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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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.

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.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection.

PubMed

Hättenschwiler, Nicole; Sterchi, Yanik; Mendes, Marcia; Schwaninger, Adrian

2018-10-01

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

Automated Processing of 2-D Gel Electrophoretograms of Genomic DNA for Hunting Pathogenic DNA Molecular Changes.

PubMed

Takahashi; Nakazawa; Watanabe; Konagaya

1999-01-01

We have developed the automated processing algorithms for 2-dimensional (2-D) electrophoretograms of genomic DNA based on RLGS (Restriction Landmark Genomic Scanning) method, which scans the restriction enzyme recognition sites as the landmark and maps them onto a 2-D electrophoresis gel. Our powerful processing algorithms realize the automated spot recognition from RLGS electrophoretograms and the automated comparison of a huge number of such images. In the final stage of the automated processing, a master spot pattern, on which all the spots in the RLGS images are mapped at once, can be obtained. The spot pattern variations which seemed to be specific to the pathogenic DNA molecular changes can be easily detected by simply looking over the master spot pattern. When we applied our algorithms to the analysis of 33 RLGS images derived from human colon tissues, we successfully detected several colon tumor specific spot pattern changes.

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood.

PubMed Central

Oosterwijk, J C; Knepflé, C F; Mesker, W E; Vrolijk, H; Sloos, W C; Pattenier, H; Ravkin, I; van Ommen, G J; Kanhai, H H; Tanke, H J

1998-01-01

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood. PMID:9837832

Automated Content Detection for Cassini Images

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Automated Microbiological Detection/Identification System

PubMed Central

Aldridge, C.; Jones, P. W.; Gibson, S.; Lanham, J.; Meyer, M.; Vannest, R.; Charles, R.

1977-01-01

An automated, computerized system, the AutoMicrobic System, has been developed for the detection, enumeration, and identification of bacteria and yeasts in clinical specimens. The biological basis for the system resides in lyophilized, highly selective and specific media enclosed in wells of a disposable plastic cuvette; introduction of a suitable specimen rehydrates and inoculates the media in the wells. An automated optical system monitors, and the computer interprets, changes in the media, with enumeration and identification results automatically obtained in 13 h. Sixteen different selective media were developed and tested with a variety of seeded (simulated) and clinical specimens. The AutoMicrobic System has been extensively tested with urine specimens, using a urine test kit (Identi-Pak) that contains selective media for Escherichia coli, Proteus species, Pseudomonas aeruginosa, Klebsiella-Enterobacter species, Serratia species, Citrobacter freundii, group D enterococci, Staphylococcus aureus, and yeasts (Candida species and Torulopsis glabrata). The system has been tested with 3,370 seeded urine specimens and 1,486 clinical urines. Agreement with simultaneous conventional (manual) cultures, at levels of 70,000 colony-forming units per ml (or more), was 92% or better for seeded specimens; clinical specimens yielded results of 93% or better for all organisms except P. aeruginosa, where agreement was 86%. System expansion in progress includes antibiotic susceptibility testing and compatibility with most types of clinical specimens. Images PMID:334798

Automated detection of exudates for diabetic retinopathy screening

NASA Astrophysics Data System (ADS)

Fleming, Alan D.; Philip, Sam; Goatman, Keith A.; Williams, Graeme J.; Olson, John A.; Sharp, Peter F.

2007-12-01

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

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

PubMed

Despins, Laurel A

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.

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.

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

PubMed

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

Automated crack detection in conductive smart-concrete structures using a resistor mesh model

NASA Astrophysics Data System (ADS)

Downey, Austin; D'Alessandro, Antonella; Ubertini, Filippo; Laflamme, Simon

2018-03-01

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a 500 × 500 × 50 mm3 reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure.

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.

Clustering approaches to feature change detection

NASA Astrophysics Data System (ADS)

G-Michael, Tesfaye; Gunzburger, Max; Peterson, Janet

2018-05-01

The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.

An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision

DTIC Science & Technology

2018-01-01

ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision...needed. Do not return it to the originator. ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques

DTIC Science & Technology

2018-01-09

ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological and...is no longer needed. Do not return it to the originator. ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy ...4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques 5a. CONTRACT NUMBER

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time.

PubMed

Greenlee, Eric T; DeLucia, Patricia R; Newton, David C

2018-06-01

The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Participants "drove" a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Automated Topographic Change Detection via Dem Differencing at Large Scales Using The Arcticdem Database

NASA Astrophysics Data System (ADS)

Candela, S. G.; Howat, I.; Noh, M. J.; Porter, C. C.; Morin, P. J.

2016-12-01

In the last decade, high resolution satellite imagery has become an increasingly accessible tool for geoscientists to quantify changes in the Arctic land surface due to geophysical, ecological and anthropomorphic processes. However, the trade off between spatial coverage and spatial-temporal resolution has limited detailed, process-level change detection over large (i.e. continental) scales. The ArcticDEM project utilized over 300,000 Worldview image pairs to produce a nearly 100% coverage elevation model (above 60°N) offering the first polar, high spatial - high resolution (2-8m by region) dataset, often with multiple repeats in areas of particular interest to geo-scientists. A dataset of this size (nearly 250 TB) offers endless new avenues of scientific inquiry, but quickly becomes unmanageable computationally and logistically for the computing resources available to the average scientist. Here we present TopoDiff, a framework for a generalized. automated workflow that requires minimal input from the end user about a study site, and utilizes cloud computing resources to provide a temporally sorted and differenced dataset, ready for geostatistical analysis. This hands-off approach allows the end user to focus on the science, without having to manage thousands of files, or petabytes of data. At the same time, TopoDiff provides a consistent and accurate workflow for image sorting, selection, and co-registration enabling cross-comparisons between research projects.

Automated detection of diabetic retinopathy on digital fundus images.

PubMed

Sinthanayothin, C; Boyce, J F; Williamson, T H; Cook, H L; Mensah, E; Lal, S; Usher, D

2002-02-01

The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.

Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

PubMed

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

2011-01-01

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

Automated Detection of Optic Disc in Fundus Images

NASA Astrophysics Data System (ADS)

Burman, R.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.

Optic disc (OD) localization is an important preprocessing step in the automated image detection of fundus image infected with glaucoma. An Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution (DE) is applied to determine the location of the OD in the right of left eye retinal fundus image. The algorithm, when applied to 460 fundus images from the MESSIDOR dataset, shows a success rate of 99.07 % for 217 normal images and 95.47 % for 243 pathological images. The mean computational time is 1.709 s for normal images and 1.753 s for pathological images. These results are important for automated detection of glaucoma and for telemedicine purposes.

[Establishment of Automation System for Detection of Alcohol in Blood].

PubMed

Tian, L L; Shen, Lei; Xue, J F; Liu, M M; Liang, L J

2017-02-01

To establish an automation system for detection of alcohol content in blood. The determination was performed by automated workstation of extraction-headspace gas chromatography （HS-GC）. The blood collection with negative pressure, sealing time of headspace bottle and sample needle were checked and optimized in the abstraction of automation system. The automatic sampling was compared with the manual sampling. The quantitative data obtained by the automated workstation of extraction-HS-GC for alcohol was stable. The relative differences of two parallel samples were less than 5%. The automated extraction was superior to the manual extraction. A good linear relationship was obtained at the alcohol concentration range of 0.1-3.0 mg/mL （ r ≥0.999） with good repeatability. The method is simple and quick, with more standard experiment process and accurate experimental data. It eliminates the error from the experimenter and has good repeatability, which can be applied to the qualitative and quantitative detections of alcohol in blood. Copyright© by the Editorial Department of Journal of Forensic Medicine

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.

Automated vehicle for railway track fault detection

NASA Astrophysics Data System (ADS)

Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

2017-11-01

For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

Towards an Automated Acoustic Detection System for Free Ranging Elephants.

PubMed

Zeppelzauer, Matthias; Hensman, Sean; Stoeger, Angela S

The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In this paper, we present a method for the automated detection of elephant vocalizations that is robust to the diverse noise sources present in the field. We evaluate the method on a dataset recorded under natural field conditions to simulate a real-world scenario. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants. Furthermore, the method may be a useful tool for scientists in bioacoustics for the study of wildlife recordings.

[Automated detection and volumetric segmentation of the spleen in CT scans].

PubMed

Hammon, M; Dankerl, P; Kramer, M; Seifert, S; Tsymbal, A; Costa, M J; Janka, R; Uder, M; Cavallaro, A

2012-08-01

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like "marginal space learning" and "boosting algorithms". 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm³ + 0.58 (width × length × thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm³ compared to 281.58 ± 130.21 cm³ in mV and 268.93 ± 104.60 cm³ in eV. The correlation coefficient was 0.99 (coefficient of determination (R²) = 0.98) for aV and mV, 0.91 (R² = 0.83) for mV and eV and 0.91 (R² = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R² = 0.84), mV and eV (0.95; R² = 0.91) and aV and eV (0.83; R² = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. © Georg Thieme Verlag KG Stuttgart · New York.

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

Automated image based prominent nucleoli detection.

PubMed

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

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. 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. 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. 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.

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.

Autofocusing and Polar Body Detection in Automated Cell Manipulation.

PubMed

Wang, Zenan; Feng, Chen; Ang, Wei Tech; Tan, Steven Yih Min; Latt, Win Tun

2017-05-01

Autofocusing and feature detection are two essential processes for performing automated biological cell manipulation tasks. In this paper, we have introduced a technique capable of focusing on a holding pipette and a mammalian cell under a bright-field microscope automatically, and a technique that can detect and track the presence and orientation of the polar body of an oocyte that is rotated at the tip of a micropipette. Both algorithms were evaluated by using mouse oocytes. Experimental results show that both algorithms achieve very high success rates: 100% and 96%. As robust and accurate image processing methods, they can be widely applied to perform various automated biological cell manipulations.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

PubMed

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

2018-03-01

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

Automated detection of a prostate Ni-Ti stent in electronic portal images.

PubMed

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-12-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins.

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.

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

PubMed

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

2013-01-01

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

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.

Automated asteroseismic peak detections

NASA Astrophysics Data System (ADS)

García Saravia Ortiz de Montellano, Andrés; Hekker, S.; Themeßl, N.

2018-05-01

Space observatories such as Kepler have provided data that can potentially revolutionize our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible in a power density spectrum. Identification of oscillation modes is usually done by visual inspection that is time-consuming and has a degree of subjectivity. Here, we present a peak-detection algorithm especially suited for the detection of solar-like oscillations. It reliably characterizes the solar-like oscillations in a power density spectrum and estimates their parameters without human intervention. Furthermore, we provide a metric to characterize the false positive and false negative rates to provide further information about the reliability of a detected oscillation mode or the significance of a lack of detected oscillation modes. The algorithm presented here opens the possibility for detailed and automated peak bagging of the thousands of solar-like oscillators observed by Kepler.

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

Treesearch

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

2007-01-01

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

Automated macromolecular crystal detection system and method

DOEpatents

Christian, Allen T [Tracy, CA; Segelke, Brent [San Ramon, CA; Rupp, Bernard [Livermore, CA; Toppani, Dominique [Fontainebleau, FR

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.

Automated face detection for occurrence and occupancy estimation in chimpanzees.

PubMed

Crunchant, Anne-Sophie; Egerer, Monika; Loos, Alexander; Burghardt, Tilo; Zuberbühler, Klaus; Corogenes, Katherine; Leinert, Vera; Kulik, Lars; Kühl, Hjalmar S

2017-03-01

Surveying endangered species is necessary to evaluate conservation effectiveness. Camera trapping and biometric computer vision are recent technological advances. They have impacted on the methods applicable to field surveys and these methods have gained significant momentum over the last decade. Yet, most researchers inspect footage manually and few studies have used automated semantic processing of video trap data from the field. The particular aim of this study is to evaluate methods that incorporate automated face detection technology as an aid to estimate site use of two chimpanzee communities based on camera trapping. As a comparative baseline we employ traditional manual inspection of footage. Our analysis focuses specifically on the basic parameter of occurrence where we assess the performance and practical value of chimpanzee face detection software. We found that the semi-automated data processing required only 2-4% of the time compared to the purely manual analysis. This is a non-negligible increase in efficiency that is critical when assessing the feasibility of camera trap occupancy surveys. Our evaluations suggest that our methodology estimates the proportion of sites used relatively reliably. Chimpanzees are mostly detected when they are present and when videos are filmed in high-resolution: the highest recall rate was 77%, for a false alarm rate of 2.8% for videos containing only chimpanzee frontal face views. Certainly, our study is only a first step for transferring face detection software from the lab into field application. Our results are promising and indicate that the current limitation of detecting chimpanzees in camera trap footage due to lack of suitable face views can be easily overcome on the level of field data collection, that is, by the combined placement of multiple high-resolution cameras facing reverse directions. This will enable to routinely conduct chimpanzee occupancy surveys based on camera trapping and semi-automated

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

PubMed

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

2010-03-01

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

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.

[Research and Design of a System for Detecting Automated External Defbrillator Performance Parameters].

PubMed

Wang, Kewu; Xiao, Shengxiang; Jiang, Lina; Hu, Jingkai

2017-09-30

In order to regularly detect the performance parameters of automated external defibrillator (AED), to make sure it is safe before using the instrument, research and design of a system for detecting automated external defibrillator performance parameters. According to the research of the characteristics of its performance parameters, combing the STM32's stability and high speed with PWM modulation control, the system produces a variety of ECG normal and abnormal signals through the digital sampling methods. Completed the design of the hardware and software, formed a prototype. This system can accurate detect automated external defibrillator discharge energy, synchronous defibrillation time, charging time and other key performance parameters.

Load-Differential Features for Automated Detection of Fatigue Cracks Using Guided Waves (Preprint)

DTIC Science & Technology

2011-11-01

AFRL-RX-WP-TP-2011-4363 LOAD-DIFFERENTIAL FEATURES FOR AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) Jennifer E...AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) 5a. CONTRACT NUMBER FA8650-09-C-5206 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...tensile loads open fatigue cracks and thus enhance their detectability using ultrasonic methods. Here we introduce a class of load-differential methods

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.

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.

The effect of JPEG compression on automated detection of microaneurysms in retinal images

NASA Astrophysics Data System (ADS)

Cree, M. J.; Jelinek, H. F.

2008-02-01

As JPEG compression at source is ubiquitous in retinal imaging, and the block artefacts introduced are known to be of similar size to microaneurysms (an important indicator of diabetic retinopathy) it is prudent to evaluate the effect of JPEG compression on automated detection of retinal pathology. Retinal images were acquired at high quality and then compressed to various lower qualities. An automated microaneurysm detector was run on the retinal images of various qualities of JPEG compression and the ability to predict the presence of diabetic retinopathy based on the detected presence of microaneurysms was evaluated with receiver operating characteristic (ROC) methodology. The negative effect of JPEG compression on automated detection was observed even at levels of compression sometimes used in retinal eye-screening programmes and these may have important clinical implications for deciding on acceptable levels of compression for a fully automated eye-screening programme.

Fully Automated Sunspot Detection and Classification Using SDO HMI Imagery in MATLAB

DTIC Science & Technology

2014-03-27

FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Gordon M. Spahr, Second Lieutenant, USAF AFIT-ENP-14-M-34...CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air...DISTRIUBUTION UNLIMITED. AFIT-ENP-14-M-34 FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB Gordon M. Spahr, BS Second

Automated detection and characterization of harmonic tremor in continuous seismic data

NASA Astrophysics Data System (ADS)

Roman, Diana C.

2017-06-01

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

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

A comparison of automated crater detection methods

NASA Astrophysics Data System (ADS)

Bandeira, L.; Barreira, C.; Pina, P.; Saraiva, J.

2008-09-01

Abstract This work presents early results of a comparison between some common methodologies for automated crater detection. The three procedures considered were applied to images of the surface of Mars, thus illustrating some pros and cons of their use. We aim to establish the clear advantages in using this type of methods in the study of planetary surfaces.

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

PubMed

Saleh, George Michael; Wawrzynski, James; Caputo, Silvestro; Peto, Tunde; Al Turk, Lutfiah Ismail; Wang, Su; Hu, Yin; Da Cruz, Lyndon; Smith, Phil; Tang, Hongying Lilian

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.

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

PubMed Central

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

2009-01-01

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

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.

PubMed

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-03-09

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

PubMed Central

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-01-01

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. PMID:29522424

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

PubMed

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

2017-04-01

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

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.

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.

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.

Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Adal, Kedir M.; van Etten, Peter G.; Martinez, Jose P.; Rouwen, Kenneth; Vermeer, Koenraad A.; van Vliet, Lucas J.

2017-03-01

Automated detection and quantification of spatio-temporal retinal changes is an important step to objectively assess disease progression and treatment effects for dynamic retinal diseases such as diabetic retinopathy (DR). However, detecting retinal changes caused by early DR lesions such as microaneurysms and dot hemorrhages from longitudinal pairs of fundus images is challenging due to intra and inter-image illumination variation between fundus images. This paper explores a method for automated detection of retinal changes from illumination normalized fundus images using a deep convolutional neural network (CNN), and compares its performance with two other CNNs trained separately on color and green channel fundus images. Illumination variation was addressed by correcting for the variability in the luminosity and contrast estimated from a large scale retinal regions. The CNN models were trained and evaluated on image patches extracted from a registered fundus image set collected from 51 diabetic eyes that were screened at two different time-points. The results show that using normalized images yield better performance than color and green channel images, suggesting that illumination normalization greatly facilitates CNNs to quickly and correctly learn distinctive local image features of DR related retinal changes.

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

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.

Automated Techniques for Quantification of Coastline Change Rates using Landsat Imagery along Caofeidian, China

NASA Astrophysics Data System (ADS)

Dong, Di; Li, Ziwei; Liu, Zhaoqin; Yu, Yang

2014-03-01

This paper focuses on automated extraction and monitoring of coastlines by remote sensing techniques using multi-temporal Landsat imagery along Caofeidian, China. Caofeidian, as one of the active economic regions in China, has experienced dramatic change due to enhanced human activities, such as land reclamation. These processes have caused morphological changes of the Caofeidian shoreline. In this study, shoreline extraction and change analysis are researched. An algorithm based on image texture and mathematical morphology is proposed to automate coastline extraction. We tested this approach and found that it's capable of extracting coastlines from TM and ETM+ images with little human modifications. Then, the detected coastline vectors are imported into Arcgis software, and the Digital Shoreline Analysis System (DSAS) is used to calculate the change rate (the end point rate and linear regression rate). The results show that in some parts of the research area, remarkable coastline changes are observed, especially the accretion rate. The abnormal accretion is mostly attributed to the large-scale land reclamation during 2003 and 2004 in Caofeidian. So we can conclude that various construction projects, especially the land reclamation project, have made Caofeidian shorelines change greatly, far above the normal.

Sunglint Detection for Unmanned and Automated Platforms

PubMed Central

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

2012-01-01

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

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…

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. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Automation of diagnostic genetic testing: mutation detection by cyclic minisequencing.

PubMed

Alagrund, Katariina; Orpana, Arto K

2014-01-01

The rising role of nucleic acid testing in clinical decision making is creating a need for efficient and automated diagnostic nucleic acid test platforms. Clinical use of nucleic acid testing sets demands for shorter turnaround times (TATs), lower production costs and robust, reliable methods that can easily adopt new test panels and is able to run rare tests in random access principle. Here we present a novel home-brew laboratory automation platform for diagnostic mutation testing. This platform is based on the cyclic minisequecing (cMS) and two color near-infrared (NIR) detection. Pipetting is automated using Tecan Freedom EVO pipetting robots and all assays are performed in 384-well micro plate format. The automation platform includes a data processing system, controlling all procedures, and automated patient result reporting to the hospital information system. We have found automated cMS a reliable, inexpensive and robust method for nucleic acid testing for a wide variety of diagnostic tests. The platform is currently in clinical use for over 80 mutations or polymorphisms. Additionally to tests performed from blood samples, the system performs also epigenetic test for the methylation of the MGMT gene promoter, and companion diagnostic tests for analysis of KRAS and BRAF gene mutations from formalin fixed and paraffin embedded tumor samples. Automation of genetic test reporting is found reliable and efficient decreasing the work load of academic personnel.

Signal amplification of FISH for automated detection using image cytometry.

PubMed

Truong, K; Boenders, J; Maciorowski, Z; Vielh, P; Dutrillaux, B; Malfoy, B; Bourgeois, C A

1997-05-01

The purpose of this study was to improve the detection of FISH signals, in order that spot counting by a fully automated image cytometer be comparable to that obtained visually under the microscope. Two systems of spot scoring, visual and automated counting, were investigated in parallel on stimulated human lymphocytes with FISH using a biotinylated centromeric probe for chromosome 3. Signal characteristics were first analyzed on images recorded with a coupled charge device (CCD) camera. Number of spots per nucleus were scored visually on these recorded images versus automatically with a DISCOVERY image analyzer. Several fluochromes, amplification and pretreatments were tested. Our results for both visual and automated scoring show that the tyramide amplification system (TSA) gives the best amplification of signal if pepsin treatment is applied prior to FISH. Accuracy of the automated scoring, however, remained low (58% of nuclei containing two spots) compared to the visual scoring because of the high intranuclear variation between FISH spots.

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

Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer.

PubMed

Lu, Hao; Papathomas, Thomas G; van Zessen, David; Palli, Ivo; de Krijger, Ronald R; van der Spek, Peter J; Dinjens, Winand N M; Stubbs, Andrew P

2014-11-25

In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI. We have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking. We have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the 'hottest' hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/Automated_Selection_of_Hotspots . The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216.

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.

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.

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

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Automated detection of microcalcification clusters in mammograms

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Wang, Xingwei; Chen, Xiaodong; Li, Yuhua; Liu, Hong; Li, Shibo; Zheng, Bin

2010-02-01

Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of

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.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform.

PubMed

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.

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.

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

PubMed

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

2010-06-01

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

Automated acoustic analysis in detection of spontaneous swallows in Parkinson's disease.

PubMed

Golabbakhsh, Marzieh; Rajaei, Ali; Derakhshan, Mahmoud; Sadri, Saeed; Taheri, Masoud; Adibi, Peyman

2014-10-01

Acoustic monitoring of swallow frequency has become important as the frequency of spontaneous swallowing can be an index for dysphagia and related complications. In addition, it can be employed as an objective quantification of ingestive behavior. Commonly, swallowing complications are manually detected using videofluoroscopy recordings, which require expensive equipment and exposure to radiation. In this study, a noninvasive automated technique is proposed that uses breath and swallowing recordings obtained via a microphone located over the laryngopharynx. Nonlinear diffusion filters were used in which a scale-space decomposition of recorded sound at different levels extract swallows from breath sounds and artifacts. This technique was compared to manual detection of swallows using acoustic signals on a sample of 34 subjects with Parkinson's disease. A speech language pathologist identified five subjects who showed aspiration during the videofluoroscopic swallowing study. The proposed automated method identified swallows with a sensitivity of 86.67 %, a specificity of 77.50 %, and an accuracy of 82.35 %. These results indicate the validity of automated acoustic recognition of swallowing as a fast and efficient approach to objectively estimate spontaneous swallow frequency.

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

PubMed

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

2016-01-01

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

Automated mitosis detection of stem cell populations in phase-contrast microscopy images.

PubMed

Huh, Seungil; Ker, Dai Fei Elmer; Bise, Ryoma; Chen, Mei; Kanade, Takeo

2011-03-01

Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

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.

Automated Terrestrial EMI Emitter Detection, Classification, and Localization

NASA Astrophysics Data System (ADS)

Stottler, R.; Ong, J.; Gioia, C.; Bowman, C.; Bhopale, A.

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 it is extremely important to track down and eliminate sources of EMI. The Terrestrial RFI-locating Automation with CasE based Reasoning (TRACER) system is being implemented to automate terrestrial EMI emitter localization and identification to improve space situational awareness, reduce manpower requirements, dramatically shorten EMI response time, enable the system to evolve without programmer involvement, and support adversarial scenarios such as jamming. The operational version of TRACER is being implemented and applied with real data (power versus frequency over time) for both satellite communication antennas and sweeping Direction Finding (DF) antennas located near them. This paper presents the design and initial implementation of TRACER’s investigation data management, automation, and data visualization capabilities. TRACER monitors DF antenna signals and detects and classifies EMI using neural network technology, trained on past cases of both normal communications and EMI events. When EMI events are detected, an Investigation Object is created automatically. The user interface facilitates the management of multiple investigations simultaneously. Using a variant of the Friis transmission equation, emissions data is used to estimate and plot the emitter’s locations over time for comparison with current flights. The data is also displayed on a set of five linked graphs to aid in the perception of patterns spanning power, time, frequency, and bearing. Based on details of the signal (its classification, direction, and strength, etc.), TRACER retrieves one or more cases of EMI investigation methodologies which are represented as graphical behavior transition networks (BTNs). These BTNs can be edited easily

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

PubMed

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

2017-10-01

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

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

USGS Publications Warehouse

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

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

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

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

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

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

An automated approach for early detection of diabetic retinopathy using SD-OCT images.

PubMed

ElTanboly, Ahmed H; Palacio, Agustina; Shalaby, Ahmed M; Switala, Andrew E; Helmy, Omar; Schaal, Shlomit; El-Baz, Ayman

2018-01-01

  This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity. 26 diabetic patients, without DR changes visible by funduscopic examination, were matched with 26 controls, according to age and sex, for purposes of statistical analysis using mixed effects ANOVA. The INL was narrower in diabetes (p = 0.14), while the NFL (p = 0.04) and IZ (p = 0.34) were thicker. Tortuosity of layers NFL through the OPL was greater in diabetes (all p < 0.1), while significantly greater normalized reflectivity was observed in the MZ and OPR (both p < 0.01) as well as ELM and IZ (both p < 0.5). A novel automated method enables to provide quantitative analysis of the changes in each layer of the retina that occur with diabetes. In turn, carries the promise to a reliable non-invasive diagnostic tool for early detection of DR.

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

Automation, consolidation, and integration in autoimmune diagnostics.

PubMed

Tozzoli, Renato; D'Aurizio, Federica; Villalta, Danilo; Bizzaro, Nicola

2015-08-01

Over the past two decades, we have witnessed an extraordinary change in autoimmune diagnostics, characterized by the progressive evolution of analytical technologies, the availability of new tests, and the explosive growth of molecular biology and proteomics. Aside from these huge improvements, organizational changes have also occurred which brought about a more modern vision of the autoimmune laboratory. The introduction of automation (for harmonization of testing, reduction of human error, reduction of handling steps, increase of productivity, decrease of turnaround time, improvement of safety), consolidation (combining different analytical technologies or strategies on one instrument or on one group of connected instruments) and integration (linking analytical instruments or group of instruments with pre- and post-analytical devices) opened a new era in immunodiagnostics. In this article, we review the most important changes that have occurred in autoimmune diagnostics and present some models related to the introduction of automation in the autoimmunology laboratory, such as automated indirect immunofluorescence and changes in the two-step strategy for detection of autoantibodies; automated monoplex immunoassays and reduction of turnaround time; and automated multiplex immunoassays for autoantibody profiling.

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.

PubMed

Rajalakshmi, Ramachandran; Subashini, Radhakrishnan; Anjana, Ranjit Mohan; Mohan, Viswanathan

2018-06-01

To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt TM ) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists' grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

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.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

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

Automated fall detection on privacy-enhanced video.

PubMed

Edgcomb, Alex; Vahid, Frank

2012-01-01

A privacy-enhanced video obscures the appearance of a person in the video. We consider four privacy enhancements: blurring of the person, silhouetting of the person, covering the person with a graphical box, and covering the person with a graphical oval. We demonstrate that an automated video-based fall detection algorithm can be as accurate on privacy-enhanced video as on raw video. The algorithm operated on video from a stationary in-home camera, using a foreground-background segmentation algorithm to extract a minimum bounding rectangle (MBR) around the motion in the video, and using time series shapelet analysis on the height and width of the rectangle to detect falls. We report accuracy applying fall detection on 23 scenarios depicted as raw video and privacy-enhanced videos involving a sole actor portraying normal activities and various falls. We found that fall detection on privacy-enhanced video, except for the common approach of blurring of the person, was competitive with raw video, and in particular that the graphical oval privacy enhancement yielded the same accuracy as raw video, namely 0.91 sensitivity and 0.92 specificity.

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

PubMed

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

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

Effects of response bias and judgment framing on operator use of an automated aid in a target detection task.

PubMed

Rice, Stephen; McCarley, Jason S

2011-12-01

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 operators' cognitive responses to different forms of automation error. The present experiments therefore examined the effects of automation false alarms and misses on human performance under conditions in which the different forms of error were matched in their perceptual characteristics. Young adult participants performed a simulated baggage x-ray screening task while assisted by an automated diagnostic aid. Judgments from the aid were rendered as text messages presented at the onset of each trial, and every trial was followed by a second text message providing response feedback. Thus, misses and false alarms from the aid were matched for their perceptual salience. Experiment 1 found that even under these conditions, false alarms from the aid produced poorer human performance and engendered lower automation use than misses from the aid. Experiment 2, however, found that the asymmetry between misses and false alarms was reduced when the aid's false alarms were framed as neutral messages rather than explicit misjudgments. Results suggest that automation false alarms and misses differ in their inherent cognitive salience and imply that changes in diagnosis framing may allow designers to encourage better use of imperfectly reliable automated aids.

Automated 3D-Printed Unibody Immunoarray for Chemiluminescence Detection of Cancer Biomarker Proteins

PubMed Central

Tang, C. K.; Vaze, A.; Rusling, J. F.

2017-01-01

A low cost three-dimensional (3D) printed clear plastic microfluidic device was fabricated for fast, low cost automated protein detection. The unibody device features three reagent reservoirs, an efficient 3D network for passive mixing, and an optically transparent detection chamber housing a glass capture antibody array for measuring chemiluminescence output with a CCD camera. Sandwich type assays were built onto the glass arrays using a multi-labeled detection antibody-polyHRP (HRP = horseradish peroxidase). Total assay time was ~30 min in a complete automated assay employing a programmable syringe pump so that the protocol required minimal operator intervention. The device was used for multiplexed detection of prostate cancer biomarker proteins prostate specific antigen (PSA) and platelet factor 4 (PF-4). Detection limits of 0.5 pg mL−1 were achieved for these proteins in diluted serum with log dynamic ranges of four orders of magnitude. Good accuracy vs ELISA was validated by analyzing human serum samples. This prototype device holds good promise for further development as a point-of-care cancer diagnostics tool. PMID:28067370

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.

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.

A self-adapting system for the automated detection of inter-ictal epileptiform discharges.

PubMed

Lodder, Shaun S; van Putten, Michel J A M

2014-01-01

Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search. Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form "IED nominations", each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections. Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20-30 min recordings 1 took approximately 5 min. The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.

A Self-Adapting System for the Automated Detection of Inter-Ictal Epileptiform Discharges

PubMed Central

Lodder, Shaun S.; van Putten, Michel J. A. M.

2014-01-01

Purpose Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search. Methods Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form “IED nominations”, each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections. Key Findings Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20–30 min recordings 1took approximately 5 min. Significance The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents. PMID:24454813

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

PubMed

Liu, Li; Gao, Simon S; Bailey, Steven T; Huang, David; Li, Dengwang; Jia, Yali

2015-09-01

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

Advances in algorithm fusion for automated sea mine detection and classification

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2002-11-01

Along with other sensors, the Navy uses high-resolution sonar to detect and classify sea mines in mine-hunting operations. Scientists and engineers have devoted substantial effort to the development of automated detection and classification (D/C) algorithms for these high-resolution systems. Several factors spurred these efforts, including: (1) aids for operators to reduce work overload; (2) more optimal use of all available data; and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and manmade clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms (Algorithm Fusion) have been studied. To date, the results have been remarkable, including reliable robustness to new environments. In this paper a brief history of existing Algorithm Fusion technology and some techniques recently used to improve performance are presented. An exploration of new developments is presented in conclusion.

[Development of an automated processing method to detect coronary motion for coronary magnetic resonance angiography].

PubMed

Asou, Hiroya; Imada, N; Sato, T

2010-06-20

On coronary MR angiography (CMRA), cardiac motions worsen the image quality. To improve the image quality, detection of cardiac especially for individual coronary motion is very important. Usually, scan delay and duration were determined manually by the operator. We developed a new evaluation method to calculate static time of individual coronary artery. At first, coronary cine MRI was taken at the level of about 3 cm below the aortic valve (80 images/R-R). Chronological change of the signals were evaluated with Fourier transformation of each pixel of the images were done. Noise reduction with subtraction process and extraction process were done. To extract higher motion such as coronary arteries, morphological filter process and labeling process were added. Using these imaging processes, individual coronary motion was extracted and individual coronary static time was calculated automatically. We compared the images with ordinary manual method and new automated method in 10 healthy volunteers. Coronary static times were calculated with our method. Calculated coronary static time was shorter than that of ordinary manual method. And scan time became about 10% longer than that of ordinary method. Image qualities were improved in our method. Our automated detection method for coronary static time with chronological Fourier transformation has a potential to improve the image quality of CMRA and easy processing.

Operator adaptation to changes in system reliability under adaptable automation.

PubMed

Chavaillaz, Alain; Sauer, Juergen

2017-09-01

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.

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

The effect of varying levels of vehicle automation on drivers' lane changing behaviour.

PubMed

Madigan, Ruth; Louw, Tyron; Merat, Natasha

2018-01-01

Much of the Human Factors research into vehicle automation has focused on driver responses to critical scenarios where a crash might occur. However, there is less knowledge about the effects of vehicle automation on drivers' behaviour during non-critical take-over situations, such as driver-initiated lane-changing or overtaking. The current driving simulator study, conducted as part of the EC-funded AdaptIVe project, addresses this issue. It uses a within-subjects design to compare drivers' lane-changing behaviour in conventional manual driving, partially automated driving (PAD) and conditionally automated driving (CAD). In PAD, drivers were required to re-take control from an automated driving system in order to overtake a slow moving vehicle, while in CAD, the driver used the indicator lever to initiate a system-performed overtaking manoeuvre. Results showed that while drivers' acceptance of both the PAD and CAD systems was high, they generally preferred CAD. A comparison of overtaking positions showed that drivers initiated overtaking manoeuvres slightly later in PAD than in manual driving or CAD. In addition, when compared to conventional driving, drivers had higher deviations in lane positioning and speed, along with higher lateral accelerations during lane changes following PAD. These results indicate that even in situations which are not time-critical, drivers' vehicle control after automation is degraded compared to conventional driving.

The effect of varying levels of vehicle automation on drivers’ lane changing behaviour

PubMed Central

Louw, Tyron; Merat, Natasha

2018-01-01

Much of the Human Factors research into vehicle automation has focused on driver responses to critical scenarios where a crash might occur. However, there is less knowledge about the effects of vehicle automation on drivers’ behaviour during non-critical take-over situations, such as driver-initiated lane-changing or overtaking. The current driving simulator study, conducted as part of the EC-funded AdaptIVe project, addresses this issue. It uses a within-subjects design to compare drivers’ lane-changing behaviour in conventional manual driving, partially automated driving (PAD) and conditionally automated driving (CAD). In PAD, drivers were required to re-take control from an automated driving system in order to overtake a slow moving vehicle, while in CAD, the driver used the indicator lever to initiate a system-performed overtaking manoeuvre. Results showed that while drivers’ acceptance of both the PAD and CAD systems was high, they generally preferred CAD. A comparison of overtaking positions showed that drivers initiated overtaking manoeuvres slightly later in PAD than in manual driving or CAD. In addition, when compared to conventional driving, drivers had higher deviations in lane positioning and speed, along with higher lateral accelerations during lane changes following PAD. These results indicate that even in situations which are not time-critical, drivers’ vehicle control after automation is degraded compared to conventional driving. PMID:29466402

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.

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

Automated detection and quantitation of bacterial RNA by using electrical microarrays.

PubMed

Elsholz, B; Wörl, R; Blohm, L; Albers, J; Feucht, H; Grunwald, T; Jürgen, B; Schweder, T; Hintsche, Rainer

2006-07-15

Low-density electrical 16S rRNA specific oligonucleotide microarrays and an automated analysis system have been developed for the identification and quantitation of pathogens. The pathogens are Escherichia coli, Pseudomonas aeruginosa, Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus epidermidis, which are typically involved in urinary tract infections. Interdigitated gold array electrodes (IDA-electrodes), which have structures in the nanometer range, have been used for very sensitive analysis. Thiol-modified oligonucleotides are immobilized on the gold IDA as capture probes. They mediate the specific recognition of the target 16S rRNA by hybridization. Additionally three unlabeled oligonucleotides are hybridized in close proximity to the capturing site. They are supporting molecules, because they improve the RNA hybridization at the capturing site. A biotin labeled detector oligonucleotide is also allowed to hybridize to the captured RNA sequence. The biotin labels enable the binding of avidin alkaline phophatase conjugates. The phosphatase liberates the electrochemical mediator p-aminophenol from its electrically inactive phosphate derivative. The electrical signals were generated by amperometric redox cycling and detected by a unique multipotentiostat. The read out signals of the microarray are position specific current and change over time in proportion to the analyte concentration. If two additional biotins are introduced into the affinity binding complex via the supporting oligonucleotides, the sensitivity of the assays increase more than 60%. The limit of detection of Escherichia coli total RNA has been determined to be 0.5 ng/microL. The control of fluidics for variable assay formats as well as the multichannel electrical read out and data handling have all been fully automated. The fast and easy procedure does not require any amplification of the targeted nucleic acids by PCR.

Building Change Detection from LIDAR Point Cloud Data Based on Connected Component Analysis

NASA Astrophysics Data System (ADS)

Awrangjeb, M.; Fraser, C. S.; Lu, G.

2015-08-01

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

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

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

PubMed

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

2017-01-01

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer 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 intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, 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.

Automated Detection of Clouds in Satellite Imagery

NASA Technical Reports Server (NTRS)

Jedlovec, Gary

2010-01-01

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

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.

Semi-automated detection of anterior cruciate ligament injury from MRI.

PubMed

Štajduhar, Ivan; Mamula, Mihaela; Miletić, Damir; Ünal, Gözde

2017-03-01

A radiologist's work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence of anterior cruciate ligament (ACL) injury in a human knee. We examine the possibility of aiding the diagnosis process by building a decision-support model for detecting the presence of milder ACL injuries (not requiring operative treatment) and complete ACL ruptures (requiring operative treatment) from sagittal plane magnetic resonance (MR) volumes of human knees. Histogram of oriented gradient (HOG) descriptors and gist descriptors are extracted from manually selected rectangular regions of interest enveloping the wider cruciate ligament area. Performance of two machine-learning models is explored, coupled with both feature extraction methods: support vector machine (SVM) and random forests model. Model generalisation properties were determined by performing multiple iterations of stratified 10-fold cross validation whilst observing the area under the curve (AUC) score. Sagittal plane knee joint MR data was retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia, from 2007 until 2014. Type of ACL injury was established in a double-blind fashion by comparing the retrospectively set diagnosis against the prospective opinion of another radiologist. After clean up, the resulting dataset consisted of 917 usable labelled exam sequences of left or right knees. Experimental results suggest that a linear-kernel SVM learned from HOG descriptors has the best generalisation properties among the experimental models compared, having an area under the curve of 0.894 for the injury-detection problem and 0.943 for the complete-rupture-detection problem. Although the problem of performing semi-automated ACL-injury diagnosis by observing knee

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

USDA-ARS?s Scientific Manuscript database

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

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy.

PubMed

Martins, Cristina; Moreira da Silva, Nadia; Silva, Guilherme; Rozanski, Verena E; Silva Cunha, Joao Paulo

2016-08-01

Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

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.

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

PubMed

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.

Automated Weight-Window Generation for Threat Detection Applications Using ADVANTG

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, Scott W; Miller, Thomas Martin; Evans, Thomas M

2009-01-01

Deterministic transport codes have been used for some time to generate weight-window parameters that can improve the efficiency of Monte Carlo simulations. As the use of this hybrid computational technique is becoming more widespread, the scope of applications in which it is being applied is expanding. An active source of new applications is the field of homeland security--particularly the detection of nuclear material threats. For these problems, automated hybrid methods offer an efficient alternative to trial-and-error variance reduction techniques (e.g., geometry splitting or the stochastic weight window generator). The ADVANTG code has been developed to automate the generation of weight-windowmore » parameters for MCNP using the Consistent Adjoint Driven Importance Sampling method and employs the TORT or Denovo 3-D discrete ordinates codes to generate importance maps. In this paper, we describe the application of ADVANTG to a set of threat-detection simulations. We present numerical results for an 'active-interrogation' problem in which a standard cargo container is irradiated by a deuterium-tritium fusion neutron generator. We also present results for two passive detection problems in which a cargo container holding a shielded neutron or gamma source is placed near a portal monitor. For the passive detection problems, ADVANTG obtains an O(10{sup 4}) speedup and, for a detailed gamma spectrum tally, an average O(10{sup 2}) speedup relative to implicit-capture-only simulations, including the deterministic calculation time. For the active-interrogation problem, an O(10{sup 4}) speedup is obtained when compared to a simulation with angular source biasing and crude geometry splitting.« less

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chandler, Darrell P.; Brown, Jeremy D.; Call, Douglas R.

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 allowedmore » 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.« less

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

Automated detection of jet contrails using the AVHRR split window

NASA Technical Reports Server (NTRS)

Engelstad, M.; Sengupta, S. K.; Lee, T.; Welch, R. M.

1992-01-01

This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases

PubMed Central

2014-01-01

Background Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. Methods We used a set of complex detection rules to take account of the patient’s clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules’ analytical quality was evaluated for ADEs. Results In terms of recall, 89.5% of ADEs with hyperkalaemia “with or without an abnormal symptom” were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs. Conclusions The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases. PMID:25212108

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.

PubMed

Ficheur, Grégoire; Chazard, Emmanuel; Beuscart, Jean-Baptiste; Merlin, Béatrice; Luyckx, Michel; Beuscart, Régis

2014-09-12

Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs. In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs. The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices

PubMed Central

Golden, J.P.; Verbarg, J.; Howell, P.B.; Shriver-Lake, L.C.; Ligler, F.S.

2012-01-01

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose–response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. PMID:22960010

Sunglass detection method for automation of video surveillance system

NASA Astrophysics Data System (ADS)

Sikandar, Tasriva; Samsudin, Wan Nur Azhani W.; Hawari Ghazali, Kamarul; Mohd, Izzeldin I.; Fazle Rabbi, Mohammad

2018-04-01

Wearing sunglass to hide face from surveillance camera is a common activity in criminal incidences. Therefore, sunglass detection from surveillance video has become a demanding issue in automation of security systems. In this paper we propose an image processing method to detect sunglass from surveillance images. Specifically, a unique feature using facial height and width has been employed to identify the covered region of the face. The presence of covered area by sunglass is evaluated using facial height-width ratio. Threshold value of covered area percentage is used to classify the glass wearing face. Two different types of glasses have been considered i.e. eye glass and sunglass. The results of this study demonstrate that the proposed method is able to detect sunglasses in two different illumination conditions such as, room illumination as well as in the presence of sunlight. In addition, due to the multi-level checking in facial region, this method has 100% accuracy of detecting sunglass. However, in an exceptional case where fabric surrounding the face has similar color as skin, the correct detection rate was found 93.33% for eye glass.

Automated detection of physiologic deterioration in hospitalized patients

PubMed Central

Evans, R Scott; Kuttler, Kathryn G; Simpson, Kathy J; Howe, Stephen; Crossno, Peter F; Johnson, Kyle V; Schreiner, Misty N; Lloyd, James F; Tettelbach, William H; Keddington, Roger K; Tanner, Alden; Wilde, Chelbi; Clemmer, Terry P

2015-01-01

Objective Develop and evaluate an automated case detection and response triggering system to monitor patients every 5 min and identify early signs of physiologic deterioration. Materials and methods A 2-year prospective, observational study at a large level 1 trauma center. All patients admitted to a 33-bed medical and oncology floor (A) and a 33-bed non-intensive care unit (ICU) surgical trauma floor (B) were monitored. During the intervention year, pager alerts of early physiologic deterioration were automatically sent to charge nurses along with access to a graphical point-of-care web page to facilitate patient evaluation. Results Nurses reported the positive predictive value of alerts was 91–100% depending on erroneous data presence. Unit A patients were significantly older and had significantly more comorbidities than unit B patients. During the intervention year, unit A patients had a significant increase in length of stay, more transfers to ICU (p = 0.23), and significantly more medical emergency team (MET) calls (p = 0.0008), and significantly fewer died (p = 0.044) compared to the pre-intervention year. No significant differences were found on unit B. Conclusions We monitored patients every 5 min and provided automated pages of early physiologic deterioration. This before–after study found a significant increase in MET calls and a significant decrease in mortality only in the unit with older patients with multiple comorbidities, and thus further study is warranted to detect potential confounding. Moreover, nurses reported the graphical alerts provided information needed to quickly evaluate patients, and they felt more confident about their assessment and more comfortable requesting help. PMID:25164256

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Semi-Disk Structure

DTIC Science & Technology

2018-01-01

statistical moments of order 2, 3, and 4. The probability density function (PDF) of the vibrational time series of a good bearing has a Gaussian...ARL-TR-8271 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...when it is no longer needed. Do not return it to the originator. ARL-TR-8271 ● JAN 2018 US Army Research Laboratory An Automated

Automated reference-free detection of motion artifacts in magnetic resonance images.

PubMed

Küstner, Thomas; Liebgott, Annika; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz; Gatidis, Sergios

2018-04-01

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis. On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively. Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Foreign object detection and removal to improve automated analysis of chest radiographs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogeweg, Laurens; Sanchez, Clara I.; Melendez, Jaime

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 methodmore » 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.« less

Automated high-grade prostate cancer detection and ranking on whole slide images

NASA Astrophysics Data System (ADS)

Huang, Chao-Hui; Racoceanu, Daniel

2017-03-01

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

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%.

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

PubMed

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

2014-03-01

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.

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera.

PubMed

Schmidt, Jürgen; Laarousi, Rihab; Stolzmann, Wolfgang; Karrer-Gauß, Katja

2018-06-01

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

Interoperable cross-domain semantic and geospatial framework for automatic change detection

NASA Astrophysics Data System (ADS)

Kuo, Chiao-Ling; Hong, Jung-Hong

2016-01-01

With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.

Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.

PubMed

Veronese, Fabio; Masciadri, Andrea; Comai, Sara; Matteucci, Matteo; Salice, Fabio

2017-01-01

Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.

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

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.

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linguraru, Marius George; Panjwani, Neil; Fletcher, Joel G.

2011-12-15

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm. Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided dosesmore » over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm. Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan. Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.« less

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

PubMed

Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2015-12-01

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.

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

Performance evaluation of three automated identification systems in detecting carbapenem-resistant Enterobacteriaceae.

PubMed

He, Qingwen; Chen, Weiyuan; Huang, Liya; Lin, Qili; Zhang, Jingling; Liu, Rui; Li, Bin

2016-06-21

Carbapenem-resistant Enterobacteriaceae (CRE) is prevalent around the world. Rapid and accurate detection of CRE is urgently needed to provide effective treatment. Automated identification systems have been widely used in clinical microbiology laboratories for rapid and high-efficient identification of pathogenic bacteria. However, critical evaluation and comparison are needed to determine the specificity and accuracy of different systems. The aim of this study was to evaluate the performance of three commonly used automated identification systems on the detection of CRE. A total of 81 non-repetitive clinical CRE isolates were collected from August 2011 to August 2012 in a Chinese university hospital, and all the isolates were confirmed to be resistant to carbapenems by the agar dilution method. The potential presence of carbapenemase genotypes of the 81 isolates was detected by PCR and sequencing. Using 81 clinical CRE isolates, we evaluated and compared the performance of three automated identification systems, MicroScan WalkAway 96 Plus, Phoenix 100, and Vitek 2 Compact, which are commonly used in China. To identify CRE, the comparator methodology was agar dilution method, while the PCR and sequencing was the comparator one to identify CPE. PCR and sequencing analysis showed that 48 of the 81 CRE isolates carried carbapenemase genes, including 23 (28.4 %) IMP-4, 14 (17.3 %) IMP-8, 5 (6.2 %) NDM-1, and 8 (9.9 %) KPC-2. Notably, one Klebsiella pneumoniae isolate produced both IMP-4 and NDM-1. One Klebsiella oxytoca isolate produced both KPC-2 and IMP-8. Of the 81 clinical CRE isolates, 56 (69.1 %), 33 (40.7 %) and 77 (95.1 %) were identified as CRE by MicroScan WalkAway 96 Plus, Phoenix 100, and Vitek 2 Compact, respectively. The sensitivities/specificities of MicroScan WalkAway, Phoenix 100 and Vitek 2 were 93.8/42.4 %, 54.2/66.7 %, and 75.0/36.4 %, respectively. The MicroScan WalkAway and Viteck2 systems are more reliable in clinical identification of

Automated Detection of Small Bodies by Space Based Observation

NASA Astrophysics Data System (ADS)

Bidstrup, P. R.; Grillmayer, G.; Andersen, A. C.; Haack, H.; Jorgensen, J. L.

The number of known comets and asteroids is increasing every year. Up till now this number is including approximately 250,000 of the largest minor planets, as they are usually referred. These discoveries are due to the Earth-based observation which has intensified over the previous decades. Additionally larger telescopes and arrays of telescopes are being used for exploring our Solar System. It is believed that all near- Earth and Main-Belt asteroids of diameters above 10 to 30 km have been discovered, leaving these groups of objects as observationally complete. However, the cataloguing of smaller bodies is incomplete as only a very small fraction of the expected number has been discovered. It is estimated that approximately 1010 main belt asteroids in the size range 1 m to 1 km are too faint to be observed using Earth-based telescopes. In order to observe these small bodies, space-based search must be initiated to remove atmospheric disturbances and to minimize the distance to the asteroids and thereby minimising the requirement for long camera integration times. A new method of space-based detection of moving non-stellar objects is currently being developed utilising the Advanced Stellar Compass (ASC) built for spacecraft attitude determination by Ørsted, Danish Technical University. The ASC serves as a backbone technology in the project as it is capable of fully automated distinction of known and unknown celestial objects. By only processing objects of particular interest, i.e. moving objects, it will be possible to discover small bodies with a minimum of ground control, with the ultimate ambition of a fully automated space search probe. Currently, the ASC is being mounted on the Flying Laptop satellite of the Institute of Space Systems, Universität Stuttgart. It will, after a launch into a low Earth polar orbit in 2008, test the detection method with the ASC equipment that already had significant in-flight experience. A future use of the ASC based automated

Automated detection of open magnetic field regions in EUV images

NASA Astrophysics Data System (ADS)

Krista, Larisza Diana; Reinard, Alysha

2016-05-01

Open magnetic regions on the Sun are either long-lived (coronal holes) or transient (dimmings) in nature, but both appear as dark regions in EUV images. For this reason their detection can be done in a similar way. As coronal holes are often large and long-lived in comparison to dimmings, their detection is more straightforward. The Coronal Hole Automated Recognition and Monitoring (CHARM) algorithm detects coronal holes using EUV images and a magnetogram. The EUV images are used to identify dark regions, and the magnetogam allows us to determine if the dark region is unipolar - a characteristic of coronal holes. There is no temporal sensitivity in this process, since coronal hole lifetimes span days to months. Dimming regions, however, emerge and disappear within hours. Hence, the time and location of a dimming emergence need to be known to successfully identify them and distinguish them from regular coronal holes. Currently, the Coronal Dimming Tracker (CoDiT) algorithm is semi-automated - it requires the dimming emergence time and location as an input. With those inputs we can identify the dimming and track it through its lifetime. CoDIT has also been developed to allow the tracking of dimmings that split or merge - a typical feature of dimmings.The advantage of these particular algorithms is their ability to adapt to detecting different types of open field regions. For coronal hole detection, each full-disk solar image is processed individually to determine a threshold for the image, hence, we are not limited to a single pre-determined threshold. For dimming regions we also allow individual thresholds for each dimming, as they can differ substantially. This flexibility is necessary for a subjective analysis of the studied regions. These algorithms were developed with the goal to allow us better understand the processes that give rise to eruptive and non-eruptive open field regions. We aim to study how these regions evolve over time and what environmental

An Automated Motion Detection and Reward System for Animal Training.

PubMed

Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

2015-12-04

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

Using microwave Doppler radar in automated manufacturing applications

NASA Astrophysics Data System (ADS)

Smith, Gregory C.

Since the beginning of the Industrial Revolution, manufacturers worldwide have used automation to improve productivity, gain market share, and meet growing or changing consumer demand for manufactured products. To stimulate further industrial productivity, manufacturers need more advanced automation technologies: "smart" part handling systems, automated assembly machines, CNC machine tools, and industrial robots that use new sensor technologies, advanced control systems, and intelligent decision-making algorithms to "see," "hear," "feel," and "think" at the levels needed to handle complex manufacturing tasks without human intervention. The investigator's dissertation offers three methods that could help make "smart" CNC machine tools and industrial robots possible: (1) A method for detecting acoustic emission using a microwave Doppler radar detector, (2) A method for detecting tool wear on a CNC lathe using a Doppler radar detector, and (3) An online non-contact method for detecting industrial robot position errors using a microwave Doppler radar motion detector. The dissertation studies indicate that microwave Doppler radar could be quite useful in automated manufacturing applications. In particular, the methods developed may help solve two difficult problems that hinder further progress in automating manufacturing processes: (1) Automating metal-cutting operations on CNC machine tools by providing a reliable non-contact method for detecting tool wear, and (2) Fully automating robotic manufacturing tasks by providing a reliable low-cost non-contact method for detecting on-line position errors. In addition, the studies offer a general non-contact method for detecting acoustic emission that may be useful in many other manufacturing and non-manufacturing areas, as well (e.g., monitoring and nondestructively testing structures, materials, manufacturing processes, and devices). By advancing the state of the art in manufacturing automation, the studies may help

Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

PubMed

Scotland, G S; McNamee, P; Fleming, A D; Goatman, K A; Philip, S; Prescott, G J; Sharp, P F; Williams, G J; Wykes, W; Leese, G P; Olson, J A

2010-06-01

To assess the cost-effectiveness of an improved automated grading algorithm for diabetic retinopathy against a previously described algorithm, and in comparison with manual grading. Efficacy of the alternative algorithms was assessed using a reference graded set of images from three screening centres in Scotland (1253 cases with observable/referable retinopathy and 6333 individuals with mild or no retinopathy). Screening outcomes and grading and diagnosis costs were modelled for a cohort of 180 000 people, with prevalence of referable retinopathy at 4%. Algorithm (b), which combines image quality assessment with detection algorithms for microaneurysms (MA), blot haemorrhages and exudates, was compared with a simpler algorithm (a) (using image quality assessment and MA/dot haemorrhage (DH) detection), and the current practice of manual grading. Compared with algorithm (a), algorithm (b) would identify an additional 113 cases of referable retinopathy for an incremental cost of pound 68 per additional case. Compared with manual grading, automated grading would be expected to identify between 54 and 123 fewer referable cases, for a grading cost saving between pound 3834 and pound 1727 per case missed. Extrapolation modelling over a 20-year time horizon suggests manual grading would cost between pound 25,676 and pound 267,115 per additional quality adjusted life year gained. Algorithm (b) is more cost-effective than the algorithm based on quality assessment and MA/DH detection. With respect to the value of introducing automated detection systems into screening programmes, automated grading operates within the recommended national standards in Scotland and is likely to be considered a cost-effective alternative to manual disease/no disease grading.

Automated Detection and Analysis of Interplanetary Shocks with Real-Time Application

NASA Astrophysics Data System (ADS)

Vorotnikov, V.; Smith, C. W.; Hu, Q.; Szabo, A.; Skoug, R. M.; Cohen, C. M.

2006-12-01

The ACE real-time data stream provides web-based now-casting capabilities for solar wind conditions upstream of Earth. Our goal is to provide an automated code that finds and analyzes interplanetary shocks as they occur for possible real-time application to space weather nowcasting. Shock analysis algorithms based on the Rankine-Hugoniot jump conditions exist and are in wide-spread use today for the interactive analysis of interplanetary shocks yielding parameters such as shock speed and propagation direction and shock strength in the form of compression ratios. Although these codes can be automated in a reasonable manner to yield solutions not far from those obtained by user-directed interactive analysis, event detection presents an added obstacle and the first step in a fully automated analysis. We present a fully automated Rankine-Hugoniot analysis code that can scan the ACE science data, find shock candidates, analyze the events, obtain solutions in good agreement with those derived from interactive applications, and dismiss false positive shock candidates on the basis of the conservation equations. The intent is to make this code available to NOAA for use in real-time space weather applications. The code has the added advantage of being able to scan spacecraft data sets to provide shock solutions for use outside real-time applications and can easily be applied to science-quality data sets from other missions. Use of the code for this purpose will also be explored.

Automated detection of physiologic deterioration in hospitalized patients.

PubMed

Evans, R Scott; Kuttler, Kathryn G; Simpson, Kathy J; Howe, Stephen; Crossno, Peter F; Johnson, Kyle V; Schreiner, Misty N; Lloyd, James F; Tettelbach, William H; Keddington, Roger K; Tanner, Alden; Wilde, Chelbi; Clemmer, Terry P

2015-03-01

Develop and evaluate an automated case detection and response triggering system to monitor patients every 5 min and identify early signs of physiologic deterioration. A 2-year prospective, observational study at a large level 1 trauma center. All patients admitted to a 33-bed medical and oncology floor (A) and a 33-bed non-intensive care unit (ICU) surgical trauma floor (B) were monitored. During the intervention year, pager alerts of early physiologic deterioration were automatically sent to charge nurses along with access to a graphical point-of-care web page to facilitate patient evaluation. Nurses reported the positive predictive value of alerts was 91-100% depending on erroneous data presence. Unit A patients were significantly older and had significantly more comorbidities than unit B patients. During the intervention year, unit A patients had a significant increase in length of stay, more transfers to ICU (p = 0.23), and significantly more medical emergency team (MET) calls (p = 0.0008), and significantly fewer died (p = 0.044) compared to the pre-intervention year. No significant differences were found on unit B. We monitored patients every 5 min and provided automated pages of early physiologic deterioration. This before-after study found a significant increase in MET calls and a significant decrease in mortality only in the unit with older patients with multiple comorbidities, and thus further study is warranted to detect potential confounding. Moreover, nurses reported the graphical alerts provided information needed to quickly evaluate patients, and they felt more confident about their assessment and more comfortable requesting help. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Automated detection of optical counterparts to GRBs with RAPTOR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wozniak, P. R.; Vestrand, W. T.; Evans, S.

2006-05-19

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

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

Improved automated lumen contour detection by novel multifrequency processing algorithm with current intravascular ultrasound system.

PubMed

Kume, Teruyoshi; Kim, Byeong-Keuk; Waseda, Katsuhisa; Sathyanarayana, Shashidhar; Li, Wenguang; Teo, Tat-Jin; Yock, Paul G; Fitzgerald, Peter J; Honda, Yasuhiro

2013-02-01

The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry. Copyright © 2011 Wiley Periodicals, Inc.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

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

Fully Automated Centrifugal Microfluidic Device for Ultrasensitive Protein Detection from Whole Blood.

PubMed

Park, Yang-Seok; Sunkara, Vijaya; Kim, Yubin; Lee, Won Seok; Han, Ja-Ryoung; Cho, Yoon-Kyoung

2016-04-16

Enzyme-linked immunosorbent assay (ELISA) is a promising method to detect small amount of proteins in biological samples. The devices providing a platform for reduced sample volume and assay time as well as full automation are required for potential use in point-of-care-diagnostics. Recently, we have demonstrated ultrasensitive detection of serum proteins, C-reactive protein (CRP) and cardiac troponin I (cTnI), utilizing a lab-on-a-disc composed of TiO2 nanofibrous (NF) mats. It showed a large dynamic range with femto molar (fM) detection sensitivity, from a small volume of whole blood in 30 min. The device consists of several components for blood separation, metering, mixing, and washing that are automated for improved sensitivity from low sample volumes. Here, in the video demonstration, we show the experimental protocols and know-how for the fabrication of NFs as well as the disc, their integration and the operation in the following order: processes for preparing TiO2 NF mat; transfer-printing of TiO2 NF mat onto the disc; surface modification for immune-reactions, disc assembly and operation; on-disc detection and representative results for immunoassay. Use of this device enables multiplexed analysis with minimal consumption of samples and reagents. Given the advantages, the device should find use in a wide variety of applications, and prove beneficial in facilitating the analysis of low abundant proteins.

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

PubMed

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

2016-10-01

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

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices.

PubMed

Golden, J P; Verbarg, J; Howell, P B; Shriver-Lake, L C; Ligler, F S

2013-02-15

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose-response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. Published by Elsevier B.V.

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

PubMed

Geertsema, Evelien E; Thijs, Roland D; Gutter, Therese; Vledder, Ben; Arends, Johan B; Leijten, Frans S; Visser, Gerhard H; Kalitzin, Stiliyan N

2018-06-01

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance. © 2018 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults.

PubMed

Rantz, Marilyn J; Skubic, Marjorie; Popescu, Mihail; Galambos, Colleen; Koopman, Richelle J; Alexander, Gregory L; Phillips, Lorraine J; Musterman, Katy; Back, Jessica; Miller, Steven J

2015-01-01

Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of ‘vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status ‘vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.

Automated inspection of bread and loaves

NASA Astrophysics Data System (ADS)

Batchelor, Bruce G.

1993-08-01

The prospects for building practical automated inspection machines, capable of detecting the following faults in ordinary, everyday loaves are reviewed: (1) foreign bodies, using X-rays, (2) texture changes, using glancing illumination, mathematical morphology and Neural Net learning techniques, and (3) shape deformations, using structured lighting and simple geometry.

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

[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

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

PubMed

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

2015-10-01

An 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.

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool

PubMed Central

Albiñana, Emma; Artes, Maite; Corcoy, Rosa; Fernández-García, Diego; García-Alemán, Jorge; García-Cuartero, Beatriz; González, Cintia; Rivero, María Teresa; Casamira, Núria; Weissmann, Jörg

2017-01-01

Abstract Background: Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. Methods: Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. Results: eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. Conclusion: eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study. PMID:29091477

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.

PubMed

Comellas, María José; Albiñana, Emma; Artes, Maite; Corcoy, Rosa; Fernández-García, Diego; García-Alemán, Jorge; García-Cuartero, Beatriz; González, Cintia; Rivero, María Teresa; Casamira, Núria; Weissmann, Jörg

2017-11-01

Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.

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.

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.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

PubMed

Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

2018-06-05

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Case-control. San Francisco Mammography Registry and Mayo Clinic. 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Neither automated nor clinical BI-RADS density was

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.

Fusion of multiple quadratic penalty function support vector machines (QPFSVM) for automated sea mine detection and classification

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2002-08-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. The Quadratic Penalty Function Support Vector Machine (QPFSVM) algorithm to aid in the automated detection and classification of sea mines is introduced in this paper. The QPFSVM algorithm is easy to train, simple to implement, and robust to feature space dimension. Outputs of successive SVM algorithms are cascaded in stages (fused) to improve the Probability of Classification (Pc) and reduce the number of false alarms. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

PubMed

Pires, Ramon; Carvalho, Tiago; Spurling, Geoffrey; Goldenstein, Siome; Wainer, Jacques; Luckie, Alan; Jelinek, Herbert F; Rocha, Anderson

2015-01-01

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

Phase II: Automated System for Aneuploidy Detection in Sperm Final Report CRADA No. TC-1554-98

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wyrobek, W. J.; Dunlay, R. T.

This was a collaborative effort between the University of California, Lawrence Livermore National Laboratory (LLNL) and Cellomics, Inc. (formerly BioDx and Biological Detection, Inc.) to develop an automated system for detecting human sperm aneuploidy. Aneuploidy (an abnormal number of chromosomes) is one of the major categories of chromosomally abnormal sperm, which results in chromosomally defective pregnancies and babies. An automated system would be used for testing the effects of toxic agents and for other research and clinical applications. This collaborated effort was funded by a National Institutes of Environmental Health Services, Phase II, Small Business Innovation Research Program (SBIR) grantmore » to Cellornics (Contract No. N44-ES-82004).« less

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

An Automated Road Roughness Detection from Mobile Laser Scanning Data

NASA Astrophysics Data System (ADS)

Kumar, P.; Angelats, E.

2017-05-01

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

Early detection of glaucoma by means of a novel 3D computer‐automated visual field test

PubMed Central

Nazemi, Paul P; Fink, Wolfgang; Sadun, Alfredo A; Francis, Brian; Minckler, Donald

2007-01-01

Purpose A recently devised 3D computer‐automated threshold Amsler grid test was used to identify early and distinctive defects in people with suspected glaucoma. Further, the location, shape and depth of these field defects were characterised. Finally, the visual fields were compared with those obtained by standard automated perimetry. Patients and methods Glaucoma suspects were defined as those having elevated intraocular pressure (>21 mm Hg) or cup‐to‐disc ratio of >0.5. 33 patients and 66 eyes with risk factors for glaucoma were examined. 15 patients and 23 eyes with no risk factors were tested as controls. The recently developed 3D computer‐automated threshold Amsler grid test was used. The test exhibits a grid on a computer screen at a preselected greyscale and angular resolution, and allows patients to trace those areas on the grid that are missing in their visual field using a touch screen. The 5‐minute test required that the patients repeatedly outline scotomas on a touch screen with varied displays of contrast while maintaining their gaze on a central fixation marker. A 3D depiction of the visual field defects was then obtained that was further characterised by the location, shape and depth of the scotomas. The exam was repeated three times per eye. The results were compared to Humphrey visual field tests (ie, achromatic standard or SITA standard 30‐2 or 24‐2). Results In this pilot study 79% of the eyes tested in the glaucoma‐suspect group repeatedly demonstrated visual field loss with the 3D perimetry. The 3D depictions of visual field loss associated with these risk factors were all characteristic of or compatible with glaucoma. 71% of the eyes demonstrated arcuate defects or a nasal step. Constricted visual fields were shown in 29% of the eyes. No visual field changes were detected in the control group. Conclusions The 3D computer‐automated threshold Amsler grid test may demonstrate visual field abnormalities characteristic of

Automated UAV-based mapping for airborne reconnaissance and video exploitation

NASA Astrophysics Data System (ADS)

Se, Stephen; Firoozfam, Pezhman; Goldstein, Norman; Wu, Linda; Dutkiewicz, Melanie; Pace, Paul; Naud, J. L. Pierre

2009-05-01

Airborne surveillance and reconnaissance are essential for successful military missions. Such capabilities are critical for force protection, situational awareness, mission planning, damage assessment and others. UAVs gather huge amount of video data but it is extremely labour-intensive for operators to analyse hours and hours of received data. At MDA, we have developed a suite of tools towards automated video exploitation including calibration, visualization, change detection and 3D reconstruction. The on-going work is to improve the robustness of these tools and automate the process as much as possible. Our calibration tool extracts and matches tie-points in the video frames incrementally to recover the camera calibration and poses, which are then refined by bundle adjustment. Our visualization tool stabilizes the video, expands its field-of-view and creates a geo-referenced mosaic from the video frames. It is important to identify anomalies in a scene, which may include detecting any improvised explosive devices (IED). However, it is tedious and difficult to compare video clips to look for differences manually. Our change detection tool allows the user to load two video clips taken from two passes at different times and flags any changes between them. 3D models are useful for situational awareness, as it is easier to understand the scene by visualizing it in 3D. Our 3D reconstruction tool creates calibrated photo-realistic 3D models from video clips taken from different viewpoints, using both semi-automated and automated approaches. The resulting 3D models also allow distance measurements and line-of- sight analysis.

Reliability of Semi-Automated Segmentations in Glioblastoma.

PubMed

Huber, T; Alber, G; Bette, S; Boeckh-Behrens, T; Gempt, J; Ringel, F; Alberts, E; Zimmer, C; Bauer, J S

2017-06-01

In glioblastoma, quantitative volumetric measurements of contrast-enhancing or fluid-attenuated inversion recovery (FLAIR) hyperintense tumor compartments are needed for an objective assessment of therapy response. The aim of this study was to evaluate the reliability of a semi-automated, region-growing segmentation tool for determining tumor volume in patients with glioblastoma among different users of the software. A total of 320 segmentations of tumor-associated FLAIR changes and contrast-enhancing tumor tissue were performed by different raters (neuroradiologists, medical students, and volunteers). All patients underwent high-resolution magnetic resonance imaging including a 3D-FLAIR and a 3D-MPRage sequence. Segmentations were done using a semi-automated, region-growing segmentation tool. Intra- and inter-rater-reliability were addressed by intra-class-correlation (ICC). Root-mean-square error (RMSE) was used to determine the precision error. Dice score was calculated to measure the overlap between segmentations. Semi-automated segmentation showed a high ICC (> 0.985) for all groups indicating an excellent intra- and inter-rater-reliability. Significant smaller precision errors and higher Dice scores were observed for FLAIR segmentations compared with segmentations of contrast-enhancement. Single rater segmentations showed the lowest RMSE for FLAIR of 3.3 % (MPRage: 8.2 %). Both, single raters and neuroradiologists had the lowest precision error for longitudinal evaluation of FLAIR changes. Semi-automated volumetry of glioblastoma was reliably performed by all groups of raters, even without neuroradiologic expertise. Interestingly, segmentations of tumor-associated FLAIR changes were more reliable than segmentations of contrast enhancement. In longitudinal evaluations, an experienced rater can detect progressive FLAIR changes of less than 15 % reliably in a quantitative way which could help to detect progressive disease earlier.

Sensitivity and specificity of automated detection of early repolarization in standard 12-lead electrocardiography.

PubMed

Kenttä, Tuomas; Porthan, Kimmo; Tikkanen, Jani T; Väänänen, Heikki; Oikarinen, Lasse; Viitasalo, Matti; Karanko, Hannu; Laaksonen, Maarit; Huikuri, Heikki V

2015-07-01

Early repolarization (ER) is defined as an elevation of the QRS-ST junction in at least two inferior or lateral leads of the standard 12-lead electrocardiogram (ECG). Our purpose was to create an algorithm for the automated detection and classification of ER. A total of 6,047 electrocardiograms were manually graded for ER by two experienced readers. The automated detection of ER was based on quantification of the characteristic slurring or notching in ER-positive leads. The ER detection algorithm was tested and its results were compared with manual grading, which served as the reference. Readers graded 183 ECGs (3.0%) as ER positive, of which the algorithm detected 176 recordings, resulting in sensitivity of 96.2%. Of the 5,864 ER-negative recordings, the algorithm classified 5,281 as negative, resulting in 90.1% specificity. Positive and negative predictive values for the algorithm were 23.2% and 99.9%, respectively, and its accuracy was 90.2%. Inferior ER was correctly detected in 84.6% and lateral ER in 98.6% of the cases. As the automatic algorithm has high sensitivity, it could be used as a prescreening tool for ER; only the electrocardiograms graded positive by the algorithm would be reviewed manually. This would reduce the need for manual labor by 90%. © 2014 Wiley Periodicals, Inc.

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.

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

PubMed Central

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

2010-01-01

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

Comparison of automated versus manual neutrophil counts for the detection of cellular abnormalities in dogs receiving chemotherapy: 50 cases (May to June 2008).

PubMed

Cora, Michelle C; Neel, Jennifer A; Grindem, Carol B; Kissling, Grace E; Hess, Paul R

2013-06-01

To determine the frequency of clinically relevant abnormalities missed by failure to perform a blood smear evaluation in a specific subset of dogs receiving chemotherapy and to compare automated and manual neutrophil counts in the same population. Retrospective case series. 50 dogs receiving chemotherapy with a total nucleated cell count > 4,000 nucleated cells/μL. 50 blood smears were evaluated for abnormalities that have strong potential to change the medical plan for a patient: presence of blast cells, band neutrophils, nucleated RBCs, toxic change, hemoparasites, schistocytes, and spherocytes. Automated and manual neutrophil counts were compared. Blood smears from 10 (20%) patients had ≥ 1 abnormalities. Blast cells were identified on 4 (8%) blood smears, increased nucleated RBCs were identified on 5 (10%), and very mild toxic change was identified on 2 (4%). Correlation coefficient of the neutrophil counts was 0.96. Analysis revealed a slight bias between the automated and manual neutrophil counts (mean ± SD difference, -0.43 × 10(3)/μL ± 1.10 × 10(3)/μL). In this series of patients, neutrophil count correlation was very good. Clinically relevant abnormalities were found on 20% of the blood smears. An automated CBC appears to be accurate for neutrophil counts, but a microscopic examination of the corresponding blood smear is still recommended; further studies are needed to determine whether the detection or frequency of these abnormalities would differ dependent on chemotherapy protocol, neoplastic disease, and decision thresholds used by the oncologist in the ordering of a CBC without a blood smear evaluation.

Infrared thermal imaging for automated detection of diabetic foot complications.

PubMed

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

2013-09-01

Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. © 2013 Diabetes Technology Society.

Infrared Thermal Imaging for Automated Detection of Diabetic Foot Complications

PubMed Central

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

2013-01-01

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

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

NASA Astrophysics Data System (ADS)

Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

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

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

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…

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.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moeglein, W. A.; Griswold, R.; Mehdi, B. L.

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

Automated Detection of Thermo-Erosion in High Latitude Ecosystems

NASA Astrophysics Data System (ADS)

Lara, M. J.; Chipman, M. L.; Hu, F.

2017-12-01

Detecting permafrost disturbance is of critical importance as the severity of climate change and associated increase in wildfire frequency and magnitude impacts regional to global carbon dynamics. However, it has not been possible to evaluate spatiotemporal patterns of permafrost degradation over large regions of the Arctic, due to limited spatial and temporal coverage of high resolution optical, radar, lidar, or hyperspectral remote sensing products. Here we present the first automated multi-temporal analysis for detecting disturbance in response to permafrost thaw, using meso-scale high-frequency remote sensing products (i.e. entire Landsat image archive). This approach was developed, tested, and applied in the Noatak National Preserve (26,500km2) in northwestern Alaska. We identified thermo-erosion (TE), by capturing the indirect spectral signal associated with episodic sediment plumes in adjacent waterbodies following TE disturbance. We isolated this turbidity signal within lakes during summer (mid-summer & late-summer) and annual time-period image composites (1986-2016), using the cloud-based geospatial parallel processing platform, Google Earth Engine™API. We validated the TE detection algorithm using seven consecutive years of sub-meter high resolution imagery (2009-2015) covering 798 ( 33%) of the 2456 total lakes in the Noatak lowlands. Our approach had "good agreement" with sediment pulses and landscape deformation in response to permafrost thaw (overall accuracy and kappa coefficient of 85% and 0.61). We identify active TE to impact 10.4% of all lakes, but was inter-annually variable, with the highest and lowest TE years represented by 1986 ( 41.1%) and 2002 ( 0.7%), respectively. We estimate thaw slumps, lake erosion, lake drainage, and gully formation to account for 23.3, 61.8, 12.5, and 1.3%, of all active TE across the Noatak National Preserve. Preliminary analysis, suggests TE may be subject to a hysteresis effect following extreme climatic

Multisensor Fusion for Change Detection

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

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

PubMed

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

2017-09-01

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

Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images.

PubMed

Seghier, Mohamed L; Kolanko, Magdalena A; Leff, Alexander P; Jäger, Hans R; Gregoire, Simone M; Werring, David J

2011-03-23

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

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.

Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care

PubMed Central

Pires, Ramon; Carvalho, Tiago; Spurling, Geoffrey; Goldenstein, Siome; Wainer, Jacques; Luckie, Alan; Jelinek, Herbert F.; Rocha, Anderson

2015-01-01

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening. PMID:26035836

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.

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jurrus, Elizabeth R.; Watanabe, Shigeki; Giuly, Richard J.

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 processmore » 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.« less

Semi-automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

PubMed Central

Jurrus, Elizabeth; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R. C.; 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. PMID:22644867

Automated Change Detection for Synthetic Aperture Sonar

DTIC Science & Technology

2014-01-01

channels, respectively. The canonical coordinates of x and y are defined as u = FHR−1/2xx x v = GHR−1/2yy y where F and G are the mapping matrices...containing the left and right singular vectors of the coherence matrix C, respectively. The canonical coordinate vectors u and v share the diagonal cross...feature set. The coherent change information between canonical coordinates v and u can be calculated using the residual, v −Ku, owing to the fact that

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.

SU-G-201-03: Automation of High Dose Rate Brachytherapy Quality Assurance: Development of a Radioluminescent Detection System for Simultaneous Detection of Activity, Timing, and Positioning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jenkins, C; Xing, L; Fahimian, B

Purpose: Accuracy of positioning, timing and activity is of critical importance for High Dose Rate (HDR) brachytherapy delivery. Respective measurements via film autoradiography, stop-watches and well chambers can be cumbersome, crude or lack dynamic source evaluation capabilities. To address such limitations, a single device radioluminescent detection system enabling automated real-time quantification of activity, position and timing accuracy is presented and experimentally evaluated. Methods: A radioluminescent sheet was fabricated by mixing Gd?O?S:Tb with PDMS and incorporated into a 3D printed device where it was fixated below a CMOS digital camera. An Ir-192 HDR source (VS2000, VariSource iX) with an effective activemore » length of 5 mm was introduced using a 17-gauge stainless steel needle below the sheet. Pixel intensity values for determining activity were taken from an ROI centered on the source location. A calibration curve relating intensity values to activity was generated and used to evaluate automated activity determination with data gathered over 6 weeks. Positioning measurements were performed by integrating images for an entire delivery and fitting peaks to the resulting profile. Timing measurements were performed by evaluating source location and timestamps from individual images. Results: Average predicted activity error over 6 weeks was .35 ± .5%. The distance between four dwell positions was determined by the automated system to be 1.99 ± .02 cm. The result from autoradiography was 2.00 ± .03 cm. The system achieved a time resolution of 10 msec and determined the dwell time to be 1.01 sec ± .02 sec. Conclusion: The system was able to successfully perform automated detection of activity, positioning and timing concurrently under a single setup. Relative to radiochromic and radiographic film-based autoradiography, which can only provide a static evaluation positioning, optical detection of temporary radiation induced luminescence enables

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.

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…

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.

The Perth Automated Supernova Search

NASA Astrophysics Data System (ADS)

Williams, A. J.

1997-12-01

An automated search for supernovae in late spiral galaxies has been established at Perth Observatory, Western Australia. This automated search uses three low-cost PC-clone computers, a liquid nitrogen cooled CCD camera built locally, and a 61-cm telescope automated for the search. The images are all analysed automatically in real-time by routines in Perth Vista, the image processing system ported to the PC architecture for the search system. The telescope control software written for the project, Teljoy, maintains open-loop position accuracy better than 30" of arc after hundreds of jumps over an entire night. Total capital cost to establish and run this supernova search over the seven years of development and operation was around US$30,000. To date, the system has discovered a total of 6 confirmed supernovae, made an independent detection of a seventh, and detected one unconfirmed event assumed to be a supernova. The various software and hardware components of the search system are described in detail, the analysis of the first three years of data is discussed, and results presented. We find a Type Ib/c rate of 0.43 +/- 0.43 SNu, and a Type IIP rate of 0.86 +/- 0.49 SNu, where SNu are 'supernova units', expressed in supernovae per 10^10 solar blue luminosity galaxy per century. These values are for a Hubble constant of 75 km/s per Mpc, and scale as (H0/75)^2. The small number of discoveries has left large statistical uncertainties, but our strategy of frequent observations has reduced systematic errors - altering detection threshold or peak supernova luminosity by +/- 0.5 mag changes estimated rates by only around 20%. Similarly, adoption of different light curve templates for Type Ia and Type IIP supernovae has a minimal effect on the final statistics (2% and 4% change, respectively).

Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

PubMed Central

Stoel, Berend C.; Marquering, Henk A.; Staring, Marius; Beenen, Ludo F.; Slump, Cornelis H.; Roos, Yvo B.; Majoie, Charles B.

2015-01-01

Abstract. The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R=−0.399 and −0.277, respectively) and with follow-up mRS (R=−0.256 and −0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring. PMID:26158082

Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments

NASA Astrophysics Data System (ADS)

Jenkins, Colleen; Jordan, Jay; Carlson, Jeff

2007-02-01

This paper presents parameter estimation techniques useful for detecting background changes in a video sequence with extreme foreground activity. A specific application of interest is automated detection of the covert placement of threats (e.g., a briefcase bomb) inside crowded public facilities. We propose that a histogram of pixel intensity acquired from a fixed mounted camera over time for a series of images will be a mixture of two Gaussian functions: the foreground probability distribution function and background probability distribution function. We will use Pearson's Method of Moments to separate the two probability distribution functions. The background function can then be "remembered" and changes in the background can be detected. Subsequent comparisons of background estimates are used to detect changes. Changes are flagged to alert security forces to the presence and location of potential threats. Results are presented that indicate the significant potential for robust parameter estimation techniques as applied to video surveillance.

Automated Detection and Annotation of Disturbance in Eastern Forests

NASA Astrophysics Data System (ADS)

Hughes, M. J.; Chen, G.; Hayes, D. J.

2013-12-01

Forest disturbances represent an important component of the terrestrial carbon budget. To generate spatially-explicit estimates of disturbance and regrowth, we developed an automated system to detect and characterize forest change in the eastern United States at 30 m resolution from a 28-year Landsat Thematic Mapper time-series (1984-2011). Forest changes are labeled as 'disturbances' or 'regrowth', assigned to a severity class, and attributed to a disturbance type: either fire, insects, harvest, or 'unknown'. The system generates cloud-free summertime composite images for each year from multiple summer scenes and calculates vegetation indices from these composites. Patches of similar terrain on the landscape are identified by segmenting the Normalized Burn Ratio image. The spatial variance within each patch, which has been found to be a good indicator of diffuse disturbances such as forest insect damage, is then calculated for each index, creating an additional set of indexes. To identify vegetation change and quantify its degree, the derivative through time is calculated for each index using total variance regularization to account for noise and create a piecewise-linear trend. These indexes and their derivatives detect areas of disturbance and regrowth and are also used as inputs into a neural network that classifies the disturbance type/agent. Disturbance and disease information from the US Forest Service Aerial Detection Surveys (ADS) geodatabase and disturbed plots from the US Forest Service Forest Inventory and Analysis (FIA) database provided training data for the neural network. Although there have been recent advances in discriminating between disturbance types in boreal forests, due to the larger number of forest species and cosmopolitan nature of overstory communities in eastern forests, separation remains difficult. The ADS database, derived from sketch maps and later digitized, commonly designates a single large area encompassing many smaller effected

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.

Automated night/day standoff detection, tracking, and identification of personnel for installation protection

NASA Astrophysics Data System (ADS)

Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

2013-06-01

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Automated detection of case clusters of waterborne acute gastroenteritis from health insurance data - pilot study in three French districts.

PubMed

Rambaud, Loïc; Galey, Catherine; Beaudeau, Pascal

2016-04-01

This pilot study was conducted to assess the utility of using a health insurance database for the automated detection of waterborne outbreaks of acute gastroenteritis (AGE). The weekly number of AGE cases for which the patient consulted a doctor (cAGE) was derived from this database for 1,543 towns in three French districts during the 2009-2012 period. The method we used is based on a spatial comparison of incidence rates and of their time trends between the target town and the district. Each municipality was tested, week by week, for the entire study period. Overall, 193 clusters were identified, 10% of the municipalities were involved in at least one cluster and less than 2% in several. We can infer that nationwide more than 1,000 clusters involving 30,000 cases of cAGE each year may be linked to tap water. The clusters discovered with this automated detection system will be reported to local operators for investigation of the situations at highest risk. This method will be compared with others before automated detection is implemented on a national level.

Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter L.

2012-02-01

Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 +/- 0.33 mm and 3.10 +/- 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images

PubMed Central

Seghier, Mohamed L.; Kolanko, Magdalena A.; Leff, Alexander P.; Jäger, Hans R.; Gregoire, Simone M.; Werring, David J.

2011-01-01

Background Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Methodology/Principal Findings Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an “extra” tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. Conclusions/Significance MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds. PMID:21448456

Changing the Tooth-to-Tail Ratio Using Robotics and Automation to Beat Sequestration

DTIC Science & Technology

2015-10-01

September–October 2015 | 75 Views Changing the Tooth-to-Tail Ratio Using Robotics and Automation to Beat Sequestration Capt Rachael L. Nussbaum...falls remains a matter of great debate. The US Air Force is the world’s leader in war-fighting automation and robotics . In fact, in accordance with the...progress in using robots to en- hance the effectiveness of the larger part of Air Force business. The amount of maintenance required by modern aerial war

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

PubMed

Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S

2018-05-01

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001). The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

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.

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.

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. © 2011 The Authors Journal of Microscopy © 2011 Royal Microscopical Society.

[Automated analyzer of enzyme immunoassay].

PubMed

Osawa, S

1995-09-01

Automated analyzers for enzyme immunoassay can be classified by several points of view: the kind of labeled antibodies or enzymes, detection methods, the number of tests per unit time, analytical time and speed per run. In practice, it is important for us consider the several points such as detection limits, the number of tests per unit time, analytical range, and precision. Most of the automated analyzers on the market can randomly access and measure samples. I will describe the recent advance of automated analyzers reviewing their labeling antibodies and enzymes, the detection methods, the number of test per unit time and analytical time and speed per test.

Automated detection of videotaped neonatal seizures based on motion segmentation methods.

PubMed

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

2006-07-01

This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures. The motion of the infants' body parts was quantified by temporal motion strength signals extracted from video recordings by motion segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by direct thresholding, by clustering of the pixel velocities, and by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The computational tools and procedures developed for automated seizure detection were tested and evaluated on 240 short video segments selected and labeled by physicians from a set of video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). The experimental study described in this paper provided the basis for selecting the most effective strategy for training neural networks to detect neonatal seizures as well as the decision scheme used for interpreting the responses of the trained neural networks. Depending on the decision scheme used for interpreting the responses of the trained neural networks, the best neural networks exhibited sensitivity above 90% or specificity above 90%. The best among the motion segmentation methods developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of this phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion trajectory signals produced by motion tracking methods. A video system based upon automated analysis potentially offers a number of advantages. Infants who are at risk for

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.

Highly Sensitive and Automated Surface Enhanced Raman Scattering-based Immunoassay for H5N1 Detection with Digital Microfluidics.

PubMed

Wang, Yang; Ruan, Qingyu; Lei, Zhi-Chao; Lin, Shui-Chao; Zhu, Zhi; Zhou, Leiji; Yang, Chaoyong

2018-04-17

Digital microfluidics (DMF) is a powerful platform for a broad range of applications, especially immunoassays having multiple steps, due to the advantages of low reagent consumption and high automatization. Surface enhanced Raman scattering (SERS) has been proven as an attractive method for highly sensitive and multiplex detection, because of its remarkable signal amplification and excellent spatial resolution. Here we propose a SERS-based immunoassay with DMF for rapid, automated, and sensitive detection of disease biomarkers. SERS tags labeled with Raman reporter 4-mercaptobenzoic acid (4-MBA) were synthesized with a core@shell nanostructure and showed strong signals, good uniformity, and high stability. A sandwich immunoassay was designed, in which magnetic beads coated with antibodies were used as solid support to capture antigens from samples to form a beads-antibody-antigen immunocomplex. By labeling the immunocomplex with a detection antibody-functionalized SERS tag, antigen can be sensitively detected through the strong SERS signal. The automation capability of DMF can greatly simplify the assay procedure while reducing the risk of exposure to hazardous samples. Quantitative detection of avian influenza virus H5N1 in buffer and human serum was implemented to demonstrate the utility of the DMF-SERS method. The DMF-SERS method shows excellent sensitivity (LOD of 74 pg/mL) and selectivity for H5N1 detection with less assay time (<1 h) and lower reagent consumption (∼30 μL) compared to the standard ELISA method. Therefore, this DMF-SERS method holds great potentials for automated and sensitive detection of a variety of infectious diseases.

Application of Novel Software Algorithms to Spectral-Domain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy.

PubMed

Adhi, Mehreen; Semy, Salim K; Stein, David W; Potter, Daniel M; Kuklinski, Walter S; Sleeper, Harry A; Duker, Jay S; Waheed, Nadia K

2016-05-01

To present novel software algorithms applied to spectral-domain optical coherence tomography (SD-OCT) for automated detection of diabetic retinopathy (DR). Thirty-one diabetic patients (44 eyes) and 18 healthy, nondiabetic controls (20 eyes) who underwent volumetric SD-OCT imaging and fundus photography were retrospectively identified. A retina specialist independently graded DR stage. Trained automated software generated a retinal thickness score signifying macular edema and a cluster score signifying microaneurysms and/or hard exudates for each volumetric SD-OCT. Of 44 diabetic eyes, 38 had DR and six eyes did not have DR. Leave-one-out cross-validation using a linear discriminant at missed detection/false alarm ratio of 3.00 computed software sensitivity and specificity of 92% and 69%, respectively, for DR detection when compared to clinical assessment. Novel software algorithms applied to commercially available SD-OCT can successfully detect DR and may have potential as a viable screening tool for DR in future. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.]. Copyright 2016, SLACK Incorporated.

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.

Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

2016-02-01

Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

Automated Detection of Repetitive Motor Behaviors as an Outcome Measurement in Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Gilchrist, Kristin H.; Hegarty-Craver, Meghan; Christian, Robert B.; Grego, Sonia; Kies, Ashley C.; Wheeler, Anne C.

2018-01-01

Repetitive sensory motor behaviors are a direct target for clinical treatment and a potential treatment endpoint for individuals with intellectual or developmental disabilities. By removing the burden associated with video annotation or direct observation, automated detection of stereotypy would allow for longer term monitoring in ecologic…

Automated Detection and Modeling of Slow Slip: Case Study of the Cascadia Subduction Zone

NASA Astrophysics Data System (ADS)

Crowell, B. W.; Bock, Y.; Liu, Z.

2012-12-01

The discovery of transient slow slip events over the past decade has changed our understanding of tectonic hazards and the earthquake cycle. Proper geodetic characterization of transient deformation is necessary for studies of regional interseismic, coseismic and postseismic tectonics, and miscalculations can affect our understanding of the regional stress field. We utilize two different methods to create a complete record of slow slip from continuous GPS stations in the Cascadia subduction zone between 1996 and 2012: spatiotemporal principal component analysis (PCA) and the relative strength index (RSI). The PCA is performed on 100 day windows of nearby stations to locate signals that exist across many stations in the network by looking at the ratio of the first two eigenvalues. The RSI is a financial momentum oscillator that looks for changes in individual time series with respect to previous epochs to locate rapid changes, indicative of transient deformation. Using both methods, we create a complete history of slow slip across the Cascadia subduction zone, fully characterizing the timing, progression, and magnitude of events. We inject the results from the automated transient detection into a time-dependent slip inversion and apply a Kalman filter based network inversion method to image the spatiotemporal variation of slip transients along the Cascadia margin.

AMSNEXRAD-Automated detection of meteorite strewnfields in doppler weather radar

NASA Astrophysics Data System (ADS)

Hankey, Michael; Fries, Marc; Matson, Rob; Fries, Jeff

2017-09-01

For several years meteorite recovery in the United States has been greatly enhanced by using Doppler weather radar images to determine possible fall zones for meteorites produced by witnessed fireballs. While most fireball events leave no record on the Doppler radar, some large fireballs do. Based on the successful recovery of 10 meteorite falls 'under the radar', and the discovery of radar on more than 10 historic falls, it is believed that meteoritic dust and or actual meteorites falling to the ground have been recorded on Doppler weather radar (Fries et al., 2014). Up until this point, the process of detecting the radar signatures associated with meteorite falls has been a manual one and dependent on prior accurate knowledge of the fall time and estimated ground track. This manual detection process is labor intensive and can take several hours per event. Recent technological developments by NOAA now help enable the automation of these tasks. This in combination with advancements by the American Meteor Society (Hankey et al., 2014) in the tracking and plotting of witnessed fireballs has opened the possibility for automatic detection of meteorites in NEXRAD Radar Archives. Here in the processes for fireball triangulation, search area determination, radar interfacing, data extraction, storage, search, detection and plotting are explained.

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.

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

DTIC Science & Technology

2008-12-01

n. , ’>, ,. Australian Government Department of Defence Defence Science and Technology Organisation Automated Detection and Classification in... Organisation DSTO-GD-0537 ABSTRACT Autonomous Underwater Vehicles (AUVs) are increasingly being used by military forces to acquire high-resolution sonar...release Published by Maritime Operations Division DsTO Defrnce sdence and Technology Organisation PO Box 1500 Edinburgh South Australia 5111 Australia

Automated seamline detection along skeleton for remote sensing image mosaicking

NASA Astrophysics Data System (ADS)

Zhang, Hansong; Chen, Jianyu; Liu, Xin

2015-08-01

The automatic generation of seamline along the overlap region skeleton is a concerning problem for the mosaicking of Remote Sensing(RS) images. Along with the improvement of RS image resolution, it is necessary to ensure rapid and accurate processing under complex conditions. So an automated seamline detection method for RS image mosaicking based on image object and overlap region contour contraction is introduced. By this means we can ensure universality and efficiency of mosaicking. The experiments also show that this method can select seamline of RS images with great speed and high accuracy over arbitrary overlap regions, and realize RS image rapid mosaicking in surveying and mapping production.

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.

Automated detection of hospital outbreaks: A systematic review of methods

PubMed Central

Buckeridge, David L.; Lepelletier, Didier

2017-01-01

Objectives Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. Methods We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Results Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Conclusion Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results. PMID:28441422

Automated detection of hospital outbreaks: A systematic review of methods.

PubMed

Leclère, Brice; Buckeridge, David L; Boëlle, Pierre-Yves; Astagneau, Pascal; Lepelletier, Didier

2017-01-01

Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.

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

PubMed

de Mol, R M

2001-02-15

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

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

NASA Astrophysics Data System (ADS)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

of continuous seismic record by the Alaskan permanent seismic network and Hi-Climb trans-Himalayan seismic network. The processing chain we developed also opens the possibility for a near-real time seismic detection of landslides, in association with remote-sensing automated detection from Sentinel 2 images for example.

Automated detection system of single nucleotide polymorphisms using two kinds of functional magnetic nanoparticles

NASA Astrophysics Data System (ADS)

Liu, Hongna; Li, Song; Wang, Zhifei; Li, Zhiyang; Deng, Yan; Wang, Hua; Shi, Zhiyang; He, Nongyue

2008-11-01

Single nucleotide polymorphisms (SNPs) comprise the most abundant source of genetic variation in the human genome wide codominant SNPs identification. Therefore, large-scale codominant SNPs identification, especially for those associated with complex diseases, has induced the need for completely high-throughput and automated SNP genotyping method. Herein, we present an automated detection system of SNPs based on two kinds of functional magnetic nanoparticles (MNPs) and dual-color hybridization. The amido-modified MNPs (NH 2-MNPs) modified with APTES were used for DNA extraction from whole blood directly by electrostatic reaction, and followed by PCR, was successfully performed. Furthermore, biotinylated PCR products were captured on the streptavidin-coated MNPs (SA-MNPs) and interrogated by hybridization with a pair of dual-color probes to determine SNP, then the genotype of each sample can be simultaneously identified by scanning the microarray printed with the denatured fluorescent probes. This system provided a rapid, sensitive and highly versatile automated procedure that will greatly facilitate the analysis of different known SNPs in human genome.

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.

Automated Epileptiform Spike Detection via Affinity Propagation-Based Template Matching

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Interictal epileptiform spikes are the key diagnostic biomarkers for epilepsy. The clinical gold standard of spike detection is visual inspection performed by neurologists. This is a tedious, time-consuming, and expert-centered process. The development of automated spike detection systems is necessary in order to provide a faster and more reliable diagnosis of epilepsy. In this paper, we propose an efficient template matching spike detector based on a combination of spike and background waveform templates. We generate a template library by clustering a collection of spikes and background waveforms extracted from a database of 50 patients with epilepsy. We benchmark the performance of five clustering techniques based on the receiver operating characteristic (ROC) curves. In addition, background templates are integrated with existing spike templates to improve the overall performance. The affinity propagation-based template matching system with a combination of spike and background templates is shown to outperform the other four conventional methods with the highest area-under-curve (AUC) of 0.953. PMID:29060543

Level of Automation and Failure Frequency Effects on Simulated Lunar Lander Performance

NASA Technical Reports Server (NTRS)

Marquez, Jessica J.; Ramirez, Margarita

2014-01-01

A human-in-the-loop experiment was conducted at the NASA Ames Research Center Vertical Motion Simulator, where instrument-rated pilots completed a simulated terminal descent phase of a lunar landing. Ten pilots participated in a 2 x 2 mixed design experiment, with level of automation as the within-subjects factor and failure frequency as the between subjects factor. The two evaluated levels of automation were high (fully automated landing) and low (manual controlled landing). During test trials, participants were exposed to either a high number of failures (75% failure frequency) or low number of failures (25% failure frequency). In order to investigate the pilots' sensitivity to changes in levels of automation and failure frequency, the dependent measure selected for this experiment was accuracy of failure diagnosis, from which D Prime and Decision Criterion were derived. For each of the dependent measures, no significant difference was found for level of automation and no significant interaction was detected between level of automation and failure frequency. A significant effect was identified for failure frequency suggesting failure frequency has a significant effect on pilots' sensitivity to failure detection and diagnosis. Participants were more likely to correctly identify and diagnose failures if they experienced the higher levels of failures, regardless of level of automation

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

What Is an Automated External Defibrillator?

MedlinePlus

ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? An automated external defibrillator (AED) is a lightweight, portable device ... ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? detect a rhythm that should be ...

Comparison of automated processing of flocked swabs with manual processing of fiber swabs for detection of nasal carriage of Staphylococcus aureus.

PubMed

Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-07-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture.

Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk.

PubMed

Gubern-Mérida, Albert; Vreemann, Suzan; Martí, Robert; Melendez, Jaime; Lardenoije, Susanne; Mann, Ritse M; Karssemeijer, Nico; Platel, Bram

2016-02-01

To evaluate the performance of an automated computer-aided detection (CAD) system to detect breast cancers that were overlooked or misinterpreted in a breast MRI screening program for women at increased risk. We identified 40 patients that were diagnosed with breast cancer in MRI and had a prior MRI examination reported as negative available. In these prior examinations, 24 lesions could retrospectively be identified by two breast radiologists in consensus: 11 were scored as visible and 13 as minimally visible. Additionally, 120 normal scans were collected from 120 women without history of breast cancer or breast surgery participating in the same MRI screening program. A fully automated CAD system was applied to this dataset to detect malignant lesions. At 4 false-positives per normal case, the sensitivity for the detection of cancer lesions that were visible or minimally visible in retrospect in prior-negative examinations was 0.71 (95% CI=0.38-1.00) and 0.31 (0.07-0.59), respectively. A substantial proportion of cancers that were misinterpreted or overlooked in an MRI screening program was detected by a CAD system in prior-negative examinations. It has to be clarified with further studies if such a CAD system has an influence on the number of misinterpreted and overlooked cancers in clinical practice when results are given to a radiologist. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Anatomic viral detection is automated: the application of a robotic molecular pathology system for the detection of DNA viruses in anatomic pathology substrates, using immunocytochemical and nucleic acid hybridization techniques.

PubMed Central

Montone, K. T.; Brigati, D. J.; Budgeon, L. R.

1989-01-01

This paper presents the first automated system for simultaneously detecting human papilloma, herpes simplex, adenovirus, or cytomegalovirus viral antigens and gene sequences in standard formalin-fixed, paraffin-embedded tissue substrates and tissue culture. These viruses can be detected by colorimetric in situ nucleic acid hybridization, using biotinylated DNA probes, or by indirect immunoperoxidase techniques, using polyclonal or monoclonal antibodies, in a 2.0-hour assay performed at a single automated robotic workstation. Images FIG. 1 FIG. 4 FIG. 5 FIG. 6 FIG. 7 FIG. 8 FIG. 9 FIG. 10 FIG. 11 PMID:2773514

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Drukker, Karen, E-mail: kdrukker@uchicago.edu; 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 patientsmore » (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.« less

Fully automated atlas-based hippocampal volumetry for detection of Alzheimer's disease in a memory clinic setting.

PubMed

Suppa, Per; Anker, Ulrich; Spies, Lothar; Bopp, Irene; Rüegger-Frey, Brigitte; Klaghofer, Richard; Gocke, Carola; Hampel, Harald; Beck, Sacha; Buchert, Ralph

2015-01-01

Hippocampal volume is a promising biomarker to enhance the accuracy of the diagnosis of dementia due to Alzheimer's disease (AD). However, whereas hippocampal volume is well studied in patient samples from clinical trials, its value in clinical routine patient care is still rather unclear. The aim of the present study, therefore, was to evaluate fully automated atlas-based hippocampal volumetry for detection of AD in the setting of a secondary care expert memory clinic for outpatients. One-hundred consecutive patients with memory complaints were clinically evaluated and categorized into three diagnostic groups: AD, intermediate AD, and non-AD. A software tool based on open source software (Statistical Parametric Mapping SPM8) was employed for fully automated tissue segmentation and stereotactical normalization of high-resolution three-dimensional T1-weighted magnetic resonance images. Predefined standard masks were used for computation of grey matter volume of the left and right hippocampus which then was scaled to the patient's total grey matter volume. The right hippocampal volume provided an area under the receiver operating characteristic curve of 84% for detection of AD patients in the whole sample. This indicates that fully automated MR-based hippocampal volumetry fulfills the requirements for a relevant core feasible biomarker for detection of AD in everyday patient care in a secondary care memory clinic for outpatients. The software used in the present study has been made freely available as an SPM8 toolbox. It is robust and fast so that it is easily integrated into routine workflow.

Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction

NASA Astrophysics Data System (ADS)

Narayan, Nikhil S.; Sivanandan, Srinivasan; Kudavelly, Srinivas; Patwardhan, Kedar A.; Ramaraju, G. A.

2018-02-01

Follicle quantification refers to the computation of the number and size of follicles in 3D ultrasound volumes of the ovary. This is one of the key factors in determining hormonal dosage during female infertility treatments. In this paper, we propose an automated algorithm to detect and segment follicles in 3D ultrasound volumes of the ovary for quantification. In a first of its kind attempt, we employ noise-robust phase symmetry feature maps as likelihood function to perform mean-shift based follicle center detection. Max-flow algorithm is used for segmentation and gray weighted distance transform is employed for post-processing the results. We have obtained state-of-the-art results with a true positive detection rate of >90% on 26 3D volumes with 323 follicles.

Automated rice leaf disease detection using color image analysis

NASA Astrophysics Data System (ADS)

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

2011-06-01

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

Automated detection and localization of bowhead whale sounds in the presence of seismic airgun surveys.

PubMed

Thode, Aaron M; Kim, Katherine H; Blackwell, Susanna B; Greene, Charles R; Nations, Christopher S; McDonald, Trent L; Macrander, A Michael

2012-05-01

An automated procedure has been developed for detecting and localizing frequency-modulated bowhead whale sounds in the presence of seismic airgun surveys. The procedure was applied to four years of data, collected from over 30 directional autonomous recording packages deployed over a 280 km span of continental shelf in the Alaskan Beaufort Sea. The procedure has six sequential stages that begin by extracting 25-element feature vectors from spectrograms of potential call candidates. Two cascaded neural networks then classify some feature vectors as bowhead calls, and the procedure then matches calls between recorders to triangulate locations. To train the networks, manual analysts flagged 219 471 bowhead call examples from 2008 and 2009. Manual analyses were also used to identify 1.17 million transient signals that were not whale calls. The network output thresholds were adjusted to reject 20% of whale calls in the training data. Validation runs using 2007 and 2010 data found that the procedure missed 30%-40% of manually detected calls. Furthermore, 20%-40% of the sounds flagged as calls are not present in the manual analyses; however, these extra detections incorporate legitimate whale calls overlooked by human analysts. Both manual and automated methods produce similar spatial and temporal call distributions.

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

Intact mass detection, interpretation, and visualization to automate Top-Down proteomics on a large scale

PubMed Central

Durbin, Kenneth R.; Tran, John C.; Zamdborg, Leonid; Sweet, Steve M. M.; Catherman, Adam D.; Lee, Ji Eun; Li, Mingxi; Kellie, John F.; Kelleher, Neil L.

2011-01-01

Applying high-throughput Top-Down MS to an entire proteome requires a yet-to-be-established model for data processing. Since Top-Down is becoming possible on a large scale, we report our latest software pipeline dedicated to capturing the full value of intact protein data in automated fashion. For intact mass detection, we combine algorithms for processing MS1 data from both isotopically resolved (FT) and charge-state resolved (ion trap) LC-MS data, which are then linked to their fragment ions for database searching using ProSight. Automated determination of human keratin and tubulin isoforms is one result. Optimized for the intricacies of whole proteins, new software modules visualize proteome-scale data based on the LC retention time and intensity of intact masses and enable selective detection of PTMs to automatically screen for acetylation, phosphorylation, and methylation. Software functionality was demonstrated using comparative LC-MS data from yeast strains in addition to human cells undergoing chemical stress. We further these advances as a key aspect of realizing Top-Down MS on a proteomic scale. PMID:20848673

Performance evaluation of an automated single-channel sleep–wake detection algorithm

PubMed Central

Kaplan, Richard F; Wang, Ying; Loparo, Kenneth A; Kelly, Monica R; Bootzin, Richard R

2014-01-01

Background A need exists, from both a clinical and a research standpoint, for objective sleep measurement systems that are both easy to use and can accurately assess sleep and wake. This study evaluates the output of an automated sleep–wake detection algorithm (Z-ALG) used in the Zmachine (a portable, single-channel, electroencephalographic [EEG] acquisition and analysis system) against laboratory polysomnography (PSG) using a consensus of expert visual scorers. Methods Overnight laboratory PSG studies from 99 subjects (52 females/47 males, 18–60 years, median age 32.7 years), including both normal sleepers and those with a variety of sleep disorders, were assessed. PSG data obtained from the differential mastoids (A1–A2) were assessed by Z-ALG, which determines sleep versus wake every 30 seconds using low-frequency, intermediate-frequency, and high-frequency and time domain EEG features. PSG data were independently scored by two to four certified PSG technologists, using standard Rechtschaffen and Kales guidelines, and these score files were combined on an epoch-by-epoch basis, using a majority voting rule, to generate a single score file per subject to compare against the Z-ALG output. Both epoch-by-epoch and standard sleep indices (eg, total sleep time, sleep efficiency, latency to persistent sleep, and wake after sleep onset) were compared between the Z-ALG output and the technologist consensus score files. Results Overall, the sensitivity and specificity for detecting sleep using the Z-ALG as compared to the technologist consensus are 95.5% and 92.5%, respectively, across all subjects, and the positive predictive value and the negative predictive value for detecting sleep are 98.0% and 84.2%, respectively. Overall κ agreement is 0.85 (approaching the level of agreement observed among sleep technologists). These results persist when the sleep disorder subgroups are analyzed separately. Conclusion This study demonstrates that the Z-ALG automated sleep�

A fully automated microfluidic-based electrochemical sensor for real-time bacteria detection.

PubMed

Altintas, Zeynep; Akgun, Mete; Kokturk, Guzin; Uludag, Yildiz

2018-02-15

A fully automated microfluidic-based electrochemical biosensor was designed and manufactured for pathogen detection. The quantification of Escherichia coli was investigated with standard and nanomaterial amplified immunoassays in the concentration ranges of 0.99 × 10 4 3.98 × 10 9 cfu mL -1 and 103.97 × 10 7 cfu mL -1 which resulted in detection limits of 1.99 × 10 4 cfu mL -1 and 50 cfu mL -1 , respectively. The developed methodology was then applied for E. coli quantification in water samples using nanomaterial modified assay. Same detection limit for E. coli was achieved for real sample analysis with a little decrease on the sensor signal. Cross-reactivity studies were conducted by testing Shigella, Salmonella spp., Salmonella typhimurium and Staphylococcus aureus on E. coli specific antibody surface that confirmed the high specificity of the developed immunoassays. The sensor surface could be regenerated multiple times which significantly reduces the cost of the system. Our custom-designed biosensor is capable of detecting bacteria with high sensitivity and specificity, and can serve as a promising tool for pathogen detection. Copyright © 2017 Elsevier B.V. All rights reserved.

[Algorithm of locally adaptive region growing based on multi-template matching applied to automated detection of hemorrhages].

PubMed

Gao, Wei-Wei; Shen, Jian-Xin; Wang, Yu-Liang; Liang, Chun; Zuo, Jing

2013-02-01

In order to automatically detect hemorrhages in fundus images, and develop an automated diabetic retinopathy screening system, a novel algorithm named locally adaptive region growing based on multi-template matching was established and studied. Firstly, spectral signature of major anatomical structures in fundus was studied, so that the right channel among RGB channels could be selected for different segmentation objects. Secondly, the fundus image was preprocessed by means of HSV brightness correction and contrast limited adaptive histogram equalization (CLAHE). Then, seeds of region growing were founded out by removing optic disc and vessel from the resulting image of normalized cross-correlation (NCC) template matching on the previous preprocessed image with several templates. Finally, locally adaptive region growing segmentation was used to find out the exact contours of hemorrhages, and the automated detection of the lesions was accomplished. The approach was tested on 90 different resolution fundus images with variable color, brightness and quality. Results suggest that the approach could fast and effectively detect hemorrhages in fundus images, and it is stable and robust. As a result, the approach can meet the clinical demands.

A landsat data tiling and compositing approach optimized for change detection in the conterminous United States

USGS Publications Warehouse

Nelson, Kurtis; Steinwand, Daniel R.

2015-01-01

Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beer, N. Reginald; Paglieroni, David W.

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 thenmore » 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.« less

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.

PubMed

Hurnanen, Tero; Lehtonen, Eero; Tadi, Mojtaba Jafari; Kuusela, Tom; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pankaala, Mikko

2017-09-01

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

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.

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.

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

Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs.

PubMed

Chakrabarty, Lipi; Joshi, Gopal Datt; Chakravarty, Arunava; Raman, Ganesh V; Krishnadas, S R; Sivaswamy, Jayanthi

2016-07-01

To describe and evaluate the performance of an automated CAD system for detection of glaucoma from color fundus photographs. Color fundus photographs of 2252 eyes from 1126 subjects were collected from 2 centers: Aravind Eye Hospital, Madurai and Coimbatore, India. The images of 1926 eyes (963 subjects) were used to train an automated image analysis-based system, which was developed to provide a decision on a given fundus image. A total of 163 subjects were clinically examined by 2 ophthalmologists independently and their diagnostic decisions were recorded. The consensus decision was defined to be the clinical reference (gold standard). Fundus images of eyes with disagreement in diagnosis were excluded from the study. The fundus images of the remaining 314 eyes (157 subjects) were presented to 4 graders and their diagnostic decisions on the same were collected. The performance of the system was evaluated on the 314 images, using the reference standard. The sensitivity and specificity of the system and 4 independent graders were determined against the clinical reference standard. The system achieved an area under receiver operating characteristic curve of 0.792 with a sensitivity of 0.716 and specificity of 0.717 at a selected threshold for the detection of glaucoma. The agreement with the clinical reference standard as determined by Cohen κ is 0.45 for the proposed system. This is comparable to that of the image-based decisions of 4 ophthalmologists. An automated system was presented for glaucoma detection from color fundus photographs. The overall evaluation results indicated that the presented system was comparable in performance to glaucoma classification by a manual grader solely based on fundus image examination.

Automated detection of videotaped neonatal seizures of epileptic origin.

PubMed

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

2006-06-01

This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements. The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion-trajectory signals extracted from video recordings by robust motion trackers based on block-motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video-frame sequence. Video segments were represented by quantitative features obtained by analyzing motion-strength and motion-trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feed-forward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements. The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity>90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural

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

NASA Astrophysics Data System (ADS)

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

2012-06-01

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

Low power multi-camera system and algorithms for automated threat detection

NASA Astrophysics Data System (ADS)

Huber, David J.; Khosla, Deepak; Chen, Yang; Van Buer, Darrel J.; Martin, Kevin

2013-05-01

A key to any robust automated surveillance system is continuous, wide field-of-view sensor coverage and high accuracy target detection algorithms. Newer systems typically employ an array of multiple fixed cameras that provide individual data streams, each of which is managed by its own processor. This array can continuously capture the entire field of view, but collecting all the data and back-end detection algorithm consumes additional power and increases the size, weight, and power (SWaP) of the package. This is often unacceptable, as many potential surveillance applications have strict system SWaP requirements. This paper describes a wide field-of-view video system that employs multiple fixed cameras and exhibits low SWaP without compromising the target detection rate. We cycle through the sensors, fetch a fixed number of frames, and process them through a modified target detection algorithm. During this time, the other sensors remain powered-down, which reduces the required hardware and power consumption of the system. We show that the resulting gaps in coverage and irregular frame rate do not affect the detection accuracy of the underlying algorithms. This reduces the power of an N-camera system by up to approximately N-fold compared to the baseline normal operation. This work was applied to Phase 2 of DARPA Cognitive Technology Threat Warning System (CT2WS) program and used during field testing.

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.

Automated multi-radionuclide separation and analysis with combined detection capability

NASA Astrophysics Data System (ADS)

Plionis, Alexander Asterios

The radiological dispersal device (RDD) is a weapon of great concern to those agencies responsible for protecting the public from the modern age of terrorism. In order to effectively respond to an RDD event, these agencies need to possess the capability to rapidly identify the radiological agents involved in the incident and assess the uptake of each individual victim. Since medical treatment for internal radiation poisoning is radionuclide-specific, it is critical to identify and quantify the radiological uptake of each individual victim. This dissertation describes the development of automated analytical components that could be used to determine and quantify multiple radionuclides in human urine bioassays. This is accomplished through the use of extraction chromatography that is plumbed in-line with one of a variety of detection instruments. Flow scintillation analysis is used for 90Sr and 210Po determination, flow gamma analysis is used assess 60 Co and 137Cs, and inductively coupled plasma mass spectrometry is used to determine actinides. Detection limits for these analytes were determined for the appropriate technique and related to their implications for health physics.

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

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

Change detection and change blindness in pigeons (Columba livia).

PubMed

Herbranson, Walter T; Trinh, Yvan T; Xi, Patricia M; Arand, Mark P; Barker, Michael S K; Pratt, Theodore H

2014-05-01

Change blindness is a phenomenon in which even obvious details in a visual scene change without being noticed. Although change blindness has been studied extensively in humans, we do not yet know if it is a phenomenon that also occurs in other animals. Thus, investigation of change blindness in a nonhuman species may prove to be valuable by beginning to provide some insight into its ultimate causes. Pigeons learned a change detection task in which pecks to the location of a change in a sequence of stimulus displays were reinforced. They were worse at detecting changes if the stimulus displays were separated by a brief interstimulus interval, during which the display was blank, and this primary result matches the general pattern seen in previous studies of change blindness in humans. A second experiment attempted to identify specific stimulus characteristics that most reliably produced a failure to detect changes. Change detection was more difficult when interstimulus intervals were longer and when the change was iterated fewer times. ©2014 APA, all rights reserved.

Orthorectified High Resolution Multispectral Imagery for Application to Change Detection and Analysis

NASA Technical Reports Server (NTRS)

Benkelman, Cody A.

1997-01-01

The project team has outlined several technical objectives which will allow the companies to improve on their current capabilities. These include modifications to the imaging system, enabling it to operate more cost effectively and with greater ease of use, automation of the post-processing software to mosaic and orthorectify the image scenes collected, and the addition of radiometric calibration to greatly aid in the ability to perform accurate change detection. Business objectives include fine tuning of the market plan plus specification of future product requirements, expansion of sales activities (including identification of necessary additional resources required to meet stated revenue objectives), development of a product distribution plan, and implementation of a world wide sales effort.

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.

Automated detection of the retinal from OCT spectral domain images of healthy eyes

NASA Astrophysics Data System (ADS)

Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

2015-06-01

Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retinal. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

Automated detection of retinal layers from OCT spectral-domain images of healthy eyes

NASA Astrophysics Data System (ADS)

Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

2015-12-01

Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retina. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral-domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

Comparison of Automated Processing of Flocked Swabs with Manual Processing of Fiber Swabs for Detection of Nasal Carriage of Staphylococcus aureus▿‡

PubMed Central

Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-01-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture. PMID:21525218

Automated hand hygiene auditing with and without an intervention.

PubMed

Kwok, Yen Lee Angela; Juergens, Craig P; McLaws, Mary-Louise

2016-12-01

Daily feedback from continuous automated auditing with a peer reminder intervention was used to improve compliance. Compliance rates from covert and overt automated auditing phases with and without intervention were compared with human mandatory audits. An automated system was installed to covertly detect hand hygiene events with each depression of the alcohol-based handrub dispenser for 5 months. The overt phase included key clinicians trained to share daily rates with clinicians, set compliance goals, and nudge each other to comply for 6 months. During a further 6 months, the intervention continued without being refreshed. Hand Hygiene Australia (HHA) human audits were performed quarterly during the intervention in accordance with the World Health Organization guidelines. Percentage point (PP) differences between compliance rates were used to determine change. HHA rates for June 2014 were 85% and 87% on the medical and surgical wards, respectively. These rates were 55 PPs and 38 PPs higher than covert automation rates for June 2014 on the medical and surgical ward at 30% and 49%, respectively. During the intervention phase, average compliance did not change on the medical ward from their covert rate, whereas the surgical ward improved compared with the covert phase by 11 PPs to 60%. On average, compliance during the intervention without being refreshed did not change on the medical ward, whereas the average rate on the surgical ward declined by 9 PPs. Automation provided a unique opportunity to respond to daily rates, but compliance will return to preintervention levels once active intervention ceases or human auditors leave the ward, unless clinicians are committed to change. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

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.

Automated location detection of injection site for preclinical stereotactic neurosurgery procedure

NASA Astrophysics Data System (ADS)

Abbaszadeh, Shiva; Wu, Hemmings C. H.

2017-03-01

Currently, during stereotactic neurosurgery procedures, the manual task of locating the proper area for needle insertion or implantation of electrode/cannula/optic fiber can be time consuming. The requirement of the task is to quickly and accurately find the location for insertion. In this study we investigate an automated method to locate the entry point of region of interest. This method leverages a digital image capture system, pattern recognition, and motorized stages. Template matching of known anatomical identifiable regions is used to find regions of interest (e.g. Bregma) in rodents. For our initial study, we tackle the problem of automatically detecting the entry point.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

PubMed Central

Schmitz, Christoph; Eastwood, Brian S.; Tappan, Susan J.; Glaser, Jack R.; Peterson, Daniel A.; Hof, Patrick R.

2014-01-01

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. PMID:24847213

Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens

NASA Astrophysics Data System (ADS)

Litjens, G.; Ehteshami Bejnordi, B.; Timofeeva, N.; Swadi, G.; Kovacs, I.; Hulsbergen-van de Kaa, C.; van der Laak, J.

2015-03-01

Automated detection of prostate cancer in digitized H and E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H and E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity.

The Development of Change Detection

ERIC Educational Resources Information Center

Shore, David I.; Burack, Jacob A.; Miller, Danny; Joseph, Shari; Enns, James T.

2006-01-01

Changes to a scene often go unnoticed if the objects of the change are unattended, making change detection an index of where attention is focused during scene perception. We measured change detection in school-age children and young adults by repeatedly alternating two versions of an image. To provide an age-fair assessment we used a bimanual…

Automated Detection of Salt Marsh Platforms : a Topographic Method

NASA Astrophysics Data System (ADS)

Goodwin, G.; Mudd, S. M.; Clubb, F. J.

2017-12-01

Monitoring the topographic evolution of coastal marshes is a crucial step toward improving the management of these valuable landscapes under the pressure of relative sea level rise and anthropogenic modification. However, determining their geometrically complex boundaries currently relies on spectral vegetation detection methods or requires labour-intensive field surveys and digitisation.We propose a novel method to reproducibly isolate saltmarsh scarps and platforms from a DEM. Field observations and numerical models show that saltmarshes mature into sub-horizontal platforms delineated by sub-vertical scarps: based on this premise, we identify scarps as lines of local maxima on a slope*relief raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. Non-dimensional search parameters allow batch-processing of data without recalibration. We test our method using lidar-derived DEMs of six saltmarshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and automatic segregation exceeds 90% for resolutions of 1m, with all but one sites maintaining this performance for resolutions up to 3.5m. For resolutions of 1m, automatically detected platforms are comparable in surface area and elevation distribution to digitised platforms. We also find that our method allows the accurate detection of local bloc failures 3 times larger than the DEM resolution.Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, automatic detection classifies them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method would benefit from a combination with existing creek detection algorithms. Fallen blocs and pioneer zones are inconsistently identified, particularly in macro-tidal marshes, leading to differences between digitisation and the automated method

Errors detected in pediatric oral liquid medication doses prepared in an automated workflow management system.

PubMed

Bledsoe, Sarah; Van Buskirk, Alex; Falconer, R James; Hollon, Andrew; Hoebing, Wendy; Jokic, Sladan

2018-02-01

The effectiveness of barcode-assisted medication preparation (BCMP) technology on detecting oral liquid dose preparation errors. From June 1, 2013, through May 31, 2014, a total of 178,344 oral doses were processed at Children's Mercy, a 301-bed pediatric hospital, through an automated workflow management system. Doses containing errors detected by the system's barcode scanning system or classified as rejected by the pharmacist were further reviewed. Errors intercepted by the barcode-scanning system were classified as (1) expired product, (2) incorrect drug, (3) incorrect concentration, and (4) technological error. Pharmacist-rejected doses were categorized into 6 categories based on the root cause of the preparation error: (1) expired product, (2) incorrect concentration, (3) incorrect drug, (4) incorrect volume, (5) preparation error, and (6) other. Of the 178,344 doses examined, 3,812 (2.1%) errors were detected by either the barcode-assisted scanning system (1.8%, n = 3,291) or a pharmacist (0.3%, n = 521). The 3,291 errors prevented by the barcode-assisted system were classified most commonly as technological error and incorrect drug, followed by incorrect concentration and expired product. Errors detected by pharmacists were also analyzed. These 521 errors were most often classified as incorrect volume, preparation error, expired product, other, incorrect drug, and incorrect concentration. BCMP technology detected errors in 1.8% of pediatric oral liquid medication doses prepared in an automated workflow management system, with errors being most commonly attributed to technological problems or incorrect drugs. Pharmacists rejected an additional 0.3% of studied doses. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Prospective study of atrial fibrillation termination during ablation guided by automated detection of fractionated electrograms.

PubMed

Porter, Michael; Spear, William; Akar, Joseph G; Helms, Ray; Brysiewicz, Neil; Santucci, Peter; Wilber, David J

2008-06-01

Complex fractionated atrial electrograms (CFAE) may identify critical sites for perpetuation of atrial fibrillation (AF) and provide useful targets for ablation. Current assessment of CFAE is subjective; automated detection algorithms may improve reproducibility, but their utility in guiding ablation has not been tested. In 67 patients presenting for initial AF ablation (42 paroxysmal, 25 persistent), LA and CS mapping were performed during induced or spontaneous AF. CFAE were identified by an online automated computer algorithm and displayed on electroanatomical maps. A mean of 28 +/- 18 sites/patient were identified (20 +/- 13% of mapped sites), and were more frequent during persistent AF. CFAE occurred most commonly within the CS, on the atrial septum, and around the pulmonary veins. Ablation initially targeting CFAE terminated AF in 88% of paroxysmal AF, but only 20% of persistent AF (P < 0.001). Subsequently, additional ablation was performed in all patients (PV isolation for paroxysmal AF, PV isolation + mitral and roof lines for persistent AF). Minimum follow-up was 1 year. One-year freedom from recurrent atrial arrhythmias without antiarrhythmic drug therapy after a single procedure was 90% for paroxysmal AF, and 68% for persistent AF. Ablation guided by automated detection of CFAE proved feasible, and was associated with a high AF termination rate in paroxysmal, but not persistent AF. As an adjunct to conventional techniques, it was associated with excellent long-term single procedure outcomes in both groups. Criteria for identifying optimal CFAE sites for ablation, and selection of patients most likely to benefit, require additional study.

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.

Automation in astronomy.

NASA Technical Reports Server (NTRS)

Wampler, E. J.

1972-01-01

Description and evaluation of the remotely operated Lick Observatory Cassegrain focus of the 120-inch telescope. The experience with this instrument has revealed that an automated system can profoundly change the observer's approach to his work. This makes it difficult to evaluate the 'advantage' of an automated telescope over a conventional instrument. Some of the problems arising with automation in astronomy are discussed.

Assessment of the impact of the change from manual to automated coding on mortality statistics in Australia.

PubMed

McKenzie, Kirsten; Walker, Sue; Tong, Shilu

It remains unclear whether the change from a manual to an automated coding system (ACS) for deaths has significantly affected the consistency of Australian mortality data. The underlying causes of 34,000 deaths registered in 1997 in Australia were dual coded, in ICD-9 manually, and by using an automated computer coding program. The diseases most affected by the change from manual to ACS were senile/presenile dementia, and pneumonia. The most common disease to which a manually assigned underlying cause of senile dementia was coded with ACS was unspecified psychoses (37.2%). Only 12.5% of codes assigned by ACS as senile dementia were coded the same by manual coders. This study indicates some important differences in mortality rates when comparing mortality data that have been coded manually with those coded using an automated computer coding program. These differences may be related to both the different interpretation of ICD coding rules between manual and automated coding, and different co-morbidities or co-existing conditions among demographic groups.

Automated gas chromatography

DOEpatents

Mowry, Curtis D.; Blair, Dianna S.; Rodacy, Philip J.; Reber, Stephen D.

1999-01-01

An apparatus and process for the continuous, near real-time monitoring of low-level concentrations of organic compounds in a liquid, and, more particularly, a water stream. A small liquid volume of flow from a liquid process stream containing organic compounds is diverted by an automated process to a heated vaporization capillary where the liquid volume is vaporized to a gas that flows to an automated gas chromatograph separation column to chromatographically separate the organic compounds. Organic compounds are detected and the information transmitted to a control system for use in process control. Concentrations of organic compounds less than one part per million are detected in less than one minute.

Automated estimation of river bathymetry using change detection based on Landsat imagery and river morphological models

NASA Astrophysics Data System (ADS)

Donchyts, G.; Jagers, B.; Van De Giesen, N.; Baart, F.; van Dam, A.

2015-12-01

Free global data sets on river bathymetry at global scale are not yet available. While one of the mostly used free elevation datasets, SRTM, provides data on location and elevation of rivers, its quality usually is very limited. This happens mainly because water mask was derived from older satellite imagery, such as Landsat 5, and also because the radar instruments perform bad near water, especially with the presence of vegetation in riparian zone. Additional corrections are required before it can be used for applications such as higher resolution surface water flow simulations. On the other hand, medium resolution satellite imagery from Landsat mission can be used to estimate water mask changes during the last 40 years. Water mask from Landsat imagery can be derived on per-image basis, in some cases, resulting in up to one thousand water masks. For rivers where significant water mask changes can be observed, this information can be used to improve quality of existing digital elevation models in the range between minimum and maximum observed water levels. Furthermore, we can use this information to further estimate river bathymetry using morphological models. We will evaluate how Landsat imagery can be used to estimate river bathymetry and will point to cases of significant inconsistencies between SRTM and Landsat-based water masks. We will also explore other challenges on a way to automated estimation of river bathymetry using fusion of numerical morphological models and remote sensing data. Some of them include automatic generation of model mesh, estimation of river morphodynamic properties and issues related to spectral method used to analyse optical satellite imagery.

Developing new automated alternation flicker using optic disc photography for the detection of glaucoma progression

PubMed Central

Ahn, J; Yun, I S; Yoo, H G; Choi, J-J; Lee, M

2017-01-01

Purpose To evaluate a progression-detecting algorithm for a new automated matched alternation flicker (AMAF) in glaucoma patients. Methods Open-angle glaucoma patients with a baseline mean deviation of visual field (VF) test>−6 dB were included in this longitudinal and retrospective study. Functional progression was detected by two VF progression criteria and structural progression by both AMAF and conventional comparison methods using optic disc and retinal nerve fiber layer (RNFL) photography. Progression-detecting performances of AMAF and the conventional method were evaluated by an agreement between functional and structural progression criteria. RNFL thickness changes measured by optical coherence tomography (OCT) were compared between progressing and stable eyes determined by each method. Results Among 103 eyes, 47 (45.6%), 21 (20.4%), and 32 (31.1%) eyes were evaluated as glaucoma progression using AMAF, the conventional method, and guided progression analysis (GPA) of the VF test, respectively. The AMAF showed better agreement than the conventional method, using GPA of the VF test (κ=0.337; P<0.001 and κ=0.124; P=0.191, respectively). The rates of RNFL thickness decay using OCT were significantly different between the progressing and stable eyes when progression was determined by AMAF (−3.49±2.86 μm per year vs −1.83±3.22 μm per year; P=0.007) but not by the conventional method (−3.24±2.42 μm per year vs −2.42±3.33 μm per year; P=0.290). Conclusions The AMAF was better than the conventional comparison method in discriminating structural changes during glaucoma progression, and showed a moderate agreement with functional progression criteria. PMID:27662466

Automated Power-Distribution System

NASA Technical Reports Server (NTRS)

Ashworth, Barry; Riedesel, Joel; Myers, Chris; Miller, William; Jones, Ellen F.; Freeman, Kenneth; Walsh, Richard; Walls, Bryan K.; Weeks, David J.; Bechtel, Robert T.

1992-01-01

Autonomous power-distribution system includes power-control equipment and automation equipment. System automatically schedules connection of power to loads and reconfigures itself when it detects fault. Potential terrestrial applications include optimization of consumption of power in homes, power supplies for autonomous land vehicles and vessels, and power supplies for automated industrial processes.

Automatic detection of slight parameter changes associated to complex biomedical signals using multiresolution q-entropy1.

PubMed

Torres, M E; Añino, M M; Schlotthauer, G

2003-12-01

It is well known that, from a dynamical point of view, sudden variations in physiological parameters which govern certain diseases can cause qualitative changes in the dynamics of the corresponding physiological process. The purpose of this paper is to introduce a technique that allows the automated temporal localization of slight changes in a parameter of the law that governs the nonlinear dynamics of a given signal. This tool takes, from the multiresolution entropies, the ability to show these changes as statistical variations at each scale. These variations are held in the corresponding principal component. Appropriately combining these techniques with a statistical changes detector, a complexity change detection algorithm is obtained. The relevance of the approach, together with its robustness in the presence of moderate noise, is discussed in numerical simulations and the automatic detector is applied to real and simulated biological signals.

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

PubMed

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

2017-04-01

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

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.

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method.

PubMed

Galletto Pregliasco, A; Collin, A; Guéguen, A; Metten, M A; Aboab, J; Deschamps, R; Gout, O; Duron, L; Sadik, J C; Savatovsky, J; Lecler, A

2018-06-07

MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging. This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient. There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10 -4 ). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions. Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care. © 2018 by American Journal of Neuroradiology.

An automated microfluidic DNA microarray platform for genetic variant detection in inherited arrhythmic diseases.

PubMed

Huang, Shu-Hong; Chang, Yu-Shin; Juang, Jyh-Ming Jimmy; Chang, Kai-Wei; Tsai, Mong-Hsun; Lu, Tzu-Pin; Lai, Liang-Chuan; Chuang, Eric Y; Huang, Nien-Tsu

2018-03-12

In this study, we developed an automated microfluidic DNA microarray (AMDM) platform for point mutation detection of genetic variants in inherited arrhythmic diseases. The platform allows for automated and programmable reagent sequencing under precise conditions of hybridization flow and temperature control. It is composed of a commercial microfluidic control system, a microfluidic microarray device, and a temperature control unit. The automated and rapid hybridization process can be performed in the AMDM platform using Cy3 labeled oligonucleotide exons of SCN5A genetic DNA, which produces proteins associated with sodium channels abundant in the heart (cardiac) muscle cells. We then introduce a graphene oxide (GO)-assisted DNA microarray hybridization protocol to enable point mutation detection. In this protocol, a GO solution is added after the staining step to quench dyes bound to single-stranded DNA or non-perfectly matched DNA, which can improve point mutation specificity. As proof-of-concept we extracted the wild-type and mutant of exon 12 and exon 17 of SCN5A genetic DNA from patients with long QT syndrome or Brugada syndrome by touchdown PCR and performed a successful point mutation discrimination in the AMDM platform. Overall, the AMDM platform can greatly reduce laborious and time-consuming hybridization steps and prevent potential contamination. Furthermore, by introducing the reciprocating flow into the microchannel during the hybridization process, the total assay time can be reduced to 3 hours, which is 6 times faster than the conventional DNA microarray. Given the automatic assay operation, shorter assay time, and high point mutation discrimination, we believe that the AMDM platform has potential for low-cost, rapid and sensitive genetic testing in a simple and user-friendly manner, which may benefit gene screening in medical practice.

Validation of a classification system to grade fractionation in atrial fibrillation and correlation with automated detection systems.

PubMed

Hunter, Ross J; Diab, Ihab; Thomas, Glyn; Duncan, Edward; Abrams, Dominic; Dhinoja, Mehul; Sporton, Simon; Earley, Mark J; Schilling, Richard J

2009-12-01

We tested application of a grading system describing complex fractionated electrograms (CFE) in atrial fibrillation (AF) and used it to validate automated CFE detection (AUTO). Ten seconds bipolar electrograms were classified by visual inspection (VI) during ablation of persistent AF and the result compared with offline manual measurement (MM) by a second blinded operator: Grade 1 uninterrupted fractionated activity (defined as segments > or =70 ms) for > or =70% of recording and uninterrupted > or =1 s; Grade 2 interrupted fractionated activity > or =70% of recording; Grade 3 intermittent fractionated activity 30-70%; Grade 4 discrete (<70 ms) complex electrogram (> or =5 direction changes); Grade 5 discrete simple electrograms (< or =4 direction changes); Grade 6 scar. Grade by VI and MM for 100 electrograms agreed in 89%. Five hundred electrograms were graded on Carto and NavX by VI to validate AUTO in (i) detection of CFE (grades 1-4 considered CFE), and (ii) assessing degree of fractionation by correlating grade and score by AUTO (data shown as sensitivity, specificity, r): NavX 'CFE mean' 92%, 91%, 0.56; Carto 'interval confidence level' using factory settings 89%, 62%, -0.72, and other published settings 80%, 74%, -0.65; Carto 'shortest confidence interval' 74%, 70%, 0.43; Carto 'average confidence interval' 86%, 66%, 0.53. Grading CFE by VI is accurate and correlates with AUTO.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

collected data. These statistical techniques are under the area of descriptive statistics, which is a methodology to condense the data in quantitative ...ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...report when it is no longer needed. Do not return it to the originator. ARL-TR-8270 ● JAN 2017 US Army Research Laboratory An

System for particle concentration and detection

DOEpatents

Morales, Alfredo M.; Whaley, Josh A.; Zimmerman, Mark D.; Renzi, Ronald F.; Tran, Huu M.; Maurer, Scott M.; Munslow, William D.

2013-03-19

A new microfluidic system comprising an automated prototype insulator-based dielectrophoresis (iDEP) triggering microfluidic device for pathogen monitoring that can eventually be run outside the laboratory in a real world environment has been used to demonstrate the feasibility of automated trapping and detection of particles. The system broadly comprised an aerosol collector for collecting air-borne particles, an iDEP chip within which to temporarily trap the collected particles and a laser and fluorescence detector with which to induce a fluorescence signal and detect a change in that signal as particles are trapped within the iDEP chip.

Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.

PubMed

Soto-Pedre, Enrique; Navea, Amparo; Millan, Saray; Hernaez-Ortega, Maria C; Morales, Jesús; Desco, Maria C; Pérez, Pablo

2015-02-01

To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme. Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded. A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9). The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes. © 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

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…

The "Sigmoid Sniffer” and the "Advanced Automated Solar Filament Detection and Characterization Code” Modules

NASA Astrophysics Data System (ADS)

Raouafi, Noureddine; Bernasconi, P. N.; Georgoulis, M. K.

2010-05-01

We present two pattern recognition algorithms, the "Sigmoid Sniffer” and the "Advanced Automated Solar Filament Detection and Characterization Code,” that are among the Feature Finding modules of the Solar Dynamic Observatory: 1) Coronal sigmoids visible in X-rays and the EUV are the result of highly twisted magnetic fields. They can occur anywhere on the solar disk and are closely related to solar eruptive activity (e.g., flares, CMEs). Their appearance is typically synonym of imminent solar eruptions, so they can serve as a tool to forecast solar activity. Automatic X-ray sigmoid identification offers an unbiased way of detecting short-to-mid term CME precursors. The "Sigmoid Sniffer” module is capable of automatically detecting sigmoids in full-disk X-ray images and determining their chirality, as well as other characteristics. It uses multiple thresholds to identify persistent bright structures on a full-disk X-ray image of the Sun. We plan to apply the code to X-ray images from Hinode/XRT, as well as on SDO/AIA images. When implemented in a near real-time environment, the Sigmoid Sniffer could allow 3-7 day forecasts of CMEs and their potential to cause major geomagnetic storms. 2)The "Advanced Automated Solar Filament Detection and Characterization Code” aims to identify, classify, and track solar filaments in full-disk Hα images. The code can reliably identify filaments; determine their chirality and other relevant parameters like filament area, length, and average orientation with respect to the equator. It is also capable of tracking the day-by-day evolution of filaments as they traverse the visible disk. The code was tested by analyzing daily Hα images taken at the Big Bear Solar Observatory from mid-2000 to early-2005. It identified and established the chirality of thousands of filaments without human intervention.

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

Automated DNA mutation detection using universal conditions direct sequencing: application to ten muscular dystrophy genes.

PubMed

Bennett, Richard R; Schneider, Hal E; Estrella, Elicia; Burgess, Stephanie; Cheng, Andrew S; Barrett, Caitlin; Lip, Va; Lai, Poh San; Shen, Yiping; Wu, Bai-Lin; Darras, Basil T; Beggs, Alan H; Kunkel, Louis M

2009-10-18

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. 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 sequences are reported in

Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior.

PubMed

Van Nuffel, Annelies; Zwertvaegher, Ingrid; Van Weyenberg, Stephanie; Pastell, Matti; Thorup, Vivi M; Bahr, Claudia; Sonck, Bart; Saeys, Wouter

2015-08-28

Despite the research on opportunities to automatically measure lameness in cattle, lameness detection systems are not widely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat them properly and in a timely fashion. Many papers have focused on the automated measurement of gait or behavioral cow characteristics related to lameness. In order for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cows need to be developed and validated. Few studies have reached this latter stage of the development process. Also, comparison between the different approaches is impeded by the wide range of practical settings used to measure the gait or behavioral characteristic (e.g., measurements during normal farming routine or during experiments; cows guided or walking at their own speed) and by the different definitions of lame cows. In the majority of the publications, mildly lame cows are included in the non-lame cow group, which limits the possibility of also detecting early lameness cases. In this review, studies that used sensor technology to measure changes in gait or behavior of cows related to lameness are discussed together with practical considerations when conducting lameness research. In addition, other prerequisites for any lameness detection system on farms (e.g., need for early detection, real-time measurements) are discussed.

Dynamic Network Change Detection

DTIC Science & Technology

2008-12-01

Change Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT...Fisher and Mackenzie, 1922). These methods are used in quality engineering to detect small changes in a process (Montgomery, 1991; Ryan , 2000). Larger...Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds

Detection of breast cancer in automated 3D breast ultrasound

NASA Astrophysics Data System (ADS)

Tan, Tao; Platel, Bram; Mus, Roel; Karssemeijer, Nico

2012-03-01

Automated 3D breast ultrasound (ABUS) is a novel imaging modality, in which motorized scans of the breasts are made with a wide transducer through a membrane under modest compression. The technology has gained high interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. ABUS has a high sensitivity for detecting solid breast lesions. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and errors. In the multi-stage system we propose, segmentations of the breast and nipple are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and locations with respect to landmarks are extracted. Using an ensemble of classifiers, a likelihood map indicating potential malignancies is computed. Local maxima in the likelihood map are determined using a local maxima detector and form a set of candidate lesions in each view. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. Region segmentation is performed using a 3D spiral-scanning dynamic programming method. Region features include descriptors of shape, acoustic behavior and texture. Performance was determined using a 78-patient dataset with 93 images, including 50 malignant lesions. We used 10-fold cross-validation. Using FROC analysis we found that the system obtains a lesion sensitivity of 60% and 70% at 2 and 4 false positives per image respectively.

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.

Automated detection of fluorescent cells in in-resin fluorescence sections for integrated light and electron microscopy.

PubMed

Delpiano, J; Pizarro, L; Peddie, C J; Jones, M L; Griffin, L D; Collinson, L M

2018-04-26

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high-resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via 'smart tracking'. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence. © 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

sFIDA automation yields sub-femtomolar limit of detection for Aβ aggregates in body fluids.

PubMed

Herrmann, Yvonne; Kulawik, Andreas; Kühbach, Katja; Hülsemann, Maren; Peters, Luriano; Bujnicki, Tuyen; Kravchenko, Kateryna; Linnartz, Christina; Willbold, Johannes; Zafiu, Christian; Bannach, Oliver; Willbold, Dieter

2017-03-01

Alzheimer's disease (AD) is a neurodegenerative disorder with yet non-existent therapeutic and limited diagnostic options. Reliable biomarker-based AD diagnostics are of utmost importance for the development and application of therapeutic substances. We have previously introduced a platform technology designated 'sFIDA' for the quantitation of amyloid β peptide (Aβ) aggregates as AD biomarker. In this study we implemented the sFIDA assay on an automated platform to enhance robustness and performance of the assay. In sFIDA (surface-based fluorescence intensity distribution analysis) Aβ species are immobilized by a capture antibody to a glass surface. Aβ aggregates are then multiply loaded with fluorescent antibodies and quantitated by high resolution fluorescence microscopy. As a model system for Aβ aggregates, we used Aβ-conjugated silica nanoparticles (Aβ-SiNaPs) diluted in PBS buffer and cerebrospinal fluid, respectively. Automation of the assay was realized on a liquid handling system in combination with a microplate washer. The automation of the sFIDA assay results in improved intra-assay precision, linearity and sensitivity in comparison to the manual application, and achieved a limit of detection in the sub-femtomolar range. Automation improves the precision and sensitivity of the sFIDA assay, which is a prerequisite for high-throughput measurements and future application of the technology in routine AD diagnostics. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Automated gas chromatography

DOEpatents

Mowry, C.D.; Blair, D.S.; Rodacy, P.J.; Reber, S.D.

1999-07-13

An apparatus and process for the continuous, near real-time monitoring of low-level concentrations of organic compounds in a liquid, and, more particularly, a water stream. A small liquid volume of flow from a liquid process stream containing organic compounds is diverted by an automated process to a heated vaporization capillary where the liquid volume is vaporized to a gas that flows to an automated gas chromatograph separation column to chromatographically separate the organic compounds. Organic compounds are detected and the information transmitted to a control system for use in process control. Concentrations of organic compounds less than one part per million are detected in less than one minute. 7 figs.

Individual differences in the calibration of trust in automation.

PubMed

Pop, Vlad L; Shrewsbury, Alex; Durso, Francis T

2015-06-01

The objective was to determine whether operators with an expectancy that automation is trustworthy are better at calibrating their trust to changes in the capabilities of automation, and if so, why. Studies suggest that individual differences in automation expectancy may be able to account for why changes in the capabilities of automation lead to a substantial change in trust for some, yet only a small change for others. In a baggage screening task, 225 participants searched for weapons in 200 X-ray images of luggage. Participants were assisted by an automated decision aid exhibiting different levels of reliability. Measures of expectancy that automation is trustworthy were used in conjunction with subjective measures of trust and perceived reliability to identify individual differences in trust calibration. Operators with high expectancy that automation is trustworthy were more sensitive to changes (both increases and decreases) in automation reliability. This difference was eliminated by manipulating the causal attribution of automation errors. Attributing the cause of automation errors to factors external to the automation fosters an understanding of tasks and situations in which automation differs in reliability and may lead to more appropriate trust. The development of interventions can lead to calibrated trust in automation. © 2014, Human Factors and Ergonomics Society.

A novel automated detection system for swallowing sounds during eating and speech under everyday conditions.

PubMed

Fukuike, C; Kodama, N; Manda, Y; Hashimoto, Y; Sugimoto, K; Hirata, A; Pan, Q; Maeda, N; Minagi, S

2015-05-01

The wave analysis of swallowing sounds has been receiving attention because the recording process is easy and non-invasive. However, up until now, an expert has been needed to visually examine the entire recorded wave to distinguish swallowing from other sounds. The purpose of this study was to establish a methodology to automatically distinguish the sound of swallowing from sound data recorded during a meal in the presence of everyday ambient sound. Seven healthy participants (mean age: 26·7 ± 1·3 years) participated in this study. A laryngeal microphone and a condenser microphone attached to the nostril were used for simultaneous recording. Recoding took place while participants were taking a meal and talking with a conversational partner. Participants were instructed to step on a foot pedal trigger switch when they swallowed, representing self-enumeration of swallowing, and also to achieve six additional noise-making tasks during the meal in a randomised manner. The automated analysis system correctly detected 342 out of the 352 self-enumerated swallowing events (sensitivity: 97·2%) and 479 out of the 503 semblable wave periods of swallowing (specificity: 95·2%). In this study, the automated detection system for swallowing sounds using a nostril microphone was able to detect the swallowing event with high sensitivity and specificity even under the conditions of daily life, thus showing potential utility in the diagnosis or screening of dysphagic patients in future studies. © 2014 John Wiley & Sons Ltd.

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network

NASA Astrophysics Data System (ADS)

Hiramatsu, Yuya; Muramatsu, Chisako; Kobayashi, Hironobu; Hara, Takeshi; Fujita, Hiroshi

2017-03-01

Breast cancer screening with mammography and ultrasonography is expected to improve sensitivity compared with mammography alone, especially for women with dense breast. An automated breast volume scanner (ABVS) provides the operator-independent whole breast data which facilitate double reading and comparison with past exams, contralateral breast, and multimodality images. However, large volumetric data in screening practice increase radiologists' workload. Therefore, our goal is to develop a computer-aided detection scheme of breast masses in ABVS data for assisting radiologists' diagnosis and comparison with mammographic findings. In this study, false positive (FP) reduction scheme using deep convolutional neural network (DCNN) was investigated. For training DCNN, true positive and FP samples were obtained from the result of our initial mass detection scheme using the vector convergence filter. Regions of interest including the detected regions were extracted from the multiplanar reconstraction slices. We investigated methods to select effective FP samples for training the DCNN. Based on the free response receiver operating characteristic analysis, simple random sampling from the entire candidates was most effective in this study. Using DCNN, the number of FPs could be reduced by 60%, while retaining 90% of true masses. The result indicates the potential usefulness of DCNN for FP reduction in automated mass detection on ABVS images.

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.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

PubMed

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Managing laboratory automation in a changing pharmaceutical industry

PubMed Central

Rutherford, Michael L.

1995-01-01

The health care reform movement in the USA and increased requirements by regulatory agencies continue to have a major impact on the pharmaceutical industry and the laboratory. Laboratory management is expected to improve effciency by providing more analytical results at a lower cost, increasing customer service, reducing cycle time, while ensuring accurate results and more effective use of their staff. To achieve these expectations, many laboratories are using robotics and automated work stations. Establishing automated systems presents many challenges for laboratory management, including project and hardware selection, budget justification, implementation, validation, training, and support. To address these management challenges, the rationale for project selection and implementation, the obstacles encountered, project outcome, and learning points for several automated systems recently implemented in the Quality Control Laboratories at Eli Lilly are presented. PMID:18925014

Unsupervised Multi-Scale Change Detection from SAR Imagery for Monitoring Natural and Anthropogenic Disasters

NASA Astrophysics Data System (ADS)

Ajadi, Olaniyi A.

Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition.. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to

Automated computer-based detection of encounter behaviours in groups of honeybees.

PubMed

Blut, Christina; Crespi, Alessandro; Mersch, Danielle; Keller, Laurent; Zhao, Linlin; Kollmann, Markus; Schellscheidt, Benjamin; Fülber, Carsten; Beye, Martin

2017-12-15

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.

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

DOEpatents

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.

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models.

PubMed

van Luijtelaar, Gilles; Lüttjohann, Annika; Makarov, Vladimir V; Maksimenko, Vladimir A; Koronovskii, Alexei A; Hramov, Alexander E

2016-02-15

Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control. Copyright © 2015 Elsevier B.V. All rights reserved.

Comparison of probability statistics for automated ship detection in SAR imagery

NASA Astrophysics Data System (ADS)

Henschel, Michael D.; Rey, Maria T.; Campbell, J. W. M.; Petrovic, D.

1998-12-01

This paper discuses the initial results of a recent operational trial of the Ocean Monitoring Workstation's (OMW) ship detection algorithm which is essentially a Constant False Alarm Rate filter applied to Synthetic Aperture Radar data. The choice of probability distribution and methodologies for calculating scene specific statistics are discussed in some detail. An empirical basis for the choice of probability distribution used is discussed. We compare the results using a l-look, k-distribution function with various parameter choices and methods of estimation. As a special case of sea clutter statistics the application of a (chi) 2-distribution is also discussed. Comparisons are made with reference to RADARSAT data collected during the Maritime Command Operation Training exercise conducted in Atlantic Canadian Waters in June 1998. Reference is also made to previously collected statistics. The OMW is a commercial software suite that provides modules for automated vessel detection, oil spill monitoring, and environmental monitoring. This work has been undertaken to fine tune the OMW algorithm's, with special emphasis on the false alarm rate of each algorithm.

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

lidar change detection using building models

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Runyon, Scott C.; Jalobeanu, Andre; Esterline, Chelsea H.; Kruse, Fred A.

2014-06-01

Terrestrial LiDAR scans of building models collected with a FARO Focus3D and a RIEGL VZ-400 were used to investigate point-to-point and model-to-model LiDAR change detection. LiDAR data were scaled, decimated, and georegistered to mimic real world airborne collects. Two physical building models were used to explore various aspects of the change detection process. The first model was a 1:250-scale representation of the Naval Postgraduate School campus in Monterey, CA, constructed from Lego blocks and scanned in a laboratory setting using both the FARO and RIEGL. The second model at 1:8-scale consisted of large cardboard boxes placed outdoors and scanned from rooftops of adjacent buildings using the RIEGL. A point-to-point change detection scheme was applied directly to the point-cloud datasets. In the model-to-model change detection scheme, changes were detected by comparing Digital Surface Models (DSMs). The use of physical models allowed analysis of effects of changes in scanner and scanning geometry, and performance of the change detection methods on different types of changes, including building collapse or subsistence, construction, and shifts in location. Results indicate that at low false-alarm rates, the point-to-point method slightly outperforms the model-to-model method. The point-to-point method is less sensitive to misregistration errors in the data. Best results are obtained when the baseline and change datasets are collected using the same LiDAR system and collection geometry.

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.

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.

Image Change Detection via Ensemble Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

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,more » 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.« less

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Mencattini, Arianna; Casti, Paola; Martinelli, Eugenio; di Natale, Corrado; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2018-02-01

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

Automation in Clinical Microbiology

PubMed Central

Ledeboer, Nathan A.

2013-01-01

Historically, the trend toward automation in clinical pathology laboratories has largely bypassed the clinical microbiology laboratory. In this article, we review the historical impediments to automation in the microbiology laboratory and offer insight into the reasons why we believe that we are on the cusp of a dramatic change that will sweep a wave of automation into clinical microbiology laboratories. We review the currently available specimen-processing instruments as well as the total laboratory automation solutions. Lastly, we outline the types of studies that will need to be performed to fully assess the benefits of automation in microbiology laboratories. PMID:23515547

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects.

PubMed

Muszynski, C; Happillon, T; Azudin, K; Tylcz, J-B; Istrate, D; Marque, C

2018-05-08

Preterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) . The study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland. Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions. EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman's pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording. EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H 2 . A curve representing the number of correlated pairs of signals according to the value of H 2 calculated between bipolar signals was then plotted. High values of H 2 indicated the presence of an event that may correspond to a contraction. Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate. The EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H 2 was

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

The Automation of Reserve Processing.

ERIC Educational Resources Information Center

Self, James

1985-01-01

Describes an automated reserve processing system developed locally at Clemons Library, University of Virginia. Discussion covers developments in the reserve operation at Clemons Library, automation of the processing and circulation functions of reserve collections, and changes in reserve operation performance and staffing needs due to automation.…

Assessment of hearing threshold in adults with hearing loss using an automated system of cortical auditory evoked potential detection.

PubMed

Durante, Alessandra Spada; Wieselberg, Margarita Bernal; Roque, Nayara; Carvalho, Sheila; Pucci, Beatriz; Gudayol, Nicolly; de Almeida, Kátia

The use of hearing aids by individuals with hearing loss brings a better quality of life. Access to and benefit from these devices may be compromised in patients who present difficulties or limitations in traditional behavioral audiological evaluation, such as newborns and small children, individuals with auditory neuropathy spectrum, autism, and intellectual deficits, and in adults and the elderly with dementia. These populations (or individuals) are unable to undergo a behavioral assessment, and generate a growing demand for objective methods to assess hearing. Cortical auditory evoked potentials have been used for decades to estimate hearing thresholds. Current technological advances have lead to the development of equipment that allows their clinical use, with features that enable greater accuracy, sensitivity, and specificity, and the possibility of automated detection, analysis, and recording of cortical responses. To determine and correlate behavioral auditory thresholds with cortical auditory thresholds obtained from an automated response analysis technique. The study included 52 adults, divided into two groups: 21 adults with moderate to severe hearing loss (study group); and 31 adults with normal hearing (control group). An automated system of detection, analysis, and recording of cortical responses (HEARLab ® ) was used to record the behavioral and cortical thresholds. The subjects remained awake in an acoustically treated environment. Altogether, 150 tone bursts at 500, 1000, 2000, and 4000Hz were presented through insert earphones in descending-ascending intensity. The lowest level at which the subject detected the sound stimulus was defined as the behavioral (hearing) threshold (BT). The lowest level at which a cortical response was observed was defined as the cortical electrophysiological threshold. These two responses were correlated using linear regression. The cortical electrophysiological threshold was, on average, 7.8dB higher than the

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

PubMed

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

PubMed

Niemeijer, Meindert; van Ginneken, Bram; Russell, Stephen R; Suttorp-Schulten, Maria S A; Abràmoff, Michael D

2007-05-01

To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

NASA Astrophysics Data System (ADS)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adal, Kedir M.; Sidebe, Desire; Ali, Sharib

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 onlymore » 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.« less

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

PubMed

Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

2014-04-01

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 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 which can 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. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Understanding human management of automation errors.

PubMed

McBride, Sara E; Rogers, Wendy A; Fisk, Arthur D

2014-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance.

Trace-Level Automated Mercury Speciation Analysis

PubMed Central

Taylor, Vivien F.; Carter, Annie; Davies, Colin; Jackson, Brian P.

2011-01-01

An automated system for methyl Hg analysis by purge and trap gas chromatography (GC) was evaluated, with comparison of several different instrument configurations including chromatography columns (packed column or capillary), detector (atomic fluorescence, AFS, or inductively coupled plasma mass spectrometry, ICP-MS, using quadrupole and sector field ICP- MS instruments). Method detection limits (MDL) of 0.042 pg and 0.030 pg for CH3Hg+ were achieved with the automated Hg analysis system configured with AFS and ICPMS detection, respectively. Capillary GC with temperature programming was effective in improving resolution and decreasing retention times of heavier Hg species (in this case C3H7Hg+) although carryover between samples was increased. With capillary GC, the MDL for CH3Hg+ was 0.25 pg for AFS detection and 0.060 pg for ICP-MS detection. The automated system was demonstrated to have high throughput (72 samples analyzed in 8 hours) requiring considerably less analyst time than the manual method for methyl mercury analysis described in EPA 1630. PMID:21572543

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 wavefrontsmore » 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

Progress in Fully Automated Abdominal CT Interpretation

PubMed Central

Summers, Ronald M.

2016-01-01

OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation. PMID:27101207

Automated Detection and Closing of Holes in Aerial Point Clouds Using AN Uas

NASA Astrophysics Data System (ADS)

Fiolka, T.; Rouatbi, F.; Bender, D.

2017-08-01

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

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

Understanding the effect of workload on automation use for younger and older adults.

PubMed

McBride, Sara E; Rogers, Wendy A; Fisk, Arthur D

2011-12-01

This study examined how individuals, younger and older, interacted with an imperfect automated system. The impact of workload on performance and automation use was also investigated. Automation is used in situations characterized by varying levels of workload. As automated systems spread to domains such as transportation and the home, a diverse population of users will interact with automation. Research is needed to understand how different segments of the population use automation. Workload was systematically manipulated to create three levels (low, moderate, high) in a dual-task scenario in which participants interacted with a 70% reliable automated aid. Two experiments were conducted to assess automation use for younger and older adults. Both younger and older adults relied on the automation more than they complied with it. Among younger adults, high workload led to poorer performance and higher compliance, even when that compliance was detrimental. Older adults' performance was negatively affected by workload, but their compliance and reliance were unaffected. Younger and older adults were both able to use and double-check an imperfect automated system. Workload affected how younger adults complied with automation, particularly with regard to detecting automation false alarms. Older adults tended to comply and rely at fairly high rates overall, and this did not change with increased workload. Training programs for imperfect automated systems should vary workload and provide feedback about error types, and strategies for identifying errors. The ability to identify automation errors varies across individuals, thereby necessitating training.

A multiplex reverse transcription PCR and automated electronic microarray assay for detection and differentiation of seven viruses affecting swine.

PubMed

Erickson, A; Fisher, M; Furukawa-Stoffer, T; Ambagala, A; Hodko, D; Pasick, J; King, D P; Nfon, C; Ortega Polo, R; Lung, O

2018-04-01

Microarray technology can be useful for pathogen detection as it allows simultaneous interrogation of the presence or absence of a large number of genetic signatures. However, most microarray assays are labour-intensive and time-consuming to perform. This study describes the development and initial evaluation of a multiplex reverse transcription (RT)-PCR and novel accompanying automated electronic microarray assay for simultaneous detection and differentiation of seven important viruses that affect swine (foot-and-mouth disease virus [FMDV], swine vesicular disease virus [SVDV], vesicular exanthema of swine virus [VESV], African swine fever virus [ASFV], classical swine fever virus [CSFV], porcine respiratory and reproductive syndrome virus [PRRSV] and porcine circovirus type 2 [PCV2]). The novel electronic microarray assay utilizes a single, user-friendly instrument that integrates and automates capture probe printing, hybridization, washing and reporting on a disposable electronic microarray cartridge with 400 features. This assay accurately detected and identified a total of 68 isolates of the seven targeted virus species including 23 samples of FMDV, representing all seven serotypes, and 10 CSFV strains, representing all three genotypes. The assay successfully detected viruses in clinical samples from the field, experimentally infected animals (as early as 1 day post-infection (dpi) for FMDV and SVDV, 4 dpi for ASFV, 5 dpi for CSFV), as well as in biological material that were spiked with target viruses. The limit of detection was 10 copies/μl for ASFV, PCV2 and PRRSV, 100 copies/μl for SVDV, CSFV, VESV and 1,000 copies/μl for FMDV. The electronic microarray component had reduced analytical sensitivity for several of the target viruses when compared with the multiplex RT-PCR. The integration of capture probe printing allows custom onsite array printing as needed, while electrophoretically driven hybridization generates results faster than conventional

Development of adapted GMR-probes for automated detection of hidden defects in thin steel sheets

NASA Astrophysics Data System (ADS)

Pelkner, Matthias; Pohl, Rainer; Kreutzbruck, Marc; Commandeur, Colin

2016-02-01

Thin steel sheets with a thickness of 0.3 mm and less are the base materials of many everyday life products (cans, batteries, etc.). Potential inhomogeneities such as non-metallic inclusions inside the steel can lead to a rupture of the sheets when it is formed into a product such as a beverage can. Therefore, there is a need to develop automated NDT techniques to detect hidden defects and inclusions in thin sheets during production. For this purpose Tata Steel Europe and BAM, the Federal Institute for Materials Research and Testing (Germany), collaborate in order to develop an automated NDT-system. Defect detection systems have to be robust against external influences, especially when used in an industrial environment. In addition, such a facility has to achieve a high sensitivity and a high spatial resolution in terms of detecting small inclusions in the μm-regime. In a first step, we carried out a feasibility study to determine which testing method is promising for detecting hidden defects and inclusions inside ferrous thin steel sheets. Therefore, two methods were investigated in more detail - magnetic flux leakage testing (MFL) using giant magneto resistance sensor arrays (GMR) as receivers [1,2] and eddy current testing (ET). The capabilities of both methods were tested with 0.2 mm-thick steel samples containing small defects with depths ranging from 5 µm up to 60 µm. Only in case of GMR-MFL-testing, we were able to detect parts of the hidden defects with a depth of 10 µm trustworthily with a SNR better than 10 dB. Here, the lift off between sensor and surface was 250 µm. On this basis, we investigated different testing scenarios including velocity tests and different lift offs. In this contribution we present the results of the feasibility study leading to first prototypes of GMR-probes which are now installed as part of a demonstrator inside a production line.

Automation of disbond detection in aircraft fuselage through thermal image processing

NASA Technical Reports Server (NTRS)

Prabhu, D. R.; Winfree, W. P.

1992-01-01

A procedure for interpreting thermal images obtained during the nondestructive evaluation of aircraft bonded joints is presented. The procedure operates on time-derivative thermal images and resulted in a disbond image with disbonds highlighted. The size of the 'black clusters' in the output disbond image is a quantitative measure of disbond size. The procedure is illustrated using simulation data as well as data obtained through experimental testing of fabricated samples and aircraft panels. Good results are obtained, and, except in pathological cases, 'false calls' in the cases studied appeared only as noise in the output disbond image which was easily filtered out. The thermal detection technique coupled with an automated image interpretation capability will be a very fast and effective method for inspecting bonded joints in an aircraft structure.

Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

NASA Astrophysics Data System (ADS)

Bonev, George

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

PubMed

Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

2016-08-01

Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.

Intelligent Transportation Systems: Automated Guided Vehicle Systems in Changing Logistics Environments

NASA Astrophysics Data System (ADS)

Schulze, L.; Behling, S.; Buhrs, S.

2008-06-01

The usage of Automated Guided Vehicle Systems (AGVS) is growing. This has not always been the case in the past. A new record of the sells numbers is the result of inventive developments, new applications and modern thinking. One market that AGVS were not able to thoroughly conquer yet were rapidly changing logistics environments. The advantages in recurrent transportation with AGVS used to be hindered by the needs of flexibility. When nowadays managers talk about Flexible Manufacturing Systems (FMS) there is no reason not to consider AGVS. Fixed guidelines, permanent transfer stations and static routes are no necessity for most AGVS producers. Flexible Manufacturing Systems can raise profitability with AGVS. When robots start saving billions in production costs, the next step at same plants are automated materials handling systems. Today, there are hundreds of instances of computer-controlled systems designed to handle and transport materials, many of which have replaced conventional human-driven platform trucks. Reduced costs due to damages and failures, tracking and tracing as well as improved production scheduling on top of fewer personnel needs are only some of the advantages.

Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients.

PubMed

Warttig, Sheryl; Alderson, Phil; Evans, David Jw; Lewis, Sharon R; Kourbeti, Irene S; Smith, Andrew F

2018-06-25

Sepsis is a life-threatening condition that is usually diagnosed when a patient has a suspected or documented infection, and meets two or more criteria for systemic inflammatory response syndrome (SIRS). The incidence of sepsis is higher among people admitted to critical care settings such as the intensive care unit (ICU) than among people in other settings. If left untreated sepsis can quickly worsen; severe sepsis has a mortality rate of 40% or higher, depending on definition. Recognition of sepsis can be challenging as it usually requires patient data to be combined from multiple unconnected sources, and interpreted correctly, which can be complex and time consuming to do. Electronic systems that are designed to connect information sources together, and automatically collate, analyse, and continuously monitor the information, as well as alerting healthcare staff when pre-determined diagnostic thresholds are met, may offer benefits by facilitating earlier recognition of sepsis and faster initiation of treatment, such as antimicrobial therapy, fluid resuscitation, inotropes, and vasopressors if appropriate. However, there is the possibility that electronic, automated systems do not offer benefits, or even cause harm. This might happen if the systems are unable to correctly detect sepsis (meaning that treatment is not started when it should be, or it is started when it shouldn't be), or healthcare staff may not respond to alerts quickly enough, or get 'alarm fatigue' especially if the alarms go off frequently or give too many false alarms. To evaluate whether automated systems for the early detection of sepsis can reduce the time to appropriate treatment (such as initiation of antibiotics, fluids, inotropes, and vasopressors) and improve clinical outcomes in critically ill patients in the ICU. We searched CENTRAL; MEDLINE; Embase; CINAHL; ISI Web of science; and LILACS, clinicaltrials.gov, and the World Health Organization trials portal. We searched all databases

Deep Learning and Image Processing for Automated Crack Detection and Defect Measurement in Underground Structures

NASA Astrophysics Data System (ADS)

Panella, F.; Boehm, J.; Loo, Y.; Kaushik, A.; Gonzalez, D.

2018-05-01

This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information). As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

THE IMPACT OF TECHNOLOGICAL CHANGE IN THE MEATPACKING INDUSTRY. AUTOMATION PROGRAM REPORT, NUMBER 1.

ERIC Educational Resources Information Center

DICK, WILLIAM G.

TWENTY AUTOMATION MANPOWER SERVICES DEMONSTRATION PROJECTS WERE STARTED TO PROVIDE EXPERIENCE WITH JOB MARKET PROBLEMS CAUSED BY CHANGING TECHNOLOGY AND MASS LAYOFFS. THE FIRST OF THE SERIES, ESTABLISHED IN LOCAL PUBLIC EMPLOYMENT SERVICE OFFICES, THIS PROJECT DEALT WITH THE LAYOFF OF 675 WORKERS, PROBLEMS OF READJUSTMENT IN THE PLANT, THE…

Understanding human management of automation errors

PubMed Central

McBride, Sara E.; Rogers, Wendy A.; Fisk, Arthur D.

2013-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance. PMID:25383042

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

DOD Major Automated Information Systems: Improvements Can Be Made in Reporting Critical Changes and Clarifying Leadership Responsibility

DTIC Science & Technology

2016-03-01

The program’s estimated program development cost increased by 45 percent over the original acquisition program baseline due to program scope changes ...DOD MAJOR AUTOMATED INFORMATION SYSTEMS Improvements Can Be Made in Reporting Critical Changes and Clarifying...INFORMATION SYSTEMS Improvements Can Be Made in Reporting Critical Changes and Clarifying Leadership Responsibility Why GAO Did This Study The National

Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT

PubMed Central

Burns, Joseph E.; Yao, Jianhua; Muñoz, Hector

2016-01-01

Purpose To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT). Materials and Methods This retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14–88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed. Results Training set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]). Conclusion The fully automated computer system detects and anatomically localizes vertebral body fractures in

A dual-process account of auditory change detection.

PubMed

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

2010-08-01

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 objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Automated Power-Distribution System

NASA Technical Reports Server (NTRS)

Thomason, Cindy; Anderson, Paul M.; Martin, James A.

1990-01-01

Automated power-distribution system monitors and controls electrical power to modules in network. Handles both 208-V, 20-kHz single-phase alternating current and 120- to 150-V direct current. Power distributed to load modules from power-distribution control units (PDCU's) via subsystem distributors. Ring busses carry power to PDCU's from power source. Needs minimal attention. Detects faults and also protects against them. Potential applications include autonomous land vehicles and automated industrial process systems.

Ice crystal characterization in cirrus clouds: a sun-tracking camera system and automated detection algorithm for halo displays

NASA Astrophysics Data System (ADS)

Forster, Linda; Seefeldner, Meinhard; Wiegner, Matthias; Mayer, Bernhard

2017-07-01

Halo displays in the sky contain valuable information about ice crystal shape and orientation: e.g., the 22° halo is produced by randomly oriented hexagonal prisms while parhelia (sundogs) indicate oriented plates. HaloCam, a novel sun-tracking camera system for the automated observation of halo displays is presented. An initial visual evaluation of the frequency of halo displays for the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign from October to mid-November 2014 showed that sundogs were observed more often than 22° halos. Thus, the majority of halo displays was produced by oriented ice crystals. During the campaign about 27 % of the cirrus clouds produced 22° halos, sundogs or upper tangent arcs. To evaluate the HaloCam observations collected from regular measurements in Munich between January 2014 and June 2016, an automated detection algorithm for 22° halos was developed, which can be extended to other halo types as well. This algorithm detected 22° halos about 2 % of the time for this dataset. The frequency of cirrus clouds during this time period was estimated by co-located ceilometer measurements using temperature thresholds of the cloud base. About 25 % of the detected cirrus clouds occurred together with a 22° halo, which implies that these clouds contained a certain fraction of smooth, hexagonal ice crystals. HaloCam observations complemented by radiative transfer simulations and measurements of aerosol and cirrus cloud optical thickness (AOT and COT) provide a possibility to retrieve more detailed information about ice crystal roughness. This paper demonstrates the feasibility of a completely automated method to collect and evaluate a long-term database of halo observations and shows the potential to characterize ice crystal properties.

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies.

PubMed

Sigoillot, Frederic D; Huckins, Jeremy F; Li, Fuhai; Zhou, Xiaobo; Wong, Stephen T C; King, Randall W

2011-01-01

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments. Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment. This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

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…

Automated analysis of retinal images for detection of referable diabetic retinopathy.

PubMed

Abràmoff, Michael D; Folk, James C; Han, Dennis P; Walker, Jonathan D; Williams, David F; Russell, Stephen R; Massin, Pascale; Cochener, Beatrice; Gain, Philippe; Tang, Li; Lamard, Mathieu; Moga, Daniela C; Quellec, Gwénolé; Niemeijer, Meindert

2013-03-01

The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard. To determine the sensitivity and specificity of the Iowa Detection Program (IDP) to detect referable diabetic retinopathy (RDR). In primary care DR clinics in France, from January 1, 2005, through December 31, 2010, patients were photographed consecutively, and retinal color images were graded for retinopathy severity according to the International Clinical Diabetic Retinopathy scale and macular edema by 3 masked independent retinal specialists and regraded with adjudication until consensus. The IDP analyzed the same images at a predetermined and fixed set point. We defined RDR as more than mild nonproliferative retinopathy and/or macular edema. A total of 874 people with diabetes at risk for DR. Sensitivity and specificity of the IDP to detect RDR, area under the receiver operating characteristic curve, sensitivity and specificity of the retinal specialists' readings, and mean interobserver difference (κ). The RDR prevalence was 21.7% (95% CI, 19.0%-24.5%). The IDP sensitivity was 96.8% (95% CI, 94.4%-99.3%) and specificity was 59.4% (95% CI, 55.7%-63.0%), corresponding to 6 of 874 false-negative results (none met treatment criteria). The area under the receiver operating characteristic curve was 0.937 (95% CI, 0.916-0.959). Before adjudication and consensus, the sensitivity/specificity of the retinal specialists were 0.80/0.98, 0.71/1.00, and 0.91/0.95, and the mean intergrader κ was 0.822. The IDP has high sensitivity and specificity to detect RDR. Computer analysis of retinal photographs for DR and automated detection of RDR can be implemented safely into the DR screening pipeline, potentially improving access to screening and health care productivity and reducing visual loss

Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.

PubMed

Rovira, Ericka; Parasuraman, Raja

2010-06-01

This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation. Research has shown that the type of automation imperfection leads to differential human performance costs. Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence. ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance. The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.

An Automated Microfluidic Assay for Photonic Crystal Enhanced Detection and Analysis of an Antiviral Antibody Cancer Biomarker in Serum

DOE PAGES

Race, Caitlin M.; Kwon, Lydia E.; Foreman, Myles T.; ...

2017-11-24

Here, we report on the implementation of an automated platform for detecting the presence of an antibody biomarker for human papillomavirus-associated oropharyngeal cancer from a single droplet of serum, in which a nanostructured photonic crystal surface is used to amplify the output of a fluorescence-linked immunosorbent assay. The platform is comprised of a microfluidic cartridge with integrated photonic crystal chips that interfaces with an assay instrument that automates the introduction of reagents, wash steps, and surface drying. Upon assay completion, the cartridge interfaces with a custom laser-scanning instrument that couples light into the photonic crystal at the optimal resonance conditionmore » for fluorescence enhancement. The instrument is used to measure the fluorescence intensity values of microarray spots corresponding to the biomarkers of interest, in addition to several experimental controls that verify correct functioning of the assay protocol. In this work, we report both dose-response characterization of the system using anti-E7 antibody introduced at known concentrations into serum and characterization of a set of clinical samples from which results were compared with a conventional enzyme-linked immunosorbent assay (ELISA) performed in microplate format. Finally, the demonstrated capability represents a simple, rapid, automated, and high-sensitivity method for multiplexed detection of protein biomarkers from a low-volume test sample.« less

An Automated Microfluidic Assay for Photonic Crystal Enhanced Detection and Analysis of an Antiviral Antibody Cancer Biomarker in Serum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Race, Caitlin M.; Kwon, Lydia E.; Foreman, Myles T.

Here, we report on the implementation of an automated platform for detecting the presence of an antibody biomarker for human papillomavirus-associated oropharyngeal cancer from a single droplet of serum, in which a nanostructured photonic crystal surface is used to amplify the output of a fluorescence-linked immunosorbent assay. The platform is comprised of a microfluidic cartridge with integrated photonic crystal chips that interfaces with an assay instrument that automates the introduction of reagents, wash steps, and surface drying. Upon assay completion, the cartridge interfaces with a custom laser-scanning instrument that couples light into the photonic crystal at the optimal resonance conditionmore » for fluorescence enhancement. The instrument is used to measure the fluorescence intensity values of microarray spots corresponding to the biomarkers of interest, in addition to several experimental controls that verify correct functioning of the assay protocol. In this work, we report both dose-response characterization of the system using anti-E7 antibody introduced at known concentrations into serum and characterization of a set of clinical samples from which results were compared with a conventional enzyme-linked immunosorbent assay (ELISA) performed in microplate format. Finally, the demonstrated capability represents a simple, rapid, automated, and high-sensitivity method for multiplexed detection of protein biomarkers from a low-volume test sample.« less

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

DOEpatents

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.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging