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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. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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…

  20. REDIR: Automated Static Detection of Obfuscated Anti-Debugging Techniques

    DTIC Science & Technology

    2014-03-27

    challenging. The Rule Engine Detection by Intermediate Representation (REDIR) system for automated static detection of obfuscated anti-debugging...understanding. 1.4 Approach The Rule Engine Detection by Intermediate Representation (REDIR) system for automated static detection of obfuscated anti...directed or automated [14]. The user interface in a knowledge-based system should provide, as natural as possible, access to the knowledge stored in

  1. 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.

  2. 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.

  3. 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…

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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.

  16. 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…

  17. 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…

  18. 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.

  19. 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.

  20. 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.

  1. 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).

  2. 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.

  3. 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

  4. 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…

  5. 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,…

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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%.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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…

  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. 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).

  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. 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

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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…

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  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. 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.…

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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…

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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.

  12. 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.

  13. 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.

  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. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. 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.

  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. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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…

  12. 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.

  13. 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.

  14. 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.

  15. Structural Change Can Be Detected in Advanced-Glaucoma Eyes

    PubMed Central

    Belghith, Akram; Medeiros, Felipe A.; Bowd, Christopher; Liebmann, Jeffrey M.; Girkin, Christopher A.; Weinreb, Robert N.; Zangwill, Linda M.

    2016-01-01

    Purpose To compare spectral-domain optical coherence tomography (SD-OCT) standard structural measures and a new three-dimensional (3D) volume optic nerve head (ONH) change detection method for detecting change over time in severely advanced-glaucoma (open-angle glaucoma [OAG]) patients. Methods Thirty-five eyes of 35 patients with very advanced glaucoma (defined as a visual field mean deviation < −21 dB) and 46 eyes of 30 healthy subjects to estimate aging changes were included. Circumpapillary retinal fiber layer thickness (cpRNFL), minimum rim width (MRW), and macular retinal ganglion cell–inner plexiform layer (GCIPL) thicknesses were measured using the San Diego Automated Layer Segmentation Algorithm (SALSA). Progression was defined as structural loss faster than 95th percentile of healthy eyes. Three-dimensional volume ONH change was estimated using the Bayesian-kernel detection scheme (BKDS), which does not require extensive retinal layer segmentation. Results The number of progressing glaucoma eyes identified was highest for 3D volume BKDS (13, 37%), followed by GCPIL (11, 31%), cpRNFL (4, 11%), and MRW (2, 6%). In advanced-OAG eyes, only the mean rate of GCIPL change reached statistical significance, −0.18 μm/y (P = 0.02); the mean rates of cpRNFL and MRW change were not statistically different from zero. In healthy eyes, the mean rates of cpRNFL, MRW, and GCIPL change were significantly different from zero. (all P < 0.001). Conclusions Ganglion cell–inner plexiform layer and 3D volume BKDS show promise for identifying change in severely advanced glaucoma. These results suggest that structural change can be detected in very advanced disease. Longer follow-up is needed to determine whether changes identified are false positives or true progression. PMID:27454660

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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

  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. 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. 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.

  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.

  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. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  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. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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%).

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. The impact of misregistration on change detection

    NASA Technical Reports Server (NTRS)

    Townshend, John R. G.; Justice, Christopher O.; Gurney, Charlotte; Mcmanus, James

    1992-01-01

    The impact of images misregistration on the detection of changes in land cover was studied using spatially degraded Landsat MSS images. Emphasis is placed on simulated images of the Normalized Difference Vegetation Index (NDVI) at spatial resolutions of 250 and 500 m. It is pointed out that there is the need to achieve high values of registration accuracy. The evidence from simulations suggests that misregistrations can have a marked effect on the ability of remotely sensed data to detect changes in land cover. Even subpixel misregistrations can have a major impact, and the most marked proportional changes will tend to occur at the finest misregistrations.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  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. 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

  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. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  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 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.

  10. 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.

  11. 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.

  12. 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.

  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. 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.

  15. 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.

  16. Automatic change detection using mobile laser scanning

    NASA Astrophysics Data System (ADS)

    Hebel, M.; Hammer, M.; Gordon, M.; Arens, M.

    2014-10-01

    Automatic change detection in 3D environments requires the comparison of multi-temporal data. By comparing current data with past data of the same area, changes can be automatically detected and identified. Volumetric changes in the scene hint at suspicious activities like the movement of military vehicles, the application of camouflage nets, or the placement of IEDs, etc. In contrast to broad research activities in remote sensing with optical cameras, this paper addresses the topic using 3D data acquired by mobile laser scanning (MLS). We present a framework for immediate comparison of current MLS data to given 3D reference data. Our method extends the concept of occupancy grids known from robot mapping, which incorporates the sensor positions in the processing of the 3D point clouds. This allows extracting the information that is included in the data acquisition geometry. For each single range measurement, it becomes apparent that an object reflects laser pulses in the measured range distance, i.e., space is occupied at that 3D position. In addition, it is obvious that space is empty along the line of sight between sensor and the reflecting object. Everywhere else, the occupancy of space remains unknown. This approach handles occlusions and changes implicitly, such that the latter are identifiable by conflicts of empty space and occupied space. The presented concept of change detection has been successfully validated in experiments with recorded MLS data streams. Results are shown for test sites at which MLS data were acquired at different time intervals.

  17. Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain

    NASA Astrophysics Data System (ADS)

    Eugene Parker, B.; Ware, H. A.; Wipf, D. P.; Tompkins, W. R.; Clark, B. R.; Larson, E. C.; Vincent Poor, H.

    2000-07-01

    It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori -known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori -known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a

  18. A Process Model of Trust in Automation: A Signal Detection Theory Based Approach

    DTIC Science & Technology

    2014-01-01

    lead to trust in automation. We also discuss a simple process model , which helps us understand the results. Our experimental paradigm suggests that...participants are agnostic to the automation s behavior; instead, they merely focus on alarm rate. A process model suggests this is the result of a simple reward structure and a non-explicit cost of trusting the automation.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification

    PubMed Central

    Flores, Francisco

    2016-01-01

    The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation. PMID:27034710

  8. A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification.

    PubMed

    Tello-Mijares, Santiago; Flores, Francisco

    2016-01-01

    The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation.

  9. Automated infrasound signal detection algorithms implemented in MatSeis - Infra Tool.

    SciTech Connect

    Hart, Darren

    2004-07-01

    MatSeis's infrasound analysis tool, Infra Tool, uses frequency slowness processing to deconstruct the array data into three outputs per processing step: correlation, azimuth and slowness. Until now, an experienced analyst trained to recognize a pattern observed in outputs from signal processing manually accomplished infrasound signal detection. Our goal was to automate the process of infrasound signal detection. The critical aspect of infrasound signal detection is to identify consecutive processing steps where the azimuth is constant (flat) while the time-lag correlation of the windowed waveform is above background value. These two statements describe the arrival of a correlated set of wavefronts at an array. The Hough Transform and Inverse Slope methods are used to determine the representative slope for a specified number of azimuth data points. The representative slope is then used in conjunction with associated correlation value and azimuth data variance to determine if and when an infrasound signal was detected. A format for an infrasound signal detection output file is also proposed. The detection output file will list the processed array element names, followed by detection characteristics for each method. Each detection is supplied with a listing of frequency slowness processing characteristics: human time (YYYY/MM/DD HH:MM:SS.SSS), epochal time, correlation, fstat, azimuth (deg) and trace velocity (km/s). As an example, a ground truth event was processed using the four-element DLIAR infrasound array located in New Mexico. The event is known as the Watusi chemical explosion, which occurred on 2002/09/28 at 21:25:17 with an explosive yield of 38,000 lb TNT equivalent. Knowing the source and array location, the array-to-event distance was computed to be approximately 890 km. This test determined the station-to-event azimuth (281.8 and 282.1 degrees) to within 1.6 and 1.4 degrees for the Inverse Slope and Hough Transform detection algorithms, respectively, and

  10. Automated software for CCD-image processing and detection of small Solar System bodies

    NASA Astrophysics Data System (ADS)

    Savanevych, V.; Bryukhovetskiy, A.; Sokovikova, N.; Bezkrovniy, M.; Khlamov, S.; Elenin, L.; Movsesian, I.; Dihtyar, M.

    2014-07-01

    Efficiency is a crucial factor in the discovery of near-Earth asteroids (NEA) and potentially-hazardous asteroids. Current asteroid surveys yield many images per night. It is no longer possible for the observer to quickly view these images in the the blinking mode. This cause a serious difficulty for large-aperture wide-field telescopes, capturing up to several tens of asteroids in one image. To achieve better asteroid-survey efficiency it is necessary to design and develop automated software for the frame processing. Currently the CoLiTec software solves the problem of the frame processing for asteroid surveys in the real mode. The automatically detected asteroids are subject to follow-up visual confirmation. The CoLiTec software is in use for the automated detection of asteroids in Andrushivka Astronomical Observatory, in the Russian remote observatory ISON-NM (Mayhill, New Mexico, USA), as well as in the observatory ISON-Kislovodsk and in ISON-Ussuriysk starting from the fall 2013. The CoLiTec led to the first automated asteroid and comet discoveries in the CIS (Commonwealth of Independent States) and Baltic countries. In 2012 (2011) 80 (86) % of observations and 74 (75) % of discoveries of asteroids in these countries were made using the CoLiTec. The comet C/2010 X1 (Elenin), discovered using the CoLiTec on December 10, 2010, was the first comet discovered by a CIS astronomer over the past 20 years. In total, out of 7 recently discovered in the CIS and Baltic countries comets 4 comets were discovered due to the CoLiTec, namely C/2010 X1 (Elenin), P/2011 NO1 (Elenin), C/2012 S1 (ISON), and P/2013 V3 (Nevski). About 500,000 CoLiTec-used measurements were reported to MPC, including over 1,500 preliminary discovered objects. These objects include 21 Jupiter Trojan asteroids, 4 NEAs and 1 Centaur. Three other discovered asteroids were reported via dedicated electronic MPC circulars. In 2012 the CoLiTec users were ranked as No. 10, 13, and 22 in the list of the most

  11. Use of an automated system for detection of canine serum antibodies against Ehrlichia canis glycoprotein 36.

    PubMed

    Moroff, Scott; Sokolchik, Irene; Woodring, Todd; Woodruff, Colby; Atkinson, Brett; Lappin, Michael R

    2014-07-01

    Ehrlichia canis is the most common cause of monocytotropic ehrlichiosis in dogs around the world. The purpose of the present study was to validate a new automated fluorescence system (Accuplex4™ BioCD system; Antech Diagnostics, Lake Success, New York) to detect antibodies against the E. canis immunodominant glycoprotein 36 (gp36). Sera and blood samples (ethylenediamine tetra-acetic acid) were collected from mixed sex beagles ( n = 8) on days 0, 3, 7, 10, 14, 17, 21, 28, 42, 49, 56, 63, 70, 77, 84, and 98 after intravenous inoculation with culture-derived E. canis. Sera were assayed using the Accuplex4 BioCD system (Accuplex4), an E. canis indirect fluorescent antibody test (IFAT), and a commercially available kit. A complete blood cell count and a proprietary E. canis polymerase chain reaction (PCR) were performed on each blood sample. On the day thrombocytopenia was first detected for each dog, E. canis DNA was amplified from blood of all dogs. At those times, E. canis antibodies were detected in 7 of 8 dogs by the Accuplex4, 1 of 8 dogs by the commercial kit, and 4 of 8 dogs by IFAT. Ehrlichia canis DNA was amplified from blood before seroconversion in any antibody assay for 6 dogs. Antibodies against gp36 were detected by Accuplex4 within 3 days of PCR-positive test results and were detected up to 25 days sooner than the commercial kit. After starting doxycycline treatment, E. canis DNA was no longer amplified by PCR assay, but serum antibodies remained detectable by all assays.

  12. Automated detection of colorectal lesions with dual-energy CT colonography

    NASA Astrophysics Data System (ADS)

    Näppi, Janne J.; Kim, Se Hyung; Yoshida, Hiroyuki

    2012-03-01

    Conventional single-energy computed tomography colonography (CTC) tends to miss polyps 6 - 9 mm in size and flat lesions. Dual-energy CTC (DE-CTC) provides more complete information about the chemical composition of tissue than does conventional CTC. We developed an automated computer-aided detection (CAD) scheme for detecting colorectal lesions in which dual-energy features were used to identify different bowel materials and their partial-volume artifacts. Based on these features, the dual-energy CAD (DE-CAD) scheme extracted the region of colon by use of a lumen-tracking method, detected lesions by use of volumetric shape features, and reduced false positives by use of a statistical classifier. For validation, 20 patients were prepared for DE-CTC by use of reduced bowel cleansing and orally administered fecal tagging with iodine and/or barium. The DE-CTC was performed in dual positions by use of a dual-energy CT scanner (SOMATOM Definition, Siemens) at 140 kVp and 80 kVp energy levels. The lesions identified by subsequent same-day colonoscopy were correlated with the DE-CTC data. The detection accuracies of the DE-CAD and conventional CAD schemes were compared by use of leave-one-patient-out evaluation and a bootstrap analysis. There were 25 colonoscopy-confirmed lesions: 22 were 6 - 9 mm and 3 were flat lesions >=10 mm in size. The DE-CAD scheme detected the large flat lesions and 95% of the 6 - 9 mm lesions with 9.9 false positives per patient. The improvement in detection accuracy by the DE-CAD was statistically significant.

  13. Image patch-based method for automated classification and detection of focal liver lesions on CT

    NASA Astrophysics Data System (ADS)

    Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

    2013-03-01

    We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

  14. Total least squares for anomalous change detection

    SciTech Connect

    Theiler, James P; Matsekh, Anna M

    2010-01-01

    A family of difference-based anomalous change detection algorithms is derived from a total least squares (TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derived from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit the largest residuals with respect to the regression of the two images against each other. The family of TLSQ-based anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly detection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms. On the other hand, whitened TLSQ is coordinate invariant, and furthermore it is shown to be equivalent to the optimized covariance equalization algorithm. What whitened TLSQ offers, in addition to connecting with a common language the derivations of two of the most popular anomalous change detection algorithms - chronochrome and covariance equalization - is a generalization of these algorithms with the potential for better performance.

  15. Automated high-pressure titration system with in situ infrared spectroscopic detection

    SciTech Connect

    Thompson, Christopher J. Martin, Paul F.; Chen, Jeffrey; Schaef, Herbert T.; Rosso, Kevin M.; Felmy, Andrew R.; Loring, John S.; Benezeth, Pascale

    2014-04-15

    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 CO{sub 2} (scCO{sub 2}) to generate an infrared calibration curve and determine the solubility of water in CO{sub 2} at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO{sub 2} 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 scCO{sub 2} hydration, and ATR measurements provided insights into competitive residency of water and CO{sub 2} 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 (Mg{sub 2}SiO{sub 4}) in water-bearing scCO{sub 2} at 50 °C and 90 bar. Immediately after water dissolved in the scCO{sub 2}, a thin film of adsorbed water formed on the mineral surface, and the film

  16. Competitive SWIFT cluster templates enhance detection of aging changes

    PubMed Central

    Rebhahn, Jonathan A.; Roumanes, David R.; Qi, Yilin; Khan, Atif; Thakar, Juilee; Rosenberg, Alex; Lee, F. Eun‐Hyung; Quataert, Sally A.; Sharma, Gaurav

    2015-01-01

    Abstract Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional

  17. Competitive SWIFT cluster templates enhance detection of aging changes.

    PubMed

    Rebhahn, Jonathan A; Roumanes, David R; Qi, Yilin; Khan, Atif; Thakar, Juilee; Rosenberg, Alex; Lee, F Eun-Hyung; Quataert, Sally A; Sharma, Gaurav; Mosmann, Tim R

    2016-01-01

    Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT--a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a "template" mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter-sample differences is increased by "competition" wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age-related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations

  18. 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...Trans. Signal Process. 49(1), 1–16 (2001). 11. A. P. Schaum and A. D. Stocker, “Hyperspectral change detection and supervised matched filtering

  19. Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms.

    PubMed

    Pereira, Franklin; Bueno, Alejandra; Rodriguez, Andrea; Perrin, Douglas; Marx, Gerald; Cardinale, Michael; Salgo, Ivan; Del Nido, Pedro

    2017-01-01

    Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).

  20. Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks

    PubMed Central

    Kreshuk, Anna; Koethe, Ullrich; Pax, Elizabeth; Bock, Davi D.; Hamprecht, Fred A.

    2014-01-01

    We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem). PMID:24516550

  1. Automated real-time detection of defects during machining of ceramics

    DOEpatents

    Ellingson, William A.; Sun, Jiangang

    1997-01-01

    Apparatus for the automated real-time detection and classification of defects during the machining of ceramic components employs an elastic optical scattering technique using polarized laser light. A ceramic specimen is continuously moved while being machined. Polarized laser light is directed onto the ceramic specimen surface at a fixed position just aft of the machining tool for examination of the newly machined surface. Any foreign material near the location of the laser light on the ceramic specimen is cleared by an air blast. As the specimen is moved, its surface is continuously scanned by the polarized laser light beam to provide a two-dimensional image presented in real-time on a video display unit, with the motion of the ceramic specimen synchronized with the data acquisition speed. By storing known "feature masks" representing various surface and sub-surface defects and comparing measured defects with the stored feature masks, detected defects may be automatically characterized. Using multiple detectors, various types of defects may be detected and classified.

  2. Automated real-time detection of defects during machining of ceramics

    DOEpatents

    Ellingson, W.A.; Sun, J.

    1997-11-18

    Apparatus for the automated real-time detection and classification of defects during the machining of ceramic components employs an elastic optical scattering technique using polarized laser light. A ceramic specimen is continuously moved while being machined. Polarized laser light is directed onto the ceramic specimen surface at a fixed position just aft of the machining tool for examination of the newly machined surface. Any foreign material near the location of the laser light on the ceramic specimen is cleared by an air blast. As the specimen is moved, its surface is continuously scanned by the polarized laser light beam to provide a two-dimensional image presented in real-time on a video display unit, with the motion of the ceramic specimen synchronized with the data acquisition speed. By storing known ``feature masks`` representing various surface and sub-surface defects and comparing measured defects with the stored feature masks, detected defects may be automatically characterized. Using multiple detectors, various types of defects may be detected and classified. 14 figs.

  3. Automated detection of heuristics and biases among pathologists in a computer-based system.

    PubMed

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

    2013-08-01

    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 diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

  4. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

    SciTech Connect

    Adal, Kedir M.; Sidebe, Desire; Ali, Sharib; Chaum, Edward; Karnowski, Thomas Paul; Meriaudeau, Fabrice

    2014-01-07

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  5. Automated Fall Detection With Quality Improvement “Rewind” to Reduce Falls in Hospital Rooms

    PubMed Central

    Rantz, Marilyn J.; Banerjee, Tanvi S.; Cattoor, Erin; Scott, Susan D.; Skubic, Marjorie; Popescu, Mihail

    2014-01-01

    The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy. PMID:24296567

  6. The normal mode analysis shape detection method for automated shape determination of lung nodules.

    PubMed

    Stember, Joseph N

    2015-04-01

    Surface morphology and shape in general are important predictors for the behavior of solid-type lung nodules detected on CT. More broadly, shape analysis is useful in many areas of computer-aided diagnosis and essentially all scientific and engineering disciplines. Automated methods for shape detection have all previously, to the author's knowledge, relied on some sort of geometric measure. I introduce Normal Mode Analysis Shape Detection (NMA-SD), an approach that measures shape indirectly via the motion it would undergo if one imagined the shape to be a pseudomolecule. NMA-SD allows users to visualize internal movements in the imaging object and thereby develop an intuition for which motions are important, and which geometric features give rise to them. This can guide the identification of appropriate classification features to distinguish among classes of interest. I employ normal mode analysis (NMA) to animate pseudomolecules representing simulated lung nodules. Doing so, I am able to assign a testing set of nodules into the classes circular, elliptical, and irregular with roughly 97 % accuracy. This represents a proof-of-principle that one can obtain shape information by treating voxels as pseudoatoms in a pseudomolecule, and analyzing the pseudomolecule's predicted motion.

  7. Detecting changes during pregnancy with Raman spectroscopy

    NASA Astrophysics Data System (ADS)

    Vargis, Elizabeth; Robertson, Kesha; Al-Hendy, Ayman; Reese, Jeff; Mahadevan-Jansen, Anita

    2010-02-01

    Preterm labor is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect changes in the cervix during pregnancy. Since Raman spectroscopy has been used to detect cancers in vivo in organs like the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy levels. We believe significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this study, Raman spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman will enter labor.

  8. Detecting hydrological changes through conceptual model

    NASA Astrophysics Data System (ADS)

    Viola, Francesco; Caracciolo, Domenico; Pumo, Dario; Francipane, Antonio; Valerio Noto, Leonardo

    2015-04-01

    Natural changes and human modifications in hydrological systems coevolve and interact in a coupled and interlinked way. If, on one hand, climatic changes are stochastic, non-steady, and affect the hydrological systems, on the other hand, human-induced changes due to over-exploitation of soils and water resources modifies the natural landscape, water fluxes and its partitioning. Indeed, the traditional assumption of static systems in hydrological analysis, which has been adopted for long time, fails whenever transient climatic conditions and/or land use changes occur. Time series analysis is a way to explore environmental changes together with societal changes; unfortunately, the not distinguishability between causes restrict the scope of this method. In order to overcome this limitation, it is possible to couple time series analysis with an opportune hydrological model, such as a conceptual hydrological model, which offers a schematization of complex dynamics acting within a basin. Assuming that model parameters represent morphological basin characteristics and that calibration is a way to detect hydrological signature at a specific moment, it is possible to argue that calibrating the model over different time windows could be a method for detecting potential hydrological changes. In order to test the capabilities of a conceptual model in detecting hydrological changes, this work presents different "in silico" experiments. A synthetic-basin is forced with an ensemble of possible future scenarios generated with a stochastic weather generator able to simulate steady and non-steady climatic conditions. The experiments refer to Mediterranean climate, which is characterized by marked seasonality, and consider the outcomes of the IPCC 5th report for describing climate evolution in the next century. In particular, in order to generate future climate change scenarios, a stochastic downscaling in space and time is carried out using realizations of an ensemble of General

  9. Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

    PubMed Central

    Irusta, Unai; Morgado, Eduardo; Aramendi, Elisabete; Ayala, Unai; Wik, Lars; Kramer-Johansen, Jo; Eftestøl, Trygve; Alonso-Atienza, Felipe

    2016-01-01

    Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s. PMID:27441719

  10. Fully automated analytical procedure for propofol determination by sequential injection technique with spectrophotometric and fluorimetric detections.

    PubMed

    Šrámková, Ivana; Amorim, Célia G; Sklenářová, Hana; Montenegro, Maria C B M; Horstkotte, Burkhard; Araújo, Alberto N; Solich, Petr

    2014-01-01

    In this work, an application of an enzymatic reaction for the determination of the highly hydrophobic drug propofol in emulsion dosage form is presented. Emulsions represent a complex and therefore challenging matrix for analysis. Ethanol was used for breakage of a lipid emulsion, which enabled optical detection. A fully automated method based on Sequential Injection Analysis was developed, allowing propofol determination without the requirement of tedious sample pre-treatment. The method was based on spectrophotometric detection after the enzymatic oxidation catalysed by horseradish peroxidase and subsequent coupling with 4-aminoantipyrine leading to a coloured product with an absorbance maximum at 485 nm. This procedure was compared with a simple fluorimetric method, which was based on the direct selective fluorescence emission of propofol in ethanol at 347 nm. Both methods provide comparable validation parameters with linear working ranges of 0.005-0.100 mg mL(-1) and 0.004-0.243 mg mL(-1) for the spectrophotometric and fluorimetric methods, respectively. The detection and quantitation limits achieved with the spectrophotometric method were 0.0016 and 0.0053 mg mL(-1), respectively. The fluorimetric method provided the detection limit of 0.0013 mg mL(-1) and limit of quantitation of 0.0043 mg mL(-1). The RSD did not exceed 5% and 2% (n=10), correspondingly. A sample throughput of approx. 14 h(-1) for the spectrophotometric and 68 h(-1) for the fluorimetric detection was achieved. Both methods proved to be suitable for the determination of propofol in pharmaceutical formulation with average recovery values of 98.1 and 98.5%.

  11. Detecting Change in Longitudinal Social Networks

    DTIC Science & Technology

    2011-01-01

    marketing campaigns and media on social behavior. Initial Construct populations, social and knowledge networks, can be hypothetical or real (Carley...patent data bases, phone-networks, email- based-networks, social- media networks and more. Page 6 of 37 Current methods of change detection in...CUSUM C Sta measured fo o be successf Average Bet ct either incre or each socia g increases in the data for fective for ch ork. tistic Over Tim

  12. Enhanced pulsar and single pulse detection via automated radio frequency interference detection in multipixel feeds

    NASA Astrophysics Data System (ADS)

    Kocz, J.; Bailes, M.; Barnes, D.; Burke-Spolaor, S.; Levin, L.

    2012-02-01

    Single pixel feeds on large aperture radio telescopes have the ability to detect weak (˜10 mJy) impulsive bursts of radio emission and sub-mJy radio pulsars. Unfortunately, in large-scale blind surveys, radio frequency interference (RFI) mimics both radio bursts and radio pulsars, greatly reducing the sensitivity to new discoveries as real signals of astronomical origin get lost among the millions of false candidates. In this paper a technique that takes advantage of multipixel feeds to use eigenvector decomposition of common signals is used to greatly facilitate radio burst and pulsar discovery. Since the majority of RFI occurs with zero dispersion, the method was tested on the total power present in the 13 beams of the Parkes multibeam receiver using data from archival intermediate-latitude surveys. The implementation of this method greatly reduced the number of false candidates and led to the discovery of one new rotating radio transient or RRAT, six new pulsars and five new pulses that shared the swept-frequency characteristics similar in nature to the `Lorimer burst'. These five new signals occurred within minutes of 11 previous detections of a similar type. When viewed together, they display temporal characteristics related to integer seconds, with non-random distributions and characteristic 'gaps' between them, suggesting they are not from a naturally occurring source. Despite the success in removing RFI, false candidates present in the data that are only visible after integrating in time or at non-zero dispersion remained. It is demonstrated that with some computational penalty, the method can be applied iteratively at all trial dispersions and time resolutions to remove the vast majority of spurious candidates.

  13. Application of singular spectrum-based change-point analysis to EMG-onset detection.

    PubMed

    Vaisman, Lev; Zariffa, José; Popovic, Milos R

    2010-08-01

    While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal's properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson's disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.

  14. Automated Immunomagnetic Separation and Microarray Detection of E. coli O157:H7 from Poultry Carcass Rinse

    SciTech Connect

    Chandler, Darrell P. ); Brown, Jeremy D.; Call, Douglas R. ); Wunschel, Sharon C. ); Grate, Jay W. ); Holman, David A.; Olson, Lydia G.; Stottlemyer, Mark S.; Bruckner-Lea, Cindy J. )

    2001-09-01

    We describe the development and application of a novel electromagnetic flow cell and fluidics system for automated immunomagnetic separation of E. coli directly from unprocessed poultry carcass rinse, and the biochemical coupling of automated sample preparation with nucleic acid microarrays without cell growth. Highly porous nickel foam was used as a magnetic flux conductor. Up to 32% recovery efficiency of 'total' E. coli was achieved within the automated system with 6 sec contact times and 15 minute protocol (from sample injection through elution), statistically similar to cell recovery efficiencies in > 1 hour 'batch' captures. The electromagnet flow cell allowed complete recovery of 2.8 mm particles directly from unprocessed poultry carcass rinse whereas the batch system did not. O157:H7 cells were reproducibly isolated directly from unprocessed poultry rinse with 39% recovery efficiency at 103 cells ml-1 inoculum. Direct plating of washed beads showed positive recovery of O 157:H7 directly from carcass rinse at an inoculum of 10 cells ml-1. Recovered beads were used for direct PCR amplification and microarray detection, with a process-level detection limit (automated cell concentration through microarray detection) of < 103 cells ml-1 carcass rinse. The fluidic system and analytical approach described here are generally applicable to most microbial detection problems and applications.

  15. Scene change detection based on multimodal integration

    NASA Astrophysics Data System (ADS)

    Zhu, Yingying; Zhou, Dongru

    2003-09-01

    Scene change detection is an essential step to automatic and content-based video indexing, retrieval and browsing. In this paper, a robust scene change detection and classification approach is presented, which analyzes audio, visual and textual sources and accounts for their inter-relations and coincidence to semantically identify and classify video scenes. Audio analysis focuses on the segmentation of audio stream into four types of semantic data such as silence, speech, music and environmental sound. Further processing on speech segments aims at locating speaker changes. Video analysis partitions visual stream into shots. Text analysis can provide a supplemental source of clues for scene classification and indexing information. We integrate the video and audio analysis results to identify video scenes and use the text information detected by the video OCR technology or derived from transcripts available to refine scene classification. Results from single source segmentation are in some cases suboptimal. By combining visual, aural features adn the accessorial text information, the scence extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to rather promising.

  16. Automated Mapping of Rapid Arctic Ocean Coastal Change Over Large Spans of Time and Geography

    NASA Astrophysics Data System (ADS)

    Hulslander, D.

    2012-12-01

    While climate change is global in scope, its impacts vary greatly from region to region. The dynamic Arctic Ocean coastline often shows greater sensitivity to climate change and more obvious impacts. Current longer ice-free conditions, rising sea level, thawing permafrost, and melting of larger ice bodies combine to produce extremely rapid coastal change and erosion. Anderson et al. (2009; Geology News) have measured erosion rates at sites along the Alaskan Arctic Ocean coast of 15 m per year and greater. Completely understanding coastal change in the Arctic requires mapping both current erosional regimes as well as changes in erosional rates over several decades. Studying coastal change and trends in the Arctic, however, presents several significant difficulties. The study area is enormous, with over 45,000 km of coastline; it is also one of the most remote, inaccessible, and hostile environments on Earth. Moreover, the region has little to no historical data from which to start. Thus, any study of the area must be able to construct its own baseline. Remote sensing offers the best solution given these difficulties. Spaceborne platforms allow for regular global coverage at temporal and spatial scales sufficient for mapping coastal erosion and deposition. The Landsat family of instruments (MSS, TM, and ETM) has data available as frequently as every 16 days and starting as early as 1972. The data are freely available from the USGS through earthexplorer.usgs.gov and are well calibrated both geometrically and spectrally, eliminating expensive pre-processing steps and making them analysis-ready. Finally, because manual coastline delineation of the quantity of data involved would be prohibitive in both budget and labor, an automated processing chain must be used. ENVI Feature Extraction can provide results in line with those generated by expert analysts (Hulslander, et al., 2008; GEOBIA 2008 Proceedings). Previous studies near Drew Point, Alaska have shown that feature

  17. Maturational changes in automated EEG spectral power analysis in preterm infants.

    PubMed

    Niemarkt, Hendrik J; Jennekens, Ward; Pasman, Jaco W; Katgert, Titia; Van Pul, Carola; Gavilanes, Antonio W D; Kramer, Boris W; Zimmermann, Luc J; Bambang Oetomo, Sidarto; Andriessen, Peter

    2011-11-01

    Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10-20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA <32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27(+4) to 36(+3) wk of postmenstrual age (PMA, GA + postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

  18. Validation of the TaqMan Influenza A Detection Kit and a rapid automated total nucleic acid extraction method to detect influenza A virus in nasopharyngeal specimens.

    PubMed

    Bolotin, Shelly; De Lima, Cedric; Choi, Kam-Wing; Lombos, Ernesto; Burton, Laura; Mazzulli, Tony; Drews, Steven J

    2009-01-01

    This study describes the validation of the TaqMan Influenza A Detection Kit v2.0 combined with an automated nucleic acid extraction method. The limit of detection of this assay was determined by probit regression (95% confidence interval) to be 2 influenza A/PR/8/34 (H1N1) virus particles per microlitre. One hundred and eleven specimens previously tested using the Seeplex RV assay and viral culture methods were tested using the TaqMan Influenza A Detection Kit. Compared to the aggregate gold-standard, the sensitivity and specificity of the TaqMan Influenza A Detection Kit were 100% (35/35) and 97% (74/76), respectively. Because of its accuracy, quick turn-around-time and lyophilized bead form, the TaqMan Influenza A Detection Kit, combined with the NucliSense easyMAG automated extraction method, constitutes a reliable protocol for influenza A diagnosis.

  19. Atlas-Based Analysis of Neurodevelopment from Infancy to Adulthood Using Diffusion Tensor Imaging and Applications for Automated Abnormality Detection

    PubMed Central

    Faria, Andreia V.; Zhang, Jiangyang; Oishi, Kenichi; Li, Xin; Jiang, Hangyi; Akhter, Kazi; Hermoye, Laurent; Lee, Seung-Koo; Hoon, Alexander; Stachinko, Elaine; Miller, Michael I.; van Zijl, Peter C.M.; Mori, Susumu

    2010-01-01

    Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2 years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated. PMID:20420929

  20. Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation.

    PubMed

    Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chua, Chua Kuang; Min, Lim Choo; Ng, E Y K; Mushrif, Milind M; Laude, Augustinus

    2013-01-01

    The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness. The identification of retinal anatomical regions is a prerequisite for the computer-aided diagnosis of several retinal diseases. The manual examination of optic disk (OD) is a standard procedure used for detecting different stages of DR and glaucoma. In this article, a novel automated, reliable, and efficient OD localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the OD due to fuzzy boundaries, inconsistent image contrast, or missing edge features. This article proposes a novel and probably the first method using the Attanassov intuitionistic fuzzy histon (A-IFSH)-based segmentation to detect OD in retinal fundus images. OD pixel intensity and column-wise neighborhood operation are employed to locate and isolate the OD. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous, and 31 DR images. Our proposed method has yielded precision of 0.93, recall of 0.91, F-score of 0.92, and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with the Otsu and gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

  1. Deep convolutional networks for automated detection of posterior-element fractures on spine CT

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Wang, Yinong; Yao, Jianhua; Lu, Le; Burns, Joseph E.; Summers, Ronald M.

    2016-03-01

    Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.

  2. Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

    PubMed Central

    Park, Han Sang; Rinehart, Matthew T.; Walzer, Katelyn A.; Chi, Jen-Tsan Ashley; Wax, Adam

    2016-01-01

    Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection

  3. Automated detection and labeling of high-density EEG electrodes from structural MR images

    NASA Astrophysics Data System (ADS)

    Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

    2016-10-01

    Objective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work

  4. Automated ABO Rh-D blood type detection using smartphone imaging for point-of-care medical diagnostics.

    PubMed

    Srivathsa, Neha; Dendukuri, Dhananjaya; Srivathsa, Neha; Dendukuri, Dhananjaya; Srivathsa, Neha; Dendukuri, Dhananjaya

    2016-08-01

    We present a novel methodology for automated ABO Rh-D blood typing using simple morphological image processing algorithms to be used in conjunction with a fabric strip based rapid diagnostic test. Images of the fabric strip post testing are acquired using low cost mobile phones and the proposed algorithm proceeds to automatically identify the blood type by processing the images using steps comprising of noise reduction, range filtering and empirically derived heuristics. The ultimate goal is to provide a simple mobile phone application to enable automated, rapid and accessible blood type detection at the point-of-care.

  5. HAT-South: A Global Network of Southern Hemisphere Automated Telescopes to Detect Transiting Exoplanets

    NASA Astrophysics Data System (ADS)

    Bakos, G.; Afonso, C.; Henning, T.; Jordán, A.; Holman, M.; Noyes, R. W.; Sackett, P. D.; Sasselov, D.; Kovács, Gábor; Csubry, Z.; Pál, A.

    2009-02-01

    HAT-South is a network of six identical, fully automated wide field telescopes, to be located at three sites (Chile: Las Campanas, Australia: Siding Springs, and Namibia: HESS site) in the Southern hemisphere. The primary purpose of the network is to detect and characterize a large number of extra-solar planets transiting nearby bright stars, and to explore their diversity. Operation of HAT-South is a collaboration among the Harvard-Smithsonian Center for Astrophysics (CfA), Max Planck Institute for Astronomy (MPIA) and the Australian National University (ANU). The network is expected to be ready for initial science operations in 2009. The three sites will permit near round-the-clock monitoring of selected fields, and the continuous data-stream will greatly enhance recovery of transits. HAT-South will be sensitive to planetary transits down to R≈14 across a 128 square-degrees combined field of view, thereby targeting a large number of dwarfs with feasible confirmation-mode follow-up. We anticipate a yearly detection rate of approximately 25 planets transiting bright stars.

  6. Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images

    PubMed Central

    Kreshuk, Anna; Straehle, Christoph N.; Sommer, Christoph; Koethe, Ullrich; Cantoni, Marco; Knott, Graham; Hamprecht, Fred A.

    2011-01-01

    We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection. PMID:22031814

  7. Automated polarization-discrimination technique to minimize lidar-detected skylight background noise

    NASA Astrophysics Data System (ADS)

    Hassebo, Yasser Y.; Ahmed, Samir

    2007-10-01

    Recently, there has been significant interest in lidar signal-to-noise ratio (SNR) improvements, particularly for lidar daytime operations. Previously, we devised in the remote sensing laboratory at the City College of New York a polarization discrimination technique to maximize lidar detected SNR taking advantage of the natural polarization properties of scattered skylight radiation to track and minimize detected sky background signal (BGS). This tracking technique was achieved by rotating, manually, a combination of polarizer and analyzer on both the lidar transmitter and receiver subsystems, respectively. The polarization orientation at which the minimum BGS occurs, follows the solar azimuth angle, even for high aerosol loading. This has been confirmed, in our previous work, both theoretically, assuming single scattering theory, and experimentally. In this paper, a design to automate the polarization discrimination technique by real time tracking of the azimuth angle to attain the minimum BGS is presented. We introduce a feedback control system to track the minimum BGS by rotating the detector analyzer and the transmission polarizer simultaneously to maximize the SNR and attainable lidar ranges, thus achieving the same results as would be done manually. Analytical results for New York City are summarized and an approach for applying the proposed design globally is investigated.

  8. Automated detection of remineralization in simulated enamel lesions with PS-OCT

    NASA Astrophysics Data System (ADS)

    Lee, Robert C.; Darling, Cynthia L.; Fried, Daniel

    2014-02-01

    Previous in vitro and in vivo studies have demonstrated that polarization-sensitive optical coherence tomography (PS-OCT) can be used to nondestructively image the subsurface structure and measure the thickness of the highly mineralized transparent surface zone of caries lesions. There are structural differences between active lesions and arrested lesions, and the surface layer thickness may correlate with activity of the lesion. The purpose of this study was to develop a method that can be used to automatically detect and measure the thickness of the transparent surface layer in PS-OCT images. Automated methods of analysis were used to measure the thickness of the transparent layer and the depth of the bovine enamel lesions produced using simulated caries models that emulate demineralization in the mouth. The transparent layer thickness measured with PS-OCT correlated well with polarization light microscopy (PLM) measurements of all regions (r2=0.9213). This study demonstrates that PS-OCT can automatically detect and measure thickness of the transparent layer formed due to remineralization in simulated caries lesions.

  9. Construction and evaluation of an automated light directed protein-detecting microarray synthesizer.

    PubMed

    Marthandan, N; Klyza, S; Li, S; Kwon, Y U; Kodadek, T; Garner, H R

    2008-03-01

    We have designed, constructed, and evaluated an automated instrument that has produced high-density arrays with more than 30 000 peptide features within a 1.5 cm(2) area of a glass slide surface. These arrays can be used for high throughput library screening for protein binding ligands, for potential drug candidate molecules, or for discovering biomarkers. The device consists of a novel fluidics system, a relay control electrical system, an optics system that implements Texas Instruments' digital micromirror device (DMD), and a microwave source for accelerated synthesis of peptide arrays. The instrument implements two novel solid phase chemical synthesis strategies for producing peptide and peptoid arrays. Biotin-streptavidin and DNP anti-DNP (dinitrophenol) models of antibody small molecule interactions were used to demonstrate and evaluate the instrument's capability to produce high-density protein detecting arrays. Several screening assay and detection schemes were explored with various levels of efficiency and assays with sensitivity of 10 nM were also possible.

  10. Portable capillary electrophoresis instrument with automated injector and contactless conductivity detection.

    PubMed

    Mai, Thanh Duc; Pham, Thi Thanh Thuy; Pham, Hung Viet; Sáiz, Jorge; Ruiz, Carmen García; Hauser, Peter C

    2013-02-19

    A portable capillary electrophoresis instrument featuring an automated, robust, valve-based injection system was developed. This significantly facilitates operation in the field compared to previous injection approaches. These generally required delicate manual operations which are difficult to perform outside the laboratory environment. The novel system relies on pressurized air for solution delivery and a micromembrane pump for sample aspiration. Contactless conductivity detection was employed for its versatility and low power requirement. The instrument has a compact design, with all components arranged in a briefcase with dimensions of 45 × 35 × 15 cm (w × d × h) and a weight of about 8 kg. It can operate continuously for 9 h in the battery-powered mode. Depending on the task at hand, the injection system allows easy optimization for high separation efficiency, for fast separations, or for low limits of detection. To illustrate these features, the separation of four anions within 16 s is demonstrated as well as the determination of nitrite below 1 μM. The determination of phosphate at a sewage treatment plant was carried out to demonstrate a field application.

  11. Evaluation of automated term groupings for detecting anaphylactic shock signals for drugs

    PubMed Central

    Souvignet, Julien; Declerck, Gunnar; Trombert, Béatrice; Rodrigues, Jean Marie; Jaulent, Marie-Christine; Bousquet, Cédric

    2012-01-01

    Signal detection in pharmacovigilance should take into account all terms related to a medical concept rather than a single term. We built an OWL-DL file with formal definitions of MedDRA and SNOMED-CT concepts and performed two queries, Query 1 and 2, to retrieve narrow and broad terms within the Standard MedDRA Query (SMQ) related to ‘anaphylactic shock’ and the terms from the High Level Term (HLT) grouping related to ‘anaphylaxis’. We compared values of the EB05 (EBGM) statistical test for disproportionality with 50 active ingredients randomly selected in the public version of the FDA pharmacovigilance database. Coefficient of correlation was R2 = 1.00 between Query 1 and HLT; R2 = 0.98 between Query 1 and SMQ narrow; R2 = 0.89 between Query 2 and SMQ Narrow+Broad. Generating automated groupings of terms for signal detection is feasible but requires additional efforts in modeling MedDRA terms in order to improve precision and recall of these groupings. PMID:23304363

  12. Automated detection of diagnostically relevant regions in H&E stained digital pathology slides

    NASA Astrophysics Data System (ADS)

    Bahlmann, Claus; Patel, Amar; Johnson, Jeffrey; Ni, Jie; Chekkoury, Andrei; Khurd, Parmeshwar; Kamen, Ali; Grady, Leo; Krupinski, Elizabeth; Graham, Anna; Weinstein, Ronald

    2012-03-01

    We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity, such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation error rate of 1.4%. We further show that the proposed method can prune ~90% of the area of pathological slides while maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.

  13. A Method for Automated Detection of Usability Problems from Client User Interface Events

    PubMed Central

    Saadawi, Gilan M.; Legowski, Elizabeth; Medvedeva, Olga; Chavan, Girish; Crowley, Rebecca S.

    2005-01-01

    Think-aloud usability analysis provides extremely useful data but is very time-consuming and expensive to perform because of the extensive manual video analysis that is required. We describe a simple method for automated detection of usability problems from client user interface events for a developing medical intelligent tutoring system. The method incorporates (1) an agent-based method for communication that funnels all interface events and system responses to a centralized database, (2) a simple schema for representing interface events and higher order subgoals, and (3) an algorithm that reproduces the criteria used for manual coding of usability problems. A correction factor was empirically determining to account for the slower task performance of users when thinking aloud. We tested the validity of the method by simultaneously identifying usability problems using TAU and manually computing them from stored interface event data using the proposed algorithm. All usability problems that did not rely on verbal utterances were detectable with the proposed method. PMID:16779121

  14. Automated monitoring of early neurobehavioral changes in mice following traumatic brain injury

    PubMed Central

    Qu, Wenrui; Liu, Nai-kui; Xie, Xin-min (Simon); Li, Rui; Xu, Xiao-ming

    2016-01-01

    Traumatic brain injury often causes a variety of behavioral and emotional impairments that can develop into chronic disorders. Therefore, there is a need to shift towards identifying early symptoms that can aid in the prediction of traumatic brain injury outcomes and behavioral endpoints in patients with traumatic brain injury after early interventions. In this study, we used the SmartCage system, an automated quantitative approach to assess behavior alterations in mice during an early phase of traumatic brain injury in their home cages. Female C57BL/6 adult mice were subjected to moderate controlled cortical impact (CCI) injury. The mice then received a battery of behavioral assessments including neurological score, locomotor activity, sleep/wake states, and anxiety-like behaviors on days 1, 2, and 7 after CCI. Histological analysis was performed on day 7 after the last assessment. Spontaneous activities on days 1 and 2 after injury were significantly decreased in the CCI group. The average percentage of sleep time spent in both dark and light cycles were significantly higher in the CCI group than in the sham group. For anxiety-like behaviors, the time spent in a light compartment and the number of transitions between the dark/light compartments were all significantly reduced in the CCI group than in the sham group. In addition, the mice suffering from CCI exhibited a preference of staying in the dark compartment of a dark/light cage. The CCI mice showed reduced neurological score and histological abnormalities, which are well correlated to the automated behavioral assessments. Our findings demonstrate that the automated SmartCage system provides sensitive and objective measures for early behavior changes in mice following traumatic brain injury. PMID:27073377

  15. Evaluation of change detection techniques for monitoring coastal zone environments

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A. (Principal Investigator); Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

    1977-01-01

    The author has identified the following significant results. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. The post classification comparison technique reliably identified areas of change and was used as the standard for qualitatively evaluating the other three techniques. The layered spectral/temporal change classification and the delta data change detection results generally agreed with the post classification comparison technique results; however, many small areas of change were not identified. Major discrepancies existed between the post classification comparison and spectral/temporal change detection results.

  16. Automated Indexing of Internet Stories for Health Behavior Change: Weight Loss Attitude Pilot Study

    PubMed Central

    Manuvinakurike, Ramesh; Velicer, Wayne F

    2014-01-01

    Background Automated health behavior change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of health behavior change that could not only provide useful information and motivation for others who are also trying to change, but an endless source of novel, entertaining stories that may keep participants more engaged than messages authored by interventionists. Objective Given a collection of relevant personal health behavior change stories gathered from the Internet, the aim of this study was to develop and evaluate an automated indexing algorithm that could select the best possible story to provide to a user to have the greatest possible impact on their attitudes toward changing a targeted health behavior, in this case weight loss. Methods An indexing algorithm was developed using features informed by theories from behavioral medicine together with text classification and machine learning techniques. The algorithm was trained using a crowdsourced dataset, then evaluated in a 2×2 between-subjects randomized pilot study. One factor compared the effects of participants reading 2 indexed stories vs 2 randomly selected stories, whereas the second factor compared the medium used to tell the stories: text or animated conversational agent. Outcome measures included changes in self-efficacy and decisional balance for weight loss before and after the stories were read. Results Participants were recruited from a crowdsourcing website (N=103; 53.4%, 55/103 female; mean age 35, SD 10.8 years; 65.0%, 67/103 precontemplation; 19.4%, 20/103 contemplation for weight loss). Participants who read indexed stories exhibited a significantly greater increase in self-efficacy for weight loss compared to the control group (F 1,107=5.5, P=.02). There were no significant effects of indexing on change in decisional balance (F 1,97=0.05, P=.83) and no significant effects of medium on change in self

  17. Computerised emission and susceptibility MIL.STD testing with automated NB/BB detection

    NASA Astrophysics Data System (ADS)

    Vanessen, J. C.

    1990-09-01

    Automation of Electromagnetic Compatibility (EMC) testing is becoming common at many EMC test facilities. Commercial automated systems have become available in the past few years. The test and operations section has developed its own EMC automation to enhance and aid in testing. A complete overview of the automated EMC test facility in operation for emission and susceptibility measurements is presented. It includes a hardware description, the program structure and some of the methods required to complete such a program on the equipment chosen, including the Narrow Band (NB) and Broad Band (BB).

  18. Nationwide Hybrid Change Detection of Buildings

    NASA Astrophysics Data System (ADS)

    Hron, V.; Halounova, L.

    2016-06-01

    The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

  19. Immunohistochemical Detection of Changes in Tumor Hypoxia

    SciTech Connect

    Russell, James Carlin, Sean; Burke, Sean A.; Wen Bixiu; Yang, Kwang Mo; Ling, C. Clifton

    2009-03-15

    Purpose: Although hypoxia is a known prognostic factor, its effect will be modified by the rate of reoxygenation and the extent to which the cells are acutely hypoxic. We tested the ability of exogenous and endogenous markers to detect reoxygenation in a xenograft model. Our technique might be applicable to stored patient samples. Methods and Materials: The human colorectal carcinoma line, HT29, was grown in nude mice. Changes in tumor hypoxia were examined by injection of pimonidazole, followed 24 hours later by EF5. Cryosections were stained for these markers and for carbonic anhydrase IX (CAIX) and hypoxia-inducible factor 1{alpha} (HIF1{alpha}). Tumor hypoxia was artificially manipulated by carbogen exposure. Results: In unstressed tumors, all four markers showed very similar spatial distributions. After carbogen treatment, pimonidazole and EF5 could detect decreased hypoxia. HIF1{alpha} staining was also decreased relative to CAIX, although the effect was less pronounced than for EF5. Control tumors displayed small regions that had undergone spontaneous changes in tumor hypoxia, as judged by pimonidazole relative to EF5; most of these changes were reflected by CAIX and HIF1{alpha}. Conclusion: HIF1{alpha} can be compared with either CAIX or a previously administered nitroimidazole to provide an estimate of reoxygenation.

  20. Lake Chapala change detection using time series

    NASA Astrophysics Data System (ADS)

    López-Caloca, Alejandra; Tapia-Silva, Felipe-Omar; Escalante-Ramírez, Boris

    2008-10-01

    The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lake's limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

  1. Automated, non-metallic measurement facility for testing and development of electromagnetic induction sensors for landmine detection

    NASA Astrophysics Data System (ADS)

    Larson, Gregg D.; Scott, Waymond R., Jr.

    2009-05-01

    For development of electromagnetic induction (EMI) sensors for landmine detection, a testing facility has been established for automated measurements of typical targets with both individual sensors and arrays of sensors. A six-degree of freedom positioner has been built with five automated axes (three translational stages and two rotational stages) and one manual axis for target characterizations with no metal within the measurement volume. Translational stages utilize commercially-available linear positioner hardware. Rotational stages have been customized using nonmetallic components to position the targets within the measurement volume. EMI sensors are held fixed in one location while the positioner orients the targets and moves them along a prescribed path through the region surrounding the sensor. The automated movement is computer-controlled and data are acquired continuously. Data are presented from three-dimensional scans of targets at various orientations. Typical targets include shell casings, wire loops, ball bearings, and landmines.

  2. 3D change detection - Approaches and applications

    NASA Astrophysics Data System (ADS)

    Qin, Rongjun; Tian, Jiaojiao; Reinartz, Peter

    2016-12-01

    Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.

  3. Sink detection on tilted terrain for automated identification of glacial cirques

    NASA Astrophysics Data System (ADS)

    Prasicek, Günther; Robl, Jörg; Lang, Andreas

    2016-04-01

    Glacial cirques are morphologically distinct but complex landforms and represent a vital part of high mountain topography. Their distribution, elevation and relief are expected to hold information on (1) the extent of glacial occupation, (2) the mechanism of glacial cirque erosion, and (3) how glacial in concert with periglacial processes can limit peak altitude and mountain range height. While easily detectably for the expert's eye both in nature and on various representations of topography, their complicated nature makes them a nemesis for computer algorithms. Consequently, manual mapping of glacial cirques is commonplace in many mountain landscapes worldwide, but consistent datasets of cirque distribution and objectively mapped cirques and their morphometrical attributes are lacking. Among the biggest problems for algorithm development are the complexity in shape and the great variability of cirque size. For example, glacial cirques can be rather circular or longitudinal in extent, exist as individual and composite landforms, show prominent topographic depressions or can entirely be filled with water or sediment. For these reasons, attributes like circularity, size, drainage area and topology of landform elements (e.g. a flat floor surrounded by steep walls) have only a limited potential for automated cirque detection. Here we present a novel, geomorphometric method for automated identification of glacial cirques on digital elevation models that exploits their genetic bowl-like shape. First, we differentiate between glacial and fluvial terrain employing an algorithm based on a moving window approach and multi-scale curvature, which is also capable of fitting the analysis window to valley width. We then fit a plane to the valley stretch clipped by the analysis window and rotate the terrain around the center cell until the plane is level. Doing so, we produce sinks of considerable size if the clipped terrain represents a cirque, while no or only very small sinks

  4. Evaluation of automated and manual DNA purification methods for detecting Ricinus communis DNA during ricin investigations.

    PubMed

    Hutchins, Anne S; Astwood, Michael J; Saah, J Royden; Michel, Pierre A; Newton, Bruce R; Dauphin, Leslie A

    2014-03-01

    In April of 2013, letters addressed to the President of United States and other government officials were intercepted and found to be contaminated with ricin, heightening awareness about the need to evaluate laboratory methods for detecting ricin. This study evaluated commercial DNA purification methods for isolating Ricinus communis DNA as measured by real-time polymerase chain reaction (PCR). Four commercially available DNA purification methods (two automated, MagNA Pure compact and MagNA Pure LC, and two manual, MasterPure complete DNA and RNA purification kit and QIAamp DNA blood mini kit) were evaluated. We compared their ability to purify detectable levels of R. communis DNA from four different sample types, including crude preparations of ricin that could be used for biological crimes or acts of bioterrorism. Castor beans, spiked swabs, and spiked powders were included to simulate sample types typically tested during criminal and public health investigations. Real-time PCR analysis indicated that the QIAamp kit resulted in the greatest sensitivity for ricin preparations; the MasterPure kit performed best with spiked powders. The four methods detected equivalent levels by real-time PCR when castor beans and spiked swabs were used. All four methods yielded DNA free of PCR inhibitors as determined by the use of a PCR inhibition control assay. This study demonstrated that DNA purification methods differ in their ability to purify R. communis DNA; therefore, the purification method used for a given sample type can influence the sensitivity of real-time PCR assays for R. communis.

  5. Robust background subtraction for automated detection and tracking of targets in wide area motion imagery

    NASA Astrophysics Data System (ADS)

    Kent, Phil; Maskell, Simon; Payne, Oliver; Richardson, Sean; Scarff, Larry

    2012-10-01

    Performing persistent surveillance of large populations of targets is increasingly important in both the defence and security domains. In response to this, Wide Area Motion Imagery (WAMI) sensors with Wide FoVs are growing in popularity. Such WAMI sensors simultaneously provide high spatial and temporal resolutions, giving extreme pixel counts over large geographical areas. The ensuing data rates are such that either very bandwidth data links are required (e.g. for human interpretation) or close-to-sensor automation is required to down-select salient information. For the latter case, we use an iterative quad-tree optical-flow algorithm to efficiently estimate the parameters of a perspective deformation of the background. We then use a robust estimator to simultaneously detect foreground pixels and infer the parameters of each background pixel in the current image. The resulting detections are referenced to the coordinates of the first frame and passed to a multi-target tracker. The multi-target tracker uses a Kalman filter per target and a Global Nearest Neighbour approach to multi-target data association, thereby including statistical models for missed detections and false alarms. We use spatial data structures to ensure that the tracker can scale to analysing thousands of targets. We demonstrate that real-time processing (on modest hardware) is feasible on an unclassified WAMI infra-red dataset consisting of 4096 by 4096 pixels at 1Hz simulating data taken from a Wide FoV sensor on a UAV. With low latency and despite intermittent obscuration and false alarms, we demonstrate persistent tracking of all but one (low-contrast) vehicular target, with no false tracks.

  6. Automated laser-based barely visible impact damage detection in honeycomb sandwich composite structures

    SciTech Connect

    Girolamo, D. Yuan, F. G.; Girolamo, L.

    2015-03-31

    Nondestructive evaluation (NDE) for detection and quantification of damage in composite materials is fundamental in the assessment of the overall structural integrity of modern aerospace systems. Conventional NDE systems have been extensively used to detect the location and size of damages by propagating ultrasonic waves normal to the surface. However they usually require physical contact with the structure and are time consuming and labor intensive. An automated, contactless laser ultrasonic imaging system for barely visible impact damage (BVID) detection in advanced composite structures has been developed to overcome these limitations. Lamb waves are generated by a Q-switched Nd:YAG laser, raster scanned by a set of galvano-mirrors over the damaged area. The out-of-plane vibrations are measured through a laser Doppler Vibrometer (LDV) that is stationary at a point on the corner of the grid. The ultrasonic wave field of the scanned area is reconstructed in polar coordinates and analyzed for high resolution characterization of impact damage in the composite honeycomb panel. Two methodologies are used for ultrasonic wave-field analysis: scattered wave field analysis (SWA) and standing wave energy analysis (SWEA) in the frequency domain. The SWA is employed for processing the wave field and estimate spatially dependent wavenumber values, related to discontinuities in the structural domain. The SWEA algorithm extracts standing waves trapped within damaged areas and, by studying the spectrum of the standing wave field, returns high fidelity damage imaging. While the SWA can be used to locate the impact damage in the honeycomb panel, the SWEA produces damage images in good agreement with X-ray computed tomographic (X-ray CT) scans. The results obtained prove that the laser-based nondestructive system is an effective alternative to overcome limitations of conventional NDI technologies.

  7. Semi-automated scar detection in delayed enhanced cardiac magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Morisi, Rita; Donini, Bruno; Lanconelli, Nico; Rosengarden, James; Morgan, John; Harden, Stephen; Curzen, Nick

    2015-06-01

    Late enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quantification is not currently offered due to time constraints. A first segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four different classifiers were trained (Support Vector Machine (SVM), k-nearest neighbor (KNN), Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region affected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classifier for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an effective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quantification.

  8. Imaging, object detection, and change detection with a polarized multistatic GPR array

    SciTech Connect

    Beer, N. Reginald; Paglieroni, David W.

    2015-07-21

    A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

  9. Detecting past changes of effective population size

    PubMed Central

    Nikolic, Natacha; Chevalet, Claude

    2014-01-01

    Understanding and predicting population abundance is a major challenge confronting scientists. Several genetic models have been developed using microsatellite markers to estimate the present and ancestral effective population sizes. However, to get an overview on the evolution of population requires that past fluctuation of population size be traceable. To address the question, we developed a new model estimating the past changes of effective population size from microsatellite by resolving coalescence theory and using approximate likelihoods in a Monte Carlo Markov Chain approach. The efficiency of the model and its sensitivity to gene flow and to assumptions on the mutational process were checked using simulated data and analysis. The model was found especially useful to provide evidence of transient changes of population size in the past. The times at which some past demographic events cannot be detected because they are too ancient and the risk that gene flow may suggest the false detection of a bottleneck are discussed considering the distribution of coalescence times. The method was applied on real data sets from several Atlantic salmon populations. The method called VarEff (Variation of Effective size) was implemented in the R package VarEff and is made available at https://qgsp.jouy.inra.fr and at http://cran.r-project.org/web/packages/VarEff. PMID:25067949

  10. Selective Automated Perimetry Under Photopic, Mesopic, and Scotopic Conditions: Detection Mechanisms and Testing Strategies

    PubMed Central

    Simunovic, Matthew P.; Moore, Anthony T.; MacLaren, Robert E.

    2016-01-01

    Purpose Automated scotopic, mesopic, and photopic perimetry are likely to be important paradigms in the assessment of emerging treatments of retinal diseases, yet our knowledge of the photoreceptor mechanisms detecting targets under these conditions remains largely dependent on simian data. We therefore aimed to establish the photoreceptor/postreceptoral mechanisms detecting perimetric targets in humans under photopic, mesopic, and scotopic conditions and to make recommendations for suitable clinical testing strategies for selective perimetry. Methods Perimetric sensitivities within 30° of fixation were determined for eight wavelengths (410, 440, 480, 520, 560, 600, 640, and 680 nm) under scotopic, mesopic (1.3 cd.m−2) and photopic (10 cd.m−2) conditions. Data were fitted with vector combinations of rod, S-cone, nonopponent M+L-cone mechanism, and opponent M- versus L-cone mechanism templates. Results Scotopicperimetric sensitivity was determined by rods peripherally and by a combination of rods and cones at, and immediately around, fixation. Mesopic perimetric sensitivity was mediated by M+L-cones and S-cones centrally and by M+L-cones and rods more peripherally. Photopic perimetric sensitivity was determined by an opponent M- versus L-cone, a nonopponent M+L-cone, and an S-cone mechanism centrally and by a combination of an S-cone and an M+L-cone mechanism peripherally. Conclusions Under scotopic conditions, a 480-nm stimulus provides adequate isolation (≥28 dB) of the rod mechanism. Several mechanisms contribute to mesopic sensitivity: this redundancy in detection may cause both insensitivity to broadband white targets and ambiguity in determining which mechanism is being probed with short-wavelength stimuli. M- and L-cone–derived mechanisms are well isolated at 10 cd.m−2: these may be selectively probed by a stimulus at 640 nm (≥ 20 dB isolation). Translation Relevance In human observers, multiple mechanisms contribute to the detection of Goldmann

  11. A longitudinal evaluation of performance of automated BCR-ABL1 quantitation using cartridge-based detection system

    PubMed Central

    Enjeti, Anoop; Granter, Neil; Ashraf, Asma; Fletcher, Linda; Branford, Susan; Rowlings, Philip; Dooley, Susan

    2015-01-01

    SummaryAn automated cartridge-based detection system (GeneXpert; Cepheid) is being widely adopted in low throughput laboratories for monitoring BCR-ABL1 transcript in chronic myelogenous leukaemia. This Australian study evaluated the longitudinal performance specific characteristics of the automated system. The automated cartridge-based system was compared prospectively with the manual qRT-PCR-based reference method at SA Pathology, Adelaide, over a period of 2.5 years. A conversion factor determination was followed by four re-validations. Peripheral blood samples (n = 129) with international scale (IS) values within detectable range were selected for assessment. The mean bias, proportion of results within specified fold difference (2-, 3- and 5-fold), the concordance rate of major molecular remission (MMR) and concordance across a range of IS values on paired samples were evaluated. The initial conversion factor for the automated system was determined as 0.43. Except for the second re-validation, where a negative bias of 1.9-fold was detected, all other biases fell within desirable limits. A cartridge-specific conversion factor and efficiency value was introduced and the conversion factor was confirmed to be stable in subsequent re-validation cycles. Concordance with the reference method/laboratory at >0.1–≤10 IS was 78.2% and at ≤0.001 was 80%, compared to 86.8% in the >0.01–≤0.1 IS range. The overall and MMR concordance were 85.7% and 94% respectively, for samples that fell within ± 5-fold of the reference laboratory value over the entire period of study. Conversion factor and performance specific characteristics for the automated system were longitudinally stable in the clinically relevant range, following introduction by the manufacturer of lot specific efficiency values. PMID:26166664

  12. Fully automated detection of corticospinal tract damage in chronic stroke patients.

    PubMed

    Yang, Ming; Yang, Ya-ru; Li, Hui-jun; Lu, Xue-song; Shi, Yong-mei; Liu, Bin; Chen, Hua-jun; Teng, Gao-jun

    2014-01-01

    Structural integrity of the corticospinal tract (CST) after stroke is closely linked to the degree of motor impairment. However, current methods for measurement of fractional atrophy (FA) of CST based on region of interest (ROI) are time-consuming and open to bias. Here, we used tract-based spatial statistics (TBSS) together with a CST template with healthy volunteers to quantify structural integrity of CST automatically. Two groups of patients after ischemic stroke were enrolled, group 1 (10 patients, 7 men, and Fugl-Meyer assessment (FMA) scores ⩽ 50) and group 2 (12 patients, 12 men, and FMA scores = 100). CST of FA(ipsi), FA(contra), and FA(ratio) was compared between the two groups. Relative to group 2, FA was decreased in group 1 in the ipsilesional CST (P < 0.01), as well as the FA(ratio) (P < 0.01). There was no significant difference between the two subgroups in the contralesional CST (P = 0.23). Compared with contralesional CST, FA of ipsilesional CST decreased in group 1 (P < 0.01). These results suggest that the automated method used in our study could detect a surrogate biomarker to quantify the CST after stroke, which would facilitate implementation of clinical practice.

  13. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection

    NASA Astrophysics Data System (ADS)

    Magalhães, F.; Cunha, A.; Caetano, E.

    2012-04-01

    In order to evaluate the usefulness of approaches based on modal parameters tracking for structural health monitoring of bridges, in September of 2007, a dynamic monitoring system was installed in a concrete arch bridge at the city of Porto, in Portugal. The implementation of algorithms to perform the continuous on-line identification of modal parameters based on structural responses to ambient excitation (automated Operational Modal Analysis) has permitted to create a very complete database with the time evolution of the bridge modal characteristics during more than 2 years. This paper describes the strategy that was followed to minimize the effects of environmental and operational factors on the bridge natural frequencies, enabling, in a subsequent stage, the identification of structural anomalies. Alternative static and dynamic regression models are tested and complemented by a Principal Components Analysis. Afterwards, the identification of damages is tried with control charts. At the end, it is demonstrated that the adopted processing methodology permits the detection of realistic damage scenarios, associated with frequency shifts around 0.2%, which were simulated with a numerical model.

  14. Semi-Automated Detection of Surface Degradation on Bridges Based on a Level Set Method

    NASA Astrophysics Data System (ADS)

    Masiero, A.; Guarnieri, A.; Pirotti, F.; Vettore, A.

    2015-08-01

    Due to the effect of climate factors, natural phenomena and human usage, buildings and infrastructures are subject of progressive degradation. The deterioration of these structures has to be monitored in order to avoid hazards for human beings and for the natural environment in their neighborhood. Hence, on the one hand, monitoring such infrastructures is of primarily importance. On the other hand, unfortunately, nowadays this monitoring effort is mostly done by expert and skilled personnel, which follow the overall data acquisition, analysis and result reporting process, making the whole monitoring procedure quite expensive for the public (and private, as well) agencies. This paper proposes the use of a partially user-assisted procedure in order to reduce the monitoring cost and to make the obtained result less subjective as well. The developed method relies on the use of images acquired with standard cameras by even inexperienced personnel. The deterioration on the infrastructure surface is detected by image segmentation based on a level sets method. The results of the semi-automated analysis procedure are remapped on a 3D model of the infrastructure obtained by means of a terrestrial laser scanning acquisition. The proposed method has been successfully tested on a portion of a road bridge in Perarolo di Cadore (BL), Italy.

  15. Point pattern match-based change detection in a constellation of previously detected objects

    SciTech Connect

    Paglieroni, David W.

    2016-06-07

    A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

  16. Automated Detection and Classification of Rockfall Induced Seismic Signals with Hidden-Markov-Models

    NASA Astrophysics Data System (ADS)

    Zeckra, M.; Hovius, N.; Burtin, A.; Hammer, C.

    2015-12-01

    Originally introduced in speech recognition, Hidden Markov Models are applied in different research fields of pattern recognition. In seismology, this technique has recently been introduced to improve common detection algorithms, like STA/LTA ratio or cross-correlation methods. Mainly used for the monitoring of volcanic activity, this study is one of the first applications to seismic signals induced by geomorphologic processes. With an array of eight broadband seismometers deployed around the steep Illgraben catchment (Switzerland) with high-level erosion, we studied a sequence of landslides triggered over a period of several days in winter. A preliminary manual classification led us to identify three main seismic signal classes that were used as a start for the HMM automated detection and classification: (1) rockslide signal, including a failure source and the debris mobilization along the slope, (2) rockfall signal from the remobilization of debris along the unstable slope, and (3) single cracking signal from the affected cliff observed before the rockslide events. Besides the ability to classify the whole dataset automatically, the HMM approach reflects the origin and the interactions of the three signal classes, which helps us to understand this geomorphic crisis and the possible triggering mechanisms for slope processes. The temporal distribution of crack events (duration > 5s, frequency band [2-8] Hz) follows an inverse Omori law, leading to the catastrophic behaviour of the failure mechanisms and the interest for warning purposes in rockslide risk assessment. Thanks to a dense seismic array and independent weather observations in the landslide area, this dataset also provides information about the triggering mechanisms, which exhibit a tight link between rainfall and freezing level fluctuations.

  17. Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease

    SciTech Connect

    Isgum, Ivana; Rutten, Annemarieke; Prokop, Mathias; Ginneken, Bram van

    2007-04-15

    A fully automated method for coronary calcification detection from non-contrast-enhanced, ECG-gated multi-slice computed tomography (CT) data is presented. Candidates for coronary calcifications are extracted by thresholding and component labeling. These candidates include coronary calcifications, calcifications in the aorta and in the heart, and other high-density structures such as noise and bone. A dedicated set of 64 features is calculated for each candidate object. They characterize the object's spatial position relative to the heart and the aorta, for which an automatic segmentation scheme was developed, its size and shape, and its appearance, which is described by a set of approximated Gaussian derivatives for which an efficient computational scheme is presented. Three classification strategies were designed. The first one tested direct classification without feature selection. The second approach also utilized direct classification, but with feature selection. Finally, the third scheme employed two-stage classification. In a computationally inexpensive first stage, the most easily recognizable false positives were discarded. The second stage discriminated between more difficult to separate coronary calcium and other candidates. Performance of linear, quadratic, nearest neighbor, and support vector machine classifiers was compared. The method was tested on 76 scans containing 275 calcifications in the coronary arteries and 335 calcifications in the heart and aorta. The best performance was obtained employing a two-stage classification system with a k-nearest neighbor (k-NN) classifier and a feature selection scheme. The method detected 73.8% of coronary calcifications at the expense of on average 0.1 false positives per scan. A calcium score was computed for each scan and subjects were assigned one of four risk categories based on this score. The method assigned the correct risk category to 93.4% of all scans.

  18. Automated determinations of selenium in thermal power plant wastewater by sequential hydride generation and chemiluminescence detection.

    PubMed

    Ezoe, Kentaro; Ohyama, Seiichi; Hashem, Md Abul; Ohira, Shin-Ichi; Toda, Kei

    2016-02-01

    After the Fukushima disaster, power generation from nuclear power plants in Japan was completely stopped and old coal-based power plants were re-commissioned to compensate for the decrease in power generation capacity. Although coal is a relatively inexpensive fuel for power generation, it contains high levels (mgkg(-1)) of selenium, which could contaminate the wastewater from thermal power plants. In this work, an automated selenium monitoring system was developed based on sequential hydride generation and chemiluminescence detection. This method could be applied to control of wastewater contamination. In this method, selenium is vaporized as H2Se, which reacts with ozone to produce chemiluminescence. However, interference from arsenic is of concern because the ozone-induced chemiluminescence intensity of H2Se is much lower than that of AsH3. This problem was successfully addressed by vaporizing arsenic and selenium individually in a sequential procedure using a syringe pump equipped with an eight-port selection valve and hot and cold reactors. Oxidative decomposition of organoselenium compounds and pre-reduction of the selenium were performed in the hot reactor, and vapor generation of arsenic and selenium were performed separately in the cold reactor. Sample transfers between the reactors were carried out by a pneumatic air operation by switching with three-way solenoid valves. The detection limit for selenium was 0.008 mg L(-1) and calibration curve was linear up to 1.0 mg L(-1), which provided suitable performance for controlling selenium in wastewater to around the allowable limit (0.1 mg L(-1)). This system consumes few chemicals and is stable for more than a month without any maintenance. Wastewater samples from thermal power plants were collected, and data obtained by the proposed method were compared with those from batchwise water treatment followed by hydride generation-atomic fluorescence spectrometry.

  19. A completely automated CAD system for mass detection in a large mammographic database

    SciTech Connect

    Bellotti, R.; De Carlo, F.; Tangaro, S.

    2006-08-15

    Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be A{sub z}=0.783{+-}0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

  20. An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection

    PubMed Central

    Kim, Woojae; Han, Tae Hwa; Kim, Hyun Jun; Park, Man Young; Kim, Ku Sang

    2011-01-01

    Objectives The mucociliary transport system is a major defense mechanism of the respiratory tract. The performance of mucous transportation in the nasal cavity can be represented by a ciliary beating frequency (CBF). This study proposes a novel method to measure CBF by using optical flow. Methods To obtain objective estimates of CBF from video images, an automated computer-based image processing technique is developed. This study proposes a new method based on optical flow for image processing and peak detection for signal processing. We compare the measuring accuracy of the method in various combinations of image processing (optical flow versus difference image) and signal processing (fast Fourier transform [FFT] vs. peak detection [PD]). The digital high-speed video method with a manual count of CBF in slow motion video play, is the gold-standard in CBF measurement. We obtained a total of fifty recorded ciliated sinonasal epithelium images to measure CBF from the Department of Otolaryngology. The ciliated sinonasal epithelium images were recorded at 50-100 frames per second using a charge coupled device camera with an inverted microscope at a magnification of ×1,000. Results The mean square errors and variance for each method were 1.24, 0.84 Hz; 11.8, 2.63 Hz; 3.22, 1.46 Hz; and 3.82, 1.53 Hz for optical flow (OF) + PD, OF + FFT, difference image [DI] + PD, and DI + FFT, respectively. Of the four methods, PD using optical flow showed the best performance for measuring the CBF of nasal mucosa. Conclusions The proposed method was able to measure CBF more objectively and efficiently than what is currently possible. PMID:21886872

  1. Automated detection of abnormalities in paranasal sinus on dental panoramic radiographs by using contralateral subtraction technique based on mandible contour

    NASA Astrophysics Data System (ADS)

    Mori, Shintaro; Hara, Takeshi; Tagami, Motoki; Muramatsu, Chicako; Kaneda, Takashi; Katsumata, Akitoshi; Fujita, Hiroshi

    2013-02-01

    Inflammation in paranasal sinus sometimes becomes chronic to take long terms for the treatment. The finding is important for the early treatment, but general dentists may not recognize the findings because they focus on teeth treatments. The purpose of this study was to develop a computer-aided detection (CAD) system for the inflammation in paranasal sinus on dental panoramic radiographs (DPRs) by using the mandible contour and to demonstrate the potential usefulness of the CAD system by means of receiver operating characteristic analysis. The detection scheme consists of 3 steps: 1) Contour extraction of mandible, 2) Contralateral subtraction, and 3) Automated detection. The Canny operator and active contour model were applied to extract the edge at the first step. At the subtraction step, the right region of the extracted contour image was flipped to compare with the left region. Mutual information between two selected regions was obtained to estimate the shift parameters of image registration. The subtraction images were generated based on the shift parameter. Rectangle regions of left and right paranasal sinus on the subtraction image were determined based on the size of mandible. The abnormal side of the regions was determined by taking the difference between the averages of each region. Thirteen readers were responded to all cases without and with the automated results. The averaged AUC of all readers was increased from 0.69 to 0.73 with statistical significance (p=0.032) when the automated detection results were provided. In conclusion, the automated detection method based on contralateral subtraction technique improves readers' interpretation performance of inflammation in paranasal sinus on DPRs.

  2. Automated detection of extradural and subdural hematoma for contrast-enhanced CT images in emergency medical care

    NASA Astrophysics Data System (ADS)

    Hara, Takeshi; Matoba, Naoto; Zhou, Xiangrong; Yokoi, Shinya; Aizawa, Hiroaki; Fujita, Hiroshi; Sakashita, Keiji; Matsuoka, Tetsuya

    2007-03-01

    We have been developing the CAD scheme for head and abdominal injuries for emergency medical care. In this work, we have developed an automated method to detect typical head injuries, rupture or strokes of brain. Extradural and subdural hematoma region were detected by comparing technique after the brain areas were registered using warping. We employ 5 normal and 15 stroke cases to estimate the performance after creating the brain model with 50 normal cases. Some of the hematoma regions were detected correctly in all of the stroke cases with no false positive findings on normal cases.

  3. Comparison between a second generation automated multicapillary electrophoresis system with an automated agarose gel electrophoresis system for the detection of M-components.

    PubMed

    Larsson, Anders; Hansson, Lars-Olof

    2008-01-01

    During the last decade, capillary electrophoresis (CE) has emerged as an interesting alternative to traditional analysis of serum, plasma and urine proteins by agarose gel electrophoresis. Initially there was a considerable difference in resolution between the two methods but the quality of CE has improved significantly. We thus wanted to evaluate a second generation of automated multicapillary instruments (Capillarys, Sebia, Paris, France) and the high resolution (HR) buffer for serum or plasma protein analysis with an automated agarose gel electrophoresis system for the detection of M-components. The comparison between the two systems was performed with patients samples with and without M-components. The comparison included 76 serum samples with M-components > 1 g/L. There was a total agreement between the two methods for detection of these M-components. When studying samples containing oligoclonal bands/small M-components, there were differences between the two systems. The capillary electrophoresis system detected a slightly higher number of samples with oligoclonal bands but the two systems found oligoclonal bands in different samples. When looking at resolution, the agarose gel electrophoresis system yielded a slightly better resolution in the alpha and beta regions, but it required an experienced interpreter to be able to benefit from the increased resolution. The capillary electrophoresis has shorter turn-around times and bar-code reader that allows positive sample identification. The Capillarys in combination with HR buffer gives better resolution of the alpha and beta regions than the same instrument with the beta1-beta2+ buffer or the Paragon CZE2000 (Beckman) which was the first generation of capillary electrophoresis systems.

  4. Long-term trends in the honeybee ‘whooping signal’ revealed by automated detection

    PubMed Central

    Newton, Michael I.

    2017-01-01

    It is known that honeybees use vibrational communication pathways to transfer information. One honeybee signal that has been previously investigated is the short vibrational pulse named the ‘stop signal’, because its inhibitory effect is generally the most accepted interpretation. The present study demonstrates long term (over 9 months) automated in-situ non-invasive monitoring of a honeybee vibrational pulse with the same characteristics of what has previously been described as a stop signal using ultra-sensitive accelerometers embedded in the honeycomb located at the heart of honeybee colonies. We show that the signal is very common and highly repeatable, occurring mainly at night with a distinct decrease in instances towards midday, and that it can be elicited en masse from bees following the gentle shaking or knocking of their hive with distinct evidence of habituation. The results of our study suggest that this vibrational pulse is generated under many different circumstances, thereby unifying previous publication’s conflicting definitions, and we demonstrate that this pulse can be generated in response to a surprise stimulus. This work suggests that, using an artificial stimulus and monitoring the changes in the features of this signal could provide a sensitive tool to assess colony status. PMID:28178291

  5. Long-term trends in the honeybee 'whooping signal' revealed by automated detection.

    PubMed

    Ramsey, Michael; Bencsik, Martin; Newton, Michael I

    2017-01-01

    It is known that honeybees use vibrational communication pathways to transfer information. One honeybee signal that has been previously investigated is the short vibrational pulse named the 'stop signal', because its inhibitory effect is generally the most accepted interpretation. The present study demonstrates long term (over 9 months) automated in-situ non-invasive monitoring of a honeybee vibrational pulse with the same characteristics of what has previously been described as a stop signal using ultra-sensitive accelerometers embedded in the honeycomb located at the heart of honeybee colonies. We show that the signal is very common and highly repeatable, occurring mainly at night with a distinct decrease in instances towards midday, and that it can be elicited en masse from bees following the gentle shaking or knocking of their hive with distinct evidence of habituation. The results of our study suggest that this vibrational pulse is generated under many different circumstances, thereby unifying previous publication's conflicting definitions, and we demonstrate that this pulse can be generated in response to a surprise stimulus. This work suggests that, using an artificial stimulus and monitoring the changes in the features of this signal could provide a sensitive tool to assess colony status.

  6. Comparison study of membrane filtration direct count and an automated coliform and Escherichia coli detection system for on-site water quality testing.

    PubMed

    Habash, Marc; Johns, Robert

    2009-10-01

    This study compared an automated Escherichia coli and coliform detection system with the membrane filtration direct count technique for water testing. The automated instrument performed equal to or better than the membrane filtration test in analyzing E. coli-spiked samples and blind samples with interference from Proteus vulgaris or Aeromonas hydrophila.

  7. Census cities experiment in urban change detection

    NASA Technical Reports Server (NTRS)

    Wray, J. R. (Principal Investigator)

    1973-01-01

    The author has identified the following significant results. Work continues on mapping of 1970 urban land use from 1970 census contemporaneous aircraft photography. In addition, change detection analysis from 1972 aircraft photography is underway for several urban test sites. Land use maps, mosaics, and census overlays for the two largest urban test sites are nearing publication readiness. Preliminary examinations of ERTS-1 imagery of San Francisco Bay have been conducted which show that tracts of land of more than 10 acres in size which are undergoing development in an urban setting can be identified. In addition, each spectral band is being evaluated as to its utility for urban analyses. It has been found that MSS infrared band 7 helps to differentiate intra-urban land use details not found in other MSS bands or in the RBV coverage of the same scene. Good quality false CIR composites have been generated from 9 x 9 inch positive MSS bands using the Diazo process.

  8. Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

    NASA Astrophysics Data System (ADS)

    Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

    2016-03-01

    Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.

  9. Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors

    NASA Astrophysics Data System (ADS)

    Gamon, J. A.; Kovalchuck, O.; Wong, C. Y. S.; Harris, A.; Garrity, S. R.

    2015-07-01

    The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use-efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "spectral reflectance sensors" (SRS sensors; recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration - one that considered sky conditions (cloud cover) at midday only, and another that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal timescale, PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different timescales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches for studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types.

  10. Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors

    NASA Astrophysics Data System (ADS)

    Gamon, J. A.; Kovalchuk, O.; Wong, C. Y. S.; Harris, A.; Garrity, S. R.

    2015-02-01

    The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "SRS" sensors recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration, one that considered sky conditions (cloud cover) at midday only, and the other that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal time scale PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different time scales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well-suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches to studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types.

  11. An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of In Vivo Mouse Models

    PubMed Central

    Fan, Jing; Zhou, Xiaobo; Dy, Jennifer G.; Zhang, Yong; Wong, Stephen T. C.

    2009-01-01

    The variations in dendritic branch morphology and spine density provide insightful information about the brain function and possible treatment to neurodegenerative disease, for example investigating structural plasticity during the course of Alzheimer's disease. Most automated image processing methods aiming at analyzing these problems are developed for in vitro data. However, in vivo neuron images provide real time information and direct observation of the dynamics of a disease process in a live animal model. This paper presents an automated approach for detecting spines and tracking spine evolution over time with in vivo image data in an animal model of Alzheimer's disease. We propose an automated pipeline starting with curvilinear structure detection to determine the medial axis of the dendritic backbone and spines connected to the backbone. We, then, propose the adaptive local binary fitting (aLBF) energy level set model to accurately locate the boundary of dendritic structures using the central line of curvilinear structure as initialization. To track the growth or loss of spines, we present a maximum likelihood based technique to find the graph homomorphism between two image graph structures at different time points. We employ dynamic programming to search for the optimum solution. The pipeline enables us to extract dynamically changing information from real time in vivo data. We validate our proposed approach by comparing with manual results generated by neurologists. In addition, we discuss the performance of 3D based segmentation and conclude that our method is more accurate in identifying weak spines. Experiments show that our approach can quickly and accurately detect and quantify spines of in vivo neuron images and is able to identify spine elimination and formation. PMID:19434521

  12. Change detection from very high resolution satellite time series with variable off-nadir angle

    NASA Astrophysics Data System (ADS)

    Barazzetti, Luigi; Brumana, Raffaella; Cuca, Branka; Previtali, Mattia

    2015-06-01

    Very high resolution (VHR) satellite images have the potential for revealing changes occurred overtime with a superior level of detail. However, their use for metric purposes requires accurate geo-localization with ancillary DEMs and GCPs to achieve sub-pixel terrain correction, in order to obtain images useful for mapping applications. Change detection with a time series of VHS images is not a simple task because images acquired with different off-nadir angles have a lack of pixel-to-pixel image correspondence, even after accurate geo-correction. This paper presents a procedure for automatic change detection able to deal with variable off-nadir angles. The case study concerns the identification of damaged buildings from pre- and post-event images acquired on the historic center of L'Aquila (Italy), which was struck by an earthquake in April 2009. The developed procedure is a multi-step approach where (i) classes are assigned to both images via object-based classification, (ii) an initial alignment is provided with an automated tile-based rubber sheeting interpolation on the extracted layers, and (iii) change detection is carried out removing residual mis-registration issues resulting in elongated features close to building edges. The method is fully automated except for some thresholds that can be interactively set to improve the visualization of the damaged buildings. The experimental results proved that damages can be automatically found without additional information, such as digital surface models, SAR data, or thematic vector layers.

  13. A comparative study of four change detection methods for aerial photography applications

    NASA Astrophysics Data System (ADS)

    Abramovich, Gil; Brooksby, Glen; Bush, Stephen F.; Manickam, Swaminathan; Ozcanli, Ozge; Garrett, Benjamin D.

    2010-04-01

    We present four new change detection methods that create an automated change map from a probability map. In this case, the probability map was derived from a 3D model. The primary application of interest is aerial photographic applications, where the appearance, disappearance or change in position of small objects of a selectable class (e.g., cars) must be detected at a high success rate in spite of variations in magnification, lighting and background across the image. The methods rely on an earlier derivation of a probability map. We describe the theory of the four methods, namely Bernoulli variables, Markov Random Fields, connected change, and relaxation-based segmentation, evaluate and compare their performance experimentally on a set probability maps derived from aerial photographs.

  14. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report

    SciTech Connect

    Duberstein, Corey A.; Matzner, Shari; Cullinan, Valerie I.; Virden, Daniel J.; Myers, Joshua R.; Maxwell, Adam R.

    2012-09-01

    Surveying wildlife at risk from offshore wind energy development is difficult and expensive. Infrared video can be used to record birds and bats that pass through the camera view, but it is also time consuming and expensive to review video and determine what was recorded. We proposed to conduct algorithm and software development to identify and to differentiate thermally detected targets of interest that would allow automated processing of thermal image data to enumerate birds, bats, and insects. During FY2012 we developed computer code within MATLAB to identify objects recorded in video and extract attribute information that describes the objects recorded. We tested the efficiency of track identification using observer-based counts of tracks within segments of sample video. We examined object attributes, modeled the effects of random variability on attributes, and produced data smoothing techniques to limit random variation within attribute data. We also began drafting and testing methodology to identify objects recorded on video. We also recorded approximately 10 hours of infrared video of various marine birds, passerine birds, and bats near the Pacific Northwest National Laboratory (PNNL) Marine Sciences Laboratory (MSL) at Sequim, Washington. A total of 6 hours of bird video was captured overlooking Sequim Bay over a series of weeks. An additional 2 hours of video of birds was also captured during two weeks overlooking Dungeness Bay within the Strait of Juan de Fuca. Bats and passerine birds (swallows) were also recorded at dusk on the MSL campus during nine evenings. An observer noted the identity of objects viewed through the camera concurrently with recording. These video files will provide the information necessary to produce and test software developed during FY2013. The annotation will also form the basis for creation of a method to reliably identify recorded objects.

  15. Automated detection of arterial input function in DSC perfusion MRI in a stroke rat model

    NASA Astrophysics Data System (ADS)

    Yeh, M.-Y.; Lee, T.-H.; Yang, S.-T.; Kuo, H.-H.; Chyi, T.-K.; Liu, H.-L.

    2009-05-01

    Quantitative cerebral blood flow (CBF) estimation requires deconvolution of the tissue concentration time curves with an arterial input function (AIF). However, image-based determination of AIF in rodent is challenged due to limited spatial resolution. We evaluated the feasibility of quantitative analysis using automated AIF detection and compared the results with commonly applied semi-quantitative analysis. Permanent occlusion of bilateral or unilateral common carotid artery was used to induce cerebral ischemia in rats. The image using dynamic susceptibility contrast method was performed on a 3-T magnetic resonance scanner with a spin-echo echo-planar-image sequence (TR/TE = 700/80 ms, FOV = 41 mm, matrix = 64, 3 slices, SW = 2 mm), starting from 7 s prior to contrast injection (1.2 ml/kg) at four different time points. For quantitative analysis, CBF was calculated by the AIF which was obtained from 10 voxels with greatest contrast enhancement after deconvolution. For semi-quantitative analysis, relative CBF was estimated by the integral divided by the first moment of the relaxivity time curves. We observed if the AIFs obtained in the three different ROIs (whole brain, hemisphere without lesion and hemisphere with lesion) were similar, the CBF ratios (lesion/normal) between quantitative and semi-quantitative analyses might have a similar trend at different operative time points. If the AIFs were different, the CBF ratios might be different. We concluded that using local maximum one can define proper AIF without knowing the anatomical location of arteries in a stroke rat model.

  16. Treehuggers: Wireless Sensor Networks for Automated Measurement and Reporting of Changes in Tree Diameter

    NASA Astrophysics Data System (ADS)

    DeLucia, E. H.; Mies, T. A.; Anderson-Teixeira, K. J.; Bohleber, A. P.; Herrmann, V.

    2014-12-01

    Ground-based measurements of changes in tree diameter and subsequent calculation of carbon storage provide validation of indirect estimates of forest productivity from remote sensing platforms, and measurements made with high temporal resolution provide critical information about the responsiveness of tree growth to variations in important physical drivers (e.g. temperature and water availability). We have developed an environmentally robust instrument for automated measurement of expansion and contraction in tree diameter that can be deployed in remote locations (TreeHuggers; TH). TH uses a membrane potentiometer to measure changes in circumference with resolution ≤ 6 mm at user-selected intervals (≥ 1 min). Simultaneous measurement of temperature is used to correct for the thermal properties of the stainless steel band. Data are stored on micro SD cards and transmitted tree-to-tree to a base station. Preliminary measurement of beech trees shows the precise initiation of growth and the emergence of diel changes in stem diameter associated with sap flow. Because of their low cost and on-board data logging and communication packages, TH will greatly increase the capacity of the scientific community and private sectors to monitor tree growth and carbon storage. Possible applications include deploying TH in the footprint of eddy covariance sites to help interpret drivers affecting net ecosystem exchange and evapotranspiration. A large scale implementation of TH will contribute to our ability to forecast changes in the carbon sink strength of forests across environmental gradients and biotic disturbances, and they could prove useful in assessing changes in forest stocks as part of evaluating carbon offsets purchased by commercial entities.

  17. Automated Non-invasive Video-Microscopy of Oyster Spat Heart Rate during Acute Temperature Change: Impact of Acclimation Temperature

    PubMed Central

    Domnik, Nicolle J.; Polymeropoulos, Elias T.; Elliott, Nicholas G.; Frappell, Peter B.; Fisher, John T.

    2016-01-01

    We developed an automated, non-invasive method to detect real-time cardiac contraction in post-larval (1.1–1.7 mm length), juvenile oysters (i.e., oyster spat) via a fiber-optic trans-illumination system. The system is housed within a temperature-controlled chamber and video microscopy imaging of the heart was coupled with video edge-detection to measure cardiac contraction, inter-beat interval, and heart rate (HR). We used the method to address the hypothesis that cool acclimation (10°C vs. 22°C—Ta10 or Ta22, respectively; each n = 8) would preserve cardiac phenotype (assessed via HR variability, HRV analysis and maintained cardiac activity) during acute temperature changes. The temperature ramp (TR) protocol comprised 2°C steps (10 min/experimental temperature, Texp) from 22°C to 10°C to 22°C. HR was related to Texp in both acclimation groups. Spat became asystolic at low temperatures, particularly Ta22 spat (Ta22: 8/8 vs. Ta10: 3/8 asystolic at Texp = 10°C). The rate of HR decrease during cooling was less in Ta10 vs. Ta22 spat when asystole was included in analysis (P = 0.026). Time-domain HRV was inversely related to temperature and elevated in Ta10 vs. Ta22 spat (P < 0.001), whereas a lack of defined peaks in spectral density precluded frequency-domain analysis. Application of the method during an acute cooling challenge revealed that cool temperature acclimation preserved active cardiac contraction in oyster spat and increased time-domain HRV responses, whereas warm acclimation enhanced asystole. These physiologic changes highlight the need for studies of mechanisms, and have translational potential for oyster aquaculture practices. PMID:27445833

  18. Automated Non-invasive Video-Microscopy of Oyster Spat Heart Rate during Acute Temperature Change: Impact of Acclimation Temperature.

    PubMed

    Domnik, Nicolle J; Polymeropoulos, Elias T; Elliott, Nicholas G; Frappell, Peter B; Fisher, John T

    2016-01-01

    We developed an automated, non-invasive method to detect real-time cardiac contraction in post-larval (1.1-1.7 mm length), juvenile oysters (i.e., oyster spat) via a fiber-optic trans-illumination system. The system is housed within a temperature-controlled chamber and video microscopy imaging of the heart was coupled with video edge-detection to measure cardiac contraction, inter-beat interval, and heart rate (HR). We used the method to address the hypothesis that cool acclimation (10°C vs. 22°C-Ta10 or Ta22, respectively; each n = 8) would preserve cardiac phenotype (assessed via HR variability, HRV analysis and maintained cardiac activity) during acute temperature changes. The temperature ramp (TR) protocol comprised 2°C steps (10 min/experimental temperature, Texp) from 22°C to 10°C to 22°C. HR was related to Texp in both acclimation groups. Spat became asystolic at low temperatures, particularly Ta22 spat (Ta22: 8/8 vs. Ta10: 3/8 asystolic at Texp = 10°C). The rate of HR decrease during cooling was less in Ta10 vs. Ta22 spat when asystole was included in analysis (P = 0.026). Time-domain HRV was inversely related to temperature and elevated in Ta10 vs. Ta22 spat (P < 0.001), whereas a lack of defined peaks in spectral density precluded frequency-domain analysis. Application of the method during an acute cooling challenge revealed that cool temperature acclimation preserved active cardiac contraction in oyster spat and increased time-domain HRV responses, whereas warm acclimation enhanced asystole. These physiologic changes highlight the need for studies of mechanisms, and have translational potential for oyster aquaculture practices.

  19. Sensitivity testing of trypanosome detection by PCR from whole blood samples using manual and automated DNA extraction methods.

    PubMed

    Dunlop, J; Thompson, C K; Godfrey, S S; Thompson, R C A

    2014-11-01

    Automated extraction of DNA for testing of laboratory samples is an attractive alternative to labour-intensive manual methods when higher throughput is required. However, it is important to maintain the maximum detection sensitivity possible to reduce the occurrence of type II errors (false negatives; failure to detect the target when it is present), especially in the biomedical field, where PCR is used for diagnosis. We used blood infected with known concentrations of Trypanosoma copemani to test the impact of analysis techniques on trypanosome detection sensitivity by PCR. We compared combinations of a manual and an automated DNA extraction method and two different PCR primer sets to investigate the impact of each on detection levels. Both extraction techniques and specificity of primer sets had a significant impact on detection sensitivity. Samples extracted using the same DNA extraction technique performed substantially differently for each of the separate primer sets. Type I errors (false positives; detection of the target when it is not present), produced by contaminants, were avoided with both extraction methods. This study highlights the importance of testing laboratory techniques with known samples to optimise accuracy of test results.

  20. Attribute and topology based change detection in a constellation of previously detected objects

    SciTech Connect

    Paglieroni, David W.; Beer, Reginald N.

    2016-01-19

    A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

  1. Detection of Hydroxychloroquine Retinal Toxicity by Automated Perimetry in 60 Rheumatoid Arthritis Patients with Normal Fundoscopic Findings.

    PubMed

    Motarjemizadeh, Qader; Aidenloo, Naser Samadi; Abbaszadeh, Mohammad

    2015-06-25

    Hydroxychloroquine (HCQ) is an antimalarial drug used extensively in treatment of autoimmune diseases such as rheumatoid arthritis. Retinal toxicity is the most important side effects of this drug. Even after the drug is discontinued, retinal degeneration from HCQ can continue to progress. Consequently, multiple ophthalmic screening tests have been developed to detect early retinopathy. The aim of the current study was to evaluate the value of central 2-10 perimetry method in early detection of retinal toxicity. This prospective cross-sectional investigation was carried out on 60 rheumatoid arthritis patients, who had been receiving HCQ for at least 6 months and still were on their medication (HCQ intake) at the time of enrollment. An ophthalmologist examined participants using direct and indirect ophthalmoscopy. Visual field testing with automated perimetry technique (central 2-10 perimetry with red target) was performed on all included subjects twice in 6 months interval: The first one at the time of enrollment and the second one 6 months later. Males and females did not show any significant difference in terms of age, duration of therapy, daily and cumulative HCQ dose, anterior or posterior segment abnormalities, hypertension, body mass index, and best corrected visual acuity. Anterior segment was abnormal in 9 individuals including 3 subjects with macular pigmentary changes, 4 individuals with cataract and 2 cases with dry eyes. Moreover, 12 subjects had retinal pigmented epithelium (RPE) in their posterior segments. After 6 months, depressive changes appeared in 12 subjects. Additionally, HCQ therapy worsened significantly the perimetric results of 5 (55.6%) patients with abnormal anterior segment. A same trend was observed in perimetric results of 6 (50.0%) subjects with abnormal posterior segments (P=0.009). The daily dose of HCQ (P=0.035) as well as the cumulative dose of hydroxychloroquine (P=0.021) displayed statistically significant associations with

  2. Feasibility of fully automated detection of fiducial markers implanted into the prostate using electronic portal imaging: A comparison of methods

    SciTech Connect

    Harris, Emma J. . E-mail: eharris@icr.ac.uk; McNair, Helen A.; Evans, Phillip M.

    2006-11-15

    Purpose: To investigate the feasibility of fully automated detection of fiducial markers implanted into the prostate using portal images acquired with an electronic portal imaging device. Methods and Materials: We have made a direct comparison of 4 different methods (2 template matching-based methods, a method incorporating attenuation and constellation analyses and a cross correlation method) that have been published in the literature for the automatic detection of fiducial markers. The cross-correlation technique requires a-priory information from the portal images, therefore the technique is not fully automated for the first treatment fraction. Images of 7 patients implanted with gold fiducial markers (8 mm in length and 1 mm in diameter) were acquired before treatment (set-up images) and during treatment (movie images) using 1MU and 15MU per image respectively. Images included: 75 anterior (AP) and 69 lateral (LAT) set-up images and 51 AP and 83 LAT movie images. Using the different methods described in the literature, marker positions were automatically identified. Results: The method based upon cross correlation techniques gave the highest percentage detection success rate of 99% (AP) and 83% (LAT) set-up (1MU) images. The methods gave detection success rates of less than 91% (AP) and 42% (LAT) set-up images. The amount of a-priory information used and how it affects the way the techniques are implemented, is discussed. Conclusions: Fully automated marker detection in set-up images for the first treatment fraction is unachievable using these methods and that using cross-correlation is the best technique for automatic detection on subsequent radiotherapy treatment fractions.

  3. Change detection experiments using Gotcha public release SAR data

    NASA Astrophysics Data System (ADS)

    Stojanovic, Ivana; Novak, Les

    2013-05-01

    In this paper we compare coherent change detection performance obtained using the maximum likelihood estimate (MLE) of the SAR image-pair coherence versus using the complex correlation coefficient coherence estimate (CCD). We also compare the non-coherent change detection performance (PD vs. PFA) versus the performance of the coherent change detection algorithms.

  4. [Application of optical flow dynamic texture in land use/cover change detection].

    PubMed

    Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

    2014-11-01

    In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better

  5. The use of adaptable automation: Effects of extended skill lay-off and changes in system reliability.

    PubMed

    Sauer, Juergen; Chavaillaz, Alain

    2017-01-01

    This experiment aimed to examine how skill lay-off and system reliability would affect operator behaviour in a simulated work environment under wide-range and large-choice adaptable automation comprising six different levels. Twenty-four participants were tested twice during a 2-hr testing session, with the second session taking place 8 months after the first. In the middle of the second testing session, system reliability changed. The results showed that after the retention interval trust increased and self-confidence decreased. Complacency was unaffected by the lay-off period. Diagnostic speed slowed down after the retention interval but diagnostic accuracy was maintained. No difference between experimental conditions was found for automation management behaviour (i.e. level of automation chosen and frequency of switching between levels). There were few effects of system reliability. Overall, the findings showed that subjective measures were more sensitive to the impact of skill lay-off than objective behavioural measures.

  6. 2006 Automation Survey: The Systems Are Changing. But School Libraries Aren't

    ERIC Educational Resources Information Center

    Fuller, Daniel

    2006-01-01

    This article presents the findings of the 2006 School Library Journal-San Jose State University Automation Survey. The study takes a close look at the systems that media specialists are using, how they are using them, and what librarians want from their future automation programs. The findings reveal that while respondents were satisfied with…

  7. Flow cytometric-membrane potential detection of sodium channel active marine toxins: application to ciguatoxins in fish muscle and feasibility of automating saxitoxin detection.

    PubMed

    Manger, Ronald; Woodle, Doug; Berger, Andrew; Dickey, Robert W; Jester, Edward; Yasumoto, Takeshi; Lewis, Richard; Hawryluk, Timothy; Hungerford, James

    2014-01-01

    Ciguatoxins are potent neurotoxins with a significant public health impact. Cytotoxicity assays have allowed the most sensitive means of detection of ciguatoxin-like activity without reliance on mouse bioassays and have been invaluable in studying outbreaks. An improvement of these cell-based assays is presented here in which rapid flow cytometric detection of ciguatoxins and saxitoxins is demonstrated using fluorescent voltage sensitive dyes. A depolarization response can be detected directly due to ciguatoxin alone; however, an approximate 1000-fold increase in sensitivity is observed in the presence of veratridine. These results demonstrate that flow cytometric assessment of ciguatoxins is possible at levels approaching the trace detection limits of our earlier cytotoxicity assays, however, with a significant reduction in analysis time. Preliminary results are also presented for detection of brevetoxins and for automation and throughput improvements to a previously described method for detecting saxitoxins in shellfish extracts.

  8. Rational Manual and Automated Scoring Thresholds for the Immunohistochemical Detection of TP53 Missense Mutations in Human Breast Carcinomas.

    PubMed

    Taylor, Nicholas J; Nikolaishvili-Feinberg, Nana; Midkiff, Bentley R; Conway, Kathleen; Millikan, Robert C; Geradts, Joseph

    2016-07-01

    Missense mutations in TP53 are common in human breast cancer, have been associated with worse prognosis, and may predict therapy effect. TP53 missense mutations are associated with aberrant accumulation of p53 protein in tumor cell nuclei. Previous studies have used relatively arbitrary cutoffs to characterize breast tumors as positive for p53 staining by immunohistochemical assays. This study aimed to objectively determine optimal thresholds for p53 positivity by manual and automated scoring methods using whole tissue sections from the Carolina Breast Cancer Study. p53-immunostained slides were available for 564 breast tumors previously assayed for TP53 mutations. Average nuclear p53 staining intensity was manually scored as negative, borderline, weak, moderate, or strong and percentage of positive tumor cells was estimated. Automated p53 signal intensity was measured using the Aperio nuclear v9 algorithm combined with the Genie histology pattern recognition tool and tuned to achieve optimal nuclear segmentation. Receiver operating characteristic curve analysis was performed to determine optimal cutoffs for average staining intensity and percent cells positive to distinguish between tumors with and without a missense mutation. Receiver operating characteristic curve analysis demonstrated a threshold of moderate average nuclear staining intensity as a good surrogate for TP53 missense mutations in both manual (area under the curve=0.87) and automated (area under the curve=0.84) scoring systems. Both manual and automated immunohistochemical scoring methods predicted missense mutations in breast carcinomas with high accuracy. Validation of the automated intensity scoring threshold suggests a role for such algorithms in detecting TP53 missense mutations in high throughput studies.

  9. Automated detection and analysis of particle beams in laser-plasma accelerator simulations

    SciTech Connect

    Ushizima, Daniela Mayumi; Geddes, C.G.; Cormier-Michel, E.; Bethel, E. Wes; Jacobsen, J.; Prabhat, ,; R.ubel, O.; Weber, G,; Hamann, B.

    2010-05-21

    scientific data mining is increasingly considered. In plasma simulations, Bagherjeiran et al. presented a comprehensive report on applying graph-based techniques for orbit classification. They used the KAM classifier to label points and components in single and multiple orbits. Love et al. conducted an image space analysis of coherent structures in plasma simulations. They used a number of segmentation and region-growing techniques to isolate regions of interest in orbit plots. Both approaches analyzed particle accelerator data, targeting the system dynamics in terms of particle orbits. However, they did not address particle dynamics as a function of time or inspected the behavior of bunches of particles. Ruebel et al. addressed the visual analysis of massive laser wakefield acceleration (LWFA) simulation data using interactive procedures to query the data. Sophisticated visualization tools were provided to inspect the data manually. Ruebel et al. have integrated these tools to the visualization and analysis system VisIt, in addition to utilizing efficient data management based on HDF5, H5Part, and the index/query tool FastBit. In Ruebel et al. proposed automatic beam path analysis using a suite of methods to classify particles in simulation data and to analyze their temporal evolution. To enable researchers to accurately define particle beams, the method computes a set of measures based on the path of particles relative to the distance of the particles to a beam. To achieve good performance, this framework uses an analysis pipeline designed to quickly reduce the amount of data that needs to be considered in the actual path distance computation. As part of this process, region-growing methods are utilized to detect particle bunches at single time steps. Efficient data reduction is essential to enable automated analysis of large data sets as described in the next section, where data reduction methods are steered to the particular requirements of our clustering analysis

  10. M-Track: A New Software for Automated Detection of Grooming Trajectories in Mice

    PubMed Central

    Zhang, Lin

    2016-01-01

    Grooming is a complex and robust innate behavior, commonly performed by most vertebrate species. In mice, grooming consists of a series of stereotyped patterned strokes, performed along the rostro-caudal axis of the body. The frequency and duration of each grooming episode is sensitive to changes in stress levels, social interactions and pharmacological manipulations, and is therefore used in behavioral studies to gain insights into the function of brain regions that control movement execution and anxiety. Traditional approaches to analyze grooming rely on manually scoring the time of onset and duration of each grooming episode, and are often performed on grooming episodes triggered by stress exposure, which may not be entirely representative of spontaneous grooming in freely-behaving mice. This type of analysis is time-consuming and provides limited information about finer aspects of grooming behaviors, which are important to understand movement stereotypy and bilateral coordination in mice. Currently available commercial and freeware video-tracking software allow automated tracking of the whole body of a mouse or of its head and tail, not of individual forepaws. Here we describe a simple experimental set-up and a novel open-source code, named M-Track, for simultaneously tracking the movement of individual forepaws during spontaneous grooming in multiple freely-behaving mice. This toolbox provides a simple platform to perform trajectory analysis of forepaw movement during distinct grooming episodes. By using M-track we show that, in C57BL/6 wild type mice, the speed and bilateral coordination of the left and right forepaws remain unaltered during the execution of distinct grooming episodes. Stress exposure induces a profound increase in the length of the forepaw grooming trajectories. M-Track provides a valuable and user-friendly interface to streamline the analysis of spontaneous grooming in biomedical research studies. PMID:27636358

  11. Automation pilot

    NASA Technical Reports Server (NTRS)

    1983-01-01

    An important concept of the Action Information Management System (AIMS) approach is to evaluate office automation technology in the context of hands on use by technical program managers in the conduct of human acceptance difficulties which may accompany the transition to a significantly changing work environment. The improved productivity and communications which result from application of office automation technology are already well established for general office environments, but benefits unique to NASA are anticipated and these will be explored in detail.

  12. Eye Movements and Display Change Detection during Reading

    ERIC Educational Resources Information Center

    Slattery, Timothy J.; Angele, Bernhard; Rayner, Keith

    2011-01-01

    In the boundary change paradigm (Rayner, 1975), when a reader's eyes cross an invisible boundary location, a preview word is replaced by a target word. Readers are generally unaware of such changes due to saccadic suppression. However, some readers detect changes on a few trials and a small percentage of them detect many changes. Two experiments…

  13. Long-Term Motor Deficits after Controlled Cortical Impact in Rats Can Be Detected by Fine Motor Skill Tests but Not by Automated Gait Analysis.

    PubMed

    Schönfeld, Lisa-Maria; Jahanshahi, Ali; Lemmens, Evi; Schipper, Sandra; Dooley, Dearbhaile; Joosten, Elbert; Temel, Yasin; Hendrix, Sven

    2017-01-15

    Animal models with constant, long-lasting motor deficits together with the right tests to assess behavioral abnormalities are needed to study the effectiveness of potential therapies to restore motor functions. In the current study, controlled cortical impact (CCI) was applied in rats to induce damage to the forelimb area of the motor cortex and the dorsal striatum. Motor behavior was assessed before and after CCI, using fine motor skill tests such as the adhesive removal test, the cylinder test, and the Montoya staircase test as well as the automated gait analysis system CatWalk XT over a 6 week period. CCI caused a variety of unilateral motor deficits, which were characterized in detail by using the selected fine motor skill tests. In striking contrast to previous studies on CCI in mice, neither forelimb impairments, nor general changes in gait, were detected with the CatWalk XT. These data suggest that the adhesive removal test, the cylinder test, and the Montoya staircase test are the methods of choice to detect long-term unilateral motor deficits in rats after CCI, whereas the use of automated gait analysis systems might not be suitable to measure these behavioral deviations.

  14. Automated JPSS VIIRS GEO code change testing by using Chain Run Scripts

    NASA Astrophysics Data System (ADS)

    Chen, W.; Wang, W.; Zhao, Q.; Das, B.; Mikles, V. J.; Sprietzer, K.; Tsidulko, M.; Zhao, Y.; Dharmawardane, V.; Wolf, W.

    2015-12-01

    The Joint Polar Satellite System (JPSS) is the next generation polar-orbiting operational environmental satellite system. The first satellite in the JPSS series of satellites, J-1, is scheduled to launch in early 2017. J1 will carry similar versions of the instruments that are on board of Suomi National Polar-Orbiting Partnership (S-NPP) satellite which was launched on October 28, 2011. The center for Satellite Applications and Research Algorithm Integration Team (STAR AIT) uses the Algorithm Development Library (ADL) to run S-NPP and pre-J1 algorithms in a development and test mode. The ADL is an offline test system developed by Raytheon to mimic the operational system while enabling a development environment for plug and play algorithms. The Perl Chain Run Scripts have been developed by STAR AIT to automate the staging and processing of multiple JPSS Sensor Data Record (SDR) and Environmental Data Record (EDR) products. JPSS J1 VIIRS Day Night Band (DNB) has anomalous non-linear response at high scan angles based on prelaunch testing. The flight project has proposed multiple mitigation options through onboard aggregation, and the Option 21 has been suggested by the VIIRS SDR team as the baseline aggregation mode. VIIRS GEOlocation (GEO) code analysis results show that J1 DNB GEO product cannot be generated correctly without the software update. The modified code will support both Op21, Op21/26 and is backward compatible with SNPP. J1 GEO code change version 0 delivery package is under development for the current change request. In this presentation, we will discuss how to use the Chain Run Script to verify the code change and Lookup Tables (LUTs) update in ADL Block2.

  15. Automated RSO Stability Analysis

    NASA Astrophysics Data System (ADS)

    Johnson, T.

    2016-09-01

    A methodology for assessing the attitude stability of a Resident Space Object (RSO) using visual magnitude data is presented and then scaled to run in an automated fashion across the entire satellite catalog. Results obtained by applying the methodology to the Commercial Space Operations Center (COMSpOC) catalog are presented and summarized, identifying objects that have changed stability. We also examine the timeline for detecting the transition from stable to unstable attitude

  16. Automated Flaw Detection Scheme For Cast Austenitic Stainless Steel Weld Specimens Using Hilbert Huang Transform Of Ultrasonic Phased Array Data

    SciTech Connect

    Khan, T.; Majumdar, Shantanu; Udpa, L.; Ramuhalli, Pradeep; Crawford, Susan L.; Diaz, Aaron A.; Anderson, Michael T.

    2012-01-01

    The objective of this work is to develop processing algorithms to detect and localize the flaws using NDE ultrasonic data. Data was collected using cast austenitic stainless steel (CASS) weld specimens on-loan from the U.S. nuclear power industry’s Pressurized Water Reactor Owners Group (PWROG) specimen set. Each specimen consists of a centrifugally cast stainless steel (CCSS) pipe section welded to a statically cast (SCSS) or wrought (WRSS) section. The paper presents a novel automated flaw detection and localization scheme using low frequency ultrasonic phased array inspection signals in the weld and heat affected zone of the base materials. The major steps of the overall scheme are preprocessing and region of interest (ROI) detection followed by the Hilbert Huang transform (HHT) of A-scans in the detected ROIs. HHT offers time-frequency-energy distribution for each ROI. The accumulation of energy in a particular frequency band is used as a classification feature for the particular ROI.

  17. Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.

    PubMed

    Hu, Zhen; Melton, Genevieve B; Arsoniadis, Elliot G; Wang, Yan; Kwaan, Mary R; Simon, Gyorgy J

    2017-03-16

    Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.

  18. Evaluation of change detection techniques for monitoring coastal zone environments

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A.; Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

    1977-01-01

    Development of satisfactory techniques for detecting change in coastal zone environments is required before operational monitoring procedures can be established. In an effort to meet this need a study was directed toward developing and evaluating different types of change detection techniques, based upon computer aided analysis of LANDSAT multispectral scanner (MSS) data, to monitor these environments. The Matagorda Bay estuarine system along the Texas coast was selected as the study area. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. Each of the four techniques was used to analyze a LANDSAT MSS temporal data set to detect areas of change of the Matagorda Bay region.

  19. Using a forehead reflectance pulse oximeter to detect changes in sympathetic tone.

    PubMed

    Wendelken, Suzanne M; McGrath, Susan P; Akay, Metin; Blike, George T

    2004-01-01

    The extreme conditions of combat and multi-casualty rescue often make field triage difficult and put the medic or first responder at risk. In an effort to improve field triage, we have developed an automated remote triage system called ARTEMIS (automated remote triage and emergency management information system) for use in the battlefield or disaster zone. Common to field injuries is a sudden change in arterial pressure resulting from massive blood loss or shock. In effort to stabilize the arterial pressure, the sympathetic system is strongly activated and sympathetic tone is increased. This preliminary research seeks to empirically demonstrate that a forehead reflectance pulse oximeter is a viable sensor for detecting sudden changes in sympathetic tone. We performed the classic supine-standing experiment and collected the raw waveform, the photoplethysmogram (PPG), continuously using a forehead reflectance pulse oximeter. The resulting waveform was processed in Matlab using various spectral analysis techniques (FFT and AR). Our preliminary results show that a relative ratio analysis (low frequency power/high frequency power) for both the raw PPG signal and its derived pulse statistics (height, beat-to-beat interval) is a useful technique for detecting change in sympathetic tone resulting from positional change.

  20. Evaluation of automated COBAS AMPLICOR PCR system for detection of several infectious agents and its impact on laboratory management.

    PubMed Central

    Jungkind, D; Direnzo, S; Beavis, K G; Silverman, N S

    1996-01-01

    We evaluated the COBAS AMPLICOR (CA) PCR system (Roche Diagnostic Systems) designed for automated PCR amplification and detection of nucleic acids from infectious agents in clinical samples. The Roche AMPLICOR microwell plate (MWP) PCR was the reference method. CA amplifies target nucleic acid, captures the biotinylated amplification products by using magnetic particles coated with specific oligonucleotide probes, and detects the bound products colorimetrically. For Mycobacterium tuberculosis, the correlation of the results of CA tests with those of MWP tests was 100% with 230 samples, including 20 culture-positive samples. For hepatitis C virus, the correlation was 100% with 214 samples, including 60 positive samples. MultiPlex CA analysis of 199 cervical specimens for Chlamydia trachomatis, Neisseria gonorrhoeae, and the internal control gave 100% concordance. These samples included 19 C. trachomatis and 3 N. gonorrhoeae culture-positive samples. Overall, the agreement between PCR methods for all 842 comparisons was 100%. Compared with culture, the sensitivities of the assays for C. trachomatis and M tuberculosis were > or = 95%. After spiking alternating amplification tubes in the CA system with 10(14) copies of the Chlamydia amplicon per ml, we were unable to demonstrate any carryover cross-contamination of negative samples. Using the criteria of the College of American Pathologists workload recording method, we found that the total hands-on time to produce CA PCR results was 4.4, 7.9, and 3.3 min for M. tuberculosis, hepatis C virus, and the MultiPlexed assay for chlamydia plus gonorrhea and an internal control, respectively. The CA system brings true PCR automation to laboratories. In addition to the accuracy of automated results, the CA system provides labor savings, provides containment of the amplification and detection components of PCR, and supports both MultiPlex amplification and sequential algorithm (ReFlex) detection of analytes. PMID:8897182

  1. Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding.

    PubMed

    Shen, Yanna; Adamou, Christina; Dowling, John N; Cooper, Gregory F

    2008-04-01

    The goals of automated biosurveillance systems are to detect disease outbreaks early, while exhibiting few false positives. Evaluation measures currently exist to estimate the expected detection time of biosurveillance systems. Researchers also have developed models that estimate clinician detection of cases of outbreak diseases, which is a process known as clinical case finding. However, little research has been done on estimating how well biosurveillance systems augment traditional outbreak detection that is carried out by clinicians. In this paper, we introduce a general approach for doing so for non-endemic disease outbreaks, which are characteristic of bioterrorist induced diseases, such as respiratory anthrax. We first layout the basic framework, which makes minimal assumptions, and then we specialize it in several ways. We illustrate the method using a Bayesian outbreak detection algorithm called PANDA, a model of clinician outbreak detection, and simulated cases of a windborne anthrax release. This analysis derives a bound on how well we would expect PANDA to augment clinician detection of an anthrax outbreak. The results support that such analyses are useful in assessing the extent to which computer-based outbreak detection systems are expected to augment traditional clinician outbreak detection.

  2. Automated Detection of Essay Revising Patterns: Applications for Intelligent Feedback in a Writing Tutor

    ERIC Educational Resources Information Center

    Roscoe, Rod D.; Snow, Erica L.; Allen, Laura K.; McNamara, Danielle S.

    2015-01-01

    The Writing Pal is an intelligent tutoring system designed to support writing proficiency and strategy acquisition for adolescent writers. A fundamental aspect of the instructional model is automated formative feedback that provides concrete information and strategies oriented toward student improvement. In this paper, the authors explore…

  3. Improving the Usefulness and Acceptability of Automated Detection and Tracking Systems

    DTIC Science & Technology

    2009-01-01

    Endsley & Kiris 1995). Our research does not support this approach. Extensive training with the manual task had no effect on misassociations...and Kiris , E. 0. 1995, The out-of-the-loop performance problem and level of control in automation, Human Factors, 37, 381-394. Kessel, C. J., and

  4. Automated pattern analysis: A newsilent partner in insect acoustic detection studies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This seminar reviews methods that have been developed for automated analysis of field-collected sounds used to estimate pest populations and guide insect pest management decisions. Several examples are presented of successful usage of acoustic technology to map insect distributions in field environ...

  5. Automated Detection of Geomorphic Features in LiDAR Point Clouds of Various Spatial Density

    NASA Astrophysics Data System (ADS)

    Dorninger, Peter; Székely, Balázs; Zámolyi, András.; Nothegger, Clemens

    2010-05-01

    LiDAR, also referred to as laser scanning, has proved to be an important tool for topographic data acquisition. Terrestrial laser scanning allows for accurate (several millimeter) and high resolution (several centimeter) data acquisition at distances of up to some hundred meters. By contrast, airborne laser scanning allows for acquiring homogeneous data for large areas, albeit with lower accuracy (decimeter) and resolution (some ten points per square meter) compared to terrestrial laser scanning. Hence, terrestrial laser scanning is preferably used for precise data acquisition of limited areas such as landslides or steep structures, while airborne laser scanning is well suited for the acquisition of topographic data of huge areas or even country wide. Laser scanners acquire more or less homogeneously distributed point clouds. These points represent natural objects like terrain and vegetation and artificial objects like buildings, streets or power lines. Typical products derived from such data are geometric models such as digital surface models representing all natural and artificial objects and digital terrain models representing the geomorphic topography only. As the LiDAR technology evolves, the amount of data produced increases almost exponentially even in smaller projects. This means a considerable challenge for the end user of the data: the experimenter has to have enough knowledge, experience and computer capacity in order to manage the acquired dataset and to derive geomorphologically relevant information from the raw or intermediate data products. Additionally, all this information might need to be integrated with other data like orthophotos. In all theses cases, in general, interactive interpretation is necessary to determine geomorphic structures from such models to achieve effective data reduction. There is little support for the automatic determination of characteristic features and their statistical evaluation. From the lessons learnt from automated

  6. A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images.

    PubMed

    Berdouses, Elias D; Koutsouri, Georgia D; Tripoliti, Evanthia E; Matsopoulos, George K; Oulis, Constantine J; Fotiadis, Dimitrios I

    2015-07-01

    The aim of this work is to present a computer-aided automated methodology for the assessment of carious lesions, according to the International Caries Detection and Assessment System (ICDAS II), which are located on the occlusal surfaces of posterior permanent teeth from photographic color tooth images. The proposed methodology consists of two stages: (a) the detection of regions of interest and (b) the classification of the detected regions according to ICDAS ΙΙ. In the first stage, pre-processing, segmentation and post-processing mechanisms were employed. For each pixel of the detected regions, a 15×15 neighborhood is used and a set of intensity-based and texture-based features were extracted. A correlation based technique was applied to select a subset of 36 features which were given as input into the classification stage, where five classifiers (J48, Random Tree, Random Forests, Support Vector Machines and Naïve Bayes) were compared to conclude to the best one, in our case, to Random Forests. The methodology was evaluated on a set of 103 digital color images where 425 regions of interest from occlusal surfaces of extracted permanent teeth were manually segmented and classified, based on visual assessments by two experts. The methodology correctly detected 337 out of 340 regions in the detection stage with accuracy of detection 80%. For the classification stage an overall accuracy 83% is achieved. The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge.

  7. A robust automated method to detect stent struts in 3D intravascular optical coherence tomographic image sequences

    NASA Astrophysics Data System (ADS)

    Wang, A.; Eggermont, J.; Dekker, N.; Garcia-Garcia, H. M.; Pawar, R.; Reiber, J. H. C.; Dijkstra, J.

    2012-03-01

    Intravascular optical coherence tomography (IVOCT) provides very high resolution cross-sectional image sequences of vessels. It has been rapidly accepted for stent implantation and its follow up evaluation. Given the large amount of stent struts in a single image sequence, only automated detection methods are feasible. In this paper, we present an automated stent strut detection technique which requires neither lumen nor vessel wall segmentation. To detect strut-pixel candidates, both global intensity histograms and local intensity profiles of the raw polar images are used. Gaussian smoothing is applied followed by specified Prewitt compass filters to detect the trailing shadow of each strut. The shadow edge positions assist the strut-pixel candidates clustering. In the end, a 3D guide wire filter is applied to remove the guide wire from the detection results. For validation, two experts marked 6738 struts in 1021 frames in 10 IVOCT image sequences from a one-year follow up study. The struts were labeled as malapposed, apposed or covered together with the image quality (high, medium, low). The inter-observer agreement was 96%. The algorithm was validated for different combinations of strut status and image quality. Compared to the manual results, 93% of the struts were correctly detected by the new method. For each combination, the lowest accuracy was 88%, which shows the robustness towards different situations. The presented method can detect struts automatically regardless of the strut status or the image quality, which can be used for quantitative measurement, 3D reconstruction and visualization of the implanted stents.

  8. A Smart Capsule System for Automated Detection of Intestinal Bleeding Using HSL Color Recognition

    PubMed Central

    Liu, Hongying; Yan, Xueping; Jia, Ziru; Pi, Xitian

    2016-01-01

    There are no ideal means for the diagnosis of intestinal bleeding diseases as of now, particularly in the small intestine. This study investigated an intelligent intestinal bleeding detection capsule system based on color recognition. After the capsule is swallowed, the bleeding detection module (containing a color-sensitive adsorptive film that changes color when absorbing intestinal juice,) is used to identify intestinal bleeding features. A hue-saturation-light color space method can be applied to detect bleeding according to the range of H and S values of the film color. Once bleeding features are recognized, a wireless transmission module is activated immediately to send an alarm signal to the outside; an in vitro module receives the signal and sends an alarm. The average power consumption of the entire capsule system is estimated to be about 2.1mW. Owing to its simplicity, reliability, and effectiveness, this system represents a new approach to the clinical diagnosis of intestinal bleeding diseases. PMID:27902728

  9. Automated kidney detection for 3D ultrasound using scan line searching

    NASA Astrophysics Data System (ADS)

    Noll, Matthias; Nadolny, Anne; Wesarg, Stefan

    2016-04-01

    Ultrasound (U/S) is a fast and non-expensive imaging modality that is used for the examination of various anatomical structures, e.g. the kidneys. One important task for automatic organ tracking or computer-aided diagnosis is the identification of the organ region. During this process the exact information about the transducer location and orientation is usually unavailable. This renders the implementation of such automatic methods exceedingly challenging. In this work we like to introduce a new automatic method for the detection of the kidney in 3D U/S images. This novel technique analyses the U/S image data along virtual scan lines. Here, characteristic texture changes when entering and leaving the symmetric tissue regions of the renal cortex are searched for. A subsequent feature accumulation along a second scan direction produces a 2D heat map of renal cortex candidates, from which the kidney location is extracted in two steps. First, the strongest candidate as well as its counterpart are extracted by heat map intensity ranking and renal cortex size analysis. This process exploits the heat map gap caused by the renal pelvis region. Substituting the renal pelvis detection with this combined cortex tissue feature increases the detection robustness. In contrast to model based methods that generate characteristic pattern matches, our method is simpler and therefore faster. An evaluation performed on 61 3D U/S data sets showed, that in 55 cases showing none or minor shadowing the kidney location could be correctly identified.

  10. Detecting holocene changes in thermohaline circulation.

    PubMed

    Keigwin, L D; Boyle, E A

    2000-02-15

    Throughout the last glacial cycle, reorganizations of deep ocean water masses were coincident with rapid millennial-scale changes in climate. Climate changes have been less severe during the present interglacial, but evidence for concurrent deep ocean circulation change is ambiguous.

  11. Change Detection in Naturalistic Pictures among Children with Autism

    ERIC Educational Resources Information Center

    Burack, Jacob A.; Joseph, Shari; Russo, Natalie; Shore, David I.; Porporino, Mafalda; Enns, James T.

    2009-01-01

    Persons with autism often show strong reactions to changes in the environment, suggesting that they may detect changes more efficiently than typically developing (TD) persons. However, Fletcher-Watson et al. (Br J Psychol 97:537-554, 2006) reported no differences between adults with autism and TD adults with a change-detection task. In this study,…

  12. AUTOMATED LEAK DETECTION OF BURIED TANKS USING GEOPHYSICAL METHODS AT THE HANFORD NUCLEAR SITE

    SciTech Connect

    CALENDINE S; SCHOFIELD JS; LEVITT MT; FINK JB; RUCKER DF

    2011-03-30

    At the Hanford Nuclear Site in Washington State, the Department of Energy oversees the containment, treatment, and retrieval of liquid high-level radioactive waste. Much of the waste is stored in single-shelled tanks (SSTs) built between 1943 and 1964. Currently, the waste is being retrieved from the SSTs and transferred into newer double-shelled tanks (DSTs) for temporary storage before final treatment. Monitoring the tanks during the retrieval process is critical to identifying leaks. An electrically-based geophysics monitoring program for leak detection and monitoring (LDM) has been successfully deployed on several SSTs at the Hanford site since 2004. The monitoring program takes advantage of changes in contact resistance that will occur when conductive tank liquid leaks into the soil. During monitoring, electrical current is transmitted on a number of different electrode types (e.g., steel cased wells and surface electrodes) while voltages are measured on all other electrodes, including the tanks. Data acquisition hardware and software allow for continuous real-time monitoring of the received voltages and the leak assessment is conducted through a time-series data analysis. The specific hardware and software combination creates a highly sensitive method of leak detection, complementing existing drywell logging as a means to detect and quantify leaks. Working in an industrial environment such as the Hanford site presents many challenges for electrical monitoring: cathodic protection, grounded electrical infrastructure, lightning strikes, diurnal and seasonal temperature trends, and precipitation, all of which create a complex environment for leak detection. In this discussion we present examples of challenges and solutions to working in the tank farms of the Hanford site.

  13. Computerized nodule detection in thin-slice CT using selective enhancement filter and automated rule-based classifier

    NASA Astrophysics Data System (ADS)

    Li, Qiang; Li, Feng; Doi, Kunio

    2005-04-01

    We have been developing computer-aided diagnostic (CAD) scheme to assist radiologists detect lung nodules in thoracic CT images. In order to improve the sensitivity for nodule detection, we developed a selective nodule enhancement filter for nodule which can simultaneously enhance nodules and suppress other normal anatomic structures such as blood vessels and airway walls. Therefore, as preprocessing steps, this filter is useful for improving the sensitivity of nodule detection and for reducing the number of false positives. Another new technique we employed in this study is an automated rule-based classifier. It can significantly reduce the extent of the disadvantages of existing rule-based classifiers, including manual design, poor reproducibility, poor evaluation methods such as re-substitution, and a large overtraining effect. Experimental results performed with Monte Carlo simulation and a real lung nodule CT dataset demonstrated that the automated method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the rule-based classifier.

  14. Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria.

    PubMed

    Joshi, Vinayak; Agurto, Carla; Barriga, Simon; Nemeth, Sheila; Soliz, Peter; MacCormick, Ian J; Lewallen, Susan; Taylor, Terrie E; Harding, Simon P

    2017-02-15

    Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis.

  15. Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

    NASA Astrophysics Data System (ADS)

    Joshi, Vinayak; Agurto, Carla; Barriga, Simon; Nemeth, Sheila; Soliz, Peter; MacCormick, Ian J.; Lewallen, Susan; Taylor, Terrie E.; Harding, Simon P.

    2017-02-01

    Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis.

  16. Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

    PubMed Central

    Joshi, Vinayak; Agurto, Carla; Barriga, Simon; Nemeth, Sheila; Soliz, Peter; MacCormick, Ian J.; Lewallen, Susan; Taylor, Terrie E.; Harding, Simon P.

    2017-01-01

    Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis. PMID:28198460

  17. An automated dust detection using SEVIRI: A multiyear climatology of summertime dustiness in the central and western Sahara

    NASA Astrophysics Data System (ADS)

    Ashpole, Ian; Washington, Richard

    2012-04-01

    Here we present an automated dust detection scheme using the Infrared (IR) channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI), carried on board Meteosat Second Generation (MSG) satellites, from which dust scheme images that are now widely used in Saharan dust studies are created. This provides an objective, readily reproducible and quick way to build up climatologies of dust presence which compares well with subjectively identified dust presence in the daytime hours. At nighttime the automated detection scheme is less reliable due to the strong diurnal cycle of surface temperatures. Our SEVIRI Dust Flag (SDF) is compared to Aerosol Optical Depth (AOD) from the surface and found to successfully and consistently identify moderate-heavy dust outbreaks, although success rate is lower in the early morning and late evening. SDF corresponds to Absorbing Aerosol Index (AAI) from the Ozone Monitoring Instrument (OMI) that is also indicative of moderate-heavy dust outbreaks across the central and western Sahara, but there are differences in the spatial patterns of climatologies created over a number of years that are likely to be due to the different sensitivities of the detection schemes. Despite these discrepancies, SDF and AAI both place dust hot spots in southern Algeria and across its southern borders with Mali and Niger, and SDF climatologies for June-August 2004-2010 reveal that there is a substantial degree of interannual variability in dust presence in the central and western Sahara in the boreal summer.

  18. Evaluation of genotoxicity using automated detection of γH2AX in metabolically competent HepaRG cells.

    PubMed

    Quesnot, Nicolas; Rondel, Karine; Audebert, Marc; Martinais, Sophie; Glaise, Denise; Morel, Fabrice; Loyer, Pascal; Robin, Marie-Anne

    2016-01-01

    The in situ detection of γH2AX was recently reported to be a promising biomarker of genotoxicity. In addition, the human HepaRG hepatoma cells appear to be relevant for investigating hepatic genotoxicity since they express most of drug metabolizing enzymes and a wild type p53. The aim of this study was to determine whether the automated in situ detection of γH2AX positive HepaRG cells could be relevant for evaluation of genotoxicity after single or long-term repeated in vitro exposure compared to micronucleus assay. Metabolically competent HepaRG cells were treated daily with environmental contaminants and genotoxicity was evaluated after 1, 7 and 14 days. Using these cells, we confirmed the genotoxicity of aflatoxin B1 and benzo(a)pyrene and demonstrated that dimethylbenzanthracene, fipronil and endosulfan previously found genotoxic with comet or micronucleus assays also induced γH2AX phosphorylation. Furthermore, we showed that fluoranthene and bisphenol A induced γH2AX while no effect had been previously reported in HepG2 cells. In addition, induction of γH2AX was observed with some compounds only after 7 days, highlighting the importance of studying long-term effects of low doses of contaminants. Together, our data demonstrate that automated γH2AX detection in metabolically competent HepaRG cells is a suitable high-through put genotoxicity screening assay.

  19. Epigenetic changes detected in micropropagated hop plants.

    PubMed

    Peredo, Elena L; Arroyo-García, Rosa; Revilla, M Angeles

    2009-07-01

    Micropropagation is a widely used technique in hops (Humulus lupulus L.). However, to the best of our knowledge, the genetic and epigenetic stability of the microplants has never been tested before. In the present study, two hop accessions were established in vitro and micropropagated for 2 years. The genetic and epigenetic stability of the in vitro plants was analyzed with several molecular techniques: random amplified DNA polymorphism (RAPD), retrotransposon microsatellite amplified polymorphism (REMAP), and methylation-sensitive amplification polymorphism (MSAP). No genetic variation among control and treated plants was found, even after 12 cycles of micropropagation. Epigenetic variation was detected, first, when field and in vitro samples were compared. Nearly a 30% of the detected fragments presented the same pattern of alterations in all the vitroplants. Second, lower levels of epigenetic variation were detected among plants from the different subcultures. Part of this detected variation seemed to be accumulated along the 12 sequential subcultures tested.

  20. Urban change detection procedures using Landsat digital data

    NASA Technical Reports Server (NTRS)

    Jensen, J. R.; Toll, D. L.

    1982-01-01

    Landsat multispectral scanner data was applied to an urban change detection problem in Denver, CO. A dichotomous key yielding ten stages of residential development at the urban fringe was developed. This heuristic model allowed one to identify certain stages of development which are difficult to detect when performing digital change detection using Landsat data. The stages of development were evaluated in terms of their spectral and derived textural characteristics. Landsat band 5 (0.6-0.7 micron) and texture data produced change detection maps which were approximately 81 percent accurate. Results indicated that the stage of development and the spectral/textural features affect the change in the spectral values used for change detection. These preliminary findings will hopefully prove valuable for improved change detection at the urban fringe.

  1. Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

    NASA Astrophysics Data System (ADS)

    Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

    2011-11-01

    Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

  2. Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques.

    PubMed

    Zhao, Ding; Lam, Henry; Peng, Huei; Bao, Shan; LeBlanc, David J; Nobukawa, Kazutoshi; Pan, Christopher S

    2016-08-05

    Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.

  3. Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques

    PubMed Central

    Zhao, Ding; Lam, Henry; Peng, Huei; Bao, Shan; LeBlanc, David J.; Nobukawa, Kazutoshi; Pan, Christopher S.

    2016-01-01

    Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs. PMID:27840592

  4. Automated detection of structural alerts (chemical fragments) in (eco)toxicology

    PubMed Central

    Lepailleur, Alban; Poezevara, Guillaume; Bureau, Ronan

    2013-01-01

    This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (eco)toxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics. The frequency of a chemical fragment in a dataset is often at the core of the process for the definition of its toxicological relevance. However, recent progresses in data mining provide new insights into the automated discovery of new rules. Particularly, this review highlights the notion of Emerging Patterns that can capture contrasts between classes of data. PMID:24688706

  5. Occupancy change detection system and method

    SciTech Connect

    Bruemmer, David J; Few, Douglas A

    2009-09-01

    A robot platform includes perceptors, locomotors, and a system controller. The system controller executes instructions for producing an occupancy grid map of an environment around the robot, scanning the environment to generate a current obstacle map relative to a current robot position, and converting the current obstacle map to a current occupancy grid map. The instructions also include processing each grid cell in the occupancy grid map. Within the processing of each grid cell, the instructions include comparing each grid cell in the occupancy grid map to a corresponding grid cell in the current occupancy grid map. For grid cells with a difference, the instructions include defining a change vector for each changed grid cell, wherein the change vector includes a direction from the robot to the changed grid cell and a range from the robot to the changed grid cell.

  6. The influence of CT dose and reconstruction parameters on automated detection of small pulmonary nodules

    NASA Astrophysics Data System (ADS)

    Ochs, Robert; Angel, Erin; Boedeker, Kirsten; Petkovska, Iva; Panknin, Christoph; Goldin, Jonathan; Aberle, Denise; McNitt-Gray, Michael; Brown, Matthew

    2006-03-01

    The aim of our investigation was to assess the influence of both CT acquisition dose and reconstruction kernel on computer-aided detection (CAD) of pulmonary nodules. Our hypothesis is that the detection of small nodules is affected by the noise characteristics of the image and the signal to noise ratio of the nodule and bronchiovascular anatomy. Knowledge gained from this experiment will assist in developing an advanced CAD system designed to detect smaller and more subtle nodules with minimal false positives. Eleven research subjects were selected from the Lung Image Database Consortium (LIDC) database based on our inclusion criteria of: 1) having at least one nodule and 2) available raw CT projection data for the series that our institution submitted to the LIDC study. Using the original raw projection data, research software simulated raw projection data acquired with a dose reduced 32-40% from the original scan. Projection data for both dose levels was reconstructed with smooth to very sharp kernels (B10f, B30f, B50f, and B70f). The resulting series were used to investigate the influence of dose and reconstruction kernel on CAD performance. A prototype CAD system was used to investigate changes in sensitivity and false positives with varying imaging parameters. In a sub-study, the prototype system was compared to a commercial CAD system. We did not have enough subjects to conclude significance, but the results indicate our research system had a higher sensitivity with the smooth or medium reconstruction kernels than with the sharper kernels. The sensitivity was similar for both dose levels. The false positive rate was higher with the smooth kernels and the lower dose levels.

  7. Examination of a Novel Method for Non-Contact, Low-Cost, and Automated Heart-Rate Detection in Ambient Light Using Photoplethysmographic Imaging

    DTIC Science & Technology

    2014-10-01

    Examination of a Novel Method for Non-Contact, Low-Cost, and Automated Heart-Rate Detection in Ambient Light Using Photoplethysmographic...Laboratory Adelphi, MD 20783-1138 ARL-TR-7136 October 2014 Examination of a Novel Method for Non-Contact, Low-Cost, and Automated Heart...REPORT DATE (DD-MM-YYYY) October 2014 2. REPORT TYPE Final 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE Examination of a Novel Method for

  8. Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

    PubMed Central

    Banister, Katie; Boachie, Charles; Bourne, Rupert; Cook, Jonathan; Burr, Jennifer M.; Ramsay, Craig; Garway-Heath, David; Gray, Joanne; McMeekin, Peter; Hernández, Rodolfo; Azuara-Blanco, Augusto

    2016-01-01

    Purpose To compare the diagnostic performance of automated imaging for glaucoma. Design Prospective, direct comparison study. Participants Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom. Methods We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies. Main Outcome Measures Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests. Results We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%–92.1%), but lowest specificity (63.9%; 95% CI, 60.2%–67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%–43.8%), but the highest specificity (97.2%; 95% CI, 95.6%–98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%–87.6%), and

  9. Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Zheng, Bin; Wood, Marc; Li, Shibo; Chen, Wei; Liu, Hong

    2005-08-01

    Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

  10. Detection of intestinal parasites by use of the cuvette-based automated microscopy analyser sediMAX(®).

    PubMed

    Intra, J; Taverna, E; Sala, M R; Falbo, R; Cappellini, F; Brambilla, P

    2016-03-01

    Microscopy is the reference method for intestinal parasite identification. The cuvette-based automated microscopy analyser, sediMAX 1, provides 15 digital images of each sediment sample. In this study, we have evaluated this fully automated instrument for detection of enteric parasites, helminths and protozoa. A total of 700 consecutively preserved samples consisting of 60 positive samples (50 protozoa, ten helminths) and 640 negative samples were analysed. Operators were blinded to each others' results. Samples were randomized and were tested both by manual microscopy and sediMAX 1 for parasite recognition. The sediMAX 1 analysis was conducted using a dilution of faecal samples, allowing determination of morphology. The data obtained using sediMAX 1 showed a specificity of 100% and a sensitivity of 100%. Some species of helminths, such as Enterobius vermicularis, Strongyloides stercolaris, the Ancylostoma duodenale/Necator americanus complex, and schistosomes were not considered in this work, because they are rare in stool specimens, are not easily detectable with microscopy analysis, and require specific recovery techniques. This study demonstrated for the first time that sediMAX 1 can be an aid in enteric parasite identification.

  11. Detection of Thermometer Clustering in the Calibration of Large Batches of Industrial Thermometers for the LHC by Automated Data Processing

    NASA Astrophysics Data System (ADS)

    Pavese, F.; Ichim, D.; Ciarlini, P.; Balle, C.; Casas-Cubillos, J.

    2003-09-01

    The complete procedure to calibrate thermometers is a complex process, especially when several thousand semiconductor-type thermometers are used and must be individually calibrated, as in the case of the instrumentation of the new Large Hadron Collider (LHC) machine at CERN. Indeed, the similarity of the characteristics of semiconducting thermometers is more limited than that of wire-wound thermometers. The span of the characteristics spread can appear as a homogeneous set, or can show clusters (groups) of characteristics. In the latter case, one of the reasons for clustering may be the fabrication process by batches of numerous devices on the same wafer. Consequently, the detection of the groups can be useful, from the supplier point of view, to give information relevant to improving the fabrication uniformity. From the user point of view, it is useful for making a guess of the possible thermometer stability with time, when this is a must, as in the LHC case. In fact, thermometers showing characteristics outlying or in small clusters should be considered to be potentially anomalous. In addition, the identification of anomalous groups allows the detection of artifacts due to the experimental process. For a large number of thermometers, this analysis requires the use of automated procedures and, consequently, automated decisions that avoid false effects. The paper describes the mathematical methodology adopted for the identification of the clusters, based on the mixed-effect modeling of the thermometer characteristics.

  12. Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Zheng, Bin; Li, Shibo; Mulvihill, John J.; Chen, Xiaodong; Liu, Hong

    2010-07-01

    Karyotyping is an important process to classify chromosomes into standard classes and the results are routinely used by the clinicians to diagnose cancers and genetic diseases. However, visual karyotyping using microscopic images is time-consuming and tedious, which reduces the diagnostic efficiency and accuracy. Although many efforts have been made to develop computerized schemes for automated karyotyping, no schemes can get be performed without substantial human intervention. Instead of developing a method to classify all chromosome classes, we develop an automatic scheme to detect abnormal metaphase cells by identifying a specific class of chromosomes (class 22) and prescreen for suspicious chronic myeloid leukemia (CML). The scheme includes three steps: (1) iteratively segment randomly distributed individual chromosomes, (2) process segmented chromosomes and compute image features to identify the candidates, and (3) apply an adaptive matching template to identify chromosomes of class 22. An image data set of 451 metaphase cells extracted from bone marrow specimens of 30 positive and 30 negative cases for CML is selected to test the scheme's performance. The overall case-based classification accuracy is 93.3% (100% sensitivity and 86.7% specificity). The results demonstrate the feasibility of applying an automated scheme to detect or prescreen the suspicious cancer cases.

  13. Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks

    NASA Astrophysics Data System (ADS)

    Eckstein, Miguel P.; Bartroff, Jay L.; Abbey, Craig K.; Whiting, James S.; Bochud, Francois O.

    2003-03-01

    We compared the ability of three model observers (nonprewhitening matched filter with an eye filter, Hotelling and channelized Hotelling) in predicting the effect of JPEG and wavelet-Crewcode image compression on human visual detection of a simulated lesion in single frame digital x-ray coronary angiograms. All three model observers predicted the JPEG superiority present in human performance, although the nonprewhitening matched filter with an eye filter (NPWE) and the channelized Hotelling models were better predictors than the Hotelling model. The commonly used root mean square error and related peak signal to noise ratio metrics incorrectly predicted a JPEG inferiority. A particular image discrimination/perceptual difference model correctly predicted a JPEG advantage at low compression ratios but incorrectly predicted a JPEG inferiority at high compression ratios. In the second part of the paper, the NPWE model was used to perform automated simulated annealing optimization of the quantization matrix of the JPEG algorithm at 25:1 compression ratio. A subsequent psychophysical study resulted in improved human detection performance for images compressed with the NPWE optimized quantization matrix over the JPEG default quantization matrix. Together, our results show how model observers can be successfully used to perform automated evaluation and optimization of diagnostic performance in clinically relevant visual tasks using real anatomic backgrounds.

  14. Comparison of AdaBoost and support vector machines for detecting Alzheimer's disease through automated hippocampal segmentation.

    PubMed

    Morra, Jonathan H; Tu, Zhuowen; Apostolova, Liana G; Green, Amity E; Toga, Arthur W; Thompson, Paul M

    2010-01-01

    We compared four automated methods for hippocampal segmentation using different machine learning algorithms: 1) hierarchical AdaBoost, 2) support vector machines (SVM) with manual feature selection, 3) hierarchical SVM with automated feature selection (Ada-SVM), and 4) a publicly available brain segmentation package (FreeSurfer). We trained our approaches using T1-weighted brain MRIs from 30 subjects [10 normal elderly, 10 mild cognitive impairment (MCI), and 10 Alzheimer's disease (AD)], and tested on an independent set of 40 subjects (20 normal, 20 AD). Manually segmented gold standard hippocampal tracings were available for all subjects (training and testing). We assessed each approach's accuracy relative to manual segmentations, and its power to map AD effects. We then converted the segmentations into parametric surfaces to map disease effects on anatomy. After surface reconstruction, we computed significance maps, and overall corrected p-values, for the 3-D profile of shape differences between AD and normal subjects. Our AdaBoost and Ada-SVM segmentations compared favorably with the manual segmentations and detected disease effects as well as FreeSurfer on the data tested. Cumulative p-value plots, in conjunction with the false discovery rate method, were used to examine the power of each method to detect correlations with diagnosis and cognitive scores. We also evaluated how segmentation accuracy depended on the size of the training set, providing practical information for future users of this technique.

  15. Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. using antimicrobial resistance phenotypes

    PubMed Central

    STELLING, J.; YIH, W. K.; GALAS, M.; KULLDORFF, M.; PICHEL, M.; TERRAGNO, R.; TUDURI, E.; ESPETXE, S.; BINSZTEIN, N.; O’BRIEN, T. F.; PLATT, R.

    2014-01-01

    SUMMARY Antimicrobial resistance is a priority emerging public health threat, and the ability to detect promptly outbreaks caused by resistant pathogens is critical for resistance containment and disease control efforts. We describe and evaluate the use of an electronic laboratory data system (WHONET) and a space–time permutation scan statistic for semi-automated disease outbreak detection. In collaboration with WHONET-Argentina, the national network for surveillance of antimicrobial resistance, we applied the system to the detection of local and regional outbreaks of Shigella spp. We searched for clusters on the basis of genus, species, and resistance phenotype and identified 19 statistical ‘events’ in a 12-month period. Of the six known outbreaks reported to the Ministry of Health, four had good or suggestive agreement with SaTScan-detected events. The most discriminating analyses were those involving resistance phenotypes. Electronic laboratory-based disease surveillance incorporating statistical cluster detection methods can enhance infectious disease outbreak detection and response. PMID:19796449

  16. A Dual-Process Account of Auditory Change Detection

    ERIC Educational Resources Information Center

    McAnally, Ken I.; Martin, Russell L.; Eramudugolla, Ranmalee; Stuart, Geoffrey W.; Irvine, Dexter R. F.; Mattingley, Jason B.

    2010-01-01

    Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed…

  17. Automated Detection and Classification in High-Resolution Sonar Imagery for Autonomous Underwater Vehicle Operations

    DTIC Science & Technology

    2008-12-01

    targets have been detected and prior to the classification of mine-like objects ... recognition for the HUGIN Mine Reconnaissance System, IntI Coni. On Detection & Classification of Underwater Targets , Proc. Institute Acoustics 29, Part...imagery, in order to detect mines and other objects of interest on the seabed. Automatic detection and classification teclmiques are being

  18. Understanding reliance on automation: effects of error type, error distribution, age and experience

    PubMed Central

    Sanchez, Julian; Rogers, Wendy A.; Fisk, Arthur D.; Rovira, Ericka

    2015-01-01

    An obstacle detection task supported by “imperfect” automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation. PMID:25642142

  19. Load and Rate of Change of Load Detection System.

    DTIC Science & Technology

    The present invention relates to a system for detecting and recording the level and rate of change of landing loads in the struts of aircraft landing...to a minimum pressure to record the level and rate of change of pressure detected by the sensor.

  20. Reference in the Age of Automation: Changes in Reference Service at Chemical Abstracts Service Library.

    ERIC Educational Resources Information Center

    Hodges, Pauline R.

    1989-01-01

    Describes the use of office automation equipment at Chemical Abstracts Services (CAS) Library Services and its impact on the reference services offered. Functions discussed include the use of electronic mail for 24-hour library access, online searching, online access to other library collections, and coordination of online searching done by CAS…

  1. Automation in Distance Learning: An Empirical Study of Unlearning and Academic Identity Change Linked to Automation of Student Messaging within Distance Learning

    ERIC Educational Resources Information Center

    Collins, Hilary; Glover, Hayley; Myers, Fran; Watson, Mor

    2016-01-01

    This paper explores the unlearning and learning undertaken by adjuncts (Associate Lecturers) during the introduction of automated messaging by the university as part replacement of adjunct pastoral support for students. Automated messages were introduced by the University to standardize the student experience in terms of qualification…

  2. Automated detection and reporting of Volatile Organic Compounds (VOCs) in complex environments

    SciTech Connect

    Hargis, P.J. Jr.; Preppernau, B.L.; Osbourn, G.C.

    1997-03-01

    This paper describes results from efforts to develop VOC sensing systems based on two complementary techniques. The first technique used a gated channeltron detector for resonant laser-induced multiphoton photoionization detection of trace organic vapors in a supersonic molecular beam. The channeltron was gated using a relatively simple circuit to generate a negative gate pulse with a width of 400 ns (FWHM), a 50 ns turn-on (rise) time, a 1.5 {mu}s turn-off (decay) time, a pulse amplitude of {minus}1000 Volts, and a DC offset adjustable from zero to {minus}1500 Volts. The gated channeltron allows rejection of spurious responses to UV laser light scattered directly into the channeltron and time-delayed ionization signals induced by photoionization of residual gas in the vacuum chamber. Detection limits in the part-per-trillion range have been demonstrated with the gated detector. The second technique used arrays of surface acoustic wave (SAW) devices coated with various chemically selective materials (e.g., polymers, self assembled monolayers) to provide unique response patterns to various chemical analytes. This work focused on polymers, formed by spin casting from solution or by plasma polymerization, as well as on self assembled monolayers. Response from coated SAWs to various concentrations of water, volatile organics, and organophosphonates (chemical warfare agent simulants) were used to provide calibration data. A novel visual empirical region of influence (VIERI) pattern recognition technique was used to evaluate the ability to use these response patterns to correctly identify chemical species. This investigation shows how the VERI technique can be used to determine the best set of coatings for an array, to predict the performance of the array even if sensor responses change due to aging of the coating materials, and to identify unknown analytes based on previous calibration data.

  3. Comparing Several Algorithms for Change Detection of Wetland

    NASA Astrophysics Data System (ADS)

    Yan, F.; Zhang, S.; Chang, L.

    2015-12-01

    As "the kidneys of the landscape" and "ecological supermarkets", wetland plays an important role in ecological equilibrium and environmental protection.Therefore, it is of great significance to understand the dynamic changes of the wetland. Nowadays, many index and many methods have been used in dynamic Monitoring of Wetland. However, there are no single method and no single index are adapted to detect dynamic change of wetland all over the world. In this paper, three digital change detection algorithms are applied to 2005 and 2010 Landsat Thematic Mapper (TM) images of a portion of the Northeast China to detect wetland dynamic between the two dates. The change vector analysis method (CVA) uses 6 bands of TM images to detect wetland dynamic. The tassled cap transformation is used to create three change images (change in brightness, greenness, and wetness). A new method--- Comprehensive Change Detection Method (CCDM) is introduced to detect forest dynamic change. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (differenced Normalized Burn Ratio (dNBR), differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) and a new index called the Relative Change Vector Maximum (RCVMAX)) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. Related test proved that CCDM method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on

  4. Change Detection in Rough Time Series

    DTIC Science & Technology

    2014-09-01

    support models. While at DSTO he has worked on applications for modelling strategic decisions, intelligence analysis, and decision support systems ...changing nature of expected droughts into the future thus indicates increasing stress on the MDB river and lake system such that pre-existing irrigation ...or inaccurate sensor data, subjective ratings of vague variables, imperfect intelligence reports, algorithmic derived measures indicating degrees

  5. Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Zheng, Bin; Li, Shibo; Zhang, Roy; Mulvihill, John J.; Chen, Wei R.; Liu, Hong

    2009-03-01

    Fluorescence in situ hybridization (FISH) technology has been widely recognized as a promising molecular and biomedical optical imaging tool to screen and diagnose cervical cancer. However, manual FISH analysis is time-consuming and may introduce large inter-reader variability. In this study, a computerized scheme is developed and tested. It automatically detects and analyzes FISH spots depicted on microscopic fluorescence images. The scheme includes two stages: (1) a feature-based classification rule to detect useful interphase cells, and (2) a knowledge-based expert classifier to identify splitting FISH spots and improve the accuracy of counting independent FISH spots. The scheme then classifies detected analyzable cells as normal or abnormal. In this study, 150 FISH images were acquired from Pap-smear specimens and examined by both an experienced cytogeneticist and the scheme. The results showed that (1) the agreement between the cytogeneticist and the scheme was 96.9% in classifying between analyzable and unanalyzable cells (Kappa=0.917), and (2) agreements in detecting normal and abnormal cells based on FISH spots were 90.5% and 95.8% with Kappa=0.867. This study demonstrated the feasibility of automated FISH analysis, which may potentially improve detection efficiency and produce more accurate and consistent results than manual FISH analysis.

  6. Synthetic circuit for exact adaptation and fold-change detection.

    PubMed

    Kim, Jongmin; Khetarpal, Ishan; Sen, Shaunak; Murray, Richard M

    2014-05-01

    Biological organisms use their sensory systems to detect changes in their environment. The ability of sensory systems to adapt to static inputs allows wide dynamic range as well as sensitivity to input changes including fold-change detection, a response that depends only on fold changes in input, and not on absolute changes. This input scale invariance underlies an important strategy for search that depends solely on the spatial profile of the input. Synthetic efforts to reproduce the architecture and response of cellular circuits provide an important step to foster understanding at the molecular level. We report the bottom-up assembly of biochemical systems that show exact adaptation and fold-change detection. Using a malachite green aptamer as the output, a synthetic transcriptional circuit with the connectivity of an incoherent feed-forward loop motif exhibits pulse generation and exact adaptation. A simple mathematical model was used to assess the amplitude and duration of pulse response as well as the parameter regimes required for fold-change detection. Upon parameter tuning, this synthetic circuit exhibits fold-change detection for four successive rounds of two-fold input changes. The experimental realization of fold-change detection circuit highlights the programmability of transcriptional switches and the ability to obtain predictive dynamical systems in a cell-free environment for technological applications.

  7. Synthetic circuit for exact adaptation and fold-change detection

    PubMed Central

    Kim, Jongmin; Khetarpal, Ishan; Murray, Richard M.

    2014-01-01

    Biological organisms use their sensory systems to detect changes in their environment. The ability of sensory systems to adapt to static inputs allows wide dynamic range as well as sensitivity to input changes including fold-change detection, a response that depends only on fold changes in input, and not on absolute changes. This input scale invariance underlies an important strategy for search that depends solely on the spatial profile of the input. Synthetic efforts to reproduce the architecture and response of cellular circuits provide an important step to foster understanding at the molecular level. We report the bottom-up assembly of biochemical systems that show exact adaptation and fold-change detection. Using a malachite green aptamer as the output, a synthetic transcriptional circuit with the connectivity of an incoherent feed-forward loop motif exhibits pulse generation and exact adaptation. A simple mathematical model was used to assess the amplitude and duration of pulse response as well as the parameter regimes required for fold-change detection. Upon parameter tuning, this synthetic circuit exhibits fold-change detection for four successive rounds of two-fold input changes. The experimental realization of fold-change detection circuit highlights the programmability of transcriptional switches and the ability to obtain predictive dynamical systems in a cell-free environment for technological applications. PMID:24728988

  8. Development of an automated processing method to detect still timing of cardiac motion for coronary magnetic resonance angiography

    NASA Astrophysics Data System (ADS)

    Asou, Hiroya; Ichikawa, Katsuhiro; Imada, Naoyuki; Masuda, Takanori; Satou, Tomoyasu

    2011-03-01

    Whole-heart coronary magnetic resonance angiography (WH-MRA) is useful noninvasive examination. Its signal acquisition is performed during very short still timing in each cardiac motion cycle, and therefore the adequate still timing selection is important to obtain the better image quality. However, since the current available selection method is only manual one using visual comparison of cine MRI images with different phases, the selected timings are often incorrect and their reproducibility is not sufficient. We developed an automated selection method to detect the best still timing for the WH-MRA and compared the automated method with conventional manual one. Cine MRI images were used for the analysis. In order to extract the high-speed cardiac cine image, each phase directional pixel set at each pixel position in all cine images were processed by a high-pass filtering using the Fourie transform. After this process, the cine images with low speed timing became dark, and the optimal timing could be determined by a threshold processing. We took ten volunteers' WH-MRA with the manually and automatically selected timings, and visually assessed image quality of each image on a 5-point scale (1=excellent, 2=very good, 3=good, 4=fair, 5=poor). The mean scores of the manual and automatic methods for right coronary arteries (RCA), LDA left anterior descending arteries (LAD) and LCX left circumflex arteries (LCX) were 4.2+/-0.38, 4.1+/-0.44, 3.9+/-0.52 and 4.1+/-0.42, 4.1+/-0.24, 3.2+/-0.35 respectively. The score were increased by our method in the RCA and LCX, and the LCX was significant (p<0.05). As the results, it was indicated that our automated method could determine the optimal cardiac phase more accurately than or equally to the conventional manual method.

  9. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd.

    PubMed

    Irshad, H; Montaser-Kouhsari, L; Waltz, G; Bucur, O; Nowak, J A; Dong, F; Knoblauch, N W; Beck, A H

    2015-01-01

    The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist

  10. CROWDSOURCING IMAGE ANNOTATION FOR NUCLEUS DETECTION AND SEGMENTATION IN COMPUTATIONAL PATHOLOGY: EVALUATING EXPERTS, AUTOMATED METHODS, AND THE CROWD

    PubMed Central

    Irshad, H.; Montaser-Kouhsari, L.; Waltz, G.; Bucur, O.; Nowak, J.A.; Dong, F.; Knoblauch, N.W.; Beck, A. H.

    2014-01-01

    The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert