45) was less than that for the men's game (0.69). This resulted in 45% of collisions for men and 62% of collisions for women being incorrectly labeled. Currently, the automated collision detection system in GPSports microtechnology units has only modest utility in rugby sevens, and it seems that a rugby sevens-specific algorithm is needed. Differences in measures between the men's and women's game may be a result of physical size, and strength, and physicality, as well as technical and tactical factors.

  3. Automated video quality measurement based on manmade object characterization and motion detection

    NASA Astrophysics Data System (ADS)

    Kalukin, Andrew; Harguess, Josh; Maltenfort, A. J.; Irvine, John; Algire, C.

    2016-05-01

    Automated video quality assessment methods have generally been based on measurements of engineering parameters such as ground sampling distance, level of blur, and noise. However, humans rate video quality using specific criteria that measure the interpretability of the video by determining the kinds of objects and activities that might be detected in the video. Given the improvements in tracking, automatic target detection, and activity characterization that have occurred in video science, it is worth considering whether new automated video assessment methods might be developed by imitating the logical steps taken by humans in evaluating scene content. This article will outline a new procedure for automatically evaluating video quality based on automated object and activity recognition, and demonstrate the method for several ground-based and maritime examples. The detection and measurement of in-scene targets makes it possible to assess video quality without relying on source metadata. A methodology is given for comparing automated assessment with human assessment. For the human assessment, objective video quality ratings can be obtained through a menu-driven, crowd-sourced scheme of video tagging, in which human participants tag objects such as vehicles and people on film clips. The size, clarity, and level of detail of features present on the tagged targets are compared directly with the Video National Image Interpretability Rating Scale (VNIIRS).

  4. Automated detection of new impact sites on Martian surface from HiRISE images

    NASA Astrophysics Data System (ADS)

    Xin, Xin; Di, Kaichang; Wang, Yexin; Wan, Wenhui; Yue, Zongyu

    2017-10-01

    In this study, an automated method for Martian new impact site detection from single images is presented. It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm. Experimental results using 100 HiRISE images show that the overall detection rate of proposed method is 84.5%, with a true positive rate of 86.9%. The detection rate and true positive rate in the flat regions are 93.0% and 91.5%, respectively.

  5. Dual approach for automated sleep spindles detection within EEG background activity in infant polysomnograms.

    PubMed

    Held, Claudio M; Causa, Leonardo; Estévez, Pablo; Pérez, Claudio; Garrido, Marcelo; Algarín, Cecilia; Peirano, Patricio

    2004-01-01

    An automated system for sleep spindles detection within EEG background activity, combining two different approaches, is presented. The first approach applies detection criteria on the sigma-band filtered EEG signal, including fuzzy thresholds. The second approach mimics an expert's procedure. A sleep spindle detection is validated if both approaches agree. The method was applied on a testing set, consisting of continuous sleep recordings of two patients, totaling 1132 epochs (pages). A total of 803 sleep spindles events were marked by the experts. Results showed an 87.7% agreement between the detection system and the medical experts.

  6. Automated detection of BB pixel clusters in digital fluoroscopic images

    NASA Astrophysics Data System (ADS)

    Cho, Paul S.; Johnson, Roger H.

    1998-09-01

    Small ball bearings (BBs) are often used to characterize and correct for geometric distortion of x-ray image intensifiers. For quantitative applications the number of BBs required for accurate distortion correction is prohibitively large for manual detection. A method to automatically determine the BB coordinates is described. The technique consists of image segmentation, pixel coalescing and centroid calculation. The dependence of calculated BB coordinates on segmentation threshold was also evaluated and found to be within the uncertainty of measurement.

  7. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection

    DTIC Science & Technology

    2015-04-13

    Language Processing ( NLP ) approaches, which tend to rely on simple statistical models built on top of word counts, are not very good at it. We...distinction has proven to be a particularly difficult classification problem. Existing Machine Learning (ML) and Natural Language Processing ( NLP ) approaches...irony detection leverage statistical natural language processing ( NLP ) and machine learning (ML) methods. These models tend to be relatively ‘shallow

  8. Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation.

    PubMed

    Huang, Sheng-Cheng; Jan, Hao-Yu; Fu, Tieh-Cheng; Lin, Wen-Chen; Lin, Geng-Hong; Lin, Wen-Chi; Tsai, Cheng-Lun; Lin, Kang-Ping

    2017-01-01

    Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.

  9. Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

    PubMed Central

    Huang, Sheng-Cheng; Jan, Hao-Yu; Fu, Tieh-Cheng; Lin, Geng-Hong; Lin, Wen-Chi; Lin, Kang-Ping

    2017-01-01

    Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep. PMID:28634497

  10. Automated sleep-spindle detection in healthy children polysomnograms.

    PubMed

    Causa, Leonardo; Held, Claudio M; Causa, Javier; Estévez, Pablo A; Perez, Claudio A; Chamorro, Rodrigo; Garrido, Marcelo; Algarín, Cecilia; Peirano, Patricio

    2010-09-01

    We present a new methodology to detect and characterize sleep spindles (SSs), based on the nonlinear algorithms, empirical-mode decomposition, and Hilbert-Huang transform, which provide adequate temporal and frequency resolutions in the electroencephalographic analysis. In addition, the application of fuzzy logic allows to emulate expert's procedures. Additionally, we built a database of 56 all-night polysomnographic recordings from children for training and testing, which is among the largest annotated databases published on the subject. The database was split into training (27 recordings), validation (10 recordings), and testing (19 recordings) datasets. The SS events were marked by sleep experts using visual inspection, and these marks were used as golden standard. The overall SS detection performance on the testing dataset of continuous all-night sleep recordings was 88.2% sensitivity, 89.7% specificity, and 11.9% false-positive (FP) rate. Considering only non-REM sleep stage 2, the results showed 92.2% sensitivity, 90.1% specificity, and 8.9% FP rate. In general, our system presents enhanced results when compared with most systems found in the literature, thus improving SS detection precision significantly without the need of hypnogram information.

  11. PCR experion automated electrophoresis system to detect Listeria monocytogenes in foods.

    PubMed

    Delibato, Elisabetta; Gattuso, Antonietta; Minucci, Angelo; Auricchio, Bruna; De Medici, Dario; Toti, Laura; Castagnola, Massimo; Capoluongo, Ettore; Gianfranceschi, Monica Virginia

    2009-11-01

    Listeria monocytogenes is frequently found as a contaminant in raw and ready-to-eat foods. The ability of L. monocytogenes to multiply at refrigeration temperatures and to grow in a wide range of pH values is of particular concern for food safety. According to the European Union regulation on microbiological criteria for foodstuffs, L. monocytogenes must be absent in some categories of ready-to-eat foods. The standard microbiological method for L. monocytogenes detection in foods (ISO 11290-1: 1996 (ISO, International Organization for Standardization)) is cost and time consuming. Developments of rapid, cost-effective and automated diagnostic methods to detect food-borne pathogens in foods continue to be a major concern for the industry and public health. The aim of this study was the development of a rapid, sensitive and specific molecular detection method for L. monocytogenes. To this purpose, we have applied a capillary electrophoresis method to a PCR protocol (PCR-EES (EES, experion automated electrophoresis system)) for detecting L. monocytogenes in food. In particular, a microfluidic chip-based automated electrophoresis system (experion automated electrophoresis system, Bio-Rad Laboratories, USA) was used for the rapid and automatic analysis of the amplicons. Fifty naturally contaminated samples were analysed with this method and the results were compared with those obtained with ISO method. Moreover, the microfluidic chip-based automated electrophoresis system was compared with classical gel electrophoresis (PCR-CGE). The results showed that after 24 h of culture enrichment, the PCR-EES showed a relative accuracy of 100% with ISO, while using PCR-CGE decreased it down to 96%. After 48 h of enrichment, both PCR-EES and PCR-CGE showed an accuracy of 100% with ISO.

  12. Development of an automated MODS plate reader to detect early growth of Mycobacterium tuberculosis.

    PubMed

    Comina, G; Mendoza, D; Velazco, A; Coronel, J; Sheen, P; Gilman, R H; Moore, D A J; Zimic, M

    2011-06-01

    In this work, an automated microscopic observation drug susceptibility (MODS) plate reader has been developed. The reader automatically handles MODS plates and after autofocussing digital images are acquired of the characteristic microscopic cording structures of Mycobacterium tuberculosis, which are the identification method utilized in the MODS technique to detect tuberculosis and multidrug resistant tuberculosis. In conventional MODS, trained technicians manually move the MODS plate on the stage of an inverted microscope while trying to locate and focus upon the characteristic microscopic cording colonies. In centres with high tuberculosis diagnostic demand, sufficient time may not be available to adequately examine all cultures. An automated reader would reduce labour time and the handling of M. tuberculosis cultures by laboratory personnel. Two hundred MODS culture images (100 from tuberculosis positive and 100 from tuberculosis negative sputum samples confirmed by a standard MODS reading using a commercial microscope) were acquired randomly using the automated MODS plate reader. A specialist analysed these digital images with the help of a personal computer and designated them as M. tuberculosis present or absent. The specialist considered four images insufficiently clear to permit a definitive reading. The readings from the 196 valid images resulted in a 100% agreement with the conventional nonautomated standard reading. The automated MODS plate reader combined with open-source MODS pattern recognition software provides a novel platform for high throughput automated tuberculosis diagnosis.

  13. Automated detection of secondary slip fronts in Cascadia

    NASA Astrophysics Data System (ADS)

    Bletery, Q.; Thomas, A.; Krogstad, R. D.; Hawthorne, J. C.; Skarbek, R. M.; Rempel, A. W.; Bostock, M. G.

    2016-12-01

    Slow slip events (SSEs) in subduction zones propagate along the plate interface at velocities on the order of 5 km/day and are largely confined to the region known as the transition zone, located down-dip of the seismogenically locked zone. As SSEs propagate, small on-fault asperities capable of generating seismic radiation fail in earthquake-like events known as low-frequency earthquakes. Recently, low-frequency earthquakes have been used to image smaller scale secondary slip fronts (SSFs) that occur within the actively slipping region of the fault after the main front associated with the SSE has passed. SSFs appear to occur over several different length and timescales and propagate both along dip and along strike. To date, most studies that have documented SSFs have relied on subjective methods, such as visual selection, to identify them. While such approaches have met with considerable success, it is likely that many small-scale fronts remain unidentifiable by visual inspection alone. We implement an algorithm to automatically detect SSFs from 2009 to 2015 along the Cascadia subduction zone. We also apply our algorithm to three large SSEs that were detected by campaign seismic instrumentation in the Vancouver Island area between 2003 and 2005. We find numerous SSFs at different time scales (from 30 min to 32 h duration). We provide a catalog of 1076 SSFs in Cascadia, including time, location, duration, area, propagation velocity, moment, stress drop, slip, slip velocity, and fracture energy for each of the detected SSFs. Analysis of their basic features indicate a wide spectra of stress drops, slip velocities, and fracture energy, as well as an intriguing relationship between SSF direction and duration that could potentially help discriminate between the different physical models proposed to explain slow slip phenomena.

  14. High-Speed Observer: Automated Streak Detection in SSME Plumes

    NASA Technical Reports Server (NTRS)

    Rieckoff, T. J.; Covan, M.; OFarrell, J. M.

    2001-01-01

    A high frame rate digital video camera installed on test stands at Stennis Space Center has been used to capture images of Space Shuttle main engine plumes during test. These plume images are processed in real time to detect and differentiate anomalous plume events occurring during a time interval on the order of 5 msec. Such speed yields near instantaneous availability of information concerning the state of the hardware. This information can be monitored by the test conductor or by other computer systems, such as the integrated health monitoring system processors, for possible test shutdown before occurrence of a catastrophic engine failure.

  15. Automated fetal cardiac valve movement detection for modified myocardial performance index calculation.

    PubMed

    Wang, Jingjing; Henry, Amanda; Welsh, Alec W; Redmond, Stephen J

    2014-01-01

    The Modified Myocardial Performance Index (Mod-MPI) is becoming an important index in fetal cardiac function evaluation. However, the current method for Mod-MPI calculation can be time-consuming and demonstrates poor inter-operator repeatability. This paper presents an automated method for detecting the opening and closing events of fetal cardiac valves with the aim of automating the Mod-MPI calculation. Fifty-four Doppler ultrasound images, showing blood inflow and outflow for the left ventricle, are analyzed to attempt to automatically detect the timings of a total of 905 opening and closing events for both aortic and mitral valves. Timings are found according to the morphological characteristics of waveforms as well as intensity information of images. The proposed method can detect the four valve movement events with high sensitivity (95.60-98.64%) and precision (96.85-100.00%). Results are verified by comparison with manual annotation of same images from an expert.

  16. Integrating Online and Offline 3D Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

    PubMed

    Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng Ann

    2016-12-07

    Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer (CRC) prevention and diagnosis. Traditional manual screening is time-consuming, operator-dependent and error-prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intra-class variations in polyp size, color, shape and texture and low inter-class variations between polyps and hard mimics. In this paper, we propose a novel offline and online 3D deep learning integration framework by leveraging the 3D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with previous methods employing hand-crafted features or 2D-CNNs, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

  17. Towards automated detection of depression from brain structural magnetic resonance images.

    PubMed

    Kipli, Kuryati; Kouzani, Abbas Z; Williams, Lana J

    2013-05-01

    Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

  18. Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements

    NASA Technical Reports Server (NTRS)

    Vaughan, Mark A.; Powell, Kathleen A.; Kuehn, Ralph E.; Young, Stuart A.; Winker, David M.; Hostetler, Chris A.; Hunt, William H.; Liu, Zhaoyan; McGill, Matthew J.; Getzewich, Brian J.

    2009-01-01

    Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth s atmosphere is critical in assessing the planet s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars.

  19. Camera image processing for automated crack detection of pressed panel products (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Moon, Hoyeon; Jung, Hwee Kwon; Lee, Changwon; Park, Gyuhae

    2017-04-01

    Crack detection on pressed panel during the press forming process is an important step to ensure the quality of panel products. Traditional crack detection technique has been generally performed by experienced human inspectors, which is subjective and expensive. Therefore, the implementation of automated and accurate crack detection is necessary during the press forming process. In this study, we performed an optimal camera positioning and automated crack detection using two image processing techniques with multi-view-camera system. The first technique is based on evaluation of the panel edge lines which are extracted from a percolated object image. This technique does not require a reference image for crack detection. Another technique is based on the comparison between a reference and a test image using the local image amplitude mapping. Before crack detection, multi-view images of a panel product are captured using multiple cameras and 3D shape information is reconstructed. Optimal camera positions are then determined based on the shape information. Afterwards, cracks are automatically detected using two crack detection techniques based on image processing. In order to demonstrate the capability of the proposed technique, experiments were performed in the laboratory and the actual manufacturing lines with the real panel products. Experimental results show that proposed techniques could effectively improve the crack detection rate with improved speed.

  20. Automated detection of off-label drug use.

    PubMed

    Jung, Kenneth; LePendu, Paea; Chen, William S; Iyer, Srinivasan V; Readhead, Ben; Dudley, Joel T; Shah, Nigam H

    2014-01-01

    Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.

  1. Towards a Single Sensor Passive Solution for Automated Fall Detection

    PubMed Central

    Belshaw, Michael; Taati, Babak; Snoek, Jasper; Mihailidis, Alex

    2012-01-01

    Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person within the field-of-view has fallen. The system addresses concerns raised in earlier work and the requirements of a widely deployable in-home solution. The presented prototype utilizes a consumer-grade camera modified with a wide-angle lens. Machine learning techniques applied to carefully engineered features allow the system to classify falls at high accuracy while maintaining invariance to lighting, environment and the presence of multiple moving objects. This paper describes the system, outlines the algorithms used and presents empirical validation of its effectiveness. PMID:22254671

  2. Automated Detection of Off-Label Drug Use

    PubMed Central

    Jung, Kenneth; LePendu, Paea; Chen, William S.; Iyer, Srinivasan V.; Readhead, Ben; Dudley, Joel T.; Shah, Nigam H.

    2014-01-01

    Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost. PMID:24586689

  3. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

    PubMed Central

    Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

    2017-01-01

    Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management. PMID:28338047

  4. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery.

    PubMed

    Seymour, A C; Dale, J; Hammill, M; Halpin, P N; Johnston, D W

    2017-03-24

    Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95-98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts' 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

  5. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

    NASA Astrophysics Data System (ADS)

    Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

    2017-03-01

    Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

  6. A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Li, Yuhua; Liu, Hong; Li, Shibo; Zhang, Roy R.; Zheng, Bin

    2012-02-01

    Fluorescence in situ hybridization (FISH) technology provides a promising molecular imaging tool to detect cervical cancer. Since manual FISH analysis is difficult, time-consuming, and inconsistent, the automated FISH image scanning systems have been developed. Due to limited focal depth of scanned microscopic image, a FISH-probed specimen needs to be scanned in multiple layers that generate huge image data. To improve diagnostic efficiency of using automated FISH image analysis, we developed a computer-aided detection (CAD) scheme. In this experiment, four pap-smear specimen slides were scanned by a dual-detector fluorescence image scanning system that acquired two spectrum images simultaneously, which represent images of interphase cells and FISH-probed chromosome X. During image scanning, once detecting a cell signal, system captured nine image slides by automatically adjusting optical focus. Based on the sharpness index and maximum intensity measurement, cells and FISH signals distributed in 3-D space were projected into a 2-D con-focal image. CAD scheme was applied to each con-focal image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm and detect FISH-probed signals using a top-hat transform. The ratio of abnormal cells was calculated to detect positive cases. In four scanned specimen slides, CAD generated 1676 con-focal images that depicted analyzable cells. FISH-probed signals were independently detected by our CAD algorithm and an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots. The study demonstrated the feasibility of applying automated FISH image and signal analysis to assist cyto-geneticists in detecting cervical cancers.

  7. Detection of clinical mastitis by changes in electrical conductivity of foremilk before visible changes in milk.

    PubMed

    Milner, P; Page, K L; Walton, A W; Hillerton, J E

    1996-01-01

    Mastitis was induced by the direct infusion of Staphylococcus aureus or Streptococcus uberis into the mammary gland of lactating cows. Changes in electrical conductivity of foremilk indicated the establishment of bacteria, increased SCC, increased clotting of milk, and, hence, disease, in advance of visible changes in the milk that could be diagnosed by a herdsperson. Clinical mastitis was detectable by changes in electrical conductivity of foremilk, 90% of cases were detectable when clots first appeared in foremilk, and 55% of cases were detectable up to 2 milkings prior to the appearance of clots. All subclinical infections from Staph. aureus were detected, but subclinical infections from Strep. uberis were not detected. The results suggested that clinical mastitis caused by these two major pathogens could be detected earlier by measuring changes in electrical conductivity of milk than by waiting for a herdsperson to detect visible changes in milk. Earlier detection would permit earlier treatment. However, the handheld sensor used in this experiment is impractical for commercial application, and reliable automated sensors and decision-making algorithms are required.

  8. An automated method for identification and ranking of hyperspectral target detections

    NASA Astrophysics Data System (ADS)

    Basener, Bill

    2011-06-01

    In this paper we present a new methodology for automated target detection and identification in hyperspectral imagery. The standard paradigm for target detection in hyperspectral imagery is to run a detection algorithm, typically statistical in nature, and visually inspect each high-scoring pixel to decide whether it is a true detection or a false alarm. Detection filters have constant false alarm rates (CFARs) approaching 10-5, but these can still result in a large number of false alarms given multiple images and a large number of target materials. Here we introduce a new methodology for target detection and identification in hyperspectral imagery that shows promise for hard targets. The result is a greatly reduced false alarm rate and a practical methodology for aiding an analyst in quantitatively evaluating detected pixels. We demonstrate the utility of the method with results on data from the HyMap sensor over the Cooke City, MT.

  9. Application of Reflectance Transformation Imaging Technique to Improve Automated Edge Detection in a Fossilized Oyster Reef

    NASA Astrophysics Data System (ADS)

    Djuricic, Ana; Puttonen, Eetu; Harzhauser, Mathias; Dorninger, Peter; Székely, Balázs; Mandic, Oleg; Nothegger, Clemens; Molnár, Gábor; Pfeifer, Norbert

    2016-04-01

    The world's largest fossilized oyster reef is located in Stetten, Lower Austria excavated during field campaigns of the Natural History Museum Vienna between 2005 and 2008. It is studied in paleontology to learn about change in climate from past events. In order to support this study, a laser scanning and photogrammetric campaign was organized in 2014 for 3D documentation of the large and complex site. The 3D point clouds and high resolution images from this field campaign are visualized by photogrammetric methods in form of digital surface models (DSM, 1 mm resolution) and orthophoto (0.5 mm resolution) to help paleontological interpretation of data. Due to size of the reef, automated analysis techniques are needed to interpret all digital data obtained from the field. One of the key components in successful automation is detection of oyster shell edges. We have tested Reflectance Transformation Imaging (RTI) to visualize the reef data sets for end-users through a cultural heritage viewing interface (RTIViewer). The implementation includes a Lambert shading method to visualize DSMs derived from terrestrial laser scanning using scientific software OPALS. In contrast to shaded RTI no devices consisting of a hardware system with LED lights, or a body to rotate the light source around the object are needed. The gray value for a given shaded pixel is related to the angle between light source and the normal at that position. Brighter values correspond to the slope surfaces facing the light source. Increasing of zenith angle results in internal shading all over the reef surface. In total, oyster reef surface contains 81 DSMs with 3 m x 2 m each. Their surface was illuminated by moving the virtual sun every 30 degrees (12 azimuth angles from 20-350) and every 20 degrees (4 zenith angles from 20-80). This technique provides paleontologists an interactive approach to virtually inspect the oyster reef, and to interpret the shell surface by changing the light source direction

  10. Rapid detection of microbial contamination in frozen vegetables by automated impedance measurements.

    PubMed Central

    Hardy, D; Kraeger, S J; Dufour, S W; Cady, P

    1977-01-01

    Automated impedance measurements can be used to rapidly assess whether a sample of frozen vegetables contains greater or less than 10(5) organisms per g. Microorganisms growing pureed food samples cause a change in the impedance of the medium when the organisms reach a threshold concentration of between 10(6) and 10(7) organisms per ml. Estimates of the concentration of microorganisms initially present in the food sample can be made by recording the time required for the organisms in the sample to replicate to threshold levels. In this study, the detection times for 357 samples of frozen vegetables were compared with standard plate counts for each sample. The agreement between the two methods in distinguishing samples containing more than 10(5) organisms per g was 92.6% for 257 assorted frozen vegetables and somewhat higher (93 to 96%) when separate cutoff times were used for each type of vegetable. The time required for analysis was about 5 h, compared to the 48 to 72 h required for standard plate counts. PMID:329759

  11. Rapid detection of microbial contamination in frozen vegetables by automated impedance measurements.

    PubMed

    Hardy, D; Kraeger, S J; Dufour, S W; Cady, P

    1977-07-01

    Automated impedance measurements can be used to rapidly assess whether a sample of frozen vegetables contains greater or less than 10(5) organisms per g. Microorganisms growing pureed food samples cause a change in the impedance of the medium when the organisms reach a threshold concentration of between 10(6) and 10(7) organisms per ml. Estimates of the concentration of microorganisms initially present in the food sample can be made by recording the time required for the organisms in the sample to replicate to threshold levels. In this study, the detection times for 357 samples of frozen vegetables were compared with standard plate counts for each sample. The agreement between the two methods in distinguishing samples containing more than 10(5) organisms per g was 92.6% for 257 assorted frozen vegetables and somewhat higher (93 to 96%) when separate cutoff times were used for each type of vegetable. The time required for analysis was about 5 h, compared to the 48 to 72 h required for standard plate counts.

  12. Automated Detection of Classical Novae with Neural Networks

    NASA Astrophysics Data System (ADS)

    Feeney, S. M.; Belokurov, V.; Evans, N. W.; An, J.; Hewett, P. C.; Bode, M.; Darnley, M.; Kerins, E.; Baillon, P.; Carr, B. J.; Paulin-Henriksson, S.; Gould, A.

    2005-07-01

    The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalog is also a prime source of light curves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough survey of the variable objects in M31, provides an excellent opportunity to locate and study an entire galactic population of CNe. This paper presents a set of 440 neural networks, working in 44 committees, designed specifically to identify fast CNe. The networks are developed using training sets consisting of simulated novae and POINT-AGAPE light curves in a novel variation on K-fold cross validation and use the binned, normalized power spectra of the light curves as input units. The networks successfully identify 9 of the 13 previously identified M31 CNe within their optimal working range (and 11 out of 13 if the network error bars are taken into account). The networks provide a catalogue of 19 new candidate fast CNe, of which four are strongly favored.

  13. MDCT for automated detection and measurement of pneumothoraces in trauma patients.

    PubMed

    Cai, Wenli; Tabbara, Malek; Takata, Noboru; Yoshida, Hiroyuki; Harris, Gordon J; Novelline, Robert A; de Moya, Marc

    2009-03-01

    The size of a pneumothorax is an important index to guide the emergency treatment of trauma patients--chest tube drainage. The purpose of this study was to develop and validate an automated computer-aided volumetry scheme for detection and measurement of pneumothoraces for trauma patients imaged with MDCT. Three pigs and 68 trauma patients with at least one diagnosed occult pneumothorax (23 women and 45 men; age range, 14-89 years; mean age, 41 +/- 19 years) were selected for the development and validation of our computer-aided volumetry scheme for pneumothorax. Computer-aided volumetry of pneumothorax consisted of five automated steps: extraction of pleural region, detection of pneumothorax candidates, delineation of the detected pneumothorax candidates, reduction of false-positive findings, and report of the volumetric measurement of pneumothoraces. In the animal study, our computer-aided volumetry scheme yielded a mean value of 24.27 +/- 0.64 mL (SD) compared with 25 mL of air volume manually injected in each scan. The correlation coefficients were 0.999 and 0.997 for the in vivo and ex vivo comparison, respectively. In the patient study, the sensitivity of our computer-aided volumetry scheme was 100% with a false-positive rate of 0.15 per case for 32 occult pneumothoraces > or = 25 mL. The correlation coefficient was 0.999 for manual volumetry comparison. This automated computer-aided volumetry scheme took approximately 3 minutes to finish the detection and measurement per case. The results show that our computer-aided volumetry scheme provides an automated method for accurate and efficient detection and measurement of pneumothoraces in MDCT images of trauma patients.

  14. An automated procedure for covariation-based detection of RNA structure

    SciTech Connect

    Winker, S.; Overbeek, R.; Woese, C.R.; Olsen, G.J.; Pfluger, N.

    1989-12-01

    This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential bondings of secondary and tertiary structure, but also provides the covariation evidence that supports the proposed bondings, and any counter-evidence that can be detected in the known sequences. A small number of previously unknown bondings have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. Currently, we are systematically studying mitochondrial rRNA. Our goal is to detect tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosome structure. Our colleagues in biology have begun experiments to test certain hypotheses suggested by an examination of our program's output. These experiments involve sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. The hope is that the 23S ribosomal RNA of these species will exhibit corresponding complementary variation or generalized covariation. 24 refs.

  15. Advances in automated deception detection in text-based computer-mediated communication

    NASA Astrophysics Data System (ADS)

    Adkins, Mark; Twitchell, Douglas P.; Burgoon, Judee K.; Nunamaker, Jay F., Jr.

    2004-08-01

    The Internet has provided criminals, terrorists, spies, and other threats to national security a means of communication. At the same time it also provides for the possibility of detecting and tracking their deceptive communication. Recent advances in natural language processing, machine learning and deception research have created an environment where automated and semi-automated deception detection of text-based computer-mediated communication (CMC, e.g. email, chat, instant messaging) is a reachable goal. This paper reviews two methods for discriminating between deceptive and non-deceptive messages in CMC. First, Document Feature Mining uses document features or cues in CMC messages combined with machine learning techniques to classify messages according to their deceptive potential. The method, which is most useful in asynchronous applications, also allows for the visualization of potential deception cues in CMC messages. Second, Speech Act Profiling, a method for quantifying and visualizing synchronous CMC, has shown promise in aiding deception detection. The methods may be combined and are intended to be a part of a suite of tools for automating deception detection.

  16. Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.

    PubMed

    Singh, P K; Hernandez-Herrera, P; Labate, D; Papadakis, M

    2017-07-14

    Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length, spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.

  17. An Investigation of Automatic Change Detection for Topographic Map Updating

    NASA Astrophysics Data System (ADS)

    Duncan, P.; Smit, J.

    2012-08-01

    Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

  18. Automating dicentric chromosome detection from cytogenetic biodosimetry data.

    PubMed

    Rogan, Peter K; Li, Yanxin; Wickramasinghe, Asanka; Subasinghe, Akila; Caminsky, Natasha; Khan, Wahab; Samarabandu, Jagath; Wilkins, Ruth; Flegal, Farrah; Knoll, Joan H

    2014-06-01

    We present a prototype software system with sufficient capacity and speed to estimate radiation exposures in a mass casualty event by counting dicentric chromosomes (DCs) in metaphase cells from many individuals. Top-ranked metaphase cell images are segmented by classifying and defining chromosomes with an active contour gradient vector field (GVF) and by determining centromere locations along the centreline. The centreline is extracted by discrete curve evolution (DCE) skeleton branch pruning and curve interpolation. Centromere detection minimises the global width and DAPI-staining intensity profiles along the centreline. A second centromere is identified by reapplying this procedure after masking the first. Dicentrics can be identified from features that capture width and intensity profile characteristics as well as local shape features of the object contour at candidate pixel locations. The correct location of the centromere is also refined in chromosomes with sister chromatid separation. The overall algorithm has both high sensitivity (85 %) and specificity (94 %). Results are independent of the shape and structure of chromosomes in different cells, or the laboratory preparation protocol followed. The prototype software was recoded in C++/OpenCV; image processing was accelerated by data and task parallelisation with Message Passaging Interface and Intel Threading Building Blocks and an asynchronous non-blocking I/O strategy. Relative to a serial process, metaphase ranking, GVF and DCE are, respectively, 100 and 300-fold faster on an 8-core desktop and 64-core cluster computers. The software was then ported to a 1024-core supercomputer, which processed 200 metaphase images each from 1025 specimens in 1.4 h.

  19. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

    NASA Astrophysics Data System (ADS)

    Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

    2012-12-01

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  20. Simplified Automated Image Analysis for Detection and Phenotyping of Mycobacterium tuberculosis on Porous Supports by Monitoring Growing Microcolonies

    PubMed Central

    den Hertog, Alice L.; Visser, Dennis W.; Ingham, Colin J.; Fey, Frank H. A. G.; Klatser, Paul R.; Anthony, Richard M.

    2010-01-01

    Background Even with the advent of nucleic acid (NA) amplification technologies the culture of mycobacteria for diagnostic and other applications remains of critical importance. Notably microscopic observed drug susceptibility testing (MODS), as opposed to traditional culture on solid media or automated liquid culture, has shown potential to both speed up and increase the provision of mycobacterial culture in high burden settings. Methods Here we explore the growth of Mycobacterial tuberculosis microcolonies, imaged by automated digital microscopy, cultured on a porous aluminium oxide (PAO) supports. Repeated imaging during colony growth greatly simplifies “computer vision” and presumptive identification of microcolonies was achieved here using existing publically available algorithms. Our system thus allows the growth of individual microcolonies to be monitored and critically, also to change the media during the growth phase without disrupting the microcolonies. Transfer of identified microcolonies onto selective media allowed us, within 1-2 bacterial generations, to rapidly detect the drug susceptibility of individual microcolonies, eliminating the need for time consuming subculturing or the inoculation of multiple parallel cultures. Significance Monitoring the phenotype of individual microcolonies as they grow has immense potential for research, screening, and ultimately M. tuberculosis diagnostic applications. The method described is particularly appealing with respect to speed and automation. PMID:20544033

  1. Effects of Response Bias and Judgment Framing on Operator Use of an Automated Aid in a Target Detection Task

    ERIC Educational Resources Information Center

    Rice, Stephen; McCarley, Jason S.

    2011-01-01

    Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in…

  2. A Chemical Sensor Pattern Recognition System Using a Self-Training Neural Network Classifier With Automated Outlier Detection

    DTIC Science & Technology

    1998-04-17

    A device and method for a pattern recognition system using a self-training neural network classifier with automated outlier detection for use in...chemical sensor array systems. The pattern recognition system uses a Probabilistic Neural Network (PNN) training computer system to develop automated

  3. Effects of Response Bias and Judgment Framing on Operator Use of an Automated Aid in a Target Detection Task

    ERIC Educational Resources Information Center

    Rice, Stephen; McCarley, Jason S.

    2011-01-01

    Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in…

  4. Anxiety, conscious awareness and change detection.

    PubMed

    Gregory, Sally M; Lambert, Anthony

    2012-03-01

    Attentional scanning was studied in anxious and non-anxious participants, using a modified change detection paradigm. Participants detected changes in pairs of emotional scenes separated by two task irrelevant slides, which contained an emotionally valenced scene (the 'distractor scene') and a visual mask. In agreement with attentional control theory, change detection latencies were slower overall for anxious participants. Change detection in anxious, but not non-anxious, participants was influenced by the emotional valence and exposure duration of distractor scenes. When negative distractor scenes were presented at subliminal exposure durations, anxious participants detected changes more rapidly than when supraliminal negative scenes or subliminal positive scenes were presented. We propose that for anxious participants, subliminal presentation of emotionally negative distractor scenes stimulated attention into a dynamic state in the absence of attentional engagement. Presentation of the same scenes at longer exposure times was accompanied by conscious awareness, attentional engagement, and slower change detection.

  5. Automated detection of geological landforms on Mars using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Palafox, Leon F.; Hamilton, Christopher W.; Scheidt, Stephen P.; Alvarez, Alexander M.

    2017-04-01

    The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

  6. Automated aortic calcification detection in low-dose chest CT images

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Htwe, Yu Maw; Padgett, Jennifer; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

    2014-03-01

    The extent of aortic calcification has been shown to be a risk indicator for vascular events including cardiac events. We have developed a fully automated computer algorithm to segment and measure aortic calcification in low-dose noncontrast, non-ECG gated, chest CT scans. The algorithm first segments the aorta using a pre-computed Anatomy Label Map (ALM). Then based on the segmented aorta, aortic calcification is detected and measured in terms of the Agatston score, mass score, and volume score. The automated scores are compared with reference scores obtained from manual markings. For aorta segmentation, the aorta is modeled as a series of discrete overlapping cylinders and the aortic centerline is determined using a cylinder-tracking algorithm. Then the aortic surface location is detected using the centerline and a triangular mesh model. The segmented aorta is used as a mask for the detection of aortic calcification. For calcification detection, the image is first filtered, then an elevated threshold of 160 Hounsfield units (HU) is used within the aorta mask region to reduce the effect of noise in low-dose scans, and finally non-aortic calcification voxels (bony structures, calcification in other organs) are eliminated. The remaining candidates are considered as true aortic calcification. The computer algorithm was evaluated on 45 low-dose non-contrast CT scans. Using linear regression, the automated Agatston score is 98.42% correlated with the reference Agatston score. The automated mass and volume score is respectively 98.46% and 98.28% correlated with the reference mass and volume score.

  7. A Statistical Analysis of Automated and Manually Detected Fires Using Environmental Satellites

    NASA Astrophysics Data System (ADS)

    Ruminski, M. G.; McNamara, D.

    2003-12-01

    The National Environmental Satellite and Data Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) has been producing an analysis of fires and smoke over the US since 1998. This product underwent significant enhancement in June 2002 with the introduction of the Hazard Mapping System (HMS), an interactive workstation based system that displays environmental satellite imagery (NOAA Geostationary Operational Environmental Satellite (GOES), NOAA Polar Operational Environmental Satellite (POES) and National Aeronautics and Space Administration (NASA) MODIS data) and fire detects from the automated algorithms for each of the satellite sensors. The focus of this presentation is to present statistics compiled on the fire detects since November 2002. The Automated Biomass Burning Algorithm (ABBA) detects fires using GOES East and GOES West imagery. The Fire Identification, Mapping and Monitoring Algorithm (FIMMA) utilizes NOAA POES 15/16/17 imagery and the MODIS algorithm uses imagery from the MODIS instrument on the Terra and Aqua spacecraft. The HMS allows satellite analysts to inspect and interrogate the automated fire detects and the input satellite imagery. The analyst can then delete those detects that are felt to be false alarms and/or add fire points that the automated algorithms have not selected. Statistics are compiled for the number of automated detects from each of the algorithms, the number of automated detects that are deleted and the number of fire points added by the analyst for the contiguous US and immediately adjacent areas of Mexico and Canada. There is no attempt to distinguish between wildfires and control or agricultural fires. A detailed explanation of the automated algorithms is beyond the scope of this presentation. However, interested readers can find a more thorough description by going to www.ssd.noaa.gov/PS/FIRE/hms.html and scrolling down to Individual Fire Layers. For the period November 2002 thru August

  8. Change Deafness: The Inability to Detect Changes Between Two Voices

    PubMed Central

    Vitevitch, Michael S.

    2008-01-01

    A shadowing task was used to demonstrate an auditory analogue of change blindness (the failure to detect a change in a visual scene), namely change deafness. Participants repeated words varying in lexical difficulty. Halfway through the word list, either the same or a different talker presented the words to participants. At least 40% of the participants failed to detect the change in talker. More interesting is that differences in shadowing times were found as a function of change detection. Alternative possibilities to the change detection phenomenon were ruled out. The results of these experiments suggest that the allocation of attention may influence the detection of changes as well as the processing of spoken words in complex ways. PMID:12760619

  9. Automated detection of broadband clicks of freshwater fish using spectro-temporal features.

    PubMed

    Kottege, Navinda; Jurdak, Raja; Kroon, Frederieke; Jones, Dean

    2015-05-01

    Large scale networks of embedded wireless sensor nodes can passively capture sound for species detection. However, the acoustic recordings result in large amounts of data requiring in-network classification for such systems to be feasible. The current state of the art in the area of in-network bioacoustics classification targets narrowband or long-duration signals, which render it unsuitable for detecting species that emit impulsive broadband signals. In this study, impulsive broadband signals were classified using a small set of spectral and temporal features to aid in their automatic detection and classification. A prototype system is presented along with an experimental evaluation of automated classification methods. The sound used was recorded from a freshwater invasive fish in Australia, the spotted tilapia (Tilapia mariae). Results show a high degree of accuracy after evaluating the proposed detection and classification method for T. mariae sounds and comparing its performance against the state of the art. Moreover, performance slightly improves when the original signal was down-sampled from 44.1 to 16 kHz. This indicates that the proposed method is well-suited for detection and classification on embedded devices, which can be deployed to implement a large scale wireless sensor network for automated species detection.

  10. Anomalous change detection in imagery

    DOEpatents

    Theiler, James P.; Perkins, Simon J.

    2011-05-31

    A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.

  11. An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

    PubMed

    Dereymaeker, Anneleen; Pillay, Kirubin; Vervisch, Jan; Van Huffel, Sabine; Naulaers, Gunnar; Jansen, Katrien; De Vos, Maarten

    2017-09-01

    Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

  12. A Real-Time Automated Point Process Method for Detection and Correction of Erroneous and Ectopic Heartbeats

    PubMed Central

    Citi, Luca; Brown, Emery N; Barbieri, Riccardo

    2012-01-01

    The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affect estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate on-line monitoring of HRV and cardiovascular control. We have developed a novel point process based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the Physionet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an on-line fashion, may provide a solid base for building a more reliable real-time HRV monitoring device. PMID:22875239

  13. Automated Guided-Wave Scanning Developed to Characterize Materials and Detect Defects

    NASA Technical Reports Server (NTRS)

    Martin, Richard E.; Gyekenyeski, Andrew L.; Roth, Don J.

    2004-01-01

    The Nondestructive Evaluation (NDE) Group of the Optical Instrumentation Technology Branch at the NASA Glenn Research Center has developed a scanning system that uses guided waves to characterize materials and detect defects. The technique uses two ultrasonic transducers to interrogate the condition of a material. The sending transducer introduces an ultrasonic pulse at a point on the surface of the specimen, and the receiving transducer detects the signal after it has passed through the material. The aim of the method is to correlate certain parameters in both the time and frequency domains of the detected waveform to characteristics of the material between the two transducers. The scanning system is shown. The waveform parameters of interest include the attenuation due to internal damping, waveform shape parameters, and frequency shifts due to material changes. For the most part, guided waves are used to gauge the damage state and defect growth of materials subjected to various mechanical or environmental loads. The technique has been applied to polymer matrix composites, ceramic matrix composites, and metal matrix composites as well as metallic alloys. Historically, guided wave analysis has been a point-by-point, manual technique with waveforms collected at discrete locations and postprocessed. Data collection and analysis of this type limits the amount of detail that can be obtained. Also, the manual movement of the sensors is prone to user error and is time consuming. The development of an automated guided-wave scanning system has allowed the method to be applied to a wide variety of materials in a consistent, repeatable manner. Experimental studies have been conducted to determine the repeatability of the system as well as compare the results obtained using more traditional NDE methods. The following screen capture shows guided-wave scan results for a ceramic matrix composite plate, including images for each of nine calculated parameters. The system can

  14. Computer automated detection of head orientation for prevention of wrong-side treatment errors.

    PubMed

    Christensen, James D; Hutchins, Gary C; McDonald, Clement J

    2006-01-01

    A medical error can occur when a patient is positioned in a medical imaging device such as an MRI scanner if information regarding their orientation is improperly entered into the device control software. If such an error is not detected and corrected, the erroneous orientation data will be stored in the image header information and will propagate with the images throughout the medical enterprise. Presented here is a fully automated algorithm for computing patient head orientation from the image data and detecting errors in image orientation labeling. This will enable errors in orientation labeling to be corrected at their source when they occur, thus preventing later medical treatment errors related to laterality.

  15. Object Level HSI-LIDAR Data Fusion for Automated Detection of Difficult Targets

    DTIC Science & Technology

    2011-10-10

    1992). 2. D. W. J. Stein, S . C. Beaven, L. E. Hoff, E. W. Winter, A. P. Schaum , and A. D. Stocker, “Anomaly detection from hyperspectral imagery...of America OCIS codes: (280.0280) Remote sensing and sensors; (280.4788) Optical sensing and sensors. References and links 1. I. S . Reed and X. Yu...A. Kolodner, “Automated target detection system for hyperspectral imaging sensors,” Appl. Opt. 47(28), F61–F70 (2008). 6. M. S . Foster, J. R

  16. Automated, per pixel Cloud Detection from High-Resolution VNIR Data

    NASA Technical Reports Server (NTRS)

    Varlyguin, Dmitry L.

    2007-01-01

    CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

  17. Automated and miniaturized detection of biological threats with a centrifugal microfluidic system

    NASA Astrophysics Data System (ADS)

    Mark, D.; van Oordt, T.; Strohmeier, O.; Roth, G.; Drexler, J.; Eberhard, M.; Niedrig, M.; Patel, P.; Zgaga-Griesz, A.; Bessler, W.; Weidmann, M.; Hufert, F.; Zengerle, R.; von Stetten, F.

    2012-06-01

    The world's growing mobility, mass tourism, and the threat of terrorism increase the risk of the fast spread of infectious microorganisms and toxins. Today's procedures for pathogen detection involve complex stationary devices, and are often too time consuming for a rapid and effective response. Therefore a robust and mobile diagnostic system is required. We present a microstructured LabDisk which performs complex biochemical analyses together with a mobile centrifugal microfluidic device which processes the LabDisk. This portable system will allow fully automated and rapid detection of biological threats at the point-of-need.

  18. Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children.

    PubMed

    Serras Pereira, Mariana; Cozijn, Reinier; Postma, Eric; Shahid, Suleman; Swerts, Marc

    2016-01-01

    The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology - the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm.

  19. Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children

    PubMed Central

    Serras Pereira, Mariana; Cozijn, Reinier; Postma, Eric; Shahid, Suleman; Swerts, Marc

    2016-01-01

    The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm. PMID:28018271

  20. Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

    PubMed Central

    Zhong, Yi; Wang, Ying; Kang, Yan; Haacke, E. Mark

    2014-01-01

    White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to be Y = 1.04X + 1.74  (R2 = 0.96). The automated algorithm estimates the number, volume, and category of WMH. PMID:25136355

  1. Evaluation of an automated fluorescent antibody procedure for detection of Salmonella in foods and feeds.

    PubMed Central

    Munson, T E; Schrade, J P; Bisciello, N B; Fantasia, L D; Hartung, W H; O'Connor, J J

    1976-01-01

    A prototype automated system using fluorescent antibody (FA) was evaluated for rapid detection of salmonellae in foods. Samples were enriched in selenite cystine and tetrathionate broths. After incubation, both were transferred into fresh selenite cystine for a 4-h "post-enrichment" to dilute possible background fluorescence from product. These cultures were then analyzed automatically, and results were compared with those obtained by the methods of the Association of Official Analytical Chemists (AOAC). Initially, 167 samples of milk powder, dried yeast, and imported frog legs were examined. The AOAC and automated FA methods correlated well with all samples but frog legs. Difficulty with the latter was caused by procedural and mechanical problems coupled with high numbers of competing microorganisms in post-enrichment cultures. Modification of procedure and partial redesign of equipment corrected these difficulties, and excellent correlation was obtained with another 116 frog leg samples. All 89 AOAC-confirmed positives were also detected by the automated FA method, and there were only 4% false FA positives. The system shows potential for screening products for salmonellae; however, all positives should be confirmed by manual biochemical and serological methods. PMID:773305

  2. An Automated Fluorescent PCR Method for Detection of Shiga Toxin-Producing Escherichia coli in Foods

    PubMed Central

    Chen, Shu; Xu, Renlin; Yee, Arlene; Wu, Kai Yuan; Wang, Chang-Ning; Read, Susan; De Grandis, Stephanie A.

    1998-01-01

    An automated fluorescence-based PCR system (a model AG-9600 AmpliSensor analyzer) was investigated to determine whether it could detect Shiga toxin-producing Escherichia coli (STEC). The AmpliSensor PCR assay involves amplification-mediated disruption of a fluorogenic DNA signal duplex (AmpliSensor) that is homologous to conserved target sequences in a 323-bp amplified fragment of Shiga toxin genes stx1, stx2, and stxe. Using the Amplisensor assay, we detected 113 strains of STEC belonging to 50 different serotypes, while 18 strains of non-Shiga-toxin-producing E. coli and 68 strains of other bacteria were not detected. The detection limits of the assay were less than 1 to 5 CFU per PCR mixture when pure cultures of five reference strains were used and 3 CFU per 25 g of food when spiked ground beef samples that were preenriched overnight were used. The performance of the assay was also evaluated by using 53 naturally contaminated meat samples and 48 raw milk samples. Thirty-two STEC-positive samples that were confirmed to be positive by the culture assay were found to be positive when the AmpliSensor assay was used. Nine samples that were found to be positive when the PCR assay was used were culture negative. The system described here is an automated PCR-based system that can be used for detection of all serotypes of STEC in food or clinical samples. PMID:9797267

  3. Real-time automated detection, tracking, classification, and geolocation of dismounts using EO and IR FMV

    NASA Astrophysics Data System (ADS)

    Muncaster, J.; Collins, G.; Waltman, J.

    2015-05-01

    The VideoPlus®-Aware (VPA) system enables autonomous video-based target detection, tracking and classification. The system stabilizes video and operates completely autonomously. A statistical background model enables robust acquisition of moving targets, while stopped targets are tracked using feature-based detectors. An ensemble classifier is trained for automated detection and classification of dismounts (i.e., humans) and a planar scene model is used to both improve system performance and reduce false positives. A formal evaluation of the VPA system was performed by the government, to quantify the system's abilities to detect, track, and classify, humans. The evaluation provided 811 separate data points gathered over a period of four days with an overall probability of sensing of 99.9%. The probability of detection was 86.2% and the percentage of correct action classification was 82%. The data provided a False Alarm Rate of 0 per hour and Nuisance Alarm Rate of 0.72 per hour. Dismounts were reliably classified with pixel heights as low as 25 pixels. Real-time automated detection, tracking, and classification of targets with low false positive rates was achieved, even with few pixels on target. The planar scene model based optimizations were sufficient to dramatically reduce the runtime of sliding-window classifiers.

  4. Multiplex RT-PCR and Automated Microarray for Detection of Eight Bovine Viruses.

    PubMed

    Lung, O; Furukawa-Stoffer, T; Burton Hughes, K; Pasick, J; King, D P; Hodko, D

    2016-11-23

    Microarrays can be a useful tool for pathogen detection as it allow for simultaneous interrogation of the presence of a large number of genetic sequences in a sample. However, conventional microarrays require extensive manual handling and multiple pieces of equipment for printing probes, hybridization, washing and signal detection. In this study, a reverse transcription (RT)-PCR with an accompanying novel automated microarray for simultaneous detection of eight viruses that affect cattle [vesicular stomatitis virus (VSV), bovine viral diarrhoea virus type 1 and type 2, bovine herpesvirus 1, bluetongue virus, malignant catarrhal fever virus, rinderpest virus (RPV) and parapox viruses] is described. The assay accurately identified a panel of 37 strains of the target viruses and identified a mixed infection. No non-specific reactions were observed with a panel of 23 non-target viruses associated with livestock. Vesicular stomatitis virus was detected as early as 2 days post-inoculation in oral swabs from experimentally infected animals. The limit of detection of the microarray assay was as low as 1 TCID50 /ml for RPV. The novel microarray platform automates the entire post-PCR steps of the assay and integrates electrophoretic-driven capture probe printing in a single user-friendly instrument that allows array layout and assay configuration to be user-customized on-site.

  5. Automated microarray system for the simultaneous detection of antibiotics in milk.

    PubMed

    Knecht, Bertram G; Strasser, Angelika; Dietrich, Richard; Märtlbauer, Erwin; Niessner, Reinhard; Weller, Michael G

    2004-02-01

    A parallel affinity sensor array (PASA) for the rapid automated analysis of 10 antibiotics in milk is presented, using multianalyte immunoassays with an indirect competitive ELISA format. Microscope glass slides modified with (3-glycidyloxypropyl)trimethoxysilane were used for the preparation of hapten microarrays. Protein conjugates of the haptens were immobilized as spots on disposable chips, which were processed in a flow cell. Monoclonal antibodies against penicillin G, cloxacillin, cephapirin, sulfadiazine, sulfamethazine, streptomycin, gentamicin, neomycin, erythromycin, and tylosin allowed the simultaneous detection of the respective analytes. Antibody binding was detected by a second antibody labeled with horseradish peroxidase generating enhanced chemiluminescence, which was recorded with a sensitive CCD camera. All liquid handling and sample processing was fully automated, and one analysis was carried out in milk within less than 5 min. The detection limits ranged from 0.12 (cephapirin) to 32 microg/L (neomycin). Penicillin G could be detected at the maximum residue limit (MRL); the detection limits for all other analytes were far below the respective MRLs. The PASA system proved to be the first immunochemical biosensor platform having the potential to test for numerous antibiotics in parallel, such being of considerable interest for the control of milk in the dairy industry.

  6. Filament Chirality over an Entire Cycle Determined with an Automated Detection Module -- a Neat Surprise!

    NASA Astrophysics Data System (ADS)

    Martens, Petrus C.; Yeates, A. R.; Mackay, D.; Pillai, K. G.

    2013-07-01

    Using metadata produced by automated solar feature detection modules developed for SDO (Martens et al. 2012) we have discovered some trends in filament chirality and filament-sigmoid relations that are new and in part contradict the current consensus. Automated detection of solar features has the advantage over manual detection of having the detection criteria applied consistently, and in being able to deal with enormous amounts of data, like the 1 Terabyte per day that SDO produces. Here we use the filament detection module developed by Bernasconi, which has metadata from 2000 on, and the sigmoid sniffer, which has been producing metadata from AIA 94 A images since October 2011. The most interesting result we find is that the hemispheric chirality preference for filaments (dextral in the north, and v.v.), studied in detail for a three year period by Pevtsov et al. (2003) seems to disappear during parts of the decline of cycle 23 and during the extended solar minimum that followed. Moreover the hemispheric chirality rule seems to be much less pronounced during the onset of cycle 24. For sigmoids we find the expected correlation between chirality and handedness (S or Z) shape but not as strong as expected.

  7. Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

    PubMed

    Gregg, Richard E; Zhou, Sophia H; Dubin, Anne M

    2016-01-01

    With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence. Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n=772) consisted of VPE ECGs (n=37), normal ECGs (n=492) and a high concentration of conduction defects, RBBB (n=232) and LBBB (n=11). The testing database was a random sample (n=763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database. The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%. This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection.

    PubMed

    Mathieson, Sean R; Livingstone, Vicki; Low, Evonne; Pressler, Ronit; Rennie, Janet M; Boylan, Geraldine B

    2016-10-01

    Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Post-phenobarbital seizures showed significantly lower amplitude (p<0.001) and involved fewer EEG channels at the peak of seizure (p<0.05). No other features or SDA detection rates showed a statistical difference. These findings show that phenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  9. Automated detection of the choroid boundary within OCT image data using quadratic measure filters

    NASA Astrophysics Data System (ADS)

    Wagner, Marcus; Scheibe, Patrick; Francke, Mike; Zimmerling, Beatrice; Frey, Katharina; Vogel, Mandy; Luckhaus, Stephan; Wiedemann, Peter; Kiess, Wieland; Rauscher, Franziska G.

    2017-02-01

    A novel method for the automated detection of the outer choroid boundary within spectral-domain optical coherence tomography image data, based on an image model within the space of functions of bounded variation and the application of quadratic measure filters, is presented. The same method is used for the segmentation of retinal layer boundaries and proves to be suitable even for data generated without special imaging modes and moderate line averaging. Based on the segmentations, an automated determination of the central fovea region and choroidal thickness measurements for this and two adjacent 1-mm regions are provided. The quality of the method is assessed by comparison with manual delineations performed by five trained graders. The study is based on data from 50 children of the ages 8 to 13 that were obtained in the framework of the LIFE Child study at Leipzig University.

  10. Rapid, automated gas chromatographic detection of organic compounds in ultra-pure water

    SciTech Connect

    MOWRY,CURTIS DALE; BLAIR,DIANNA S.; MORRISON,DENNIS J.; REBER,STEPHEN D.; RODACY,PHILIP J.

    2000-02-15

    An automated gas chromatography was used to analyze water samples contaminated with trace (parts-per-billion) concentrations of organic analytes. A custom interface introduced the liquid sample to the chromatography. This was followed by rapid chromatographic analysis. Characteristics of the analysis include response times less than one minute and automated data processing. Analytes were chosen based on their known presence in the recycle water streams of semiconductor manufacturers and their potential to reduce process yield. These include acetone, isopropanol, butyl acetate, ethyl benzene, p-xylene, methyl ethyl ketone and 2-ethoxy ethyl acetate. Detection limits below 20 ppb were demonstrated for all analytes and quantitative analysis with limited speciation was shown for multianalyte mixtures. Results are discussed with respect to the potential for on-line liquid process monitoring by this method.

  11. Automated Detection of Benzodiazepine Dosage in ICU Patients through a Computational Analysis of Electrocardiographic Data

    PubMed Central

    Spadafore, Maxwell T.; Syed, Zeeshan; Rubinfeld, Ilan S.

    2015-01-01

    To enable automated maintenance of patient sedation in an intensive care unit (ICU) setting, more robust, quantitative metrics of sedation depth must be developed. In this study, we demonstrated the feasibility of a fully computational system that leverages low-quality electrocardiography (ECG) from a single lead to detect the presence of benzodiazepine sedatives in a subject’s system. Starting with features commonly examined manually by cardiologists searching for evidence of poisonings, we generalized the extraction of these features to a fully automated process. We tested the predictive power of these features using nine subjects from an intensive care clinical database. Features were found to be significantly indicative of a binary relationship between dose and ECG morphology, but we were unable to find evidence of a predictable continuous relationship. Fitting this binary relationship to a classifier, we achieved a sensitivity of 89% and a specificity of 95%. PMID:26958308

  12. An ex ante analysis on the use of activity meters for automated estrus detection: to invest or not to invest?

    PubMed

    Rutten, C J; Steeneveld, W; Inchaisri, C; Hogeveen, H

    2014-11-01

    The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a sensor system for detection of estrus and appraises the feasibility of an investment in such a system. A stochastic dynamic simulation model was used to simulate reproductive performance of a dairy herd. The number of cow places in this herd was fixed at 130. The model started with 130 randomly drawn cows (in a Monte Carlo process) and simulated calvings and replacement of these cows in subsequent years. Default herd characteristics were a conception rate of 50%, an 8-wk dry-off period, and an average milk production level of 8,310 kg per cow per 305 d. Model inputs were derived from real farm data and expertise. For the analysis, visual detection by the farmer ("without" situation) was compared with automated detection with activity meters ("with" situation). For visual estrus detection, an estrus detection rate of 50% and a specificity of 100% were assumed. For automated estrus detection, an estrus detection rate of 80% and a specificity of 95% were assumed. The results of the cow simulation model were used to estimate the difference between the annual net cash flows in the "with" and "without" situations (marginal financial effect) and the internal rate of return (IRR) as profitability indicators. The use of activity meters led to improved estrus detection and, therefore, to a decrease in the average calving interval and subsequent increase in annual milk production. For visual estrus detection, the average calving interval was 419 d and average annual milk production was 1,032,278 kg. For activity meters, the average calving interval was 403 d and the average annual milk production was 1,043,398 kg. It was estimated that the initial investment in activity meters

  13. Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

    SciTech Connect

    Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

    2006-02-01

    The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

  14. A fully automated IIF system for the detection of antinuclear antibodies and antineutrophil cytoplasmic antibodies.

    PubMed

    Shovman, O; Agmon-Levin, N; Gilburd, B; Martins, T; Petzold, A; Matthias, T; Shoenfeld, Y

    2015-02-01

    Indirect immunofluorescence (IIF) is the main technique for the detection of antinuclear antibodies (ANA) and antineutrophil cytoplasmic antibodies (ANCA). The fully automated IIF processor HELIOS(®) is the first IIF processor that is able to automatically prepare slides and perform automatic reading. The objective of the present study was to determine the diagnostic performance of this system for ANA and ANCA IIF interpretation, in comparison with visual IIF. ANA detection by visual IIF or HELIOS(®) was performed on 425 sera samples including: 218 consecutive samples submitted to a reference laboratory for routine ANA testing, 137 samples from healthy subjects and 70 ANA/ENA positive samples. For ANCA determination, 170 sera samples were collected: 40 samples for routine testing, 90 samples from healthy blood donors and 40 anti-PR3/anti-MPO positive subjects. Good correlation was found for the visual and automated ANA IIF approach regarding positive/negative discrimination of these samples (kappa = 0.633 for ANA positive samples and kappa = 0.657 for ANA negative samples, respectively). Positive/negative IIF ANCA discrimination by HELIOS(®) and visual IIF revealed a complete agreement of 100% in sera from healthy patients and PR3/MPO positive samples (kappa = 1.00). There was 95% agreement between the ANCA IIF performed by automated and visual IIF on the investigation of routine samples. Based on these results, HELIOS(®) demonstrated a high diagnostic performance for the automated ANA and ANCA IIF interpretation that was similar to a visual reading in all groups of samples.

  15. Image Change Detection via Ensemble Learning

    SciTech Connect

    Martin, Benjamin W; Vatsavai, Raju

    2013-01-01

    The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.

  16. Image change detection via ensemble learning

    NASA Astrophysics Data System (ADS)

    Martin, Benjamin W.; Vatsavai, Ranga R.

    2013-05-01

    The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a "mixture of experts" in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.

  17. Development of an Automated DNA Detection System Using an Electrochemical DNA Chip Technology

    NASA Astrophysics Data System (ADS)

    Hongo, Sadato; Okada, Jun; Hashimoto, Koji; Tsuji, Koichi; Nikaido, Masaru; Gemma, Nobuhiro

    A new compact automated DNA detection system Genelyzer™ has been developed. After injecting a sample solution into a cassette with a built-in electrochemical DNA chip, processes from hybridization reaction to detection and analysis are all operated fully automatically. In order to detect a sample DNA, electrical currents from electrodes due to an oxidization reaction of electrochemically active intercalator molecules bound to hybridized DNAs are detected. The intercalator is supplied as a reagent solution by a fluid supply unit of the system. The feasibility test proved that the simultaneous typing of six single nucleotide polymorphisms (SNPs) associated with a rheumatoid arthritis (RA) was carried out within two hours and that all the results were consistent with those by conventional typing methods. It is expected that this system opens a new way to a DNA testing such as a test for infectious diseases, a personalized medicine, a food inspection, a forensic application and any other applications.

  18. A rich Internet application for automated detection of road blockage in post-disaster scenarios

    NASA Astrophysics Data System (ADS)

    Liu, W.; Dong, P.; Liu, S.; Liu, J.

    2014-02-01

    This paper presents the development of a rich Internet application for automated detection of road blockage in post-disaster scenarios using volunteered geographic information from OpenStreetMap street centerlines and airborne light detection and ranging (LiDAR) data. The architecture of the application on the client-side and server-side was described. The major functionality of the application includes shapefile uploading, Web editing for spatial features, road blockage detection, and blockage points downloading. An example from the 2010 Haiti earthquake was included to demonstrate the effectiveness of the application. The results suggest that the prototype application can effectively detect (1) road blockage caused by earthquakes, and (2) some human errors caused by contributors of volunteered geographic information.

  19. Automated Detection of Coronal Mass Ejections in STEREO Heliospheric Imager Data

    NASA Astrophysics Data System (ADS)

    Pant, V.; Willems, S.; Rodriguez, L.; Mierla, M.; Banerjee, D.; Davies, J. A.

    2016-12-01

    We have performed, for the first time, the successful automated detection of coronal mass ejections (CMEs) in data from the inner heliospheric imager (HI-1) cameras on the STEREO-A spacecraft. Detection of CMEs is done in time-height maps based on the application of the Hough transform, using a modified version of the CACTus software package, conventionally applied to coronagraph data. In this paper, we describe the method of detection. We present the results of the application of the technique to a few CMEs, which are well detected in the HI-1 imagery, and compare these results with those based on manual-cataloging methodologies. We discuss, in detail, the advantages and disadvantages of this method.

  20. Automated sinkhole detection using a DEM subsetting technique and fill tools at Mammoth Cave National Park

    NASA Astrophysics Data System (ADS)

    Wall, J.; Bohnenstiehl, D. R.; Levine, N. S.

    2013-12-01

    An automated workflow for sinkhole detection is developed using Light Detection and Ranging (Lidar) data from Mammoth Cave National Park (MACA). While the park is known to sit within a karst formation, the generally dense canopy cover and the size of the park (~53,000 acres) creates issues for sinkhole inventorying. Lidar provides a useful remote sensing technology for peering beneath the canopy in hard to reach areas of the park. In order to detect sinkholes, a subsetting technique is used to interpolate a Digital Elevation Model (DEM) thereby reducing edge effects. For each subset, standard GIS fill tools are used to fill depressions within the DEM. The initial DEM is then subtracted from the filled DEM resulting in detected depressions or sinkholes. Resulting depressions are then described in terms of size and geospatial trend.

  1. Automatic detection of surface changes on Mars - a status report

    NASA Astrophysics Data System (ADS)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2016-10-01

    Orbiter missions have acquired approximately 500,000 high-resolution visible images of the Martian surface, covering an area approximately 6 times larger than the overall area of Mars. This data abundance allows the scientific community to examine the Martian surface thoroughly and potentially make exciting new discoveries. However, the increased data volume, as well as its complexity, generate problems at the data processing stages, which are mainly related to a number of unresolved issues that batch-mode planetary data processing presents. As a matter of fact, the scientific community is currently struggling to scale the common ("one-at-a-time" processing of incoming products by expert scientists) paradigm to tackle the large volumes of input data. Moreover, expert scientists are more or less forced to use complex software in order to extract input information for their research from raw data, even though they are not data scientists themselves.Our work within the STFC and EU FP7 i-Mars projects aims at developing automated software that will process all of the acquired data, leaving domain expert planetary scientists to focus on their final analysis and interpretation. Moreover, after completing the development of a fully automated pipeline that processes automatically the co-registration of high-resolution NASA images to ESA/DLR HRSC baseline, our main goal has shifted to the automated detection of surface changes on Mars. In particular, we are developing a pipeline that uses as an input multi-instrument image pairs, which are processed by an automated pipeline, in order to identify changes that are correlated with Mars surface dynamic phenomena. The pipeline has currently been tested in anger on 8,000 co-registered images and by the time of DPS/EPSC we expect to have processed many tens of thousands of image pairs, producing a set of change detection results, a subset of which will be shown in the presentation.The research leading to these results has received

  2. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

    SciTech Connect

    Drukker, Karen Sennett, Charlene A.; Giger, Maryellen L.

    2014-01-15

    Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

  3. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

    SciTech Connect

    Drukker, Karen Sennett, Charlene A.; Giger, Maryellen L.

    2014-01-15

    Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

  4. Probabilistic Change Detection Framework for Analyzing Settlement Dynamics Using Very High-resolution Satellite Imagery

    SciTech Connect

    Vatsavai, Raju; Graesser, Jordan B

    2012-01-01

    Global human population growth and an increasingly urbanizing world have led to rapid changes in human settlement landscapes and patterns. Timely monitoring and assessment of these changes and dissemination of accurate information is important for policy makers, city planners, and humanitarian relief workers. Satellite imagery provides useful data for the aforementioned applications, and remote sensing can be used to identify and quantify change areas. We explore a probabilistic framework to identify changes in human settlements using very high-resolution satellite imagery. As compared to predominantly pixel-based change detection systems which are highly sensitive to image registration errors, our grid (block) based approach is more robust to registration errors. The presented framework is an automated change detection system applicable to both panchromatic and multi-spectral imagery. The detection system provides comprehensible information about change areas, and minimizes the post-detection thresholding procedure often needed in traditional change detection algorithms.

  5. Automated detection of presence of mucus foci in airway diseases: preliminary results

    NASA Astrophysics Data System (ADS)

    Odry, Benjamin L.; Kiraly, Atilla P.; Novak, Carol L.; Naidich, David P.; Ko, Jane; Godoy, Myrna C. B.

    2009-02-01

    Chronic Obstructive Pulmonary Disease (COPD) is often characterized by partial or complete obstruction of airflow in the lungs. This can be due to airway wall thickening and retained secretions, resulting in foci of mucoid impactions. Although radiologists have proposed scoring systems to assess extent and severity of airway diseases from CT images, these scores are seldom used clinically due to impracticality. The high level of subjectivity from visual inspection and the sheer number of airways in the lungs mean that automation is critical in order to realize accurate scoring. In this work we assess the feasibility of including an automated mucus detection method in a clinical scoring system. Twenty high-resolution datasets of patients with mild to severe bronchiectasis were randomly selected, and used to test the ability of the computer to detect the presence or absence of mucus in each lobe (100 lobes in all). Two experienced radiologists independently scored the presence or absence of mucus in each lobe based on the visual assessment method recommended by Sheehan et al [1]. These results were compared with an automated method developed for mucus plug detection [2]. Results showed agreement between the two readers on 44% of the lobes for presence of mucus, 39% of lobes for absence of mucus, and discordant opinions on 17 lobes. For 61 lobes where 1 or both readers detected mucus, the computer sensitivity was 75.4%, the specificity was 69.2%, and the positive predictive value (PPV) was 79.3%. Six computer false positives were a-posteriori reviewed by the experts and reassessed as true positives, yielding results of 77.6% sensitivity, 81.8% for specificity, and 89.6% PPV.

  6. Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone

    PubMed Central

    Ascenzi, Maria-Grazia; Du, Xia; Harding, James I; Beylerian, Emily N; de Silva, Brian M; Gross, Ben J; Kastein, Hannah K; Wang, Weiguang; Lyons, Karen M; Schaeffer, Hayden

    2014-01-01

    Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth. PMID:25525552

  7. Automated high-grade prostate cancer detection and ranking on whole slide images

    NASA Astrophysics Data System (ADS)

    Huang, Chao-Hui; Racoceanu, Daniel

    2017-03-01

    Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

  8. Phase editing as a signal pre-processing step for automated bearing fault detection

    NASA Astrophysics Data System (ADS)

    Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

    2017-07-01

    Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

  9. Automated Detection of coronal mass ejections in three-dimensions using multi-viewpoint observations

    NASA Astrophysics Data System (ADS)

    Hutton, Joseph; Morgan, Huw

    2016-10-01

    A new, automated method of detecting Solar Wind transients such as Coronal Mass Ejections (CMEs) in three dimensions for the LASCO C2 and STEREO COR2 coronagraphs is presented. By triangulating isolated CME signal from the three coronagraphs over a sliding window of five hours, the most likely region through which CMEs pass at 5 solar radii is identified. The centre and size of the region gives the most likely direction of propagation and angular extent. The Automated CME Triangulation (ACT) method is tested extensively using a series of synthetic CME images created using a flux rope density model, and on a sample of real coronagraph data; including Halo CMEs. The accuracy of the detection remains acceptable regardless of CME position relative to the observer, the relative separation of the three observers, and even through the loss of one coronagraph. By comparing the detection results with the input parameters of the synthetic CMEs, and the low coronal sources of the real CMEs, it is found that the detection is on average accurate to within 7.14 degrees. All current CME catalogues (CDAW, CACTus, SEEDS, ARTEMIS and CORIMP) rely on plane-of-sky measurements for key parameters such as height and velocity. Estimating the true geometry using the new method gains considerable accuracy for kinematics and mass/density. The results of the new method will be incorporated into the CORIMP database in the near future, enabling improved space weather diagnostics and forecasting.

  10. Detecting Concentration Changes with Cooperative Receptors

    NASA Astrophysics Data System (ADS)

    Bo, Stefano; Celani, Antonio

    2016-03-01

    Cells constantly need to monitor the state of the environment to detect changes and timely respond. The detection of concentration changes of a ligand by a set of receptors can be cast as a problem of hypothesis testing, and the cell viewed as a Neyman-Pearson detector. Within this framework, we investigate the role of receptor cooperativity in improving the cell's ability to detect changes. We find that cooperativity decreases the probability of missing an occurred change. This becomes especially beneficial when difficult detections have to be made. Concerning the influence of cooperativity on how fast a desired detection power is achieved, we find in general that there is an optimal value at finite levels of cooperation, even though easy discrimination tasks can be performed more rapidly by noncooperative receptors.

  11. Evaluating Changes in Ocular Redness Using a Novel Automated Method.

    PubMed

    Amparo, Francisco; Yin, Jia; Di Zazzo, Antonio; Abud, Tulio; Jurkunas, Ula V; Hamrah, Pedram; Dana, Reza

    2017-07-01

    To evaluate interobserver concordance in measured ocular redness among a group of raters using an objective computer-assisted method (ocular redness index [ORI]) and a group of clinicians using an ordinal comparative scale. We conducted a prospective study to evaluate ocular redness in clinical photographs of 12 patients undergoing pterygium surgery. Photographs were acquired preoperatively, and at 1 week and 1 month postoperatively. One group of clinicians graded conjunctival redness in the photographs using an image-based comparative scale. A second group applied the ORI to measure redness in the same photographs. We evaluated redness change between time points, level of agreement among raters, and assessed redness score differences among observers within each group. Interobserver agreement using the image-based redness scale was 0.458 (P < 0.001). Interobserver agreement with the ORI was 0.997 (P < 0.001). We observed statistically significant differences among clinicians' measurements obtained with the image-based redness scale (P < 0.001). There were no significant differences among measurements obtained with the ORI (P = 0.27). We observed a significant change in redness between baseline and follow-up visits with all scoring methods. Detailed analysis of redness change was performed only in the ORI group due to availability of continuous scores. Our findings suggest that the ORI scores provide higher consistency among raters than ordinal scales, and can discriminate redness changes that clinical observers often can miss. The ORI may be a reliable alternative to measure ocular redness objectively in the clinic and in clinical trials.

  12. Evaluating Changes in Ocular Redness Using a Novel Automated Method

    PubMed Central

    Amparo, Francisco; Yin, Jia; Di Zazzo, Antonio; Abud, Tulio; Jurkunas, Ula V.; Hamrah, Pedram; Dana, Reza

    2017-01-01

    Purpose To evaluate interobserver concordance in measured ocular redness among a group of raters using an objective computer-assisted method (ocular redness index [ORI]) and a group of clinicians using an ordinal comparative scale. Methods We conducted a prospective study to evaluate ocular redness in clinical photographs of 12 patients undergoing pterygium surgery. Photographs were acquired preoperatively, and at 1 week and 1 month postoperatively. One group of clinicians graded conjunctival redness in the photographs using an image-based comparative scale. A second group applied the ORI to measure redness in the same photographs. We evaluated redness change between time points, level of agreement among raters, and assessed redness score differences among observers within each group. Results Interobserver agreement using the image-based redness scale was 0.458 (P < 0.001). Interobserver agreement with the ORI was 0.997 (P < 0.001). We observed statistically significant differences among clinicians' measurements obtained with the image-based redness scale (P < 0.001). There were no significant differences among measurements obtained with the ORI (P = 0.27). We observed a significant change in redness between baseline and follow-up visits with all scoring methods. Detailed analysis of redness change was performed only in the ORI group due to availability of continuous scores. Conclusion Our findings suggest that the ORI scores provide higher consistency among raters than ordinal scales, and can discriminate redness changes that clinical observers often can miss. Translational Relevance The ORI may be a reliable alternative to measure ocular redness objectively in the clinic and in clinical trials. PMID:28736686

  13. Indigenous people's detection of rapid ecological change.

    PubMed

    Aswani, Shankar; Lauer, Matthew

    2014-06-01

    When sudden catastrophic events occur, it becomes critical for coastal communities to detect and respond to environmental transformations because failure to do so may undermine overall ecosystem resilience and threaten people's livelihoods. We therefore asked how capable of detecting rapid ecological change following massive environmental disruptions local, indigenous people are. We assessed the direction and periodicity of experimental learning of people in the Western Solomon Islands after a tsunami in 2007. We compared the results of marine science surveys with local ecological knowledge of the benthos across 3 affected villages and 3 periods before and after the tsunami. We sought to determine how people recognize biophysical changes in the environment before and after catastrophic events such as earthquakes and tsunamis and whether people have the ability to detect ecological changes over short time scales or need longer time scales to recognize changes. Indigenous people were able to detect changes in the benthos over time. Detection levels differed between marine science surveys and local ecological knowledge sources over time, but overall patterns of statistically significant detection of change were evident for various habitats. Our findings have implications for marine conservation, coastal management policies, and disaster-relief efforts because when people are able to detect ecological changes, this, in turn, affects how they exploit and manage their marine resources. © 2014 Society for Conservation Biology.

  14. Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

    NASA Astrophysics Data System (ADS)

    Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

    2016-02-01

    Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

  15. Foreign object detection and removal to improve automated analysis of chest radiographs

    SciTech Connect

    Hogeweg, Laurens; Sanchez, Clara I.; Melendez, Jaime; Maduskar, Pragnya; Ginneken, Bram van; Story, Alistair; Hayward, Andrew

    2013-07-15

    Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.

  16. Foreign object detection and removal to improve automated analysis of chest radiographs.

    PubMed

    Hogeweg, Laurens; Sánchez, Clara I; Melendez, Jaime; Maduskar, Pragnya; Story, Alistair; Hayward, Andrew; van Ginneken, Bram

    2013-07-01

    Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs. Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting. The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an Az value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores. The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.

  17. Automated detection system of single nucleotide polymorphisms using two kinds of functional magnetic nanoparticles

    NASA Astrophysics Data System (ADS)

    Liu, Hongna; Li, Song; Wang, Zhifei; Li, Zhiyang; Deng, Yan; Wang, Hua; Shi, Zhiyang; He, Nongyue

    2008-11-01

    Single nucleotide polymorphisms (SNPs) comprise the most abundant source of genetic variation in the human genome wide codominant SNPs identification. Therefore, large-scale codominant SNPs identification, especially for those associated with complex diseases, has induced the need for completely high-throughput and automated SNP genotyping method. Herein, we present an automated detection system of SNPs based on two kinds of functional magnetic nanoparticles (MNPs) and dual-color hybridization. The amido-modified MNPs (NH 2-MNPs) modified with APTES were used for DNA extraction from whole blood directly by electrostatic reaction, and followed by PCR, was successfully performed. Furthermore, biotinylated PCR products were captured on the streptavidin-coated MNPs (SA-MNPs) and interrogated by hybridization with a pair of dual-color probes to determine SNP, then the genotype of each sample can be simultaneously identified by scanning the microarray printed with the denatured fluorescent probes. This system provided a rapid, sensitive and highly versatile automated procedure that will greatly facilitate the analysis of different known SNPs in human genome.

  18. Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers.

    PubMed

    Maduskar, P; Muyoyeta, M; Ayles, H; Hogeweg, L; Peters-Bax, L; van Ginneken, B

    2013-12-01

    A busy urban health centre in Lusaka, Zambia. To compare the accuracy of automated reading (CAD4TB) with the interpretation of digital chest radiograph (CXR) by clinical officers for the detection of tuberculosis (TB). A retrospective analysis was performed on 161 subjects enrolled in a TB specimen bank study. CXRs were analysed using CAD4TB, which computed an image abnormality score (0-100). Four clinical officers scored the CXRs for abnormalities consistent with TB. We compared the automated readings and the readings by clinical officers against the bacteriological and radiological results used as reference. We report here the area under the receiver operating characteristic curve (AUC) and kappa (κ) statistics. Of 161 enrolled subjects, 97 had bacteriologically confirmed TB and 120 had abnormal CXR. The AUCs for CAD4TB and the clinical officers were respectively 0.73 and 0.65-0.75 in comparison with the bacteriological reference, and 0.91 and 0.89-0.94 in comparison with the radiological reference. P values indicated no significant differences, except for one clinical officer who performed significantly worse than CAD4TB (P < 0.05) using the bacteriological reference. κ values for CAD4TB and clinical officers with radiological reference were respectively 0.61 and 0.49-0.67. CXR assessment using CAD4TB and by clinical officers is comparable. CAD4TB has potential as a point-of-care test and for the automated identification of subjects who require further examinations.

  19. Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

    SciTech Connect

    Jurrus, Elizabeth R.; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R.; Ellisman, Mark H.; Jorgensen, Erik M.; Tasdizen, Tolga

    2013-01-01

    Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

  20. Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

    NASA Astrophysics Data System (ADS)

    Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

    2009-09-01

    We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

  1. Development of automated high throughput single molecular microfluidic detection platform for signal transduction analysis

    NASA Astrophysics Data System (ADS)

    Huang, Po-Jung; Baghbani Kordmahale, Sina; Chou, Chao-Kai; Yamaguchi, Hirohito; Hung, Mien-Chie; Kameoka, Jun

    2016-03-01

    Signal transductions including multiple protein post-translational modifications (PTM), protein-protein interactions (PPI), and protein-nucleic acid interaction (PNI) play critical roles for cell proliferation and differentiation that are directly related to the cancer biology. Traditional methods, like mass spectrometry, immunoprecipitation, fluorescence resonance energy transfer, and fluorescence correlation spectroscopy require a large amount of sample and long processing time. "microchannel for multiple-parameter analysis of proteins in single-complex (mMAPS)"we proposed can reduce the process time and sample volume because this system is composed by microfluidic channels, fluorescence microscopy, and computerized data analysis. In this paper, we will present an automated mMAPS including integrated microfluidic device, automated stage and electrical relay for high-throughput clinical screening. Based on this result, we estimated that this automated detection system will be able to screen approximately 150 patient samples in a 24-hour period, providing a practical application to analyze tissue samples in a clinical setting.

  2. Anger superiority effect for change detection and change blindness.

    PubMed

    Lyyra, Pessi; Hietanen, Jari K; Astikainen, Piia

    2014-11-01

    In visual search, an angry face in a crowd "pops out" unlike a happy or a neutral face. This "anger superiority effect" conflicts with views of visual perception holding that complex stimulus contents cannot be detected without focused top-down attention. Implicit visual processing of threatening changes was studied by recording event-related potentials (ERPs) using facial stimuli using the change blindness paradigm, in which conscious change detection is eliminated by presenting a blank screen before the changes. Already before their conscious detection, angry faces modulated relatively early emotion sensitive ERPs when appearing among happy and neutral faces, but happy faces only among neutral, not angry faces. Conscious change detection was more efficient for angry than happy faces regardless of background. These findings indicate that the brain can implicitly extract complex emotional information from facial stimuli, and the biological relevance of threatening contents can speed up their break up into visual consciousness. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Detectability of onsets versus offsets in the change detection paradigm.

    PubMed

    Cole, Geoff G; Kentridge, Robert W; Gellatly, Angus R H; Heywood, Charles A

    2003-01-01

    The human visual system is particularly sensitive to abrupt onset of new objects that appear in the visual field. Onsets have been shown to capture attention even when other transients simultaneously occur. This has led some authors to argue for the special role that object onset plays in attentional capture. However, evidence from the change detection paradigm appears contradictory to such findings. Studies of change blindness demonstrate that the onset of new objects can often go unnoticed. Assessing the relative detectability of onsets compared with other visual transients in a change detection procedure may help resolve this contradiction. We report the results of four experiments investigating the efficacy with which onsets capture attention compared with offsets. In Experiment 1, we employed a standard flicker procedure and assessed whether participants were more likely to detect the change following a frame containing an onset or following a frame containing an offset. In Experiment 2, we employed the one-shot method and investigated whether participants detected more onsets or offsets. Experiment 3 used the same method but assessed whether onsets would be detected more rapidly than offsets. In Experiment 4, we investigated whether the effect obtained in Experiments 1-3 using simple shapes would replicate when images of real-world objects were used. Results showed that onsets were less susceptible to change blindness than were offsets. We argue that the preservation of information is greater in onsets than in offsets.

  4. Airborne change detection system for the detection of route mines

    NASA Astrophysics Data System (ADS)

    Donzelli, Thomas P.; Jackson, Larry; Yeshnik, Mark; Petty, Thomas E.

    2003-09-01

    The US Army is interested in technologies that will enable it to maintain the free flow of traffic along routes such as Main Supply Routes (MSRs). Mines emplaced in the road by enemy forces under cover of darkness represent a major threat to maintaining a rapid Operational Tempo (OPTEMPO) along such routes. One technique that shows promise for detecting enemy mining activity is Airborne Change Detection, which allows an operator to detect suspicious day-to-day changes in and around the road that may be indicative of enemy mining. This paper presents an Airborne Change Detection that is currently under development at the US Army Night Vision and Electronic Sensors Directorate (NVESD). The system has been tested using a longwave infrared (LWIR) sensor on a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) and a midwave infrared (MWIR) sensor on a fixed wing aircraft. The system is described and results of the various tests conducted to date are presented.

  5. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

    SciTech Connect

    Petrick, N.; Chan, H.; Wei, D.; Sahiner, B.; Helvie, M.A.; Adler, D.D.

    1996-10-01

    This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90{percent} and 2.3 false positives per image at a true positive rate of 80{percent}. {copyright} {ital 1996 American Association of Physicists in Medicine.}

  6. Symmetry and appearance based automated detection of salient anatomical regions in ultrasound.

    PubMed

    Patwardhan, Kedar A

    2012-01-01

    In this paper we present a method for automated detection of enclosed anatomical regions in ultrasound images by utilizing the coarse shape symmetry as well as relative homogeneity of their sonographic appearance. The proposed method comprises of two steps: First, local phase based filtering [2] is used to detect points in the image which are roughly positioned along the axes of spatial symmetry with respect to structures around them. Secondly, the sonographic 'appearance' and location of these points is used to define a distance-map on the image, which is supplied to a simple fast-marching algorithm in order to provide the final feature detections. The method is robust to ultrasound speckle and works well with or without specialized pre-processing (e.g. speckle-reduction filtering). We illustrate the proposed method with qualitative results on in-vivo Ultrasound images.

  7. Automated feature detection and identification in digital point-ordered signals

    DOEpatents

    Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

    1998-01-01

    A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

  8. Automated detection and localization of bowhead whale sounds in the presence of seismic airgun surveys.

    PubMed

    Thode, Aaron M; Kim, Katherine H; Blackwell, Susanna B; Greene, Charles R; Nations, Christopher S; McDonald, Trent L; Macrander, A Michael

    2012-05-01

    An automated procedure has been developed for detecting and localizing frequency-modulated bowhead whale sounds in the presence of seismic airgun surveys. The procedure was applied to four years of data, collected from over 30 directional autonomous recording packages deployed over a 280 km span of continental shelf in the Alaskan Beaufort Sea. The procedure has six sequential stages that begin by extracting 25-element feature vectors from spectrograms of potential call candidates. Two cascaded neural networks then classify some feature vectors as bowhead calls, and the procedure then matches calls between recorders to triangulate locations. To train the networks, manual analysts flagged 219 471 bowhead call examples from 2008 and 2009. Manual analyses were also used to identify 1.17 million transient signals that were not whale calls. The network output thresholds were adjusted to reject 20% of whale calls in the training data. Validation runs using 2007 and 2010 data found that the procedure missed 30%-40% of manually detected calls. Furthermore, 20%-40% of the sounds flagged as calls are not present in the manual analyses; however, these extra detections incorporate legitimate whale calls overlooked by human analysts. Both manual and automated methods produce similar spatial and temporal call distributions.

  9. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    NASA Astrophysics Data System (ADS)

    Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

    2014-03-01

    Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

  10. Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification.

    PubMed

    Kennedy, Alan; Finlay, Dewar D; Guldenring, Daniel; Bond, Raymond R; Moran, Kieran; McLaughlin, James

    Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%).

  11. An automated method for detecting architectural distortions on mammograms using direction analysis of linear structures.

    PubMed

    Matsubara, T; Ito, A; Tsunomori, A; Hara, T; Muramatsu, C; Endo, T; Fujita, H

    2015-08-01

    Architectural distortion is one of the most important findings when evaluating mammograms for breast cancer. Abnormal breast architecture is characterized by the presence of spicules, which are distorted mammary structures that are not accompanied by an increased density or mass. We have been developing an automated method for detecting spiculated architectural distortions by analyzing linear structures extracted by normal curvature. However, some structures that are possibly related to distorted areas are not extracted using this method. The purpose of this study was to develop a new automated method for direction analysis of linear structures to improve detection performance in mammography. The direction of linear structures in each region of interest (ROI) was first determined using a direction filter and a background filter that can define one of eight directions (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°). The concentration and isotropic indexes were calculated using the determined direction of the linear structures in order to extract the candidate areas. Discriminant analysis was performed to eliminate false positives results. Our database consisted of 168 abnormal images containing 174 distorted areas and 580 normal images. The sensitivity of the new method was 81%. There were 2.6 and 4.2 false positives per image using the new and previous methods, respectively. These findings show that our new method is effective for detecting spiculated architectural distortions.

  12. Primer effect in the detection of mitochondrial DNA point heteroplasmy by automated sequencing.

    PubMed

    Calatayud, Marta; Ramos, Amanda; Santos, Cristina; Aluja, Maria Pilar

    2013-06-01

    The correct detection of mitochondrial DNA (mtDNA) heteroplasmy by automated sequencing presents methodological constraints. The main goals of this study are to investigate the effect of sense and distance of primers in heteroplasmy detection and to test if there are differences in the accurate determination of heteroplasmy involving transitions or transversions. A gradient of the heteroplasmy levels was generated for mtDNA positions 9477 (transition G/A) and 15,452 (transversion C/A). Amplification and subsequent sequencing with forward and reverse primers, situated at 550 and 150 bp from the heteroplasmic positions, were performed. Our data provide evidence that there is a significant difference between the use of forward and reverse primers. The forward primer is the primer that seems to give a better approximation to the real proportion of the variants. No significant differences were found concerning the distance at which the sequencing primers were placed neither between the analysis of transitions and transversions. The data collected in this study are a starting point that allows to glimpse the importance of the sequencing primers in the accurate detection of point heteroplasmy, providing additional insight into the overall automated sequencing strategy.

  13. Procedure for Automated Eddy Current Crack Detection in Thin Titanium Plates

    NASA Technical Reports Server (NTRS)

    Wincheski, Russell A.

    2012-01-01

    This procedure provides the detailed instructions for conducting Eddy Current (EC) inspections of thin (5-30 mils) titanium membranes with thickness and material properties typical of the development of Ultra-Lightweight diaphragm Tanks Technology (ULTT). The inspection focuses on the detection of part-through, surface breaking fatigue cracks with depths between approximately 0.002" and 0.007" and aspect ratios (a/c) of 0.2-1.0 using an automated eddy current scanning and image processing technique.

  14. Automated location detection of injection site for preclinical stereotactic neurosurgery procedure

    NASA Astrophysics Data System (ADS)

    Abbaszadeh, Shiva; Wu, Hemmings C. H.

    2017-03-01

    Currently, during stereotactic neurosurgery procedures, the manual task of locating the proper area for needle insertion or implantation of electrode/cannula/optic fiber can be time consuming. The requirement of the task is to quickly and accurately find the location for insertion. In this study we investigate an automated method to locate the entry point of region of interest. This method leverages a digital image capture system, pattern recognition, and motorized stages. Template matching of known anatomical identifiable regions is used to find regions of interest (e.g. Bregma) in rodents. For our initial study, we tackle the problem of automatically detecting the entry point.

  15. Updating National Topographic Data Base Using Change Detection Methods

    NASA Astrophysics Data System (ADS)

    Keinan, E.; Felus, Y. A.; Tal, Y.; Zilberstien, O.; Elihai, Y.

    2016-06-01

    The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

  16. Semi-automated unidirectional sequence analysis for mutation detection in a clinical diagnostic setting.

    PubMed

    Ellard, Sian; Shields, Beverley; Tysoe, Carolyn; Treacy, Rebecca; Yau, Shu; Mattocks, Christopher; Wallace, Andrew

    2009-06-01

    The past 10 years have seen an improvement in sequence data quality due to the introduction of capillary sequencers and new sequencing chemistries. In parallel, new software programs for automated mutation detection have been developed. We evaluated the sensitivity of semiautomated unidirectional sequence analysis for the detection of heterozygous base substitutions using the Mutation Surveyor software package. Detection rates for heterozygous base substitutions in 29 genes by automated and visual inspection were compared. Examples of heterozygous bases not detected in one direction during bidirectional analysis were also sought through a national survey of United Kingdom (UK) genetics laboratories. Sequence quality was assessed in a consecutive cohort of 50 patients for whom the 39 exons of the ABCC8 gene had been sequenced in one direction. A total of 701 different heterozygous base substitutions were detected by the software with no false negatives (sensitivity >or=99.57%). Four examples of heterozygous bases missed in one direction during bidirectional analysis were reported. Two were detected using unidirectional analysis settings, and the other two bases had low-quality scores. Of the 1950 amplicons examined, 97.2% had a quality score >or=30 and an average PHRED-like score >or=50 for the defined region of interest, and 98.1% of the 323,650 bases had a PHRED score >40. We found no evidence to support a requirement for bidirectional sequencing. Semiautomated analysis of good quality unidirectional sequence data has high sensitivity and is suitable for heterozygote mutation scanning in clinical diagnostic laboratories. Further work is required to determine minimum quality parameters for semiautomated analysis.

  17. Change Detection via Morphological Comparative Filters

    NASA Astrophysics Data System (ADS)

    Vizilter, Y. V.; Rubis, A. Y.; Zheltov, S. Y.; Vygolov, O. V.

    2016-06-01

    In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

  18. A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans.

    PubMed

    Tulum, Gökalp; Bolat, Bülent; Osman, Onur

    2017-04-01

    Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives. The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier. Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset. To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.

  19. Change Detection Experiments Using Low Cost UAVs

    NASA Technical Reports Server (NTRS)

    Logan, Michael J.; Vranas, Thomas L.; Motter, Mark; Hines, Glenn D.; Rahman, Zia-ur

    2005-01-01

    This paper presents the progress in the development of a low-cost change-detection system. This system is being developed to provide users with the ability to use a low-cost unmanned aerial vehicle (UAV) and image processing system that can detect changes in specific fixed ground locations using video provided by an autonomous UAV. The results of field experiments conducted with the US Army at Ft. A.P.Hill are presented.

  20. Automated High-Pressure Titration System with In Situ Infrared Spectroscopic Detection

    SciTech Connect

    Thompson, Christopher J.; Martin, Paul F.; Chen, Jeffrey; Benezeth, Pascale; Schaef, Herbert T.; Rosso, Kevin M.; Felmy, Andrew R.; Loring, John S.

    2014-04-17

    A fully automated titration system with infrared detection was developed for investigating interfacial chemistry at high pressures. The apparatus consists of a high-pressure fluid generation and delivery system coupled to a high-pressure cell with infrared optics. A manifold of electronically actuated valves is used to direct pressurized fluids into the cell. Precise reagent additions to the pressurized cell are made with calibrated tubing loops that are filled with reagent and placed in-line with the cell and a syringe pump. The cell’s infrared optics facilitate both transmission and attenuated total reflection (ATR) measurements to monitor bulk-fluid composition and solid-surface phenomena such as adsorption, desorption, complexation, dissolution, and precipitation. Switching between the two measurement modes is accomplished with moveable mirrors that direct radiation from a Fourier transform infrared spectrometer into the cell along transmission or ATR light paths. The versatility of the high-pressure IR titration system is demonstrated with three case studies. First, we titrated water into supercritical CO2 (scCO2) to generate an infrared calibration curve and determine the solubility of water in CO2 at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO2 at 50 °C and 90 bar. Transmission-mode spectra were used to quantify changes in the clay’s sorbed water concentration as a function of scCO2 hydration, and ATR measurements provided insights into competitive residency of water and CO2 on the clay surface and in the interlayer. Finally, we demonstrated how time-dependent studies can be conducted with the system by monitoring the carbonation reaction of forsterite (Mg2SiO4) in water-bearing scCO2 at 50 °C and 90 bar. Immediately after water dissolved in the scCO2, a thin film of adsorbed water formed on the mineral surface, and the film thickness increased with time as the forsterite began to dissolve

  1. Automated high-pressure titration system with in situ infrared spectroscopic detection

    NASA Astrophysics Data System (ADS)

    Thompson, Christopher J.; Martin, Paul F.; Chen, Jeffrey; Benezeth, Pascale; Schaef, Herbert T.; Rosso, Kevin M.; Felmy, Andrew R.; Loring, John S.

    2014-04-01

    A fully automated titration system with infrared detection was developed for investigating interfacial chemistry at high pressures. The apparatus consists of a high-pressure fluid generation and delivery system coupled to a high-pressure cell with infrared optics. A manifold of electronically actuated valves is used to direct pressurized fluids into the cell. Precise reagent additions to the pressurized cell are made with calibrated tubing loops that are filled with reagent and placed in-line with the cell and a syringe pump. The cell's infrared optics facilitate both transmission and attenuated total reflection (ATR) measurements to monitor bulk-fluid composition and solid-surface phenomena such as adsorption, desorption, complexation, dissolution, and precipitation. Switching between the two measurement modes is accomplished with moveable mirrors that direct the light path of a Fourier transform infrared spectrometer into the cell along transmission or ATR light paths. The versatility of the high-pressure IR titration system was demonstrated with three case studies. First, we titrated water into supercritical CO2 (scCO2) to generate an infrared calibration curve and determine the solubility of water in CO2 at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO2 at 50 °C and 90 bar. Transmission-mode spectra were used to quantify changes in the clay's sorbed water concentration as a function of scCO2 hydration, and ATR measurements provided insights into competitive residency of water and CO2 on the clay surface and in the interlayer. Finally, we demonstrated how time-dependent studies can be conducted with the system by monitoring the carbonation reaction of forsterite (Mg2SiO4) in water-bearing scCO2 at 50 °C and 90 bar. Immediately after water dissolved in the scCO2, a thin film of adsorbed water formed on the mineral surface, and the film thickness increased with time as the forsterite began to dissolve

  2. Automated high-pressure titration system with in situ infrared spectroscopic detection.

    PubMed

    Thompson, Christopher J; Martin, Paul F; Chen, Jeffrey; Benezeth, Pascale; Schaef, Herbert T; Rosso, Kevin M; Felmy, Andrew R; Loring, John S

    2014-04-01

    A fully automated titration system with infrared detection was developed for investigating interfacial chemistry at high pressures. The apparatus consists of a high-pressure fluid generation and delivery system coupled to a high-pressure cell with infrared optics. A manifold of electronically actuated valves is used to direct pressurized fluids into the cell. Precise reagent additions to the pressurized cell are made with calibrated tubing loops that are filled with reagent and placed in-line with the cell and a syringe pump. The cell's infrared optics facilitate both transmission and attenuated total reflection (ATR) measurements to monitor bulk-fluid composition and solid-surface phenomena such as adsorption, desorption, complexation, dissolution, and precipitation. Switching between the two measurement modes is accomplished with moveable mirrors that direct the light path of a Fourier transform infrared spectrometer into the cell along transmission or ATR light paths. The versatility of the high-pressure IR titration system was demonstrated with three case studies. First, we titrated water into supercritical CO2 (scCO2) to generate an infrared calibration curve and determine the solubility of water in CO2 at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO2 at 50 °C and 90 bar. Transmission-mode spectra were used to quantify changes in the clay's sorbed water concentration as a function of scCO2 hydration, and ATR measurements provided insights into competitive residency of water and CO2 on the clay surface and in the interlayer. Finally, we demonstrated how time-dependent studies can be conducted with the system by monitoring the carbonation reaction of forsterite (Mg2SiO4) in water-bearing scCO2 at 50 °C and 90 bar. Immediately after water dissolved in the scCO2, a thin film of adsorbed water formed on the mineral surface, and the film thickness increased with time as the forsterite began to

  3. Development of an Automated Microfluidic System for DNA Collection, Amplification, and Detection of Pathogens

    SciTech Connect

    Hagan, Bethany S.; Bruckner-Lea, Cynthia J.

    2002-12-01

    This project was focused on developing and testing automated routines for a microfluidic Pathogen Detection System. The basic pathogen detection routine has three primary components; cell concentration, DNA amplification, and detection. In cell concentration, magnetic beads are held in a flow cell by an electromagnet. Sample liquid is passed through the flow cell and bacterial cells attach to the beads. These beads are then released into a small volume of fluid and delivered to the peltier device for cell lysis and DNA amplification. The cells are lysed during initial heating in the peltier device, and the released DNA is amplified using polymerase chain reaction (PCR) or strand displacement amplification (SDA). Once amplified, the DNA is then delivered to a laser induced fluorescence detection unit in which the sample is detected. These three components create a flexible platform that can be used for pathogen detection in liquid and sediment samples. Future developments of the system will include on-line DNA detection during DNA amplification and improved capture and release methods for the magnetic beads during cell concentration.

  4. Change Point Detection in Correlation Networks

    NASA Astrophysics Data System (ADS)

    Barnett, Ian; Onnela, Jukka-Pekka

    2016-01-01

    Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.

  5. Change Point Detection in Correlation Networks

    PubMed Central

    Barnett, Ian; Onnela, Jukka-Pekka

    2016-01-01

    Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data. PMID:26739105

  6. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data

    NASA Astrophysics Data System (ADS)

    Ali, Abder-Rahman A.; Deserno, Thomas M.

    2012-02-01

    Malignant melanoma is the third most frequent type of skin cancer and one of the most malignant tumors, accounting for 79% of skin cancer deaths. Melanoma is highly curable if diagnosed early and treated properly as survival rate varies between 15% and 65% from early to terminal stages, respectively. So far, melanoma diagnosis is depending subjectively on the dermatologist's expertise. Computer-aided diagnosis (CAD) systems based on epiluminescense light microscopy can provide an objective second opinion on pigmented skin lesions (PSL). This work systematically analyzes the evidence of the effectiveness of automated melanoma detection in images from a dermatoscopic device. Automated CAD applications were analyzed to estimate their diagnostic outcome. Searching online databases for publication dates between 1985 and 2011, a total of 182 studies on dermatoscopic CAD were found. With respect to the systematic selection criterions, 9 studies were included, published between 2002 and 2011. Those studies formed databases of 14,421 dermatoscopic images including both malignant "melanoma" and benign "nevus", with 8,110 images being available ranging in resolution from 150 x 150 to 1568 x 1045 pixels. Maximum and minimum of sensitivity and specificity are 100.0% and 80.0% as well as 98.14% and 61.6%, respectively. Area under the receiver operator characteristics (AUC) and pooled sensitivity, specificity and diagnostics odds ratio are respectively 0.87, 0.90, 0.81, and 15.89. So, although that automated melanoma detection showed good accuracy in terms of sensitivity, specificity, and AUC, but diagnostic performance in terms of DOR was found to be poor. This might be due to the lack of dermatoscopic image resources (ground truth) that are needed for comprehensive assessment of diagnostic performance. In future work, we aim at testing this hypothesis by joining dermatoscopic images into a unified database that serves as a standard reference for dermatology related research in

  7. Sensor for detecting changes in magnetic fields

    DOEpatents

    Praeg, W.F.

    1980-02-26

    A sensor is described for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device that comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

  8. Sensor for detecting changes in magnetic fields

    DOEpatents

    Praeg, Walter F.

    1981-01-01

    A sensor for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

  9. Automated detection of extended sources in radio maps: progress from the SCORPIO survey

    NASA Astrophysics Data System (ADS)

    Riggi, S.; Ingallinera, A.; Leto, P.; Cavallaro, F.; Bufano, F.; Schillirò, F.; Trigilio, C.; Umana, G.; Buemi, C. S.; Norris, R. P.

    2016-08-01

    Automated source extraction and parametrization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper, we present a new algorithm, called CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parametrization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, also including different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the Evolutionary Map of the Universe (EMU) survey at the Australian Square Kilometre Array Pathfinder (ASKAP). The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.

  10. Automated Aflatoxin Analysis Using Inline Reusable Immunoaffinity Column Cleanup and LC-Fluorescence Detection.

    PubMed

    Rhemrev, Ria; Pazdanska, Monika; Marley, Elaine; Biselli, Scarlett; Staiger, Simone

    2015-01-01

    A novel reusable immunoaffinity cartridge containing monoclonal antibodies to aflatoxins coupled to a pressure resistant polymer has been developed. The cartridge is used in conjunction with a handling system inline to LC with fluorescence detection to provide fully automated aflatoxin analysis for routine monitoring of a variety of food matrixes. The handling system selects an immunoaffinity cartridge from a tray and automatically applies the sample extract. The cartridge is washed, then aflatoxins B1, B2, G1, and G2 are eluted and transferred inline to the LC system for quantitative analysis using fluorescence detection with postcolumn derivatization using a KOBRA® cell. Each immunoaffinity cartridge can be used up to 15 times without loss in performance, offering increased sample throughput and reduced costs compared to conventional manual sample preparation and cleanup. The system was validated in two independent laboratories using samples of peanuts and maize spiked at 2, 8, and 40 μg/kg total aflatoxins, and paprika, nutmeg, and dried figs spiked at 5, 20, and 100 μg/kg total aflatoxins. Recoveries exceeded 80% for both aflatoxin B1 and total aflatoxins. The between-day repeatability ranged from 2.1 to 9.6% for aflatoxin B1 for the six levels and five matrixes. Satisfactory Z-scores were obtained with this automated system when used for participation in proficiency testing (FAPAS®) for samples of chilli powder and hazelnut paste containing aflatoxins.

  11. Automated detection of coronal mass ejections in three-dimensions using multi-viewpoint observations

    NASA Astrophysics Data System (ADS)

    Hutton, J.; Morgan, H.

    2017-03-01

    A new, automated method of detecting coronal mass ejections (CMEs) in three dimensions for the LASCO C2 and STEREO COR2 coronagraphs is presented. By triangulating isolated CME signal from the three coronagraphs over a sliding window of five hours, the most likely region through which CMEs pass at 5 R⊙ is identified. The centre and size of the region gives the most likely direction of propagation and approximate angular extent. The Automated CME Triangulation (ACT) method is tested extensively using a series of synthetic CME images created using a wireframe flux rope density model, and on a sample of real coronagraph data; including halo CMEs. The accuracy of the angular difference (σ) between the detection and true input of the synthetic CMEs is σ = 7.14°, and remains acceptable for a broad range of CME positions relative to the observer, the relative separation of the three observers and even through the loss of one coronagraph. For real data, the method gives results that compare well with the distribution of low coronal sources and results from another instrument and technique made further from the Sun. The true three dimension (3D)-corrected kinematics and mass/density are discussed. The results of the new method will be incorporated into the CORIMP database in the near future, enabling improved space weather diagnostics and forecasting.

  12. Semi-Automated, Occupationally Safe Immunofluorescence Microtip Sensor for Rapid Detection of Mycobacterium Cells in Sputum

    PubMed Central

    Soelberg, Scott D.; Weigel, Kris M.; Hiraiwa, Morgan; Cairns, Andrew; Lee, Hyun-Boo; Furlong, Clement E.; Oh, Kieseok; Lee, Kyong-Hoon; Gao, Dayong; Chung, Jae-Hyun; Cangelosi, Gerard A.

    2014-01-01

    An occupationally safe (biosafe) sputum liquefaction protocol was developed for use with a semi-automated antibody-based microtip immunofluorescence sensor. The protocol effectively liquefied sputum and inactivated microorganisms including Mycobacterium tuberculosis, while preserving the antibody-binding activity of Mycobacterium cell surface antigens. Sputum was treated with a synergistic chemical-thermal protocol that included moderate concentrations of NaOH and detergent at 60°C for 5 to 10 min. Samples spiked with M. tuberculosis complex cells showed approximately 106-fold inactivation of the pathogen after treatment. Antibody binding was retained post-treatment, as determined by analysis with a microtip immunosensor. The sensor correctly distinguished between Mycobacterium species and other cell types naturally present in biosafe-treated sputum, with a detection limit of 100 CFU/mL for M. tuberculosis, in a 30-minute sample-to-result process. The microtip device was also semi-automated and shown to be compatible with low-cost, LED-powered fluorescence microscopy. The device and biosafe sputum liquefaction method opens the door to rapid detection of tuberculosis in settings with limited laboratory infrastructure. PMID:24465845

  13. A fully automated liquid–liquid extraction system utilizing interface detection

    PubMed Central

    Maslana, Eugene; Schmitt, Robert; Pan, Jeffrey

    2000-01-01

    The development of the Abbott Liquid-Liquid Extraction Station was a result of the need for an automated system to perform aqueous extraction on large sets of newly synthesized organic compounds used for drug discovery. The system utilizes a cylindrical laboratory robot to shuttle sample vials between two loading racks, two identical extraction stations, and a centrifuge. Extraction is performed by detecting the phase interface (by difference in refractive index) of the moving column of fluid drawn from the bottom of each vial containing a biphasic mixture. The integration of interface detection with fluid extraction maximizes sample throughput. Abbott-developed electronics process the detector signals. Sample mixing is performed by high-speed solvent injection. Centrifuging of the samples reduces interface emulsions. Operating software permits the user to program wash protocols with any one of six solvents per wash cycle with as many cycle repeats as necessary. Station capacity is eighty, 15 ml vials. This system has proven successful with a broad spectrum of both ethyl acetate and methylene chloride based chemistries. The development and characterization of this automated extraction system will be presented. PMID:18924693

  14. Electrochemical pesticide detection with AutoDip--a portable platform for automation of crude sample analyses.

    PubMed

    Drechsel, Lisa; Schulz, Martin; von Stetten, Felix; Moldovan, Carmen; Zengerle, Roland; Paust, Nils

    2015-02-07

    Lab-on-a-chip devices hold promise for automation of complex workflows from sample to answer with minimal consumption of reagents in portable devices. However, complex, inhomogeneous samples as they occur in environmental or food analysis may block microchannels and thus often cause malfunction of the system. Here we present the novel AutoDip platform which is based on the movement of a solid phase through the reagents and sample instead of transporting a sequence of reagents through a fixed solid phase. A ball-pen mechanism operated by an external actuator automates unit operations such as incubation and washing by consecutively dipping the solid phase into the corresponding liquids. The platform is applied to electrochemical detection of organophosphorus pesticides in real food samples using an acetylcholinesterase (AChE) biosensor. Minimal sample preparation and an integrated reagent pre-storage module hold promise for easy handling of the assay. Detection of the pesticide chlorpyrifos-oxon (CPO) spiked into apple samples at concentrations of 10(-7) M has been demonstrated. This concentration is below the maximum residue level for chlorpyrifos in apples defined by the European Commission.

  15. Parallax mitigation for hyperspectral change detection

    NASA Astrophysics Data System (ADS)

    Vongsy, Karmon; Eismann, Michael T.; Mendenhall, Michael J.; Velten, Vincent J.

    2014-06-01

    A pixel-level Generalized Likelihood Ratio Test (GLRT) statistic for hyperspectral change detection is developed to mitigate false change caused by image parallax. Change detection, in general, represents the difficult problem of discriminating significant changes opposed to insignificant changes caused by radiometric calibration, image registration issues, and varying view geometries. We assume that the images have been registered, and each pixel pair provides a measurement from the same spatial region in the scene. Although advanced image registration methods exist that can reduce mis-registration to subpixel levels; residual spatial mis-registration can still be incorrectly detected as significant changes. Similarly, changes in sensor viewing geometry can lead to parallax error in an urban cluttered scene where height structures, such as buildings, appear to move. Our algorithm looks to the inherent relationship between the image views and the theory of stereo vision to perform parallax mitigation leading to a search result in the assumed parallax direction. Mitigation of the parallax-induced false alarms is demonstrated using hyperspectral data in the experimental analysis. The algorithm is examined and compared to the existing chronochrome anomalous change detection algorithm to assess performance.

  16. How Small Can Impact Craters Be Detected at Large Scale by Automated Algorithms?

    NASA Astrophysics Data System (ADS)

    Bandeira, L.; Machado, M.; Pina, P.; Marques, J. S.

    2013-12-01

    The last decade has seen a widespread publication of crater detection algorithms (CDA) with increasing detection performances. The adaptive nature of some of the algorithms [1] has permitting their use in the construction or update of global catalogues for Mars and the Moon. Nevertheless, the smallest craters detected in these situations by CDA have 10 pixels in diameter (or about 2 km in MOC-WA images) [2] or can go down to 16 pixels or 200 m in HRSC imagery [3]. The availability of Martian images with metric (HRSC and CTX) and centimetric (HiRISE) resolutions is permitting to unveil craters not perceived before, thus automated approaches seem a natural way of detecting the myriad of these structures. In this study we present the efforts, based on our previous algorithms [2-3] and new training strategies, to push the automated detection of craters to a dimensional threshold as close as possible to the detail that can be perceived on the images, something that has not been addressed yet in a systematic way. The approach is based on the selection of candidate regions of the images (portions that contain crescent highlight and shadow shapes indicating a possible presence of a crater) using mathematical morphology operators (connected operators of different sizes) and on the extraction of texture features (Haar-like) and classification by Adaboost, into crater and non-crater. This is a supervised approach, meaning that a training phase, in which manually labelled samples are provided, is necessary so the classifier can learn what crater and non-crater structures are. The algorithm is intensively tested in Martian HiRISE images, from different locations on the planet, in order to cover the largest surface types from the geological point view (different ages and crater densities) and also from the imaging or textural perspective (different degrees of smoothness/roughness). The quality of the detections obtained is clearly dependent on the dimension of the craters

  17. A Validation of Automated and Quality Controlled Satellite Based Fire Detection

    NASA Astrophysics Data System (ADS)

    Ruminski, M. G.; Hanna, J.

    2010-12-01

    The Satellite Analysis Branch (SAB) of NOAA/NESDIS performs a daily fire analysis for North America utilizing GOES, NOAA POES and MODIS satellite data. Automated fire detection algorithms are employed for each of the sensors. The automated detections are evaluated against the underlying satellite imagery by analysts, with detections that are believed to be false positives removed and missed fires added to the analysis. Previous validation of automated detections has typically utilized very high resolution satellite data, such as ASTER (30m), coincident in space and time with the sensor being validated. While this approach is useful for evaluating algorithm detection capability at a specific time for fires that are not obscured there is a high likelihood that it does not provide a comprehensive evaluation based on all fire occurrences for the day. Fires that occur before or after the satellite overpass would not be included and those that are obscured by clouds would also not be accounted for. These are important considerations in assessing climatology and for emission estimates. This study utilizes ground based reports from Florida, Montana, Idaho and South Carolina which have well established reporting and permitting procedures. These ground reports are primarily agricultural and prescribe burns for which permits are required. While it is possible that permits are obtained but the burn is not performed it is felt that this represents a small fraction of the number reported based on communication with permitting officials. Only the Probability Of Detection (POD) is computed. A positive detection occurs for satellite detections within 8km of a reported fire. This buffer is employed to allow for known satellite navigation errors. Determining false positive detects would not be reliable since there is no way of knowing with certainty that a detected fire did not actually occur at a location. It could easily be an unreported fire. Results for Florida based on daily

  18. An automated and integrated framework for dust storm detection based on ogc web processing services

    NASA Astrophysics Data System (ADS)

    Xiao, F.; Shea, G. Y. K.; Wong, M. S.; Campbell, J.

    2014-11-01

    Dust storms are known to have adverse effects on public health. Atmospheric dust loading is also one of the major uncertainties in global climatic modelling as it is known to have a significant impact on the radiation budget and atmospheric stability. The complexity of building scientific dust storm models is coupled with the scientific computation advancement, ongoing computing platform development, and the development of heterogeneous Earth Observation (EO) networks. It is a challenging task to develop an integrated and automated scheme for dust storm detection that combines Geo-Processing frameworks, scientific models and EO data together to enable the dust storm detection and tracking processes in a dynamic and timely manner. This study develops an automated and integrated framework for dust storm detection and tracking based on the Web Processing Services (WPS) initiated by Open Geospatial Consortium (OGC). The presented WPS framework consists of EO data retrieval components, dust storm detecting and tracking component, and service chain orchestration engine. The EO data processing component is implemented based on OPeNDAP standard. The dust storm detecting and tracking component combines three earth scientific models, which are SBDART model (for computing aerosol optical depth (AOT) of dust particles), WRF model (for simulating meteorological parameters) and HYSPLIT model (for simulating the dust storm transport processes). The service chain orchestration engine is implemented based on Business Process Execution Language for Web Service (BPEL4WS) using open-source software. The output results, including horizontal and vertical AOT distribution of dust particles as well as their transport paths, were represented using KML/XML and displayed in Google Earth. A serious dust storm, which occurred over East Asia from 26 to 28 Apr 2012, is used to test the applicability of the proposed WPS framework. Our aim here is to solve a specific instance of a complex EO data

  19. VirusDetect: An automated pipeline for efficient virus discovery using deep sequencing of small RNAs.

    PubMed

    Zheng, Yi; Gao, Shan; Padmanabhan, Chellappan; Li, Rugang; Galvez, Marco; Gutierrez, Dina; Fuentes, Segundo; Ling, Kai-Shu; Kreuze, Jan; Fei, Zhangjun

    2017-01-01

    Accurate detection of viruses in plants and animals is critical for agriculture production and human health. Deep sequencing and assembly of virus-derived small interfering RNAs has proven to be a highly efficient approach for virus discovery. Here we present VirusDetect, a bioinformatics pipeline that can efficiently analyze large-scale small RNA (sRNA) datasets for both known and novel virus identification. VirusDetect performs both reference-guided assemblies through aligning sRNA sequences to a curated virus reference database and de novo assemblies of sRNA sequences with automated parameter optimization and the option of host sRNA subtraction. The assembled contigs are compared to a curated and classified reference virus database for known and novel virus identification, and evaluated for their sRNA size profiles to identify novel viruses. Extensive evaluations using plant and insect sRNA datasets suggest that VirusDetect is highly sensitive and efficient in identifying known and novel viruses. VirusDetect is freely available at http://bioinfo.bti.cornell.edu/tool/VirusDetect/. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Automated Detection and Extraction of Coronal Dimmings from SDO/AIA Data

    NASA Astrophysics Data System (ADS)

    Davey, Alisdair R.; Attrill, G. D. R.; Wills-Davey, M. J.

    2010-05-01

    The sheer volume of data anticipated from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) highlights the necessity for the development of automatic detection methods for various types of solar activity. Initially recognised in the 1970s, it is now well established that coronal dimmings are closely associated with coronal mass ejections (CMEs), and are particularly recognised as an indicator of front-side (halo) CMEs, which can be difficult to detect in white-light coronagraph data. An automated coronal dimming region detection and extraction algorithm removes visual observer bias from determination of physical quantities such as spatial location, area and volume. This allows reproducible, quantifiable results to be mined from very large datasets. The information derived may facilitate more reliable early space weather detection, as well as offering the potential for conducting large-sample studies focused on determining the geoeffectiveness of CMEs, coupled with analysis of their associated coronal dimmings. We present examples of dimming events extracted using our algorithm from existing EUV data, demonstrating the potential for the anticipated application to SDO/AIA data. Metadata returned by our algorithm include: location, area, volume, mass and dynamics of coronal dimmings. As well as running on historic datasets, this algorithm is capable of detecting and extracting coronal dimmings in near real-time. The coronal dimming detection and extraction algorithm described in this poster is part of the SDO/Computer Vision Center effort hosted at SAO (Martens et al., 2009). We acknowledge NASA grant NNH07AB97C.

  1. An automated lung nodule detection system for CT images using synthetic minority oversampling

    NASA Astrophysics Data System (ADS)

    Mehre, Shrikant A.; Mukhopadhyay, Sudipta; Dutta, Anirvan; Harsha, Nagam Chaithan; Dhara, Ashis Kumar; Khandelwal, Niranjan

    2016-03-01

    Pulmonary nodules are a potential manifestation of lung cancer, and their early detection can remarkably enhance the survival rate of patients. This paper presents an automated pulmonary nodule detection algorithm for lung CT images. The algorithm utilizes a two-stage approach comprising nodule candidate detection followed by reduction of false positives. The nodule candidate detection involves thresholding, followed by morphological opening. The geometrical features at this stage are selected from properties of nodule size and compactness, and lead to reduced number of false positives. An SVM classifier is used with a radial basis function kernel. The data imbalance, due to uneven distribution of nodules and non-nodules as a result of the candidate detection stage, is proposed to be addressed by oversampling of minority class using Synthetic Minority Over-sampling Technique (SMOTE), and over-imposition of its misclassification penalty. Experiments were performed on 97 CT scans of a publically-available (LIDC-IDRI) database. Performance is evaluated in terms of sensitivity and false positives per scan (FP/scan). Results indicate noteworthy performance of the proposed approach (nodule detection sensitivity after 4-fold cross-validation is 92.91% with 3 FP/scan). Comparative analysis also reflects a comparable and often better performance of the proposed setup over some of the existing techniques.

  2. Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units.

    PubMed

    Sackellares, J Chris; Shiau, Deng-Shan; Halford, Jonathon J; LaRoche, Suzette M; Kelly, Kevin M

    2011-12-01

    Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Evaluation of experimental UAV video change detection

    NASA Astrophysics Data System (ADS)

    Bartelsen, J.; Saur, G.; Teutsch, C.

    2016-10-01

    During the last ten years, the availability of images acquired from unmanned aerial vehicles (UAVs) has been continuously increasing due to the improvements and economic success of flight and sensor systems. From our point of view, reliable and automatic image-based change detection may contribute to overcoming several challenging problems in military reconnaissance, civil security, and disaster management. Changes within a scene can be caused by functional activities, i.e., footprints or skid marks, excavations, or humidity penetration; these might be recognizable in aerial images, but are almost overlooked when change detection is executed manually. With respect to the circumstances, these kinds of changes may be an indication of sabotage, terroristic activity, or threatening natural disasters. Although image-based change detection is possible from both ground and aerial perspectives, in this paper we primarily address the latter. We have applied an extended approach to change detection as described by Saur and Kr uger,1 and Saur et al.2 and have built upon the ideas of Saur and Bartelsen.3 The commercial simulation environment Virtual Battle Space 3 (VBS3) is used to simulate aerial "before" and "after" image acquisition concerning flight path, weather conditions and objects within the scene and to obtain synthetic videos. Video frames, which depict the same part of the scene, including "before" and "after" changes and not necessarily from the same perspective, are registered pixel-wise against each other by a photogrammetric concept, which is based on a homography. The pixel-wise registration is used to apply an automatic difference analysis, which, to a limited extent, is able to suppress typical errors caused by imprecise frame registration, sensor noise, vegetation and especially parallax effects. The primary concern of this paper is to seriously evaluate the possibilities and limitations of our current approach for image-based change detection with respect

  4. Support vector machine as a binary classifier for automated object detection in remotely sensed data

    NASA Astrophysics Data System (ADS)

    Wardaya, P. D.

    2014-02-01

    In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

  5. An evaluation of some factors affecting the detection of blood group antibodies by automated methods.

    PubMed

    Kolberg, J; Nordhagen, R

    1975-01-01

    Some factors affecting the sensitivity of the automated methods for blood group antibody detection have been evaluated. The experiments revealed influencing differences between various albumin preparations. In the BMC method, one lot of albumin permitted no significant antibody detection. In the LISP technique, a plateau of maximum Polybrene activity was found. The beginning of this plateau depended on both the albumin preparation and the Polybrene lot. In the BMC method, methyl cellulose gave optimal sensitivity within a concentration range of 0.3 to 0.5 per cent. The stability of test cells stored in ACD at 4 C was studied. All test cells could be used safely up to two weeks. Cells from different donors showed variable reactivity after three weeks.

  6. Joint Dictionary Learning for Multispectral Change Detection.

    PubMed

    Lu, Xiaoqiang; Yuan, Yuan; Zheng, Xiangtao

    2017-04-01

    Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors' knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data.

  7. Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

    PubMed Central

    Abuzaghleh, Omar; Barkana, Buket D.

    2015-01-01

    Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors’ subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively. PMID:27170906

  8. Holistic processing improves change detection but impairs change identification.

    PubMed

    Mathis, Katherine M; Kahan, Todd A

    2014-10-01

    It has been just over a century since Gestalt psychologists described the factors that contribute to the holistic processing of visually presented stimuli. Recent research indicates that holistic processing may come at a cost; specifically, the perception of holistic forms may reduce the visibility of constituent parts. In the present experiment, we examined change detection and change identification accuracy with Kanizsa rectangle patterns that were arranged to either form a Gestalt whole or not. Results from an experiment with 62 participants support this trade-off in processing holistic forms. Holistic processing improved the detection of change but obstructed its identification. Results are discussed in terms of both their theoretical significance and their application in areas ranging from baggage screening and the detection of changes in radiological images to the systems that are used to generate composite images of perpetrators on the basis of eyewitness reports.

  9. Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

    PubMed

    Akram, Usman M; Khan, Shoab A

    2012-10-01

    There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

  10. Genomic Data Quality Impacts Automated Detection of Lateral Gene Transfer in Fungi

    PubMed Central

    Dupont, Pierre-Yves; Cox, Murray P.

    2017-01-01

    Lateral gene transfer (LGT, also known as horizontal gene transfer), an atypical mechanism of transferring genes between species, has almost become the default explanation for genes that display an unexpected composition or phylogeny. Numerous methods of detecting LGT events all rely on two fundamental strategies: primary structure composition or gene tree/species tree comparisons. Discouragingly, the results of these different approaches rarely coincide. With the wealth of genome data now available, detection of laterally transferred genes is increasingly being attempted in large uncurated eukaryotic datasets. However, detection methods depend greatly on the quality of the underlying genomic data, which are typically complex for eukaryotes. Furthermore, given the automated nature of genomic data collection, it is typically impractical to manually verify all protein or gene models, orthology predictions, and multiple sequence alignments, requiring researchers to accept a substantial margin of error in their datasets. Using a test case comprising plant-associated genomes across the fungal kingdom, this study reveals that composition- and phylogeny-based methods have little statistical power to detect laterally transferred genes. In particular, phylogenetic methods reveal extreme levels of topological variation in fungal gene trees, the vast majority of which show departures from the canonical species tree. Therefore, it is inherently challenging to detect LGT events in typical eukaryotic genomes. This finding is in striking contrast to the large number of claims for laterally transferred genes in eukaryotic species that routinely appear in the literature, and questions how many of these proposed examples are statistically well supported. PMID:28235827

  11. Density estimation of Yangtze finless porpoises using passive acoustic sensors and automated click train detection.

    PubMed

    Kimura, Satoko; Akamatsu, Tomonari; Li, Songhai; Dong, Shouyue; Dong, Lijun; Wang, Kexiong; Wang, Ding; Arai, Nobuaki

    2010-09-01

    A method is presented to estimate the density of finless porpoises using stationed passive acoustic monitoring. The number of click trains detected by stereo acoustic data loggers (A-tag) was converted to an estimate of the density of porpoises. First, an automated off-line filter was developed to detect a click train among noise, and the detection and false-alarm rates were calculated. Second, a density estimation model was proposed. The cue-production rate was measured by biologging experiments. The probability of detecting a cue and the area size were calculated from the source level, beam patterns, and a sound-propagation model. The effect of group size on the cue-detection rate was examined. Third, the proposed model was applied to estimate the density of finless porpoises at four locations from the Yangtze River to the inside of Poyang Lake. The estimated mean density of porpoises in a day decreased from the main stream to the lake. Long-term monitoring during 466 days from June 2007 to May 2009 showed variation in the density 0-4.79. However, the density was fewer than 1 porpoise/km(2) during 94% of the period. These results suggest a potential gap and seasonal migration of the population in the bottleneck of Poyang Lake.

  12. Facile electrochemical method and corresponding automated instrument for the detection of furfural in insulation oil.

    PubMed

    Wang, Ruili; Huang, Xinjian; Wang, Lishi

    2016-02-01

    Determining the concentration of furfural contained in the insulation oil of a transformer has been established as a method to evaluate the health status of the transformer. However, the detection of furfural involves the employment of expensive instruments and/or time-consuming laboratorial operations. In this paper, we proposed a convenient electrochemical method to make the detection. The quantification of furfural was realized by extraction of furfural from oil phase to aqueous phase followed by reductive detection of furfural with differential pulse voltammetry (DPV) at a mercury electrode. This method is very sensitive and the limit of detection, corresponding to furfural contained in oil, is estimated to be 0.03 μg g(-1). Furthermore, excellent linearity can be obtained in the range of 0-10 μg g(-1). These features make the method very suitable for the determination of furfural in real situation. A fully automated instrument that can perform the operations of extraction and detection was developed, and this instrument enables the whole measurement to be finished within eight minutes. The methodology and the instrument were tested with real samples, and very favorable agreement between results obtained with this instrument and HPLC indicates that the proposed method along with instrument can be employed as a facile tool to diagnose the health status of aged transformers. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Automated detection and analysis of depolarization events in human cardiomyocytes using MaDEC.

    PubMed

    Szymanska, Agnieszka F; Heylman, Christopher; Datta, Rupsa; Gratton, Enrico; Nenadic, Zoran

    2016-08-01

    Optical imaging-based methods for assessing the membrane electrophysiology of in vitro human cardiac cells allow for non-invasive temporal assessment of the effect of drugs and other stimuli. Automated methods for detecting and analyzing the depolarization events (DEs) in image-based data allow quantitative assessment of these different treatments. In this study, we use 2-photon microscopy of fluorescent voltage-sensitive dyes (VSDs) to capture the membrane voltage of actively beating human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). We built a custom and freely available Matlab software, called MaDEC, to detect, quantify, and compare DEs of hiPS-CMs treated with the β-adrenergic drugs, propranolol and isoproterenol. The efficacy of our software is quantified by comparing detection results against manual DE detection by expert analysts, and comparing DE analysis results to known drug-induced electrophysiological effects. The software accurately detected DEs with true positive rates of 98-100% and false positive rates of 1-2%, at signal-to-noise ratios (SNRs) of 5 and above. The MaDEC software was also able to distinguish control DEs from drug-treated DEs both immediately as well as 10min after drug administration.

  14. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators

    PubMed Central

    Amann, Anton; Tratnig, Robert; Unterkofler, Karl

    2005-01-01

    Background A pivotal component in automated external defibrillators (AEDs) is the detection of ventricular fibrillation by means of appropriate detection algorithms. In scientific literature there exists a wide variety of methods and ideas for handling this task. These algorithms should have a high detection quality, be easily implementable, and work in real time in an AED. Testing of these algorithms should be done by using a large amount of annotated data under equal conditions. Methods For our investigation we simulated a continuous analysis by selecting the data in steps of one second without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and the files 7001 – 8210 of the AHA database. All algorithms were tested under equal conditions. Results For 5 well-known standard and 5 new ventricular fibrillation detection algorithms we calculated the sensitivity, specificity, and the area under their receiver operating characteristic. In addition, two QRS detection algorithms were included. These results are based on approximately 330 000 decisions (per algorithm). Conclusion Our values for sensitivity and specificity differ from earlier investigations since we used no preselection. The best algorithm is a new one, presented here for the first time. PMID:16253134

  15. Automated detection of asteroids in real-time with the Spacewatch telescope

    NASA Technical Reports Server (NTRS)

    Scotti, James Vernon; Gehrels, T.; Rabinowitz, David L.

    1992-01-01

    The Spacewatch telescope on Kitt Peak is being used to survey for near-earth asteroids using a Tektronix TK2048 CCD in scanning mode. We hope to identify suitable low delta v candidates amongst the near-earth asteroid population as possible exploration targets, to identify those objects which pose a danger to life on earth, and to study the physical properties of the objects in near-earth space. Between Sep. 1990 and Jun. 1991, 14 new earth-approaching asteroids including 1 Aten, 9 Apollo, and 4 Amor type asteroids were detected by automated software and discriminated by their angular rates from the rest of the detected asteroids in near-real time by the observer. The average of about 1.5 earth-approaching asteroids per month is comparable to the total number found by all other observatories combined. One other Apollo type asteroid was detected by the observer as a long trailed image. The positions of this last object were measured and the object was tracked by the observer in real time. This object was determined to be a 5-10 meter diameter object which passed within 170,000 kilometers of earth. Of the 14 automatically detected earth-approaching asteroids, 10 have been found at distances in excess of 0.5 AU from earth. An average of more than 2000 asteroids are detected each month. Positions, angular rates, and brightnesses are determined for each of these asteroids in real-time.

  16. NOVELTY DETECTION UNDER CHANGING ENVIRONMENTAL CONDITIONS

    SciTech Connect

    H. SOHN; K. WORDER; C. R. FARRAR

    2001-04-01

    The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

  17. Novelty detection under changing environmental conditions

    NASA Astrophysics Data System (ADS)

    Sohn, Hoon; Worden, Keith; Farrar, Charles R.

    2001-07-01

    The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

  18. Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

    PubMed Central

    Aguzzi, Jacopo; Costa, Corrado; Robert, Katleen; Matabos, Marjolaine; Antonucci, Francesca; Juniper, S. Kim; Menesatti, Paolo

    2011-01-01

    The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage

  19. Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network.

    PubMed

    Aguzzi, Jacopo; Costa, Corrado; Robert, Katleen; Matabos, Marjolaine; Antonucci, Francesca; Juniper, S Kim; Menesatti, Paolo

    2011-01-01

    The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage

  20. Automated detection of submerged navigational obstructions in freshwater impoundments with hull mounted sidescan sonar

    NASA Astrophysics Data System (ADS)

    Morris, Phillip A.

    The prevalence of low-cost side scanning sonar systems mounted on small recreational vessels has created improved opportunities to identify and map submerged navigational hazards in freshwater impoundments. However, these economical sensors also present unique challenges for automated techniques. This research explores related literature in automated sonar imagery processing and mapping technology, proposes and implements a framework derived from these sources, and evaluates the approach with video collected from a recreational grade sonar system. Image analysis techniques including optical character recognition and an unsupervised computer automated detection (CAD) algorithm are employed to extract the transducer GPS coordinates and slant range distance of objects protruding from the lake bottom. The retrieved information is formatted for inclusion into a spatial mapping model. Specific attributes of the sonar sensors are modeled such that probability profiles may be projected onto a three dimensional gridded map. These profiles are computed from multiple points of view as sonar traces crisscross or come near each other. As lake levels fluctuate over time so do the elevation points of view. With each sonar record, the probability of a hazard existing at certain elevations at the respective grid points is updated with Bayesian mechanics. As reinforcing data is collected, the confidence of the map improves. Given a lake's current elevation and a vessel draft, a final generated map can identify areas of the lake that have a high probability of containing hazards that threaten navigation. The approach is implemented in C/C++ utilizing OpenCV, Tesseract OCR, and QGIS open source software and evaluated in a designated test area at Lake Lavon, Collin County, Texas.

  1. Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy.

    PubMed

    Ernstsen, Christina L; Login, Frédéric H; Jensen, Helene H; Nørregaard, Rikke; Møller-Jensen, Jakob; Nejsum, Lene N

    2017-08-01

    To target bacterial pathogens that invade and proliferate inside host cells, it is necessary to design intervention strategies directed against bacterial attachment, cellular invasion and intracellular proliferation. We present an automated microscopy-based, fast, high-throughput method for analyzing size and number of intracellular bacterial colonies in infected tissue culture cells. Cells are seeded in 48-well plates and infected with a GFP-expressing bacterial pathogen. Following gentamicin treatment to remove extracellular pathogens, cells are fixed and cell nuclei stained. This is followed by automated microscopy and subsequent semi-automated spot detection to determine the number of intracellular bacterial colonies, their size distribution, and the average number per host cell. Multiple 48-well plates can be processed sequentially and the procedure can be completed in one working day. As a model we quantified intracellular bacterial colonies formed by uropathogenic Escherichia coli (UPEC) during infection of human kidney cells (HKC-8). Urinary tract infections caused by UPEC are among the most common bacterial infectious diseases in humans. UPEC can colonize tissues of the urinary tract and is responsible for acute, chronic, and recurrent infections. In the bladder, UPEC can form intracellular quiescent reservoirs, thought to be responsible for recurrent infections. In the kidney, UPEC can colonize renal epithelial cells and pass to the blood stream, either via epithelial cell disruption or transcellular passage, to cause sepsis. Intracellular colonies are known to be clonal, originating from single invading UPEC. In our experimental setup, we found UPEC CFT073 intracellular bacterial colonies to be heterogeneous in size and present in nearly one third of the HKC-8 cells. This high-throughput experimental format substantially reduces experimental time and enables fast screening of the intracellular bacterial load and cellular distribution of multiple

  2. Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

    PubMed

    Wickering, Ellis; Gaspard, Nicolas; Zafar, Sahar; Moura, Valdery J; Biswal, Siddharth; Bechek, Sophia; OʼConnor, Kathryn; Rosenthal, Eric S; Westover, M Brandon

    2016-06-01

    The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.

  3. Systematic comparison of automated geological feature detection methods for impact craters

    NASA Astrophysics Data System (ADS)

    Vinogradova, T.; Mjolsness, E.

    2001-12-01

    Accurate, automated crater counts will be essential in extrapolating from existing Mars crater catalogs to much larger catalogs of impact craters in high-resolution orbital imagery for use in relative dating of surfaces in such imagery. Once validated, automatic methods for performing crater counts could be integrated into tools such as the Planetary Image Atlas, which is designed to be a convenient interface through which a user can search for, display, and download images and other ancillary data for planetary Missions, and the Diamond Eye image mining system. Here we report on preliminary computational experiments in using a trainable feature detection algorithm [Burl et al. 2001] to detect craters in real and simulated Mars orbital imagery, and to derive approximate impact crater counts for geological use. In these experiments, we consider two uses of the trainable feature detector: first, directly as a crater detector, and second, as two detectors for sunlit and shadowed inner walls of craters which can then be assembled into a single crater detection based on multiple pieces of evidence. For both of these methods, we consider two data sources: one consisting of real Viking Orbiter imagery of Mars with human expert-supplied ground truth labels, and the other consisting of computer generated renderings of simplified, synthetic cratered terrain with 100% accurate ground truth labels and known, controllable crater density. Each detector reports out a numeric detection ``likelihood'' for every candidate crater. This likelihood must then be thresholded to produce a detection decision. For each combination of two data sources (one natural and one synthetic) and two crater detection methods (whole-crater and parts-model), we vary image complexity and finally measure detection accuracy. Detection accuracy is measured by a Receiver Operator Characteristic (ROC) curve in which detection efficiency (the fraction of true craters detected) and purity (the fraction of

  4. An Architecture for Automated Fire Detection Early Warning System Based on Geoprocessing Service Composition

    NASA Astrophysics Data System (ADS)

    Samadzadegan, F.; Saber, M.; Zahmatkesh, H.; Joze Ghazi Khanlou, H.

    2013-09-01

    Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in a Service Oriented Architecture (SOA) platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

  5. New Approaches on Automated Wrinkle Detection in Sheet Metal Components by Forming Simulation

    NASA Astrophysics Data System (ADS)

    Liewald, M.; Wurster, K.; Blaich, C.

    2011-05-01

    In production of passenger cars, geometry complexity of deep drawn body panels increases constantly. For that reason, sheet metal components are analyzed within finite element analysis (FEA) with regard to their feasibility in production and expected quality before production equipment, such as drawing dies, is manufactured. Main criteria for characterizing component quality are cracks and sidewall wrinkles. In particular, cracks occur due to local overload in sheet metal plane caused by inadequate process parameters such as too high friction or forming forces. In contrast, sidewall wrinkles are caused by an inadequate level of compressive stress in component areas without contact between sheet metal component and drawing die. In FEA, failure by cracks can be analyzed evaluating scalar values of thinning or strain distribution in forming limit diagram with regard to forming limit curve. In contrast, detecting sidewall wrinkles often requires a manual and visual inspection of simulation results by the user. Therefore, a procedure to detect sidewall wrinkles in an automated manner is presented in this paper. The presented method determines occurrence of sidewall wrinkles based on strain distribution in forming limit diagram. Utilization of the disclosed calculation strategy allows estimation of cracks and sidewall wrinkles simultaneously after one run of simulation code. The presented approach for automated detection of sidewall wrinkles in combination with multivariate statistics shows a tool for virtual engineering to optimize deep drawing processes. Prior to die manufacturing, optimization with regard to both sides of the process window is possible. Hence, an increase in design efficiency, design space and reduction of development time and costs can be achieved at the same time.

  6. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

    PubMed

    Abràmoff, Michael David; Lou, Yiyue; Erginay, Ali; Clarida, Warren; Amelon, Ryan; Folk, James C; Niemeijer, Meindert

    2016-10-01

    To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

  7. Automated coronary artery calcification detection on low-dose chest CT images

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Cham, Matthew D.; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

    2014-03-01

    Coronary artery calcification (CAC) measurement from low-dose CT images can be used to assess the risk of coronary artery disease. A fully automatic algorithm to detect and measure CAC from low-dose non-contrast, non-ECG-gated chest CT scans is presented. Based on the automatically detected CAC, the Agatston score (AS), mass score and volume score were computed. These were compared with scores obtained manually from standard-dose ECG-gated scans and low-dose un-gated scans of the same patient. The automatic algorithm segments the heart region based on other pre-segmented organs to provide a coronary region mask. The mitral valve and aortic valve calcification is identified and excluded. All remaining voxels greater than 180HU within the mask region are considered as CAC candidates. The heart segmentation algorithm was evaluated on 400 non-contrast cases with both low-dose and regular dose CT scans. By visual inspection, 371 (92.8%) of the segmentations were acceptable. The automated CAC detection algorithm was evaluated on 41 low-dose non-contrast CT scans. Manual markings were performed on both low-dose and standard-dose scans for these cases. Using linear regression, the correlation of the automatic AS with the standard-dose manual scores was 0.86; with the low-dose manual scores the correlation was 0.91. Standard risk categories were also computed. The automated method risk category agreed with manual markings of gated scans for 24 cases while 15 cases were 1 category off. For low-dose scans, the automatic method agreed with 33 cases while 7 cases were 1 category off.

  8. Automated microfluidically controlled electrochemical biosensor for the rapid and highly sensitive detection of Francisella tularensis.

    PubMed

    Dulay, Samuel B; Gransee, Rainer; Julich, Sandra; Tomaso, Herbert; O'Sullivan, Ciara K

    2014-09-15

    Tularemia is a highly infectious zoonotic disease caused by a Gram-negative coccoid rod bacterium, Francisella tularensis. Tularemia is considered as a life-threatening potential biological warfare agent due to its high virulence, transmission, mortality and simplicity of cultivation. In the work reported here, different electrochemical immunosensor formats for the detection of whole F. tularensis bacteria were developed and their performance compared. An anti-Francisella antibody (FB11) was used for the detection that recognises the lipopolysaccharide found in the outer membrane of the bacteria. In the first approach, gold-supported self-assembled monolayers of a carboxyl terminated bipodal alkanethiol were used to covalently cross-link with the FB11 antibody. In an alternative second approach F(ab) fragments of the FB11 antibody were generated and directly chemisorbed onto the gold electrode surface. The second approach resulted in an increased capture efficiency and higher sensitivity. Detection limits of 4.5 ng/mL for the lipopolysaccharide antigen and 31 bacteria/mL for the F. tularensis bacteria were achieved. Having demonstrated the functionality of the immunosensor, an electrode array was functionalised with the antibody fragment and integrated with microfluidics and housed in a tester set-up that facilitated complete automation of the assay. The only end-user intervention is sample addition, requiring less than one-minute hands-on time. The use of the automated microfluidic set-up not only required much lower reagent volumes but also the required incubation time was considerably reduced and a notable increase of 3-fold in assay sensitivity was achieved with a total assay time from sample addition to read-out of less than 20 min.

  9. Automated image classification applied to reconstituted human corneal epithelium for the early detection of toxic damage

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni Franco; Urani, Chiara; De Servi, Barbara; Meloni, Marisa

    2010-02-01

    For a long time acute eye irritation has been assessed by means of the DRAIZE rabbit test, the limitations of which are known. Alternative tests based on in vitro models have been proposed. This work focuses on the "reconstituted human corneal epithelium" (R-HCE), which resembles the corneal epithelium of the human eye by thickness, morphology and marker expression. Testing a substance on R-HCE involves a variety of methods. Herewith quantitative morphological analysis is applied to optical microscope images of R-HCE cross sections resulting from exposure to benzalkonium chloride (BAK). The short term objectives and the first results are the analysis and classification of said images. Automated analysis relies on feature extraction by the spectrum-enhancement algorithm, which is made sensitive to anisotropic morphology, and classification based on principal components analysis. The winning strategy has been the separate analysis of the apical and basal layers, which carry morphological information of different types. R-HCE specimens have been ranked by gross damage. The onset of early damage has been detected and an R-HCE specimen exposed to a low BAK dose has been singled out from the negative and positive control. These results provide a proof of principle for the automated classification of the specimens of interest on a purely morphological basis by means of the spectrum enhancement algorithm.

  10. Automated Detection of Dwarf Galaxies and Star Clusters in SMASH through the NOAO Data Lab

    NASA Astrophysics Data System (ADS)

    Olsen, Knut A.; Nidever, David L.; Fitzpatrick, Michael J.; Mighell, Kenneth J.; SMASH Collaboration; NOAO Data Lab Team

    2017-01-01

    We present an automated method, using the NOAO Data Lab environment, for the detection of dwarf galaxy-scale objects in catalog data from the Survey of the Magellanic Stellar History (SMASH). SMASH has imaged ~480 square degrees of the southern sky, over a partially filled area of 2400 square degrees, to 24th mag in gri (uz~23) using the Dark Energy Camera (DECam). The NOAO Data Lab (http://datalab.noao.edu) is being developed to support community research of the massive data sets now being derived from NOAO’s wide-field telescopes, in particular DECam. A key feature of the Data Lab is the ability to perform efficient automated analysis of catalog and imaging data. Our method, which is an example of this feature, allows for the rapid search of candidate dwarf galaxies and stellar clusters in deep catalog data. Using SMASH as the catalog data source, we easily recover the previously discovered Hydra II dwarf galaxy and SMASH-I LMC globular cluster, as well as a number of other potentially interesting candidate stellar systems.

  11. Automated Detection/Characterization of EUV Waves in SDO/AIA Data

    NASA Astrophysics Data System (ADS)

    Shih, A. Y.; Ireland, J.; Christe, S.; Hughitt, V. K.; Young, C.; Earnshaw, M. D.; Mayer, F.

    2012-12-01

    Although EUV waves in the solar corona (also called coronal bright fronts or "EIT waves") were first observed in 1996, many questions still remain about their nature and their association with other phenomena such as flares, CMEs, and Moreton waves. The high-resolution, high-cadence data from the Atmospheric Imaging Assembly (AIA) instrument on the Solar Dynamics Observatory (SDO) allows for unprecedented analysis of the kinematics and morphology of EUV waves. This information can be crucial for constraining and differentiating between theoretical models. While this analysis can be performed "by hand", the large volume of AIA data is well-suited for automated algorithms to detect and characterize these waves. We are developing such algorithms, which will generate a comprehensive catalog that can be used for statistical studies, and the biases of the algorithms can be well-studied using simulated data. We take advantage of imaging processing methods developed in Python, a general-purpose scientific computing language widely used used by multiple communities, as well as the SunPy Python library. We compare the results of our automated algorithms with other efforts that use more traditional, human-powered methods to identify and characterize EUV waves.

  12. Automated detection of the retinal from OCT spectral domain images of healthy eyes

    NASA Astrophysics Data System (ADS)

    Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

    2015-06-01

    Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retinal. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

  13. Automated detection of retinal layers from OCT spectral-domain images of healthy eyes

    NASA Astrophysics Data System (ADS)

    Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

    2015-12-01

    Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retina. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral-domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

  14. Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

    NASA Astrophysics Data System (ADS)

    Harvey, Neal R.; Levenson, Richard M.; Rimm, David L.

    2003-05-01

    Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

  15. Evaluation of an automated procedure for detecting frequency-following responses in American and Chinese neonates.

    PubMed

    Jeng, Fuh-Cherng; Peris, Kevin S; Hu, Jiong; Lin, Chia-Der

    2013-04-01

    To date, observations of the scalp-recorded frequency-following response (FFR) to voice pitch have depended on subjective interpretation of the experimenter. The purpose of this study was to develop and evaluate an automated procedure for detecting the presence of a response. Twenty American (9 boys, 1-3 days) and 20 Chinese (10 boys, 1-3 days) neonates were recruited. A Chinese monosyllable that mimicked the English vowel /i/ with a rising pitch (117-166 Hz) was used as the stimulus. Three objective indices (Frequency Error, Tracking Accuracy, and Pitch Strength) were computed from the recorded brain waves and the test results were compared with human judgments to calculate the sensitivity and specificity values. Results demonstrated that the automated procedure produced sensitivity values between 53-90% and specificity values between 80-100%, and could be used to assess the presence of an FFR for neonates who were born in a tonal or non-tonal language environment.

  16. Automated DNA mutation detection using universal conditions direct sequencing: application to ten muscular dystrophy genes

    PubMed Central

    2009-01-01

    Background One of the most common and efficient methods for detecting mutations in genes is PCR amplification followed by direct sequencing. Until recently, the process of designing PCR assays has been to focus on individual assay parameters rather than concentrating on matching conditions for a set of assays. Primers for each individual assay were selected based on location and sequence concerns. The two primer sequences were then iteratively adjusted to make the individual assays work properly. This generally resulted in groups of assays with different annealing temperatures that required the use of multiple thermal cyclers or multiple passes in a single thermal cycler making diagnostic testing time-consuming, laborious and expensive. These factors have severely hampered diagnostic testing services, leaving many families without an answer for the exact cause of a familial genetic disease. A search of GeneTests for sequencing analysis of the entire coding sequence for genes that are known to cause muscular dystrophies returns only a small list of laboratories that perform comprehensive gene panels. The hypothesis for the study was that a complete set of universal assays can be designed to amplify and sequence any gene or family of genes using computer aided design tools. If true, this would allow automation and optimization of the mutation detection process resulting in reduced cost and increased throughput. Results An automated process has been developed for the detection of deletions, duplications/insertions and point mutations in any gene or family of genes and has been applied to ten genes known to bear mutations that cause muscular dystrophy: DMD; CAV3; CAPN3; FKRP; TRIM32; LMNA; SGCA; SGCB; SGCG; SGCD. Using this process, mutations have been found in five DMD patients and four LGMD patients (one in the FKRP gene, one in the CAV3 gene, and two likely causative heterozygous pairs of variations in the CAPN3 gene of two other patients). Methods and assay

  17. Parametric probability distributions for anomalous change detection

    SciTech Connect

    Theiler, James P; Foy, Bernard R; Wohlberg, Brendt E; Scovel, James C

    2010-01-01

    The problem of anomalous change detection arises when two (or possibly more) images are taken of the same scene, but at different times. The aim is to discount the 'pervasive differences' that occur thoughout the imagery, due to the inevitably different conditions under which the images were taken (caused, for instance, by differences in illumination, atmospheric conditions, sensor calibration, or misregistration), and to focus instead on the 'anomalous changes' that actually take place in the scene. In general, anomalous change detection algorithms attempt to model these normal or pervasive differences, based on data taken directly from the imagery, and then identify as anomalous those pixels for which the model does not hold. For many algorithms, these models are expressed in terms of probability distributions, and there is a class of such algorithms that assume the distributions are Gaussian. By considering a broader class of distributions, however, a new class of anomalous change detection algorithms can be developed. We consider several parametric families of such distributions, derive the associated change detection algorithms, and compare the performance with standard algorithms that are based on Gaussian distributions. We find that it is often possible to significantly outperform these standard algorithms, even using relatively simple non-Gaussian models.

  18. Automatic change detection in spaceborne SAR imagery

    NASA Astrophysics Data System (ADS)

    Corr, Douglas G.; Whitehouse, Simon W.; Mott, David H.; Baldwin, Jim F.

    1996-06-01

    This paper describes a new technique of the automatic detection of change within synthetic aperture radar (SAR) images produced from satellite data. The interpretation of this type of imagery is difficult due to the combined effect of speckle, low resolution and the complexity of the radar signatures. The change detection technique that has been developed overcomes these problems by automatically measuring the degree of change between two images. The principle behind the technique used is that when satellite repeat orbits are at almost the same position in space then unless the scene has changed, the speckle pattern in the image will be unchanged. Comparison of images therefore reveals real change, not change due to fluctuating speckle patterns. The degree of change between two SAR images was measured by using the coherence function. Coherence has been studied for a variety of scene types: agricultural, forestry, domestic housing, small and large scale industrial complexes. Fuzzy set techniques, as well as direct threshold methods, have bee applied to the coherence data to determine places where change has occurred. The method has been validated using local information on building changes due to construction or demolition.

  19. Automated Algorithms to Identify Geostationary Satellites and Detect Mistagging using Concurrent Spatio-Temporal and Brightness Information

    NASA Astrophysics Data System (ADS)

    Dao, P.; Heinrich-Josties, E.; Boroson, T.

    2016-09-01

    Automated detection of changes of GEO satellites using photometry is fundamentally dependent on near real time association of non-resolved signatures and object identification. Non-statistical algorithms which rely on fixed positional boundaries for associating objects often results in mistags [1]. Photometry has been proposed to reduce the occurrence of mistags. In past attempts to include photometry, (1) the problem of correlation (with the catalog) has been decoupled from the photometry-based detection of change and mistagging and (2) positional information has not been considered simultaneously with photometry. The technique used in this study addresses both problems. It takes advantage of the fusion of both types of information and processes all information concurrently in a single statistics-based framework. This study demonstrates with Las Cumbres Observatory Global Telescope Network (LCOGT) data that metric information, i.e. right ascension, declination, photometry and GP element set, can be used concurrently to confidently associate (identify) GEO objects. All algorithms can easily be put into a framework to process data in near-real-time.

  20. Validation of a classification system to grade fractionation in atrial fibrillation and correlation with automated detection systems.

    PubMed

    Hunter, Ross J; Diab, Ihab; Thomas, Glyn; Duncan, Edward; Abrams, Dominic; Dhinoja, Mehul; Sporton, Simon; Earley, Mark J; Schilling, Richard J

    2009-12-01

    We tested application of a grading system describing complex fractionated electrograms (CFE) in atrial fibrillation (AF) and used it to validate automated CFE detection (AUTO). Ten seconds bipolar electrograms were classified by visual inspection (VI) during ablation of persistent AF and the result compared with offline manual measurement (MM) by a second blinded operator: Grade 1 uninterrupted fractionated activity (defined as segments > or =70 ms) for > or =70% of recording and uninterrupted > or =1 s; Grade 2 interrupted fractionated activity > or =70% of recording; Grade 3 intermittent fractionated activity 30-70%; Grade 4 discrete (<70 ms) complex electrogram (> or =5 direction changes); Grade 5 discrete simple electrograms (< or =4 direction changes); Grade 6 scar. Grade by VI and MM for 100 electrograms agreed in 89%. Five hundred electrograms were graded on Carto and NavX by VI to validate AUTO in (i) detection of CFE (grades 1-4 considered CFE), and (ii) assessing degree of fractionation by correlating grade and score by AUTO (data shown as sensitivity, specificity, r): NavX 'CFE mean' 92%, 91%, 0.56; Carto 'interval confidence level' using factory settings 89%, 62%, -0.72, and other published settings 80%, 74%, -0.65; Carto 'shortest confidence interval' 74%, 70%, 0.43; Carto 'average confidence interval' 86%, 66%, 0.53. Grading CFE by VI is accurate and correlates with AUTO.

  1. Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

    NASA Astrophysics Data System (ADS)

    Gaffney, Kevin P.; Aghaei, Faranak; Battiste, James; Zheng, Bin

    2017-03-01

    Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

  2. The automated system of detection and research of pollution in the atmosphere

    NASA Astrophysics Data System (ADS)

    Isakova, Anna I.; Smal, Oksana V.; Chistyakova, Liliya K.; Penin, Sergei T.

    2004-02-01

    In the paper, the automated system of data processing (ASDP) for a hardware complex DAN-2, assigned for registration of emission and absorption of optical and the microwave radiation initiated by gas-aerosol pollution in the atmosphere, is presented. The complex DAN-2 has been developed in the Institute of Atsmospheric Optics of the Siberian Branch of the Russian Academy of Science. In the ASDP, a problem of automation of recording processes, storage and processing of the information measured in experiment has been solved. Using in ASDP subsystems of the forecast of optical noise, the forecast of distribution of an impurity in a plume of gas-aerosol emission from industrial plants allows us to carry out the express-analysis of ecological pollution in the inspection zone. Application of a modular principle has created an opportunity to realize all subsystems ASPD independently from each other, thus, they can operate as independently, and in the general complex of programs. As a tool for creation of the system software, the object-oriented instrument of programming Delphi 5.0 has been chosen. It has a number of advantages and distinctive features such as the convenient graphic interface with displaying of calculation results as uniform scrolling tables and graphics, access to the data files, high speed of mathematical calculations, an opportunity of the further expansion and change of the calculation algorithms. Use of the ASPD has allowed us to improve quality of data recording, their processing, and visualization of the processed results. For the first time in the automated system, the complex estimation of ecological situation with use of experimental data in real time has been realized. The ASPD can be used also by other experimental equipment intended for the solution of problems of the atmospheric optics.

  3. Automated 3D dendritic spine detection and analysis from two-photon microscopy

    NASA Astrophysics Data System (ADS)

    Koh, Ingrid Y. Y.; Lindquist, W. Brent

    2001-04-01

    The functional significance of dendritic spines and their plasticity to a wide spectrum of developmental and pathological conditions has led to extensive studies based on spine morphology. The advances in image acquisition techniques and the associated generation of large 3D data sets of optical micrographs have not been accompanied by comparable advances in data analysis techniques. We present an automated 3D spine detection and quantification procedure suitable for images obtained by laser scanning microscopy. The image is first processed by deconvolution and the dendritic phase consisting of the neuronal cytoplasm is extracted by segmentation. Spines are detected as geometrical protrusions relative to the dendritic backbone. As very thin necks may not be imaged, some spine `heads' may be detached from the dendrite and are detected as detached components. These detected heads are merged with spine `bases' where appropriate. Morphological characterizations on spine length, volume, density and shape classifications are obtained. For time-lapse data, images are registered and individual spines are traced through the image sequence. Successful comparison results on spine lengths and densities with manual analysis are obtained. This method is highly automatic and allows detailed and objective quantification of the structure and dynamics of dendritic spines, which can be important predictors for the function of neural networks.

  4. Time-Gated Orthogonal Scanning Automated Microscopy (OSAM) for High-speed Cell Detection and Analysis

    NASA Astrophysics Data System (ADS)

    Lu, Yiqing; Xi, Peng; Piper, James A.; Huo, Yujing; Jin, Dayong

    2012-11-01

    We report a new development of orthogonal scanning automated microscopy (OSAM) incorporating time-gated detection to locate rare-event organisms regardless of autofluorescent background. The necessity of using long-lifetime (hundreds of microseconds) luminescent biolabels for time-gated detection implies long integration (dwell) time, resulting in slow scan speed. However, here we achieve high scan speed using a new 2-step orthogonal scanning strategy to realise on-the-fly time-gated detection and precise location of 1-μm lanthanide-doped microspheres with signal-to-background ratio of 8.9. This enables analysis of a 15 mm × 15 mm slide area in only 3.3 minutes. We demonstrate that detection of only a few hundred photoelectrons within 100 μs is sufficient to distinguish a target event in a prototype system using ultraviolet LED excitation. Cytometric analysis of lanthanide labelled Giardia cysts achieved a signal-to-background ratio of two orders of magnitude. Results suggest that time-gated OSAM represents a new opportunity for high-throughput background-free biosensing applications.

  5. Automated detection of pain from facial expressions: a rule-based approach using AAM

    NASA Astrophysics Data System (ADS)

    Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

    2012-02-01

    In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

  6. An automated dental caries detection and scoring system for optical images of tooth occlusal surface.

    PubMed

    Ghaedi, Leila; Gottlieb, Riki; Sarrett, David C; Ismail, Amid; Belle, Ashwin; Najarian, Kayvan; Hargraves, Rosalyn Hobson

    2014-01-01

    Dental caries are one of the most prevalent chronic diseases. The management of dental caries demands detection of carious lesions at early stages. This study aims to design an automated system to detect and score caries lesions based on optical images of the occlusal tooth surface according to the International Caries Detection and Assessment System (ICDAS) guidelines. The system detects the tooth boundaries and irregular regions, and extracts 77 features from each image. These features include statistical measures of color space, grayscale image, as well as Wavelet Transform and Fourier Transform based features. Used in this study were 88 occlusal surface photographs of extracted teeth examined and scored by ICDAS experts. Seven ICDAS codes which show the different stages in caries development were collapsed into three classes: score 0, scores 1 and 2, and scores 3 to 6. The system shows accuracy of 86.3%, specificity of 91.7%, and sensitivity of 83.0% in ten-fold cross validation in classification of the tooth images. While the system needs further improvement and validation using larger datasets, it presents promising potential for clinical diagnostics with high accuracy and minimal cost. This is a notable advantage over existing systems requiring expensive imaging and external hardware.

  7. Multi-laboratory evaluation of an automated microbial detection/identification system.

    PubMed

    Smith, P B; Gavan, T L; Isenberg, H D; Sonnenwirth, A; Taylor, W I; Washington, J A; Balows, A

    1978-12-01

    An automated and computerized system (Automicrobic System [AMS]) for the detection of frequently encountered bacteria in clinical urine specimens was tested in a collaborative study among six laboratories. The sensitivity, specificity, reliability, and reproducibility of the AMS were determined, and the system was compared with conventional detection and identification systems. In this study, pure cultures and mixtures of pure cultures were used to simulate clinical urine specimens. With pure cultures, the sensitivity of the AMS in identifying the nine groups of organisms most commonly found in urine averaged 92.8%. The specificity averaged 99.4%, and the reliability of a positive result averaged 92.1%. The latter value was strongly influenced by a relatively high occurrence of false positive Escherichia coli results. The AMS was capable of detecting growth of most organisms, including those which it was not designed to identify. However, it identified some of these incorrectly as common urinary tract flora. Reproducibility of results, both within laboratories and among different laboratories, was high. Fast-growing organisms, such as E. coli and Klebsiella/Enterobacter species, were detected often at cell populations well below the AMS enumeration threshold of 70,000/ml. In mixed culture studies, high levels of sensitivity and specificity were maintained but when Serratia species were present in mixtures with other organisms, there was often a false positive report of E. coli. The overall performance of the AMS was considered satisfactory under the test conditions used.

  8. Knee X-ray image analysis method for automated detection of Osteoarthritis

    PubMed Central

    Shamir, Lior; Ling, Shari M.; Scott, William W.; Bos, Angelo; Orlov, Nikita; Macura, Tomasz; Eckley, D. Mark; Ferrucci, Luigi; Goldberg, Ilya G.

    2008-01-01

    We describe a method for automated detection of radiographic Osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays, and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org. PMID:19342330

  9. Automated detection of exudates and macula for grading of diabetic macular edema.

    PubMed

    Akram, M Usman; Tariq, Anam; Khan, Shoab A; Javed, M Younus

    2014-04-01

    Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Automated type specific ELISA probe detection of amplified NS3 gene products of dengue viruses.

    PubMed Central

    Chow, V T; Yong, R Y; Ngoh, B L; Chan, Y C

    1997-01-01

    AIM: To apply an automated system of nucleic acid hybridisation coupled with the enzyme linked immunosorbent assay (ELISA) for the type specific detection of amplification products of dengue viruses. METHODS: Non-structural 3 (NS3) gene targets of reference strains of all four dengue and other flaviviruses, as well as dengue patient viraemic sera, were subjected to reverse transcription and polymerase chain reaction using consensus and dengue type specific primers and digoxigenin-11-dUTP label incorporation. The amplification products were detected by biotinylated type specific primers which served as ELISA capture probes bound to streptavidin coated tubes. RESULTS: Significantly high spectrophotometric absorbance readings were obtained by hybridisation of the consensus and seminested amplification products of all four dengue viruses with their respective capture probes. In contrast, extremely low absorbances were observed for consensus products of Japanese encephalitis, yellow fever, and Kunjin viruses, which served as negative controls. These ELISA data correlated well with agarose gel electrophoresis of dengue type specific amplified products of diagnostic sizes. CONCLUSIONS: The combination of in vitro amplification and antibody based detection offers rapid, type specific, high throughput, and gel-free detection of amplified products of dengue viruses. Images PMID:9215155

  11. AMSNEXRAD-Automated detection of meteorite strewnfields in doppler weather radar

    NASA Astrophysics Data System (ADS)

    Hankey, Michael; Fries, Marc; Matson, Rob; Fries, Jeff

    2017-09-01

    For several years meteorite recovery in the United States has been greatly enhanced by using Doppler weather radar images to determine possible fall zones for meteorites produced by witnessed fireballs. While most fireball events leave no record on the Doppler radar, some large fireballs do. Based on the successful recovery of 10 meteorite falls 'under the radar', and the discovery of radar on more than 10 historic falls, it is believed that meteoritic dust and or actual meteorites falling to the ground have been recorded on Doppler weather radar (Fries et al., 2014). Up until this point, the process of detecting the radar signatures associated with meteorite falls has been a manual one and dependent on prior accurate knowledge of the fall time and estimated ground track. This manual detection process is labor intensive and can take several hours per event. Recent technological developments by NOAA now help enable the automation of these tasks. This in combination with advancements by the American Meteor Society (Hankey et al., 2014) in the tracking and plotting of witnessed fireballs has opened the possibility for automatic detection of meteorites in NEXRAD Radar Archives. Here in the processes for fireball triangulation, search area determination, radar interfacing, data extraction, storage, search, detection and plotting are explained.

  12. UAS imaging for automated crop lodging detection: a case study over an experimental maize field

    NASA Astrophysics Data System (ADS)

    Chu, Tianxing; Starek, Michael J.; Brewer, Michael J.; Masiane, Tiisetso; Murray, Seth C.

    2017-05-01

    Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in corn. A variety of contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may lead to lodging before harvesting season. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address this problem, this research focuses on the use of unmanned aircraft systems (UAS) for automated detection of crop lodging. The study was conducted over an experimental corn field at the Texas A and M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the corn field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height, and rates of growth. A lodging index for detecting corn lodging was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates well with the ground-measured height. More importantly, the lodging index can effectively reflect severity of corn lodging and yield after harvesting.

  13. Development and validation of a fully automated system for detection and diagnosis of mammographic lesions.

    PubMed

    Casti, Paola; Mencattini, Arianna; Salmeri, Marcello; Ancona, Antonietta; Mangieri, Fabio; Rangayyan, Rangaraj M

    2014-01-01

    We present a comprehensive and fully automated system for computer-aided detection and diagnosis of masses in mammograms. Novel methods for detection include: selection of suspicious focal areas based on analysis of the gradient vector field, rejection of oriented components of breast tissue using multidirectional Gabor filtering, and use of differential features for rejection of false positives (FPs) via clustering of the surrounding fibroglandular tissue. The diagnosis step is based on extraction of contour-independent features for characterization of lesions as benign or malignant from automatically detected circular and annular regions. A new unified 3D free-response receiver operating characteristic framework is introduced for global analysis of two binary categorization problems in cascade. In total, 3,080 suspicious focal areas were extracted from a set of 156 full-field digital mammograms, including 26 malignant tumors, 120 benign lesions, and 18 normal mammograms. The proposed system detected and diagnosed malignant tumors with a sensitivity of 0.96, 0.92, and 0.88 at, respectively, 1.83, 0.46, and 0.45 FPs/image, with two stages of stepwise logistic regression for selection of features, a cascade of Fisher linear discriminant analysis and an artificial neural network with radial basis functions, and leave-one-patient-out cross-validation.

  14. Creating an automated chiller fault detection and diagnostics tool using a data fault library.

    PubMed

    Bailey, Margaret B; Kreider, Jan F

    2003-07-01

    Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.

  15. Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances.

    PubMed

    Navarrete, Miguel; Pyrzowski, Jan; Corlier, Juliana; Valderrama, Mario; Le Van Quyen, Michel

    2017-02-21

    In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of high-frequency oscillations (HFOs, > 40 Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions.

  16. A Novel Fully Automated Molecular Diagnostic System (AMDS) for Colorectal Cancer Mutation Detection

    PubMed Central

    Kitano, Shiro; Myers, Jamie; Nakamura, Junko; Yamane, Akio; Yamashita, Mami; Nakayama, Masato; Tsukahara, Yusuke; Ushida, Hiroshi; Liu, Wanqing; Ratain, Mark J.; Amano, Masahiko

    2013-01-01

    Background KRAS, BRAF and PIK3CA mutations are frequently observed in colorectal cancer (CRC). In particular, KRAS mutations are strong predictors for clinical outcomes of EGFR-targeted treatments such as cetuximab and panitumumab in metastatic colorectal cancer (mCRC). For mutation analysis, the current methods are time-consuming, and not readily available to all oncologists and pathologists. We have developed a novel, simple, sensitive and fully automated molecular diagnostic system (AMDS) for point of care testing (POCT). Here we report the results of a comparison study between AMDS and direct sequencing (DS) in the detection of KRAS, BRAF and PI3KCA somatic mutations. Methodology/Principal Finding DNA was extracted from a slice of either frozen (n = 89) or formalin-fixed and paraffin-embedded (FFPE) CRC tissue (n = 70), and then used for mutation analysis by AMDS and DS. All mutations (n = 41 among frozen and 27 among FFPE samples) detected by DS were also successfully (100%) detected by the AMDS. However, 8 frozen and 6 FFPE samples detected as wild-type in the DS analysis were shown as mutants in the AMDS analysis. By cloning-sequencing assays, these discordant samples were confirmed as true mutants. One sample had simultaneous “hot spot” mutations of KRAS and PIK3CA, and cloning assay comfirmed that E542K and E545K were not on the same allele. Genotyping call rates for DS were 100.0% (89/89) and 74.3% (52/70) in frozen and FFPE samples, respectively, for the first attempt; whereas that of AMDS was 100.0% for both sample sets. For automated DNA extraction and mutation detection by AMDS, frozen tissues (n = 41) were successfully detected all mutations within 70 minutes. Conclusions/Significance AMDS has superior sensitivity and accuracy over DS, and is much easier to execute than conventional labor intensive manual mutation analysis. AMDS has great potential for POCT equipment for mutation analysis. PMID:23671647

  17. Monitoring gypsy moth defoliation by applying change detection techniques to Landsat imagery

    NASA Technical Reports Server (NTRS)

    Williams, D. L.; Stauffer, M. L.

    1978-01-01

    The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.

  18. Monitoring gypsy moth defoliation by applying change detection techniques to Landsat imagery

    NASA Technical Reports Server (NTRS)

    Williams, D. L.; Stauffer, M. L.

    1978-01-01

    The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.

  19. Digital breast tomosynthesis: feasibility of automated detection of microcalcification clusters on projections views

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir M.; Chan, Heang-Ping; Wei, Jun; Sahiner, Berkman; Zhou, Chuan; Helvie, Mark A.

    2010-03-01

    We are developing a computer-aided detection (CAD) system to assist radiologists in detecting microcalcification clusters in digital breast tomosynthesis (DBT). The purpose of this study is to investigate the feasibility of a 2D approach using the projection-view (PV) images as input. In the first stage, automated detection of the microcalcification clusters on the PVs is performed. In the second stage, the detected cluster candidates or the individual microcalcifications on the PVs are back-projected to the 3D volume. The true clusters or microcalcifications will therefore converge at their focal planes and ideally will result in higher cluster or microcalcification scores than the FPs. In the final step an analysis of the back-projected cluster or microcalcification candidates is performed to differentiate the true and false clusters. In this pilot study, a limited data set of 39 cases with biopsy proven microcalcification clusters (17 malignant, 22 benign) was used. The DBT scans were obtained in both CC and MLO views using a GE GEN2 prototype system which acquires 21 PVs over a 60º arc in 3º increments. In the 78 DBT volumes, a total of 74 clusters (33 malignant clusters in 34 breasts and 41 benign clusters in 44 breasts) were identified by an experienced radiologist. The computer detected 61% (956/1554) of the clusters on the PVs from the 74 scans. After back-projection of the microcalcification candidates detected on the individual PVs and excluding the first few PVs that had higher noise in back-projection stage, 84% (62/74) of the true clusters were detected in the 3D volume. Study is underway to develop methods to reduce FPs and to compare this 2D approach with 3D or combined 2D and 3D approaches.

  20. Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images.

    PubMed

    Gao, Zhifan; Guo, Wei; Liu, Xin; Huang, Wenhua; Zhang, Heye; Tan, Ning; Hau, William Kongto; Zhang, Yuan-Ting; Liu, Huafeng

    2014-01-01

    Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

  1. Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

    PubMed Central

    Liu, Xin; Huang, Wenhua; Zhang, Heye; Tan, Ning; Hau, William Kongto; Zhang, Yuan-Ting; Liu, Huafeng

    2014-01-01

    Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images. PMID:25372784

  2. Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors

    NASA Astrophysics Data System (ADS)

    Migliorelli, Carolina; Alonso, Joan F.; Romero, Sergio; Nowak, Rafał; Russi, Antonio; Mañanas, Miguel A.

    2017-08-01

    Objective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of

  3. A thesis on the Development of an Automated SWIFT Edge Detection Algorithm

    SciTech Connect

    Trujillo, Christopher J.

    2016-07-28

    Throughout the world, scientists and engineers such as those at Los Alamos National Laboratory, perform research and testing unique only to applications aimed towards advancing technology, and understanding the nature of materials. With this testing, comes a need for advanced methods of data acquisition and most importantly, a means of analyzing and extracting the necessary information from such acquired data. In this thesis, I aim to produce an automated method implementing advanced image processing techniques and tools to analyze SWIFT image datasets for Detonator Technology at Los Alamos National Laboratory. Such an effective method for edge detection and point extraction can prove to be advantageous in analyzing such unique datasets and provide for consistency in producing results.

  4. Automated Detection of Small-scale Magnetic Flux Ropes and Their Association with Shocks

    NASA Astrophysics Data System (ADS)

    Zheng, Jinlei; Hu, Qiang; Chen, Yu; le Roux, Jakobus

    2017-09-01

    We have quantitatively examined one type of fundamental space plasma structures in the solar wind, the magnetic flux ropes, especially those of relatively small scales. They usually are of durations ranging from a few minutes to a few hours. The main objectives are to reveal the existence in terms of their occurrence and distributions in the solar wind, to quantitatively examine their configurations and properties, and to relate to other relevant processes, involving particle energization and intermittent structures in the solar wind. The technical approach is a combination of time-series analysis methods with the Grad-Shafranov reconstruction technique. This modeling method is capable of characterizing two and a half dimensional cross section of space plasma structures, based on in-situ spacecraft measurements along a single path across. We present the automated detection of flux ropes, construction of an online magnetic flux rope database, and detailed case studies of such structures identified downstream of interplanetary shocks.

  5. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

    PubMed Central

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

    2016-01-01

    Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively. PMID:27974882

  6. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

    PubMed

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

    2016-01-01

    Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  7. An automated system for lung nodule detection in low-dose computed tomography

    NASA Astrophysics Data System (ADS)

    Gori, I.; Fantacci, M. E.; Preite Martinez, A.; Retico, A.

    2007-03-01

    A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

  8. Automated detection and characterization of meteoroid from simultaneous optical and radar observations

    NASA Astrophysics Data System (ADS)

    Limonta, L.; Sugar, G.

    2015-12-01

    Many uncertainties remain to be determined in meteoroid science: the distribution of meteor sources as well as each sources' mass flux; the effects of meteoroids on the ionosphere and thermosphere as both depositary of heavy metals and modifiers of the plasma background; and a correct characterization of their ablation process. These uncertainties strongly depend on the meteoroids' composition and consequentially on their mass. Classical mass computation techniques relies on single instrument observations, mainly optical and radar data, which give high error bounds on the mass estimate due to the use of luminous efficiency τ (for optical) and ionization probability β (for radar) parameters. In the following talk we will show the results from our experiments at the poker flat facility and highlight the benefits of using multiple data collection instruments. We will present an automated technique for detection of meteoroids in the acquired data and use it to cross calibrate τ and β and thus better infer meteorids' mass and bound their error.

  9. Automated EEG detection algorithms and clinical semiology in epilepsy: importance of correlations.

    PubMed

    Hogan, R Edward

    2011-12-01

    With advances in technological innovation, electroencephalography has remained the gold standard for classification and localization of epileptic seizures. Like other diagnostic modalities, technological advances have opened new avenues for assessment of data, and hold great promise to improve interpretive capabilities. However, proper overall interpretation and application of electroencephalographic findings relies on valid correlations of associated clinical semiology. This article addresses interpretation of clinical signs and symptoms in the context of the diagnostic predictive value of electroencephalographic, clinical, and electrographic definitions of seizures, and upcoming challenges of interpreting intracranial high-frequency electroencephalographic data. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Navigating Longitudinal Clinical Notes with an Automated Method for Detecting New Information

    PubMed Central

    Zhang, Rui; Pakhomov, Serguei; Lee, Janet T.; Melton, Genevieve B.

    2015-01-01

    Automated methods to detect new information in clinical notes may be valuable for navigating and using information in these documents for patient care. Statistical language models were evaluated as a means to quantify new information over longitudinal clinical notes for a given patient. The new information proportion (NIP) in target notes decreased logarithmically with increasing numbers of previous notes to create the language model. For a given patient, the amount of new information had cyclic patterns. Higher NIP scores correlated with notes having more new information often with clinically significant events, and lower NIP scores indicated notes with less new information. Our analysis also revealed “copying and pasting” to be widely used in generating clinical notes by copying information from the most recent historical clinical notes forward. These methods can potentially aid clinicians in finding notes with more clinically relevant new information and in reviewing notes more purposefully which may increase the efficiency of clinicians in delivering patient care. PMID:23920658

  11. Detection of abrupt changes in dynamic systems

    NASA Technical Reports Server (NTRS)

    Willsky, A. S.

    1984-01-01

    Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

  12. Detecting Landscape Change: The View from Above

    ERIC Educational Resources Information Center

    Porter, Jess

    2008-01-01

    This article will demonstrate an approach for discovering and assessing local landscape change through the use of remotely sensed images. A brief introduction to remotely sensed imagery is followed by a discussion of relevant ways to introduce this technology into the college science classroom. The Map Detective activity demonstrates the…

  13. NOTE: Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

    NASA Astrophysics Data System (ADS)

    Holan, Scott H.; Viator, John A.

    2008-06-01

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

  14. Estimation of left ventricular compliance using on-line echocardiographic automated border detection and pressure data.

    PubMed

    Gorcsan, J; Mandarino, W A; Deneault, L G; Morita, S; Kawai, A; Griffith, B P; Kormos, R L

    1994-06-01

    The end-diastolic pressure-volume relationship can be used to describe left ventricular (LV) compliance. The objective of this study was to utilize measurements of LV cavity area by echocardiographic automated border detection and pressure data to estimate the end-diastolic pressure-volume curve in an isolated heart preparation where true volume could be measured by an intraventricular balloon. Six dog hearts were excised for placement of an intraventricular balloon and a micromanometer catheter and perfused in an ex vivo circuit. Mid-ventricular short-axis images were used to measure cross-sectional area by automated border detection while LV volumes were increased from 5 ml to maximal volume (30-40 ml) in each preparation. Simultaneous area and pressure data were recorded on a computer workstation through a customized interface with the ultrasound system. Three runs of varying LV volumes at 1 ml increments were performed on each of 6 hearts for a total of 1,080 simultaneous measurements. Pressure-volume and pressure-area curves were analyzed by linear regression analyses, the slope of which was used to estimate compliance. End-diastolic pressure-area and pressure-volume relationships were significantly correlated with mean r = 0.97 +/- 0.02 (p < 0.001) from individual hearts. The slopes which served to estimate compliance of the individual pressure-area and pressure-volume curves were similar and differed by only 7 +/- 4%. A similar correlation was observed by second order regression analyses with r = 0.97 +/- 0.01 (p < 0.001) for pressure-area and r = 0.98 +/- 0.01 (p < 0.001) for pressure-volume relationships.(ABSTRACT TRUNCATED AT 250 WORDS)

  15. Fully automated detection of the counting area in blood smears for computer aided hematology.

    PubMed

    Rupp, Stephan; Schlarb, Timo; Hasslmeyer, Erik; Zerfass, Thorsten

    2011-01-01

    For medical diagnosis, blood is an indispensable indicator for a wide variety of diseases, i.e. hemic, parasitic and sexually transmitted diseases. A robust detection and exact segmentation of white blood cells (leukocytes) in stained blood smears of the peripheral blood provides the base for a fully automated, image based preparation of the so called differential blood cell count in the context of medical laboratory diagnostics. Especially for the localization of the blood cells and in particular for the segmentation of the cells it is necessary to detect the working area of the blood smear. In this contribution we present an approach for locating the so called counting area on stained blood smears that is the region where cells are predominantly separated and do not interfere with each other. For this multiple images of a blood smear are taken and analyzed in order to select the image corresponding to this area. The analysis involves the computation of an unimodal function from image content that serves as indicator for the corresponding image. This requires a prior segmentation of the cells that is carried out by a binarization in the HSV color space. Finally, the indicator function is derived from the number of cells and the cells' surface area. Its unimodality guarantees to find a maximum value that corresponds to the counting areas image index. By this, a fast lookup of the counting area is performed enabling a fully automated analysis of blood smears for medical diagnosis. For an evaluation the algorithm's performance on a number of blood smears was compared with the ground truth information that has been defined by an adept hematologist.

  16. Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images.

    PubMed

    Wu, Shandong; Weinstein, Susan P; Conant, Emily F; Schnall, Mitchell D; Kontos, Despina

    2013-04-01

    Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts