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.

[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.

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.

Systems and methods for data quality control and cleansing

DOEpatents

Wenzel, Michael; Boettcher, Andrew; Drees, Kirk; Kummer, James

2016-05-31

A method for detecting and cleansing suspect building automation system data is shown and described. The method includes using processing electronics to automatically determine which of a plurality of error detectors and which of a plurality of data cleansers to use with building automation system data. The method further includes using processing electronics to automatically detect errors in the data and cleanse the data using a subset of the error detectors and a subset of the cleansers.

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 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.

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

PubMed Central

Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

2011-01-01

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

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 Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation

PubMed Central

2015-01-01

Background To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website’s HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. Objective The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Methods Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. Results For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the “document” definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Conclusions Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed. PMID:26036669

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.

Human versus automation in responding to failures: an expected-value analysis

NASA Technical Reports Server (NTRS)

Sheridan, T. B.; Parasuraman, R.

2000-01-01

A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.

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.

Defect analysis and detection of micro nano structured optical thin film

NASA Astrophysics Data System (ADS)

Xu, Chang; Shi, Nuo; Zhou, Lang; Shi, Qinfeng; Yang, Yang; Li, Zhuo

2017-10-01

This paper focuses on developing an automated method for detecting defects on our wavelength conversion thin film. We analyzes the operating principle of our wavelength conversion Micro/Nano thin film which absorbing visible light and emitting infrared radiation, indicates the relationship between the pixel's pattern and the radiation of the thin film, and issues the principle of defining blind pixels and their categories due to the calculated and experimental results. An effective method is issued for the automated detection based on wavelet transform and template matching. The results reveal that this method has desired accuracy and processing speed.

Automated measurement of office, home and ambulatory blood pressure in atrial fibrillation.

PubMed

Kollias, Anastasios; Stergiou, George S

2014-01-01

1. Hypertension and atrial fibrillation (AF) often coexist and are strong risk factors for stroke. Current guidelines for blood pressure (BP) measurement in AF recommend repeated measurements using the auscultatory method, whereas the accuracy of the automated devices is regarded as questionable. This review presents the current evidence on the feasibility and accuracy of automated BP measurement in the presence of AF and the potential for automated detection of undiagnosed AF during such measurements. 2. Studies evaluating the use of automated BP monitors in AF are limited and have significant heterogeneity in methodology and protocols. Overall, the oscillometric method is feasible for static (office or home) and ambulatory use and appears to be more accurate for systolic than diastolic BP measurement. 3. Given that systolic hypertension is particularly common and important in the elderly, the automated BP measurement method may be acceptable for self-home and ambulatory monitoring, but not for professional office or clinic measurement. 4. An embedded algorithm for the detection of asymptomatic AF during routine automated BP measurement with high diagnostic accuracy has been developed and appears to be a useful screening tool for elderly hypertensives. © 2013 Wiley Publishing Asia Pty Ltd.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

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.

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 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.

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 model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.

PubMed

Ulrich, Ethan J; Sunderland, John J; Smith, Brian J; Mohiuddin, Imran; Parkhurst, Jessica; Plichta, Kristin A; Buatti, John M; Beichel, Reinhard R

2018-01-01

Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R 2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ≪ 0.001) compared to manual analysis (mean: 247.8 s). The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts. © 2017 American Association of Physicists in Medicine.

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

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.

Segmentation of 3D ultrasound computer tomography reflection images using edge detection and surface fitting

NASA Astrophysics Data System (ADS)

Hopp, T.; Zapf, M.; Ruiter, N. V.

2014-03-01

An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

Apparatus and method for automated monitoring of airborne bacterial spores

NASA Technical Reports Server (NTRS)

Ponce, Adrian (Inventor)

2009-01-01

An apparatus and method for automated monitoring of airborne bacterial spores. The apparatus is provided with an air sampler, a surface for capturing airborne spores, a thermal lysis unit to release DPA from bacterial spores, a source of lanthanide ions, and a spectrometer for excitation and detection of the characteristic fluorescence of the aromatic molecules in bacterial spores complexed with lanthanide ions. In accordance with the method: computer-programmed steps allow for automation of the apparatus for the monitoring of airborne bacterial spores.

ABO Mistyping of cis-AB Blood Group by the Automated Microplate Technique.

PubMed

Chun, Sejong; Ryu, Mi Ra; Cha, Seung-Yeon; Seo, Ji-Young; Cho, Duck

2018-01-01

The cis -AB phenotype, although rare, is the relatively most frequent of ABO subgroups in Koreans. To prevent ABO mistyping of cis -AB samples, our hospital has applied a combination of the manual tile method with automated devices. Herein, we report cases of ABO mistyping detected by the combination testing system. Cases that showed discrepant results by automated devices and the manual tile method were evaluated. These samples were also tested by the standard tube method. The automated devices used in this study were a QWALYS-3 and Galileo NEO. Exons 6 and 7 of the ABO gene were sequenced. 13 cases that had the cis -AB allele showed results suggestive of the cis -AB subgroup by manual methods, but were interpreted as AB by either automated device. This happened in 87.5% of these cases by QWALYS-3 and 70.0% by Galileo NEO. Genotyping results showed that 12 cases were ABO*cis-AB01/ABO*O01 or ABO*cis-AB01/ABO*O02 , and one case was ABO*cis-AB01/ ABO*A102. Cis -AB samples were mistyped as AB by the automated microplate technique in some cases. We suggest that the manual tile method can be a simple supplemental test for the detection of the cis -AB phenotype, especially in countries with relatively high cis- AB prevalence.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

Automated determination of arterial input function for DCE-MRI of the prostate

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Chang, Ming-Ching; Gupta, Sandeep

2011-03-01

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.

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 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…

A novel tool for high-throughput screening of granulocyte-specific antibodies using the automated flow cytometric granulocyte immunofluorescence test (Flow-GIFT).

PubMed

Nguyen, Xuan Duc; Dengler, Thomas; Schulz-Linkholt, Monika; Klüter, Harald

2011-02-03

Transfusion-related acute lung injury (TRALI) is a severe complication related with blood transfusion. TRALI has usually been associated with antibodies against leukocytes. The flow cytometric granulocyte immunofluorescence test (Flow-GIFT) has been introduced for routine use when investigating patients and healthy blood donors. Here we describe a novel tool in the automation of the Flow-GIFT that enables a rapid screening of blood donations. We analyzed 440 sera from healthy female blood donors for the presence of granulocyte antibodies. As positive controls, 12 sera with known antibodies against anti-HNA-1a, -b, -2a; and -3a were additionally investigated. Whole-blood samples from HNA-typed donors were collected and the test cells isolated using cell sedimentation in a Ficoll density gradient. Subsequently, leukocytes were incubated with the respective serum and binding of antibodies was detected using FITC-conjugated antihuman antibody. 7-AAD was used to exclude dead cells. Pipetting steps were automated using the Biomek NXp Multichannel Automation Workstation. All samples were prepared in the 96-deep well plates and analyzed by flow cytometry. The standard granulocyte immunofluorescence test (GIFT) and granulocyte agglutination test (GAT) were also performed as reference methods. Sixteen sera were positive in the automated Flow-GIFT, while five of these sera were negative in the standard GIFT (anti-HNA 3a, n = 3; anti-HNA-1b, n = 1) and GAT (anti-HNA-2a, n = 1). The automated Flow-GIFT was able to detect all granulocyte antibodies, which could be only detected in GIFT in combination with GAT. In serial dilution tests, the automated Flow-GIFT detected the antibodies at higher dilutions than the reference methods GIFT and GAT. The Flow-GIFT proved to be feasible for automation. This novel high-throughput system allows an effective antigranulocyte antibody detection in a large donor population in order to prevent TRALI due to transfusion of blood products.

Automated measurement of estrogen receptor in breast cancer: a comparison of fluorescent and chromogenic methods of measurement

PubMed Central

Zarrella, Elizabeth; Coulter, Madeline; Welsh, Allison; Carvajal, Daniel; Schalper, Kurt; Harigopal, Malini; Rimm, David; Neumeister, Veronique

2016-01-01

While FDA approved methods of assessment of Estrogen Receptor (ER) are “fit for purpose”, they represent a 30-year-old technology. New quantitative methods, both chromogenic and fluorescent, have been developed and studies have shown that these methods increase the accuracy of assessment of ER. Here, we compare three methods of ER detection and assessment on two retrospective tissue microarray cohorts of breast cancer patients: estimates of percent nuclei positive by pathologists and by Aperio’s nuclear algorithm (standard chromogenic immunostaining), and immunofluorescence as quantified with the AQUA® method of quantitative immunofluorescence (QIF). Reproducibility was excellent (R2 > 0.95) between users for both automated analysis methods, and the Aperio and QIF scoring results were also highly correlated, despite the different detection systems. The subjective readings show lower levels of reproducibility and a discontinuous, bimodal distribution of scores not seen by either mechanized method. Kaplan-Meier analysis of 10-year disease-free survival was significant for each method (Pathologist, P=0.0019; Aperio, P=0.0053, AQUA, P=0.0026), but there were discrepancies in patient classification in 19 out of 233 cases analyzed. Out of these, 11 were visually positive by both chromogenic and fluorescent detection. In 10 cases, the Aperio nuclear algorithm labeled the nuclei as negative, in 1 case, the AQUA score was just under the cutoff for positivity (determined by an Index TMA). In contrast, 8 out of 19 discrepant cases had clear nuclear positivity by fluorescence that was unable to be visualized by chromogenic detection, perhaps due to low positivity masked by the hematoxylin counterstain. These results demonstrate that automated systems enable objective, precise quantification of ER. Furthermore immunofluorescence detection offers the additional advantage of a signal that cannot be masked by a counterstaining agent. These data support the usage of automated methods for measurement of this and other biomarkers that may be used in companion diagnostic tests. PMID:27348626

A New Automated Method and Sample Data Flow for Analysis of Volatile Nitrosamines in Human Urine*

PubMed Central

Hodgson, James A.; Seyler, Tiffany H.; McGahee, Ernest; Arnstein, Stephen; Wang, Lanqing

2016-01-01

Volatile nitrosamines (VNAs) are a group of compounds classified as probable (group 2A) and possible (group 2B) carcinogens in humans. Along with certain foods and contaminated drinking water, VNAs are detected at high levels in tobacco products and in both mainstream and sidestream smoke. Our laboratory monitors six urinary VNAs—N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosopiperidine (NPIP), N-nitrosopyrrolidine (NPYR), and N-nitrosomorpholine (NMOR)—using isotope dilution GC-MS/MS (QQQ) for large population studies such as the National Health and Nutrition Examination Survey (NHANES). In this paper, we report for the first time a new automated sample preparation method to more efficiently quantitate these VNAs. Automation is done using Hamilton STAR™ and Caliper Staccato™ workstations. This new automated method reduces sample preparation time from 4 hours to 2.5 hours while maintaining precision (inter-run CV < 10%) and accuracy (85% - 111%). More importantly this method increases sample throughput while maintaining a low limit of detection (<10 pg/mL) for all analytes. A streamlined sample data flow was created in parallel to the automated method, in which samples can be tracked from receiving to final LIMs output with minimal human intervention, further minimizing human error in the sample preparation process. This new automated method and the sample data flow are currently applied in bio-monitoring of VNAs in the US non-institutionalized population NHANES 2013-2014 cycle. PMID:26949569

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.

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.

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 processing of footage. Using semi-automated ape face detection technology for processing camera trap footage requires only 2-4% of the time compared to manual analysis and allows to estimate site use by chimpanzees relatively reliably. © 2017 Wiley Periodicals, Inc.

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.

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.

Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans

PubMed Central

Smits, Loek P.; van Wijk, Diederik F.; Duivenvoorden, Raphael; Xu, Dongxiang; Yuan, Chun; Stroes, Erik S.; Nederveen, Aart J.

2016-01-01

Purpose To study the interscan reproducibility of manual versus automated segmentation of carotid artery plaque components, and the agreement between both methods, in high and lower quality MRI scans. Methods 24 patients with 30–70% carotid artery stenosis were planned for 3T carotid MRI, followed by a rescan within 1 month. A multicontrast protocol (T1w,T2w, PDw and TOF sequences) was used. After co-registration and delineation of the lumen and outer wall, segmentation of plaque components (lipid-rich necrotic cores (LRNC) and calcifications) was performed both manually and automated. Scan quality was assessed using a visual quality scale. Results Agreement for the detection of LRNC (Cohen’s kappa (k) is 0.04) and calcification (k = 0.41) between both manual and automated segmentation methods was poor. In the high-quality scans (visual quality score ≥ 3), the agreement between manual and automated segmentation increased to k = 0.55 and k = 0.58 for, respectively, the detection of LRNC and calcification larger than 1 mm2. Both manual and automated analysis showed good interscan reproducibility for the quantification of LRNC (intraclass correlation coefficient (ICC) of 0.94 and 0.80 respectively) and calcified plaque area (ICC of 0.95 and 0.77, respectively). Conclusion Agreement between manual and automated segmentation of LRNC and calcifications was poor, despite a good interscan reproducibility of both methods. The agreement between both methods increased to moderate in high quality scans. These findings indicate that image quality is a critical determinant of the performance of both manual and automated segmentation of carotid artery plaque components. PMID:27930665

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

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.

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

High-throughput analysis of sub-visible mAb aggregate particles using automated fluorescence microscopy imaging.

PubMed

Paul, Albert Jesuran; Bickel, Fabian; Röhm, Martina; Hospach, Lisa; Halder, Bettina; Rettich, Nina; Handrick, René; Herold, Eva Maria; Kiefer, Hans; Hesse, Friedemann

2017-07-01

Aggregation of therapeutic proteins is a major concern as aggregates lower the yield and can impact the efficacy of the drug as well as the patient's safety. It can occur in all production stages; thus, it is essential to perform a detailed analysis for protein aggregates. Several methods such as size exclusion high-performance liquid chromatography (SE-HPLC), light scattering, turbidity, light obscuration, and microscopy-based approaches are used to analyze aggregates. None of these methods allows determination of all types of higher molecular weight (HMW) species due to a limited size range. Furthermore, quantification and specification of different HMW species are often not possible. Moreover, automation is a perspective challenge coming up with automated robotic laboratory systems. Hence, there is a need for a fast, high-throughput-compatible method, which can detect a broad size range and enable quantification and classification. We describe a novel approach for the detection of aggregates in the size range 1 to 1000 μm combining fluorescent dyes for protein aggregate labelling and automated fluorescence microscope imaging (aFMI). After appropriate selection of the dye and method optimization, our method enabled us to detect various types of HMW species of monoclonal antibodies (mAbs). Using 10 μmol L -1 4,4'-dianilino-1,1'-binaphthyl-5,5'-disulfonate (Bis-ANS) in combination with aFMI allowed the analysis of mAb aggregates induced by different stresses occurring during downstream processing, storage, and administration. Validation of our results was performed by SE-HPLC, UV-Vis spectroscopy, and dynamic light scattering. With this new approach, we could not only reliably detect different HMW species but also quantify and classify them in an automated approach. Our method achieves high-throughput requirements and the selection of various fluorescent dyes enables a broad range of applications.

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.

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

[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.

On the Agreement between Manual and Automated Methods for Single-Trial Detection and Estimation of Features from Event-Related Potentials

PubMed Central

Biurrun Manresa, José A.; Arguissain, Federico G.; Medina Redondo, David E.; Mørch, Carsten D.; Andersen, Ole K.

2015-01-01

The agreement between humans and algorithms on whether an event-related potential (ERP) is present or not and the level of variation in the estimated values of its relevant features are largely unknown. Thus, the aim of this study was to determine the categorical and quantitative agreement between manual and automated methods for single-trial detection and estimation of ERP features. To this end, ERPs were elicited in sixteen healthy volunteers using electrical stimulation at graded intensities below and above the nociceptive withdrawal reflex threshold. Presence/absence of an ERP peak (categorical outcome) and its amplitude and latency (quantitative outcome) in each single-trial were evaluated independently by two human observers and two automated algorithms taken from existing literature. Categorical agreement was assessed using percentage positive and negative agreement and Cohen’s κ, whereas quantitative agreement was evaluated using Bland-Altman analysis and the coefficient of variation. Typical values for the categorical agreement between manual and automated methods were derived, as well as reference values for the average and maximum differences that can be expected if one method is used instead of the others. Results showed that the human observers presented the highest categorical and quantitative agreement, and there were significantly large differences between detection and estimation of quantitative features among methods. In conclusion, substantial care should be taken in the selection of the detection/estimation approach, since factors like stimulation intensity and expected number of trials with/without response can play a significant role in the outcome of a study. PMID:26258532

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

Performance Equivalence and Validation of the Soleris Automated System for Quantitative Microbial Content Testing Using Pure Suspension Cultures.

PubMed

Limberg, Brian J; Johnstone, Kevin; Filloon, Thomas; Catrenich, Carl

2016-09-01

Using United States Pharmacopeia-National Formulary (USP-NF) general method <1223> guidance, the Soleris(®) automated system and reagents (Nonfermenting Total Viable Count for bacteria and Direct Yeast and Mold for yeast and mold) were validated, using a performance equivalence approach, as an alternative to plate counting for total microbial content analysis using five representative microbes: Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Candida albicans, and Aspergillus brasiliensis. Detection times (DTs) in the alternative automated system were linearly correlated to CFU/sample (R(2) = 0.94-0.97) with ≥70% accuracy per USP General Chapter <1223> guidance. The LOD and LOQ of the automated system were statistically similar to the traditional plate count method. This system was significantly more precise than plate counting (RSD 1.2-2.9% for DT, 7.8-40.6% for plate counts), was statistically comparable to plate counting with respect to variations in analyst, vial lots, and instruments, and was robust when variations in the operating detection thresholds (dTs; ±2 units) were used. The automated system produced accurate results, was more precise and less labor-intensive, and met or exceeded criteria for a valid alternative quantitative method, consistent with USP-NF general method <1223> guidance.

Decision support system for the detection and grading of hard exudates from color fundus photographs

NASA Astrophysics Data System (ADS)

Jaafar, Hussain F.; Nandi, Asoke K.; Al-Nuaimy, Waleed

2011-11-01

Diabetic retinopathy is a major cause of blindness, and its earliest signs include damage to the blood vessels and the formation of lesions in the retina. Automated detection and grading of hard exudates from the color fundus image is a critical step in the automated screening system for diabetic retinopathy. We propose novel methods for the detection and grading of hard exudates and the main retinal structures. For exudate detection, a novel approach based on coarse-to-fine strategy and a new image-splitting method are proposed with overall sensitivity of 93.2% and positive predictive value of 83.7% at the pixel level. The average sensitivity of the blood vessel detection is 85%, and the success rate of fovea localization is 100%. For exudate grading, a polar fovea coordinate system is adopted in accordance with medical criteria. Because of its competitive performance and ability to deal efficiently with images of variable quality, the proposed technique offers promising and efficient performance as part of an automated screening system for diabetic retinopathy.

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.

[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

Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.

PubMed

Karnowski, T P; Aykac, D; Giancardo, L; Li, Y; Nichols, T; Tobin, K W; Chaum, E

2011-01-01

The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

NASA Astrophysics Data System (ADS)

Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

2015-07-01

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

Automated Microfluidic Filtration and Immunocytochemistry Detection System for Capture and Enumeration of Circulating Tumor Cells and Other Rare Cell Populations in Blood.

PubMed

Pugia, Michael; Magbanua, Mark Jesus M; Park, John W

2017-01-01

Isolation by size using a filter membrane offers an antigen-independent method for capturing rare cells present in blood of cancer patients. Multiple cell types, including circulating tumor cells (CTCs), captured on the filter membrane can be simultaneously identified via immunocytochemistry (ICC) analysis of specific cellular biomarkers. Here, we describe an automated microfluidic filtration method combined with a liquid handling system for sequential ICC assays to detect and enumerate non-hematologic rare cells in blood.

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

PubMed

Boyer, Célia; Dolamic, Ljiljana

2015-06-02

To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website's HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the "document" definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed.

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

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

NASA Astrophysics Data System (ADS)

Amrute, Junedh M.; Athanasiou, Lambros S.; Rikhtegar, Farhad; de la Torre Hernández, José M.; Camarero, Tamara García; Edelman, Elazer R.

2018-03-01

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

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.

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.

High precision automated face localization in thermal images: oral cancer dataset as test case

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Raman, S. K.; Mukhopadhyay, S.; Patsa, S.; Anjum, N.; Ray, J. G.

2017-02-01

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

Comparison of two drug safety signals in a pharmacovigilance data mining framework.

PubMed

Tubert-Bitter, Pascale; Bégaud, Bernard; Ahmed, Ismaïl

2016-04-01

Since adverse drug reactions are a major public health concern, early detection of drug safety signals has become a top priority for regulatory agencies and the pharmaceutical industry. Quantitative methods for analyzing spontaneous reporting material recorded in pharmacovigilance databases through data mining have been proposed in the last decades and are increasingly used to flag potential safety problems. While automated data mining is motivated by the usually huge size of pharmacovigilance databases, it does not systematically produce relevant alerts. Moreover, each detected signal requires appropriate assessment that may involve investigation of the whole therapeutic class. The goal of this article is to provide a methodology for comparing two detected signals. It is nested within the automated surveillance framework as (1) no extra information is required and (2) no simple inference on the actual risks can be extrapolated from spontaneous reporting data. We designed our methodology on the basis of two classical methods used for automated signal detection: the Bayesian Gamma Poisson Shrinker and the frequentist Proportional Reporting Ratio. A simulation study was conducted to assess the performances of both proposed methods. The latter were used to compare cardiovascular signals for two HIV treatments from the French pharmacovigilance database. © The Author(s) 2012.

Vision Marker-Based In Situ Examination of Bacterial Growth in Liquid Culture Media.

PubMed

Kim, Kyukwang; Choi, Duckyu; Lim, Hwijoon; Kim, Hyeongkeun; Jeon, Jessie S

2016-12-18

The detection of bacterial growth in liquid media is an essential process in determining antibiotic susceptibility or the level of bacterial presence for clinical or research purposes. We have developed a system, which enables simplified and automated detection using a camera and a striped pattern marker. The quantification of bacterial growth is possible as the bacterial growth in the culturing vessel blurs the marker image, which is placed on the back of the vessel, and the blurring results in a decrease in the high-frequency spectrum region of the marker image. The experiment results show that the FFT (fast Fourier transform)-based growth detection method is robust to the variations in the type of bacterial carrier and vessels ranging from the culture tubes to the microfluidic devices. Moreover, the automated incubator and image acquisition system are developed to be used as a comprehensive in situ detection system. We expect that this result can be applied in the automation of biological experiments, such as the Antibiotics Susceptibility Test or toxicity measurement. Furthermore, the simple framework of the proposed growth measurement method may be further utilized as an effective and convenient method for building point-of-care devices for developing countries.

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.

Chest wall segmentation in automated 3D breast ultrasound scans.

PubMed

Tan, Tao; Platel, Bram; Mann, Ritse M; Huisman, Henkjan; Karssemeijer, Nico

2013-12-01

In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm. Copyright © 2012 Elsevier B.V. All rights reserved.

Semi-automated identification of cones in the human retina using circle Hough transform

PubMed Central

Bukowska, Danuta M.; Chew, Avenell L.; Huynh, Emily; Kashani, Irwin; Wan, Sue Ling; Wan, Pak Ming; Chen, Fred K

2015-01-01

A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately. PMID:26713186

Automated accident detection at intersections.

DOT National Transportation Integrated Search

2004-03-01

This research aims to provide a timely and accurate accident detection method at intersections, which is : very important for the Traffic Management System(TMS). This research uses acoustic signals to detect : accident at intersections. A system is c...

Comparison of visual microscopic and computer-automated fluorescence detection of rabies virus neutralizing antibodies.

PubMed

Péharpré, D; Cliquet, F; Sagné, E; Renders, C; Costy, F; Aubert, M

1999-07-01

The rapid fluorescent focus inhibition test (RFFIT) and the fluorescent antibody virus neutralization test (FAVNT) are both diagnostic tests for determining levels of rabies neutralizing antibodies. An automated method for determining fluorescence has been implemented to reduce the work time required for fluorescent visual microscopic observations. The automated method offers several advantages over conventional visual observation, such as the ability to rapidly test many samples. The antibody titers obtained with automated techniques were similar to those obtained with both the RFFIT (n = 165, r = 0.93, P < 0.001) and the FAVNT (n = 52, r = 0.99, P < 0.001).

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

The use of automated bioacoustic recorders to replace human wildlife surveys: an example using nightjars.

PubMed

Zwart, Mieke C; Baker, Andrew; McGowan, Philip J K; Whittingham, Mark J

2014-01-01

To be able to monitor and protect endangered species, we need accurate information on their numbers and where they live. Survey methods using automated bioacoustic recorders offer significant promise, especially for species whose behaviour or ecology reduces their detectability during traditional surveys, such as the European nightjar. In this study we examined the utility of automated bioacoustic recorders and the associated classification software as a way to survey for wildlife, using the nightjar as an example. We compared traditional human surveys with results obtained from bioacoustic recorders. When we compared these two methods using the recordings made at the same time as the human surveys, we found that recorders were better at detecting nightjars. However, in practice fieldworkers are likely to deploy recorders for extended periods to make best use of them. Our comparison of this practical approach with human surveys revealed that recorders were significantly better at detecting nightjars than human surveyors: recorders detected nightjars during 19 of 22 survey periods, while surveyors detected nightjars on only six of these occasions. In addition, there was no correlation between the amount of vocalisation captured by the acoustic recorders and the abundance of nightjars as recorded by human surveyors. The data obtained from the recorders revealed that nightjars were most active just before dawn and just after dusk, and least active during the middle of the night. As a result, we found that recording at both dusk and dawn or only at dawn would give reasonably high levels of detection while significantly reducing recording time, preserving battery life. Our analyses suggest that automated bioacoustic recorders could increase the detection of other species, particularly those that are known to be difficult to detect using traditional survey methods. The accuracy of detection is especially important when the data are used to inform conservation.

Automated volumetric segmentation of retinal fluid on optical coherence tomography

PubMed Central

Wang, Jie; Zhang, Miao; Pechauer, Alex D.; Liu, Liang; Hwang, Thomas S.; Wilson, David J.; Li, Dengwang; Jia, Yali

2016-01-01

We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation. PMID:27446676

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 circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Huang, Alex S.; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M.; Weinreb, Robert N.

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm's canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC's was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

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.

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 assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. National Cancer Institute.

Automated detection of nerve fiber layer defects on retinal fundus images using fully convolutional network for early diagnosis of glaucoma

NASA Astrophysics Data System (ADS)

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

2017-03-01

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. We have been studying an automated scheme for detection of a retinal nerve fiber layer defect (NFLD), which is one of the earliest signs of glaucoma on retinal fundus images. In our previous study, we proposed a multi-step detection scheme which consists of Gabor filtering, clustering and adaptive thresholding. The problems of the previous method were that the number of false positives (FPs) was still large and that the method included too many rules. In attempt to solve these problems, we investigated the end-to-end learning system without pre-specified features. A deep convolutional neural network (DCNN) with deconvolutional layers was trained to detect NFLD regions. In this preliminary investigation, we investigated effective ways of preparing the input images and compared the detection results. The optimal result was then compared with the result obtained by the previous method. DCNN training was carried out using original images of abnormal cases, original images of both normal and abnormal cases, ellipse-based polar transformed images, and transformed half images. The result showed that use of both normal and abnormal cases increased the sensitivity as well as the number of FPs. Although NFLDs are visualized with the highest contrast in green plane, the use of color images provided higher sensitivity than the use of green image only. The free response receiver operating characteristic curve using the transformed color images, which was the best among seven different sets studied, was comparable to that of the previous method. Use of DCNN has a potential to improve the generalizability of automated detection method of NFLDs and may be useful in assisting glaucoma diagnosis on retinal fundus images.

Machine-vision-based roadway health monitoring and assessment : development of a shape-based pavement-crack-detection approach.

DOT National Transportation Integrated Search

2016-01-01

State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level : pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and : manag...

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.

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.

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.

Development and Validation of an Automated High-Throughput System for Zebrafish In Vivo Screenings

PubMed Central

Virto, Juan M.; Holgado, Olaia; Diez, Maria; Izpisua Belmonte, Juan Carlos; Callol-Massot, Carles

2012-01-01

The zebrafish is a vertebrate model compatible with the paradigms of drug discovery. The small size and transparency of zebrafish embryos make them amenable for the automation necessary in high-throughput screenings. We have developed an automated high-throughput platform for in vivo chemical screenings on zebrafish embryos that includes automated methods for embryo dispensation, compound delivery, incubation, imaging and analysis of the results. At present, two different assays to detect cardiotoxic compounds and angiogenesis inhibitors can be automatically run in the platform, showing the versatility of the system. A validation of these two assays with known positive and negative compounds, as well as a screening for the detection of unknown anti-angiogenic compounds, have been successfully carried out in the system developed. We present a totally automated platform that allows for high-throughput screenings in a vertebrate organism. PMID:22615792

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.

Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.

PubMed

Tan, Tao; Gubern-Mérida, Albert; Borelli, Cristina; Manniesing, Rashindra; van Zelst, Jan; Wang, Lei; Zhang, Wei; Platel, Bram; Mann, Ritse M; Karssemeijer, Nico

2016-07-01

Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening modality to mammography for early detection of breast cancers. To facilitate the interpretation of ABUS images, automated diagnosis and detection techniques are being developed, in which malignant lesion segmentation plays an important role. However, automated segmentation of cancer in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim at developing an automated segmentation method for malignant lesions in ABUS that is robust to ill-defined cancer edges and posterior shadowing. A segmentation method using depth-guided dynamic programming based on spiral scanning is proposed. The method automatically adjusts aggressiveness of the segmentation according to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer), where posterior shadowing is usually visible. The authors used Dice similarity coefficient (Dice) for evaluation. The proposed method is compared to existing state of the art approaches such as graph cut, level set, and smart opening and an existing dynamic programming method without depth dependence. In a dataset of 78 cancers, our proposed segmentation method achieved a mean Dice of 0.73 ± 0.14. The method outperforms an existing dynamic programming method (0.70 ± 0.16) on this task (p = 0.03) and it is also significantly (p < 0.001) better than graph cut (0.66 ± 0.18), level set based approach (0.63 ± 0.20) and smart opening (0.65 ± 0.12). The proposed depth-guided dynamic programming method achieves accurate breast malignant lesion segmentation results in automated breast ultrasound.

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

USDA-ARS?s Scientific Manuscript database

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

Lesion Border Detection in Dermoscopy Images

PubMed Central

Celebi, M. Emre; Schaefer, Gerald; Iyatomi, Hitoshi; Stoecker, William V.

2009-01-01

Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses. PMID:19121917

Automation for deep space vehicle monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1991-01-01

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Extraction of the number of peroxisomes in yeast cells by automated image analysis.

PubMed

Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

2006-01-01

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

a Novel Method for Automation of 3d Hydro Break Line Generation from LIDAR Data Using Matlab

NASA Astrophysics Data System (ADS)

Toscano, G. J.; Gopalam, U.; Devarajan, V.

2013-08-01

Water body detection is necessary to generate hydro break lines, which are in turn useful in creating deliverables such as TINs, contours, DEMs from LiDAR data. Hydro flattening follows the detection and delineation of water bodies (lakes, rivers, ponds, reservoirs, streams etc.) with hydro break lines. Manual hydro break line generation is time consuming and expensive. Accuracy and processing time depend on the number of vertices marked for delineation of break lines. Automation with minimal human intervention is desired for this operation. This paper proposes using a novel histogram analysis of LiDAR elevation data and LiDAR intensity data to automatically detect water bodies. Detection of water bodies using elevation information was verified by checking against LiDAR intensity data since the spectral reflectance of water bodies is very small compared with that of land and vegetation in near infra-red wavelength range. Detection of water bodies using LiDAR intensity data was also verified by checking against LiDAR elevation data. False detections were removed using morphological operations and 3D break lines were generated. Finally, a comparison of automatically generated break lines with their semi-automated/manual counterparts was performed to assess the accuracy of the proposed method and the results were discussed.

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

PubMed Central

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

2005-01-01

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

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

Novel Automated Blood Separations Validate Whole Cell Biomarkers

PubMed Central

Burger, Douglas E.; Wang, Limei; Ban, Liqin; Okubo, Yoshiaki; Kühtreiber, Willem M.; Leichliter, Ashley K.; Faustman, Denise L.

2011-01-01

Background Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs). Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs) of fresh blood samples. Methods and Findings To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes. Conclusions Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials. PMID:21799852

Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization

PubMed Central

Ray, Laura B.; Sockeel, Stéphane; Soon, Melissa; Bore, Arnaud; Myhr, Ayako; Stojanoski, Bobby; Cusack, Rhodri; Owen, Adrian M.; Doyon, Julien; Fogel, Stuart M.

2015-01-01

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing “background” sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11–16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles. PMID:26441604

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 retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

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.

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.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography.

PubMed

Huang, Alex S; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M; Weinreb, Robert N

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images.

PubMed

Shahidi, Shoaleh; Bahrampour, Ehsan; Soltanimehr, Elham; Zamani, Ali; Oshagh, Morteza; Moattari, Marzieh; Mehdizadeh, Alireza

2014-09-16

Two-dimensional projection radiographs have been traditionally considered the modality of choice for cephalometric analysis. To overcome the shortcomings of two-dimensional images, three-dimensional computed tomography (CT) has been used to evaluate craniofacial structures. However, manual landmark detection depends on medical expertise, and the process is time-consuming. The present study was designed to produce software capable of automated localization of craniofacial landmarks on cone beam (CB) CT images based on image registration and to evaluate its accuracy. The software was designed using MATLAB programming language. The technique was a combination of feature-based (principal axes registration) and voxel similarity-based methods for image registration. A total of 8 CBCT images were selected as our reference images for creating a head atlas. Then, 20 CBCT images were randomly selected as the test images for evaluating the method. Three experts twice located 14 landmarks in all 28 CBCT images during two examinations set 6 weeks apart. The differences in the distances of coordinates of each landmark on each image between manual and automated detection methods were calculated and reported as mean errors. The combined intraclass correlation coefficient for intraobserver reliability was 0.89 and for interobserver reliability 0.87 (95% confidence interval, 0.82 to 0.93). The mean errors of all 14 landmarks were <4 mm. Additionally, 63.57% of landmarks had a mean error of <3 mm compared with manual detection (gold standard method). The accuracy of our approach for automated localization of craniofacial landmarks, which was based on combining feature-based and voxel similarity-based methods for image registration, was acceptable. Nevertheless we recommend repetition of this study using other techniques, such as intensity-based methods.

Rapid Change Detection Algorithm for Disaster Management

NASA Astrophysics Data System (ADS)

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

2012-07-01

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

Automated extraction for the analysis of 11-nor-delta9-tetrahydrocannabinol-9-carboxylic acid (THCCOOH) in urine using a six-head probe Hamilton Microlab 2200 system and gas chromatography-mass spectrometry.

PubMed

Whitter, P D; Cary, P L; Leaton, J I; Johnson, J E

1999-01-01

An automated extraction scheme for the analysis of 11 -nor-delta9-tetrahydrocannabinol-9-carboxylic acid using the Hamilton Microlab 2200, which was modified for gravity-flow solid-phase extraction, has been evaluated. The Hamilton was fitted with a six-head probe, a modular valve positioner, and a peristaltic pump. The automated method significantly increased sample throughput, improved assay consistency, and reduced the time spent performing the extraction. Extraction recovery for the automated method was > 90%. The limit of detection, limit of quantitation, and upper limit of linearity were equivalent to the manual method: 1.5, 3.0, and 300 ng/mL, respectively. Precision at the 15-ng/mL cut-off was as follows: mean = 14.4, standard deviation = 0.5, coefficient of variation = 3.5%. Comparison of 38 patient samples, extracted by the manual and automated extraction methods, demonstrated the following correlation statistics: r = .991, slope 1.029, and y-intercept -2.895. Carryover was < 0.3% at 1000 ng/mL. Aliquoting/extraction time for the automated method (48 urine samples) was 50 min, and the manual procedure required approximately 2.5 h. The automated aliquoting/extraction method on the Hamilton Microlab 2200 and its use in forensic applications are reviewed.

Processing bronchial sonograms to detect respiratory cycle fragments

NASA Astrophysics Data System (ADS)

Bureev, A. Sh; Zhdanov, D. S.; Zemlyakov, I. Yu; Svetlik, M. V.

2014-10-01

This article describes the authors' results of work on the development of a method for the automated assessment of the state of the human bronchopulmonary system based on acoustic data. In particular, the article covers the method of detecting breath sounds on bronchial sonograms obtained during the auscultation process.

Automated quantitative cytological analysis using portable microfluidic microscopy.

PubMed

Jagannadh, Veerendra Kalyan; Murthy, Rashmi Sreeramachandra; Srinivasan, Rajesh; Gorthi, Sai Siva

2016-06-01

In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide-based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in-suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rapid System to Quantitatively Characterize the Airborne Microbial Community

NASA Technical Reports Server (NTRS)

Macnaughton, Sarah J.

1998-01-01

Bioaerosols have been linked to a wide range of different allergies and respiratory illnesses. Currently, microorganism culture is the most commonly used method for exposure assessment. Such culture techniques, however, generally fail to detect between 90-99% of the actual viable biomass. Consequently, an unbiased technique for detecting airborne microorganisms is essential. In this Phase II proposal, a portable air sampling device his been developed for the collection of airborne microbial biomass from indoor (and outdoor) environments. Methods were evaluated for extracting and identifying lipids that provide information on indoor air microbial biomass, and automation of these procedures was investigated. Also, techniques to automate the extraction of DNA were explored.

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.

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 capillary Western dot blot method for the identity of a 15-valent pneumococcal conjugate vaccine.

PubMed

Hamm, Melissa; Ha, Sha; Rustandi, Richard R

2015-06-01

Simple Western is a new technology that allows for the separation, blotting, and detection of proteins similar to a traditional Western except in a capillary format. Traditionally, identity assays for biological products are performed using either an enzyme-linked immunosorbent assay (ELISA) or a manual dot blot Western. Both techniques are usually very tedious, labor-intensive, and complicated for multivalent vaccines, and they can be difficult to transfer to other laboratories. An advantage this capillary Western technique has over the traditional manual dot blot Western method is the speed and the automation of electrophoresis separation, blotting, and detection steps performed in 96 capillaries. This article describes details of the development of an automated identity assay for a 15-valent pneumococcal conjugate vaccine, PCV15-CRM197, using capillary Western technology. Copyright © 2015 Elsevier Inc. All rights reserved.

Computerised electronic foetal heart rate monitoring in labour: automated contraction identification.

PubMed

Georgieva, A; Payne, S J; Redman, C W G

2009-12-01

The foetal heart rate (FHR) response to uterine contractions is crucial to detect foetal distress by electronic FHR monitoring during labour. We are developing a new automated system (OxSys) for decision support in labour, using the Oxford database of intrapartum FHR records. We describe here a novel technique for automated detection of uterus contractions. In addition, we present a comparison of the new method with four other computerised approaches. During training, OxSys achieved sensitivity above 95% and positive predictive value (PPV) of up to 90% for traces of good quality. During testing, OxSys achieved sensitivity = 87% and PPV = 75%. For comparison, a second clinical expert obtained sensitivity = 93% and PPV = 80%, and all other computerised approaches achieved lower values. It was concluded that the proposed method can be employed with confidence in our study on foetal health assessment in labour and future OxSys development.

Picking vs Waveform based detection and location methods for induced seismicity monitoring

NASA Astrophysics Data System (ADS)

Grigoli, Francesco; Boese, Maren; Scarabello, Luca; Diehl, Tobias; Weber, Bernd; Wiemer, Stefan; Clinton, John F.

2017-04-01

Microseismic monitoring is a common operation in various industrial activities related to geo-resouces, such as oil and gas and mining operations or geothermal energy exploitation. In microseismic monitoring we generally deal with large datasets from dense monitoring networks that require robust automated analysis procedures. The seismic sequences being monitored are often characterized by very many events with short inter-event times that can even provide overlapped seismic signatures. In these situations, traditional approaches that identify seismic events using dense seismic networks based on detections, phase identification and event association can fail, leading to missed detections and/or reduced location resolution. In recent years, to improve the quality of automated catalogues, various waveform-based methods for the detection and location of microseismicity have been proposed. These methods exploit the coherence of the waveforms recorded at different stations and do not require any automated picking procedure. Although this family of methods have been applied to different induced seismicity datasets, an extensive comparison with sophisticated pick-based detection and location methods is still lacking. We aim here to perform a systematic comparison in term of performance using the waveform-based method LOKI and the pick-based detection and location methods (SCAUTOLOC and SCANLOC) implemented within the SeisComP3 software package. SCANLOC is a new detection and location method specifically designed for seismic monitoring at local scale. Although recent applications have proved an extensive test with induced seismicity datasets have been not yet performed. This method is based on a cluster search algorithm to associate detections to one or many potential earthquake sources. On the other hand, SCAUTOLOC is more a "conventional" method and is the basic tool for seismic event detection and location in SeisComp3. This approach was specifically designed for regional and teleseismic applications, thus its performance with microseismic data might be limited. We analyze the performance of the three methodologies for a synthetic dataset with realistic noise conditions as well as for the first hour of continuous waveform data, including the Ml 3.5 St. Gallen earthquake, recorded by a microseismic network deployed in the area. We finally compare the results obtained all these three methods with a manually revised catalogue.

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.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

An automated method for the analysis of phenolic acids in plasma based on ion-pairing micro-extraction coupled on-line to gas chromatography/mass spectrometry with in-liner derivatisation.

PubMed

Peters, Sonja; Kaal, Erwin; Horsting, Iwan; Janssen, Hans-Gerd

2012-02-24

A new method is presented for the analysis of phenolic acids in plasma based on ion-pairing 'Micro-extraction in packed sorbent' (MEPS) coupled on-line to in-liner derivatisation-gas chromatography-mass spectrometry (GC-MS). The ion-pairing reagent served a dual purpose. It was used both to improve extraction yields of the more polar analytes and as the methyl donor in the automated in-liner derivatisation method. In this way, a fully automated procedure for the extraction, derivatisation and injection of a wide range of phenolic acids in plasma samples has been obtained. An extensive optimisation of the extraction and derivatisation procedure has been performed. The entire method showed excellent repeatabilities of under 10% and linearities of 0.99 or better for all phenolic acids. The limits of detection of the optimised method for the majority of phenolic acids were 10ng/mL or lower with three phenolic acids having less-favourable detection limits of around 100 ng/mL. Finally, the newly developed method has been applied in a human intervention trial in which the bioavailability of polyphenols from wine and tea was studied. Forty plasma samples could be analysed within 24h in a fully automated method including sample extraction, derivatisation and gas chromatographic analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

PubMed

Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

2015-04-01

A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

Surface plasmon resonance (SPR) detection of Staphylococcal Enterotoxin A in food samples

USDA-ARS?s Scientific Manuscript database

An automated and rapid method for detection of staphylococcal enterotoxins (SE) is needed. A sandwich assay was developed using a surface plasmon resonance (SPR) biosensor for detection of staphylococcal enterotoxin A (SEA) at subpicomolar concentration. Assay conditions were optimized for capturing...

A thesis on the Development of an Automated SWIFT Edge Detection Algorithm

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

Trujillo, Christopher J.

Throughout the world, scientists and engineers such as those at Los Alamos National Laboratory, perform research and testing unique only to applications aimed towards advancing technology, and understanding the nature of materials. With this testing, comes a need for advanced methods of data acquisition and most importantly, a means of analyzing and extracting the necessary information from such acquired data. In this thesis, I aim to produce an automated method implementing advanced image processing techniques and tools to analyze SWIFT image datasets for Detonator Technology at Los Alamos National Laboratory. Such an effective method for edge detection and point extractionmore » can prove to be advantageous in analyzing such unique datasets and provide for consistency in producing results.« less

Detection of the nipple in automated 3D breast ultrasound using coronal slab-average-projection and cumulative probability map

NASA Astrophysics Data System (ADS)

Kim, Hannah; Hong, Helen

2014-03-01

We propose an automatic method for nipple detection on 3D automated breast ultrasound (3D ABUS) images using coronal slab-average-projection and cumulative probability map. First, to identify coronal images that appeared remarkable distinction between nipple-areola region and skin, skewness of each coronal image is measured and the negatively skewed images are selected. Then, coronal slab-average-projection image is reformatted from selected images. Second, to localize nipple-areola region, elliptical ROI covering nipple-areola region is detected using Hough ellipse transform in coronal slab-average-projection image. Finally, to separate the nipple from areola region, 3D Otsu's thresholding is applied to the elliptical ROI and cumulative probability map in the elliptical ROI is generated by assigning high probability to low intensity region. False detected small components are eliminated using morphological opening and the center point of detected nipple region is calculated. Experimental results show that our method provides 94.4% nipple detection rate.

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

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

Real-time Bayesian anomaly detection in streaming environmental data

NASA Astrophysics Data System (ADS)

Hill, David J.; Minsker, Barbara S.; Amir, Eyal

2009-04-01

With large volumes of data arriving in near real time from environmental sensors, there is a need for automated detection of anomalous data caused by sensor or transmission errors or by infrequent system behaviors. This study develops and evaluates three automated anomaly detection methods using dynamic Bayesian networks (DBNs), which perform fast, incremental evaluation of data as they become available, scale to large quantities of data, and require no a priori information regarding process variables or types of anomalies that may be encountered. This study investigates these methods' abilities to identify anomalies in eight meteorological data streams from Corpus Christi, Texas. The results indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering, outperform a DBN-based detector using Kalman filtering, with the former having false positive/negative rates of less than 2%. These methods were successful at identifying data anomalies caused by two real events: a sensor failure and a large storm.

Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk Hα Images

NASA Astrophysics Data System (ADS)

Yang, Meng; Tian, Yu; Liu, Yangyi; Rao, Changhui

2018-05-01

In this article, an automated solar flare detection method applied to both full-disk and local high-resolution Hα images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of Hα solar flares.

Evaluation of mericon E. coli O157 Screen Plus and mericon E. coli STEC O-Type Pathogen Detection Assays in Select Foods: Collaborative Study, First Action 2017.05.

PubMed

Bird, Patrick; Benzinger, M Joseph; Bastin, Benjamin; Crowley, Erin; Agin, James; Goins, David; Armstrong, Marcia

2018-05-01

QIAGEN mericon Escherichia coli O157 Screen Plus and mericon E. coli Shiga toxin-producing E. coli (STEC) O-Type Pathogen Detection Assays use Real-Time PCR technology for the rapid, accurate detection of E. coli O157 and the "big six" (O26, O45, O103, O111, O121, O145) (non-O157 STEC) in select food types. Using a paired study design, the assays were compared with the U.S. Department of Agriculture, Food Safety Inspection Service Microbiology Laboratory Guidebook Chapter 5.09 reference method for the detection of E. coli O157:H7 in raw ground beef. Both mericon assays were evaluated using the manual and an automated DNA extraction method. Thirteen technicians from five laboratories located within the continental United States participated in the collaborative study. Three levels of contamination were evaluated. Statistical analysis was conducted according to the probability of detection (POD) statistical model. Results obtained for the low-inoculum level test portions produced a difference between laboratories POD (dLPOD) value with a 95% confidence interval of 0.00 (-0.12, 0.12) for the mericon E. coli O157 Screen Plus with manual and automated extraction and mericon E. coli STEC O-Type with manual extraction and -0.01 (-0.13, 0.10) for the mericon E. coli STEC O-Type with automated extraction. The dLPOD results indicate equivalence between the candidate methods and the reference method.

Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

2015-11-01

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

Automated scoring of regional lung perfusion in children from contrast enhanced 3D MRI

NASA Astrophysics Data System (ADS)

Heimann, Tobias; Eichinger, Monika; Bauman, Grzegorz; Bischoff, Arved; Puderbach, Michael; Meinzer, Hans-Peter

2012-03-01

MRI perfusion images give information about regional lung function and can be used to detect pulmonary pathologies in cystic fibrosis (CF) children. However, manual assessment of the percentage of pathologic tissue in defined lung subvolumes features large inter- and intra-observer variation, making it difficult to determine disease progression consistently. We present an automated method to calculate a regional score for this purpose. First, lungs are located based on thresholding and morphological operations. Second, statistical shape models of left and right children's lungs are initialized at the determined locations and used to precisely segment morphological images. Segmentation results are transferred to perfusion maps and employed as masks to calculate perfusion statistics. An automated threshold to determine pathologic tissue is calculated and used to determine accurate regional scores. We evaluated the method on 10 MRI images and achieved an average surface distance of less than 1.5 mm compared to manual reference segmentations. Pathologic tissue was detected correctly in 9 cases. The approach seems suitable for detecting early signs of CF and monitoring response to therapy.

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.

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.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

PubMed

Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

2014-07-01

To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

Electrophoretic mobility shift scanning using an automated infrared DNA sequencer.

PubMed

Sano, M; Ohyama, A; Takase, K; Yamamoto, M; Machida, M

2001-11-01

Electrophoretic mobility shift assay (EMSA) is widely used in the study of sequence-specific DNA-binding proteins, including transcription factors and mismatch binding proteins. We have established a non-radioisotope-based protocol for EMSA that features an automated DNA sequencer with an infrared fluorescent dye (IRDye) detection unit. Our modification of the elec- trophoresis unit, which includes cooling the gel plates with a reduced well-to-read length, has made it possible to detect shifted bands within 1 h. Further, we have developed a rapid ligation-based method for generating IRDye-labeled probes with an approximately 60% cost reduction. This method has the advantages of real-time scanning, stability of labeled probes, and better safety associated with nonradioactive methods of detection. Analysis of a promoter from an industrially important filamentous fungus, Aspergillus oryzae, in a prototype experiment revealed that the method we describe has potential for use in systematic scanning and identification of the functionally important elements to which cellular factors bind in a sequence-specific manner.

Practical Considerations for Optic Nerve Estimation in Telemedicine

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

Karnowski, Thomas Paul; Aykac, Deniz; Chaum, Edward

The projected increase in diabetes in the United States and worldwide has created a need for broad-based, inexpensive screening for diabetic retinopathy (DR), an eye disease which can lead to vision impairment. A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening. In this work we report on the effect of quality estimation on an optic nerve (ON) detection method with a confidence metric. We report on an improvement of the fusion technique using a data set from an ophthalmologists practice then show themore » results of the method as a function of image quality on a set of images from an on-line telemedicine network collected in Spring 2009 and another broad-based screening program. We show that the fusion method, combined with quality estimation processing, can improve detection performance and also provide a method for utilizing a physician-in-the-loop for images that may exceed the capabilities of automated processing.« less

System and Method for Automated Rendezvous, Docking and Capture of Autonomous Underwater Vehicles

NASA Technical Reports Server (NTRS)

Clark, Evan (Inventor); Richmond, Kristof (Inventor); Paulus, Jeremy (Inventor); Kimball, Peter (Inventor); Scully, Mark (Inventor); Kapit, Jason (Inventor); Stone, William C. (Inventor)

2018-01-01

A system for automated rendezvous, docking, and capture of autonomous underwater vehicles at the conclusion of a mission comprising of comprised of a docking rod having lighted, pulsating (in both frequency and light intensity) series of LED light strips thereon, with the LEDs at a known spacing, and the autonomous underwater vehicle specially designed to detect and capture the docking rod and then be lifted structurally by a spherical end strop about which the vehicle can be pivoted and hoisted up (e.g., onto a ship). The method of recovery allows for very routine and reliable automated recovery of an unmanned underwater asset.

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.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

NASA Astrophysics Data System (ADS)

Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

2012-12-01

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

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.

A universal method for automated gene mapping

PubMed Central

Zipperlen, Peder; Nairz, Knud; Rimann, Ivo; Basler, Konrad; Hafen, Ernst; Hengartner, Michael; Hajnal, Alex

2005-01-01

Small insertions or deletions (InDels) constitute a ubiquituous class of sequence polymorphisms found in eukaryotic genomes. Here, we present an automated high-throughput genotyping method that relies on the detection of fragment-length polymorphisms (FLPs) caused by InDels. The protocol utilizes standard sequencers and genotyping software. We have established genome-wide FLP maps for both Caenorhabditis elegans and Drosophila melanogaster that facilitate genetic mapping with a minimum of manual input and at comparatively low cost. PMID:15693948

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

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

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

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.

COMPARISON BETWEEN AUTOMATED SYSTEM AND PCR-BASED METHOD FOR IDENTIFICATION AND ANTIMICROBIAL SUSCEPTIBILITY PROFILE OF CLINICAL Enterococcus spp

PubMed Central

Furlaneto-Maia, Luciana; Rocha, Kátia Real; Siqueira, Vera Lúcia Dias; Furlaneto, Márcia Cristina

2014-01-01

Enterococci are increasingly responsible for nosocomial infections worldwide. This study was undertaken to compare the identification and susceptibility profile using an automated MicrosScan system, PCR-based assay and disk diffusion assay of Enterococcus spp. We evaluated 30 clinical isolates of Enterococcus spp. Isolates were identified by MicrosScan system and PCR-based assay. The detection of antibiotic resistance genes (vancomycin, gentamicin, tetracycline and erythromycin) was also determined by PCR. Antimicrobial susceptibilities to vancomycin (30 µg), gentamicin (120 µg), tetracycline (30 µg) and erythromycin (15 µg) were tested by the automated system and disk diffusion method, and were interpreted according to the criteria recommended in CLSI guidelines. Concerning Enterococcus identification the general agreement between data obtained by the PCR method and by the automatic system was 90.0% (27/30). For all isolates of E. faecium and E. faecalis we observed 100% agreement. Resistance frequencies were higher in E. faecium than E. faecalis. The resistance rates obtained were higher for erythromycin (86.7%), vancomycin (80.0%), tetracycline (43.35) and gentamicin (33.3%). The correlation between disk diffusion and automation revealed an agreement for the majority of the antibiotics with category agreement rates of > 80%. The PCR-based assay, the van(A) gene was detected in 100% of vancomycin resistant enterococci. This assay is simple to conduct and reliable in the identification of clinically relevant enterococci. The data obtained reinforced the need for an improvement of the automated system to identify some enterococci. PMID:24626409

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.

Acoustic-sensor-based detection of damage in composite aircraft structures

NASA Astrophysics Data System (ADS)

Foote, Peter; Martin, Tony; Read, Ian

2004-03-01

Acoustic emission detection is a well-established method of locating and monitoring crack development in metal structures. The technique has been adapted to test facilities for non-destructive testing applications. Deployment as an operational or on-line automated damage detection technology in vehicles is posing greater challenges. A clear requirement of potential end-users of such systems is a level of automation capable of delivering low-level diagnosis information. The output from the system is in the form of "go", "no-go" indications of structural integrity or immediate maintenance actions. This level of automation requires significant data reduction and processing. This paper describes recent trials of acoustic emission detection technology for the diagnosis of damage in composite aerospace structures. The technology comprises low profile detection sensors using piezo electric wafers encapsulated in polymer film ad optical sensors. Sensors are bonded to the structure"s surface and enable acoustic events from the loaded structure to be located by triangulation. Instrumentation has been enveloped to capture and parameterise the sensor data in a form suitable for low-bandwidth storage and transmission.

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

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.

Isomorphic red blood cells using automated urine flow cytometry is a reliable method in diagnosis of bladder cancer.

PubMed

Muto, Satoru; Sugiura, Syo-Ichiro; Nakajima, Akiko; Horiuchi, Akira; Inoue, Masahiro; Saito, Keisuke; Isotani, Shuji; Yamaguchi, Raizo; Ide, Hisamitsu; Horie, Shigeo

2014-10-01

We aimed to identify patients with a chief complaint of hematuria who could safely avoid unnecessary radiation and instrumentation in the diagnosis of bladder cancer (BC), using automated urine flow cytometry to detect isomorphic red blood cells (RBCs) in urine. We acquired urine samples from 134 patients over the age of 35 years with a chief complaint of hematuria and a positive urine occult blood test or microhematuria. The data were analyzed using the UF-1000i (®) (Sysmex Co., Ltd., Kobe, Japan) automated urine flow cytometer to determine RBC morphology, which was classified as isomorphic or dysmorphic. The patients were divided into two groups (BC versus non-BC) for statistical analysis. Multivariate logistic regression analysis was used to determine the predictive value of flow cytometry versus urine cytology, the bladder tumor antigen test, occult blood in urine test, and microhematuria test. BC was confirmed in 26 of 134 patients (19.4 %). The area under the curve for RBC count using the automated urine flow cytometer was 0.94, representing the highest reference value obtained in this study. Isomorphic RBCs were detected in all patients in the BC group. On multivariate logistic regression analysis, only isomorphic RBC morphology was significantly predictive for BC (p < 0.001). Analytical parameters such as sensitivity, specificity, positive predictive value, and negative predictive value of isomorphic RBCs in urine were 100.0, 91.7, 74.3, and 100.0 %, respectively. Detection of urinary isomorphic RBCs using automated urine flow cytometry is a reliable method in the diagnosis of BC with hematuria.

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.

EDTA analysis on the Roche MODULAR analyser.

PubMed

Davidson, D F

2007-05-01

Patient specimens can be subject to subtle interference from cross contamination by liquid-based, potassium-containing EDTA anticoagulant, leading to misinterpretation of results. A rapid method for EDTA analysis to detect such contamination is described. An in-house EDTA assay on the Roche MODULAR analyser was assessed for accuracy and precision by comparison with an adjusted calcium difference measurement (atomic absorption and o-cresolphthalein complexone colorimetry). EDTA method versus adjusted calcium difference showed: slope = 1.038 (95% confidence interval [CI] 0.949-1.131); intercept = 0.073 (95% CI 0.018-0.132) mmol/L; r = 0.914; n = 94. However, inter-assay precision of the calcium difference method was estimated to be poorer (coefficient of variation 24.8% versus 3.4% for the automated colorimetric method at an EDTA concentration of 0.25 mmol/L). Unequivocal contamination was observed at an EDTA concentration of > or =0.2 mmol/L. The automated method showed positive interference from haemolysis and negative interference from oxalate. The method was unaffected by lipaemia (triglycerides <20 mmol/L), icterus (bilirubin <500 micromol/L), glucose (<100 mmol/L), iron (<100 micromol/L), and citrate, phosphate or fluoride (all <2.5 mmol/L). The automated colorimetric assay described is an accurate, precise and rapid (3 min) means of detecting EDTA contamination of unhaemolysed biochemistry specimens.

[Advances in automatic detection technology for images of thin blood film of malaria parasite].

PubMed

Juan-Sheng, Zhang; Di-Qiang, Zhang; Wei, Wang; Xiao-Guang, Wei; Zeng-Guo, Wang

2017-05-05

This paper reviews the computer vision and image analysis studies aiming at automated diagnosis or screening of malaria in microscope images of thin blood film smears. On the basis of introducing the background and significance of automatic detection technology, the existing detection technologies are summarized and divided into several steps, including image acquisition, pre-processing, morphological analysis, segmentation, count, and pattern classification components. Then, the principles and implementation methods of each step are given in detail. In addition, the promotion and application in automatic detection technology of thick blood film smears are put forwarded as questions worthy of study, and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Automated Detector of High Frequency Oscillations in Epilepsy Based on Maximum Distributed Peak Points.

PubMed

Ren, Guo-Ping; Yan, Jia-Qing; Yu, Zhi-Xin; Wang, Dan; Li, Xiao-Nan; Mei, Shan-Shan; Dai, Jin-Dong; Li, Xiao-Li; Li, Yun-Lin; Wang, Xiao-Fei; Yang, Xiao-Feng

2018-02-01

High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.

Automated Telerobotic Inspection Of Surfaces

NASA Technical Reports Server (NTRS)

Balaram, J.; Prasad, K. Venkatesh

1996-01-01

Method of automated telerobotic inspection of surfaces undergoing development. Apparatus implementing method includes video camera that scans over surfaces to be inspected, in manner of mine detector. Images of surfaces compared with reference images to detect flaws. Developed for inspecting external structures of Space Station Freedom for damage from micrometeorites and debris from prior artificial satellites. On Earth, applied to inspection for damage, missing parts, contamination, and/or corrosion on interior surfaces of pipes or exterior surfaces of bridges, towers, aircraft, and ships.

[Comparison of manual and automated (MagNA Pure) nucleic acid isolation methods in molecular diagnosis of HIV infections].

PubMed

Alp, Alpaslan; Us, Dürdal; Hasçelik, Gülşen

2004-01-01

Rapid quantitative molecular methods are very important for the diagnosis of human immunodeficiency virus (HIV) infections, assessment of prognosis and follow up. The purpose of this study was to compare and evaluate the performances of conventional manual extraction method and automated MagNA Pure system, for the nucleic acid isolation step which is the first and most important step in molecular diagnosis of HIV infections. Plasma samples of 35 patients in which anti-HIV antibodies were found as positive by microparticule enzyme immunoassay and confirmed by immunoblotting method, were included in the study. The nucleic acids obtained simultaneously by manual isolation kit (Cobas Amplicor, HIV-1 Monitor Test, version 1.5, Roche Diagnostics) and automated system (MagNA Pure LC Total Nucleic Acid Isolation Kit, Roche Diagnostics), were amplified and detected in Cobas Amplicor (Roche Diagnostics) instrument. Twenty three of 35 samples (65.7%) were found to be positive, and 9 (25.7%) were negative by both of the methods. The agreement between the methods were detected as 91.4%, for qualitative results. Viral RNA copies detected by manual and MagNA Pure isolation methods were found between 76.0-7.590.000 (mean: 487.143) and 113.0-20.300.0000 (mean: 2.174.097) copies/ml, respectively. When both of the overall and individual results were evaluated, the number of RNA copies obtained with automatized system, were found higher than the manual method (p<0.05). Three samples which had low numbers of nucleic acids (113, 773, 857, respectively) with MagNA Pure, yielded negative results with manual method. In conclusion, the automatized MagNA Pure system was found to be a reliable, rapid and practical method for the isolation of HIV-RNA.

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.

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.

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Automated Detection of Events of Scientific Interest

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

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

Digital pathology: elementary, rapid and reliable automated image analysis.

PubMed

Bouzin, Caroline; Saini, Monika L; Khaing, Kyi-Kyi; Ambroise, Jérôme; Marbaix, Etienne; Grégoire, Vincent; Bol, Vanesa

2016-05-01

Slide digitalization has brought pathology to a new era, including powerful image analysis possibilities. However, while being a powerful prognostic tool, immunostaining automated analysis on digital images is still not implemented worldwide in routine clinical practice. Digitalized biopsy sections from two independent cohorts of patients, immunostained for membrane or nuclear markers, were quantified with two automated methods. The first was based on stained cell counting through tissue segmentation, while the second relied upon stained area proportion within tissue sections. Different steps of image preparation, such as automated tissue detection, folds exclusion and scanning magnification, were also assessed and validated. Quantification of either stained cells or the stained area was found to be correlated highly for all tested markers. Both methods were also correlated with visual scoring performed by a pathologist. For an equivalent reliability, quantification of the stained area is, however, faster and easier to fine-tune and is therefore more compatible with time constraints for prognosis. This work provides an incentive for the implementation of automated immunostaining analysis with a stained area method in routine laboratory practice. © 2015 John Wiley & Sons Ltd.

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.

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.

Detection of concealed cars in complex cargo X-ray imagery using Deep Learning.

PubMed

Jaccard, Nicolas; Rogers, Thomas W; Morton, Edward J; Griffin, Lewis D

2017-01-01

Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery. X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images. CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data.

Automated indirect immunofluorescence evaluation of antinuclear autoantibodies on HEp-2 cells.

PubMed

Voigt, Jörn; Krause, Christopher; Rohwäder, Edda; Saschenbrecker, Sandra; Hahn, Melanie; Danckwardt, Maick; Feirer, Christian; Ens, Konstantin; Fechner, Kai; Barth, Erhardt; Martinetz, Thomas; Stöcker, Winfried

2012-01-01

Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability.

Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells

PubMed Central

Voigt, Jörn; Krause, Christopher; Rohwäder, Edda; Saschenbrecker, Sandra; Hahn, Melanie; Danckwardt, Maick; Feirer, Christian; Ens, Konstantin; Fechner, Kai; Barth, Erhardt; Martinetz, Thomas; Stöcker, Winfried

2012-01-01

Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability. PMID:23251220

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 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 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.

Comparison of automated BAX polymerase chain reaction and standard culture methods for detection of Listeria monocyogenes in blue crab meat (Callinectus sapidus) and blue crab processing plants

USDA-ARS?s Scientific Manuscript database

This study compared the BAX Polymerase Chain Reaction method (BAX PCR) with the Standard Culture Method (SCM) for detection of L. monocytogenes in blue crab meat and crab processing plants. The aim of this study was to address this data gap. Raw crabs, finished products and environmental sponge samp...

SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

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

Verburg, E; Waard, SN de; Veldhuis, WB

Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selectedmore » participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.« less

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

Rapid-viability PCR method for detection of live, virulent Bacillus anthracis in environmental samples.

PubMed

Létant, Sonia E; Murphy, Gloria A; Alfaro, Teneile M; Avila, Julie R; Kane, Staci R; Raber, Ellen; Bunt, Thomas M; Shah, Sanjiv R

2011-09-01

In the event of a biothreat agent release, hundreds of samples would need to be rapidly processed to characterize the extent of contamination and determine the efficacy of remediation activities. Current biological agent identification and viability determination methods are both labor- and time-intensive such that turnaround time for confirmed results is typically several days. In order to alleviate this issue, automated, high-throughput sample processing methods were developed in which real-time PCR analysis is conducted on samples before and after incubation. The method, referred to as rapid-viability (RV)-PCR, uses the change in cycle threshold after incubation to detect the presence of live organisms. In this article, we report a novel RV-PCR method for detection of live, virulent Bacillus anthracis, in which the incubation time was reduced from 14 h to 9 h, bringing the total turnaround time for results below 15 h. The method incorporates a magnetic bead-based DNA extraction and purification step prior to PCR analysis, as well as specific real-time PCR assays for the B. anthracis chromosome and pXO1 and pXO2 plasmids. A single laboratory verification of the optimized method applied to the detection of virulent B. anthracis in environmental samples was conducted and showed a detection level of 10 to 99 CFU/sample with both manual and automated RV-PCR methods in the presence of various challenges. Experiments exploring the relationship between the incubation time and the limit of detection suggest that the method could be further shortened by an additional 2 to 3 h for relatively clean samples.

Fully automated two-step assay for detection of metallothionein through magnetic isolation using functionalized γ-Fe2O3 particles.

PubMed

Merlos Rodrigo, Miguel Angel; Krejcova, Ludmila; Kudr, Jiri; Cernei, Natalia; Kopel, Pavel; Richtera, Lukas; Moulick, Amitava; Hynek, David; Adam, Vojtech; Stiborova, Marie; Eckschlager, Tomas; Heger, Zbynek; Zitka, Ondrej

2016-12-15

Metallothioneins (MTs) are involved in heavy metal detoxification in a wide range of living organisms. Currently, it is well known that MTs play substantial role in many pathophysiological processes, including carcinogenesis, and they can serve as diagnostic biomarkers. In order to increase the applicability of MT in cancer diagnostics, an easy-to-use and rapid method for its detection is required. Hence, the aim of this study was to develop a fully automated and high-throughput assay for the estimation of MT levels. Here, we report the optimal conditions for the isolation of MTs from rabbit liver and their characterization using MALDI-TOF MS. In addition, we described a two-step assay, which started with an isolation of the protein using functionalized paramagnetic particles and finished with their electrochemical analysis. The designed easy-to-use, cost-effective, error-free and fully automated procedure for the isolation of MT coupled with a simple analytical detection method can provide a prototype for the construction of a diagnostic instrument, which would be appropriate for the monitoring of carcinogenesis or MT-related chemoresistance of tumors. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

PubMed

Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

2010-12-01

Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Direct-methods structure determination of a trypanosome RNA-editing substrate fragment with translational pseudosymmetry

DOE PAGES

Mooers, Blaine H. M.

2016-03-24

Using direct methods starting from random phases, the crystal structure of a 32-base-pair RNA (675 non-H RNA atoms in the asymmetric unit) was determined using only the native diffraction data (resolution limit 1.05 Å) and the computer program SIR2014. The almost three helical turns of the RNA in the asymmetric unit introduced partial or imperfect translational pseudosymmetry (TPS) that modulated the intensities when averaged by the lMiller indices but still escaped automated detection. Almost six times as many random phase sets had to be tested on average to reach a correct structure compared with a similar-sized RNA hairpin (27 nucleotides,more » 580 non-H RNA atoms) without TPS. Lastly, more sensitive methods are needed for the automated detection of partial TPS.« less

Determination of Low Concentrations of Acetochlor in Water by Automated Solid-Phase Extraction and Gas Chromatography with Mass-Selective Detection

USGS Publications Warehouse

Lindley, C.E.; Stewart, J.T.; Sandstrom, M.W.

1996-01-01

A sensitive and reliable gas chromatographic/mass spectrometric (GC/MS) method for determining acetochlor in environmental water samples was developed. The method involves automated extraction of the herbicide from a filtered 1 L water sample through a C18 solid-phase extraction column, elution from the column with hexane-isopropyl alcohol (3 + 1), and concentration of the extract with nitrogen gas. The herbicide is quantitated by capillary/column GC/MS with selected-ion monitoring of 3 characteristic ions. The single-operator method detection limit for reagent water samples is 0.0015 ??g/L. Mean recoveries ranged from about 92 to 115% for 3 water matrixes fortified at 0.05 and 0.5 ??g/L. Average single-operator precision, over the course of 1 week, was better than 5%.

Direct-methods structure determination of a trypanosome RNA-editing substrate fragment with translational pseudosymmetry

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

Mooers, Blaine H. M.

Using direct methods starting from random phases, the crystal structure of a 32-base-pair RNA (675 non-H RNA atoms in the asymmetric unit) was determined using only the native diffraction data (resolution limit 1.05 Å) and the computer program SIR2014. The almost three helical turns of the RNA in the asymmetric unit introduced partial or imperfect translational pseudosymmetry (TPS) that modulated the intensities when averaged by the lMiller indices but still escaped automated detection. Almost six times as many random phase sets had to be tested on average to reach a correct structure compared with a similar-sized RNA hairpin (27 nucleotides,more » 580 non-H RNA atoms) without TPS. Lastly, more sensitive methods are needed for the automated detection of partial TPS.« less

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.

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

Automated Eddy Current Inspection on Space Shuttle Hardware

NASA Technical Reports Server (NTRS)

Hartmann, John; Felker, Jeremy

2007-01-01

Over the life time of the Space Shuttle program, metal parts used for the Reusable Solid Rocket Motors (RSRMs) have been nondestructively inspected for cracks and surface breaking discontinuities using magnetic particle (steel) and penetrant methods. Although these inspections adequately screened for critical sized cracks in most regions of the hardware, it became apparent after detection of several sub-critical flaws that the processes were very dependent on operator attentiveness and training. Throughout the 1990's, eddy current inspections were added to areas that had either limited visual access or were more fracture critical. In the late 1990's. a project was initiated to upgrade NDE inspections with the overall objective of improving inspection reliability and control. An automated eddy current inspection system was installed in 2001. A figure shows one of the inspection bays with the robotic axis of the system highlighted. The system was programmed to inspect the various case, nozzle, and igniter metal components that make up an RSRM. both steel and aluminum. For the past few years, the automated inspection system has been a part of the baseline inspection process for steel components. Although the majority of the RSRM metal part inventory ts free of detectable surface flaws, a few small, sub-critical manufacturing defects have been detected with the automated system. This paper will summarize the benefits that have been realized with the current automated eddy current system, as well as the flaws that have been detected.

Automated measurement of stent strut coverage in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Ahn, Chi Young; Kim, Byeong-Keuk; Hong, Myeong-Ki; Jang, Yangsoo; Heo, Jung; Joo, Chulmin; Seo, Jin Keun

2015-02-01

Optical coherence tomography (OCT) is a non-invasive, cross-sectional imaging modality that has become a prominent imaging method in percutaneous intracoronary intervention. We present an automated detection algorithm for stent strut coordinates and coverage in OCT images. The algorithm for stent strut detection is composed of a coordinate transformation from the polar to the Cartesian domains and application of second derivative operators in the radial and the circumferential directions. Local region-based active contouring was employed to detect lumen boundaries. We applied the method to the OCT pullback images acquired from human patients in vivo to quantitatively measure stent strut coverage. The validation studies against manual expert assessments demonstrated high Pearson's coefficients ( R = 0.99) in terms of the stent strut coordinates, with no significant bias. An averaged Hausdorff distance of < 120 μm was obtained for vessel border detection. Quantitative comparison in stent strut to vessel wall distance found a bias of < 12.3 μm and a 95% confidence of < 110 μm.

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

PubMed

Park, Albert; Hartzler, Andrea L; Huh, Jina; McDonald, David W; Pratt, Wanda

2015-08-31

The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap's mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text.

New fully automated software for assessment of brachial artery flow- mediated dilation with advantages of continuous measurement.

PubMed

Ercan, Ertuğrul; Kırılmaz, Bahadır; Kahraman, İsmail; Bayram, Vildan; Doğan, Hüseyin

2012-11-01

Flow-mediated dilation (FMD) is used to evaluate endothelial functions. Computer-assisted analysis utilizing edge detection permits continuous measurements along the vessel wall. We have developed a new fully automated software program to allow accurate and reproducible measurement. FMD has been measured and analyzed in 18 coronary artery disease (CAD) patients and 17 controls both by manually and by the software developed (computer supported) methods. The agreement between methods was assessed by Bland-Altman analysis. The mean age, body mass index and cardiovascular risk factors were higher in CAD group. Automated FMD% measurement for the control subjects was 18.3±8.5 and 6.8±6.5 for the CAD group (p=0.0001). The intraobserver and interobserver correlation for automated measurement was high (r=0.974, r=0.981, r=0.937, r=0.918, respectively). Manual FMD% at 60th second was correlated with automated FMD % (r=0.471, p=0.004). The new fully automated software© can be used to precise measurement of FMD with low intra- and interobserver variability than manual assessment.

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

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 structure solution, density modification and model building.

PubMed

Terwilliger, Thomas C

2002-11-01

The approaches that form the basis of automated structure solution in SOLVE and RESOLVE are described. The use of a scoring scheme to convert decision making in macromolecular structure solution to an optimization problem has proven very useful and in many cases a single clear heavy-atom solution can be obtained and used for phasing. Statistical density modification is well suited to an automated approach to structure solution because the method is relatively insensitive to choices of numbers of cycles and solvent content. The detection of non-crystallographic symmetry (NCS) in heavy-atom sites and checking of potential NCS operations against the electron-density map has proven to be a reliable method for identification of NCS in most cases. Automated model building beginning with an FFT-based search for helices and sheets has been successful in automated model building for maps with resolutions as low as 3 A. The entire process can be carried out in a fully automatic fashion in many cases.

EPA Method 3135.2I: Cyanide, Total and Amenable in Aqueous and Solid Samples Automated Colorimetric With Manual Digestion

EPA Pesticide Factsheets

This method describes procedures for preparation and analysis of solid, water and wipe samples for detection and measurement of cyanide amendable to chlorination using acid digestion and spectrophotometry.

A Fully Automated Method for Quantifying and Localizing White Matter Hyperintensities on MR Images

PubMed Central

Wu, Minjie; Rosano, Caterina; Butters, Meryl; Whyte, Ellen; Nable, Megan; Crooks, Ryan; Meltzer, Carolyn C.; Reynolds, Charles F.; Aizenstein3, Howard J.

2006-01-01

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer’s disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies e.g. (Taylor, et al. 2003)), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large neuroimage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders. PMID:17097277

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.

Shape indexes for semi-automated detection of windbreaks in thematic tree cover maps from the central United States

Treesearch

Greg C. Liknes; Dacia M. Meneguzzo; Todd A. Kellerman

2017-01-01

Windbreaks are an important ecological resource across the large expanse of agricultural land in the central United States and are often planted in straight-line or L-shaped configurations to serve specific functions. As high-resolution (i.e., <5 m) land cover datasets become more available for these areas, semi-or fully-automated methods for distinguishing...

An automated tool-joint inspection device for the drillstring

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

Moyer, M.C.; Dale, B.A.; Kusenberger, F.N.

1984-06-01

This paper discusses the development of an automated tool joint inspection device-i.e., the fatigue crack detector (FCD), which can detect defects in the threaded region of drillpipe and drill collars. Inspection tests conducted at a research test facility and at drilling rig sites indicate that this device can detect both simulated defects (saw slots and drilled holes) and service-induced defects, such as fatigue cracks, pin stretch (plastic deformation), mashed threads, and corrosion pitting. The system operates on an electromagnetic-flux leakage principle and has several advantages over the conventional method of magnetic particle inspection.

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

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

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

Open-source software for collision detection in external beam radiation therapy

NASA Astrophysics Data System (ADS)

Suriyakumar, Vinith M.; Xu, Renee; Pinter, Csaba; Fichtinger, Gabor

2017-03-01

PURPOSE: Collision detection for external beam radiation therapy (RT) is important for eliminating the need for dryruns that aim to ensure patient safety. Commercial treatment planning systems (TPS) offer this feature but they are expensive and proprietary. Cobalt-60 RT machines are a viable solution to RT practice in low-budget scenarios. However, such clinics are hesitant to invest in these machines due to a lack of affordable treatment planning software. We propose the creation of an open-source room's eye view visualization module with automated collision detection as part of the development of an open-source TPS. METHODS: An openly accessible linac 3D geometry model is sliced into the different components of the treatment machine. The model's movements are based on the International Electrotechnical Commission standard. Automated collision detection is implemented between the treatment machine's components. RESULTS: The room's eye view module was built in C++ as part of SlicerRT, an RT research toolkit built on 3D Slicer. The module was tested using head and neck and prostate RT plans. These tests verified that the module accurately modeled the movements of the treatment machine and radiation beam. Automated collision detection was verified using tests where geometric parameters of the machine's components were changed, demonstrating accurate collision detection. CONCLUSION: Room's eye view visualization and automated collision detection are essential in a Cobalt-60 treatment planning system. Development of these features will advance the creation of an open-source TPS that will potentially help increase the feasibility of adopting Cobalt-60 RT.

Automated Fast Screening Method for Cocaine Identification in Seized Drug Samples Using a Portable Fourier Transform Infrared (FT-IR) Instrument.

PubMed

Mainali, Dipak; Seelenbinder, John

2016-05-01

Quick and presumptive identification of seized drug samples without destroying evidence is necessary for law enforcement officials to control the trafficking and abuse of drugs. This work reports an automated screening method to detect the presence of cocaine in seized samples using portable Fourier transform infrared (FT-IR) spectrometers. The method is based on the identification of well-defined characteristic vibrational frequencies related to the functional group of the cocaine molecule and is fully automated through the use of an expert system. Traditionally, analysts look for key functional group bands in the infrared spectra and characterization of the molecules present is dependent on user interpretation. This implies the need for user expertise, especially in samples that likely are mixtures. As such, this approach is biased and also not suitable for non-experts. The method proposed in this work uses the well-established "center of gravity" peak picking mathematical algorithm and combines it with the conditional reporting feature in MicroLab software to provide an automated method that can be successfully employed by users with varied experience levels. The method reports the confidence level of cocaine present only when a certain number of cocaine related peaks are identified by the automated method. Unlike library search and chemometric methods that are dependent on the library database or the training set samples used to build the calibration model, the proposed method is relatively independent of adulterants and diluents present in the seized mixture. This automated method in combination with a portable FT-IR spectrometer provides law enforcement officials, criminal investigators, or forensic experts a quick field-based prescreening capability for the presence of cocaine in seized drug samples. © The Author(s) 2016.

A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations.

PubMed

Alsalem, M A; Zaidan, A A; Zaidan, B B; Hashim, M; Madhloom, H T; Azeez, N D; Alsyisuf, S

2018-05-01

Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

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.

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.

Automated Detection of Stereotypical Motor Movements

ERIC Educational Resources Information Center

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

2011-01-01

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

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

A semi-automated technique for labeling and counting of apoptosing retinal cells

PubMed Central

2014-01-01

Background Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. Results A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods. Conclusions The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly. PMID:24902592

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy.

PubMed

Rasta, Seyed Hossein; Nikfarjam, Shima; Javadzadeh, Alireza

2015-01-01

Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images. Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts. Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%.The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals. This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.

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

Automatic Fringe Detection for Oil Film Interferometry Measurement of Skin Friction

NASA Technical Reports Server (NTRS)

Naughton, Jonathan W.; Decker, Robert K.; Jafari, Farhad

2001-01-01

This report summarizes two years of work on investigating algorithms for automatically detecting fringe patterns in images acquired using oil-drop interferometry for the determination of skin friction. Several different analysis methods were tested, and a combination of a windowed Fourier transform followed by a correlation was found to be most effective. The implementation of this method is discussed and details of the process are described. The results indicate that this method shows promise for automating the fringe detection process, but further testing is required.

Non-destructive automated sampling of mycotoxins in bulk food and feed - A new tool for required harmonization.

PubMed

Spanjer, M; Stroka, J; Patel, S; Buechler, S; Pittet, A; Barel, S

2001-06-01

Mycotoxins contamination is highly non-uniformly distributed as is well recog-nized by the EC, by not only setting legal limits in a series of commodities, but also schedule a sampling plan that takes this heterogeneity into account. In practice however, it turns out that it is very difficult to carry out this sampling plan in a harmonised way. Applying the sampling plan to a container filled with pallets of bags (i.e. with nuts or coffee beans) varies from very laborious to almost impossible. The presented non-destructive automated method to sample bulk food could help to overcome these practical problems and to enforcing of EC directives. It is derived from a tested and approved technology for detection of illicit substances in security applications. It has capability to collect and iden-tify ultra trace contaminants, i.e. from a fingerprint of chemical substance in a bulk of goods, a cargo pallet load (~ 1000 kg) with boxes and commodities.The technology, patented for explosives detection, uses physical and chemistry processes for excitation and remote rapid enhanced release of contaminant residues, vapours and particulate, of the inner/outer surfaces of inspected bulk and collect them on selective probes. The process is automated, takes only 10 minutes, is non-destructive and the bulk itself remains unharmed. The system design is based on applicable international regulations for shipped cargo hand-ling and transportation by road, sea and air. After this process the pallet can be loaded on a truck, ship or plane. Analysis can be carried out before the cargo leaves the place of shipping. The potent application of this technology for myco-toxins detection, has been demonstrated by preliminary feasibility experiments. Aflatoxins were detected in pistachios and ochratoxin A in green coffee beans bulk. Both commodities were naturally contaminated, priory found and confirm-ed by common methods as used at routine inspections. Once the contaminants are extracted from a bulk shipment, an appropriate existing analytical method, i.e. a CEN method, can be used to measure the mycotoxins.The system, routinely in use for explosives detection, was able to screen bulk food and feed for mycotoxins, through non-destructive automated sampling of a whole batch/lot/sublot of commodities. The opportunity to sample a whole bulk would provide more effective tools for inspection at seaports, production facili-ties and distri-bution points. It will advance the current process of myco-toxins check because: (i) Checks will be automated and harmonized, (ii) Checks will be non-destructive, (iii) Checks will be faster and allow a greater amount of bulk commodities to be inspected and (iv) The ability to check, with automated equipment, larger portions of lots of a shipment will increase the probability to detect the heterogeneous mycotoxins contamination in bulk foods. The poster provides some results of feasibility experiments indicating the capability of this technology for inspection of commodities bulks for the detection of mycotoxins, at legal limits, in naturally contaminated food.

Development and validation of an automated operational modal analysis algorithm for vibration-based monitoring and tensile load estimation

NASA Astrophysics Data System (ADS)

Rainieri, Carlo; Fabbrocino, Giovanni

2015-08-01

In the last few decades large research efforts have been devoted to the development of methods for automated detection of damage and degradation phenomena at an early stage. Modal-based damage detection techniques are well-established methods, whose effectiveness for Level 1 (existence) and Level 2 (location) damage detection is demonstrated by several studies. The indirect estimation of tensile loads in cables and tie-rods is another attractive application of vibration measurements. It provides interesting opportunities for cheap and fast quality checks in the construction phase, as well as for safety evaluations and structural maintenance over the structure lifespan. However, the lack of automated modal identification and tracking procedures has been for long a relevant drawback to the extensive application of the above-mentioned techniques in the engineering practice. An increasing number of field applications of modal-based structural health and performance assessment are appearing after the development of several automated output-only modal identification procedures in the last few years. Nevertheless, additional efforts are still needed to enhance the robustness of automated modal identification algorithms, control the computational efforts and improve the reliability of modal parameter estimates (in particular, damping). This paper deals with an original algorithm for automated output-only modal parameter estimation. Particular emphasis is given to the extensive validation of the algorithm based on simulated and real datasets in view of continuous monitoring applications. The results point out that the algorithm is fairly robust and demonstrate its ability to provide accurate and precise estimates of the modal parameters, including damping ratios. As a result, it has been used to develop systems for vibration-based estimation of tensile loads in cables and tie-rods. Promising results have been achieved for non-destructive testing as well as continuous monitoring purposes. They are documented in the last sections of the paper.

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

Interfacing a robotic station with a gas chromatograph for the full automation of the determination of organochlorine pesticides in vegetables

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

Torres, P.; Luque de Castro, M.D.

1996-12-31

A fully automated method for the determination of organochlorine pesticides in vegetables is proposed. The overall system acts as an {open_quotes}analytical black box{close_quotes} because a robotic station performs the prelimninary operations, from weighing to capping the leached analytes and location in an autosampler of an automated gas chromatograph with electron capture detection. The method has been applied to the determination of lindane, heptachlor, captan, chlordane and metoxcychlor in tea, marjoram, cinnamon, pennyroyal, and mint with good results in most cases. A gas chromatograph has been interfaced to a robotic station for the determination of pesticides in vegetables. 15 refs., 4more » figs., 2 tabs.« less

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

PubMed

Kayasandik, Cihan Bilge; Labate, Demetrio

2016-12-01

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community. Copyright © 2016 Elsevier B.V. All rights reserved.

An experience of the introduction of a blood bank automation system (Ortho AutoVue Innova) in a regional acute hospital.

PubMed

Cheng, Yuk Wah; Wilkinson, Jenny M

2015-08-01

This paper reports on an evaluation of the introduction of a blood bank automation system (Ortho AutoVue(®) Innova) in a hospital blood bank by considering the performance and workflow as compared with manual methods. The turnaround time was found to be 45% faster than the manual method. The concordance rate was found to be 100% for both ABO/Rh(D) typing and antibody screening in both of the systems and there was no significant difference in detection sensitivity for clinically significant antibodies. The Ortho AutoVue(®) Innova automated blood banking system streamlined the routine pre-transfusion testing in hospital blood bank with high throughput, equivalent sensitivity and reliability as compared with conventional manual method. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Automated chromatographic laccase-mediator-system activity assay.

PubMed

Anders, Nico; Schelden, Maximilian; Roth, Simon; Spiess, Antje C

2017-08-01

To study the interaction of laccases, mediators, and substrates in laccase-mediator systems (LMS), an on-line measurement was developed using high performance anion exchange chromatography equipped with a CarboPac™ PA 100 column coupled to pulsed amperometric detection (HPAEC-PAD). The developed method was optimized for overall chromatographic run time (45 to 120 min) and automated sample drawing. As an example, the Trametes versicolor laccase induced oxidation of 1-(3,4-dimethoxyphenyl)-2-(2-methoxyphenoxy)-1,3-dihydroxypropane (adlerol) using 1-hydroxybenzotriazole (HBT) as mediator was measured and analyzed on-line. Since the Au electrode of the PAD detects only hydroxyl group containing substances with a limit of detection being in the milligram/liter range, not all products are measureable. Therefore, this method was applied for the quantification of adlerol, and-based on adlerol conversion-for the quantification of the LMS activity at a specific T. versicolor laccase/HBT ratio. The automated chromatographic activity assay allowed for a defined reaction start of all laccase-mediator-system reactions mixtures, and the LMS reaction progress was automatically monitored for 48 h. The automatization enabled an integrated monitoring overnight and over-weekend and minimized all manual errors such as pipetting of solutions accordingly. The activity of the LMS based on adlerol consumption was determined to 0.47 U/mg protein for a laccase/mediator ratio of 1.75 U laccase/g HBT. In the future, the automated method will allow for a fast screening of combinations of laccases, mediators, and substrates which are efficient for lignin modification. In particular, it allows for a fast and easy quantification of the oxidizing activity of an LMS on a lignin-related substrate which is not covered by typical colorimetric laccase assays. ᅟ.

Automation of laboratory testing for infectious diseases using the polymerase chain reaction-- our past, our present, our future.

PubMed

Jungkind, D

2001-01-01

While it is an extremely powerful and versatile assay method, polymerase chain reaction (PCR) can be a labor-intensive process. Since the advent of commercial test kits from Roche and the semi-automated microwell Amplicor system, PCR has become an increasingly useful and widespread clinical tool. However, more widespread acceptance of molecular testing will depend upon automation that allows molecular assays to enter the routine clinical laboratory. The forces driving the need for automated PCR are the requirements for diagnosis and treatment of chronic viral diseases, economic pressures to develop more automated and less expensive test procedures similar to those in the clinical chemistry laboratories, and a shortage in many areas of qualified laboratory personnel trained in the types of manual procedures used in past decades. The automated Roche COBAS AMPLICOR system has automated the amplification and detection process. Specimen preparation remains the most labor-intensive part of the PCR testing process, accounting for the majority of the hands-on-time in most of the assays. A new automated specimen preparation system, the COBAS AmpliPrep, was evaluated. The system automatically releases the target nucleic acid, captures the target with specific oligonucleotide probes, which become attached to magnetic beads via a biotin-streptavidin binding reaction. Once attached to the beads, the target is purified and concentrated automatically. Results of 298 qualitative and 57 quantitative samples representing a wide range of virus concentrations analyzed after the COBAS AmpliPrep and manual specimen preparation methods, showed that there was no significant difference in qualitative or quantitative hepatitis C virus (HCV) assay performance, respectively. The AmpliPrep instrument decreased the time required to prepare serum or plasma samples for HCV PCR to under 1 min per sample. This was a decrease of 76% compared to the manual specimen preparation method. Systems that can analyze more samples with higher throughput and that can answer more questions about the nature of the microbes that we can presently only detect and quantitate will be needed in the future.

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.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection.

PubMed

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

2017-07-01

False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively address the challenges in this task and therefore to accurately discriminate the true nodules from a large number of candidates. We propose a novel method employing three-dimensional (3-D) convolutional neural networks (CNNs) for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans. Compared with its 2-D counterparts, the 3-D CNNs can encode richer spatial information and extract more representative features via their hierarchical architecture trained with 3-D samples. More importantly, we further propose a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules. The proposed framework has been extensively validated in the LUNA16 challenge held in conjunction with ISBI 2016, where we achieved the highest competition performance metric (CPM) score in the false positive reduction track. Experimental results demonstrated the importance and effectiveness of integrating multilevel contextual information into 3-D CNN framework for automated pulmonary nodule detection in volumetric CT data. While our method is tailored for pulmonary nodule detection, the proposed framework is general and can be easily extended to many other 3-D object detection tasks from volumetric medical images, where the targeting objects have large variations and are accompanied by a number of hard mimics.

Evaluation of automated time-lapse microscopy for assessment of in vitro activity of antibiotics.

PubMed

Ungphakorn, Wanchana; Malmberg, Christer; Lagerbäck, Pernilla; Cars, Otto; Nielsen, Elisabet I; Tängdén, Thomas

2017-01-01

This study aimed to evaluate the potential of a new time-lapse microscopy based method (oCelloScope) to efficiently assess the in vitro antibacterial effects of antibiotics. Two E. coli and one P. aeruginosa strain were exposed to ciprofloxacin, colistin, ertapenem and meropenem in 24-h experiments. Background corrected absorption (BCA) derived from the oCelloScope was used to detect bacterial growth. The data obtained with the oCelloScope were compared with those of the automated Bioscreen C method and standard time-kill experiments and a good agreement in results was observed during 6-24h of experiments. Viable counts obtained at 1, 4, 6 and 24h during oCelloScope and Bioscreen C experiments were well correlated with the corresponding BCA and optical density (OD) data. Initial antibacterial effects during the first 6h of experiments were difficult to detect with the automated methods due to their high detection limits (approximately 10 5 CFU/mL for oCelloScope and 10 7 CFU/mL for Bioscreen C), the inability to distinguish between live and dead bacteria and early morphological changes of bacteria during exposure to ciprofloxacin, ertapenem and meropenem. Regrowth was more frequently detected in time-kill experiments, possibly related to the larger working volume with an increased risk of pre-existing or emerging resistance. In comparison with Bioscreen C, the oCelloScope provided additional information on bacterial growth dynamics in the range of 10 5 to 10 7 CFU/mL and morphological features. In conclusion, the oCelloScope would be suitable for detection of in vitro effects of antibiotics, especially when a large number of regimens need to be tested. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of two commercial real-time PCR assays for detecting Campylobacter in broiler carcass rinses.

USDA-ARS?s Scientific Manuscript database

Traditional plating methods are reliable means for Campylobacter identification from poultry samples but automated gene-based detection systems now available can reduce assay time, data collection and analysis. Bio-Rad and DuPont Qualicon recently introduced Campylobacter assays for their real-time ...

Automated on-line fecal detection - digital eye guards against fecal contamination

USDA-ARS?s Scientific Manuscript database

Agricultural Research Service scientists in Athens, GA., have been granted a patent on a method to detect contaminants on food surfaces with imaging systems. Using a real-time imaging system in the processing plant, researchers Bob Windham, Kurt, Lawrence, Bosoon Park, and Doug Smith in the ARS Poul...

Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

PubMed

Suleimanov, Yury V; Green, William H

2015-09-08

We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

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.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

PubMed

Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

2015-10-01

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

Rapid-Viability PCR Method for Detection of Live, Virulent Bacillus anthracis in Environmental Samples ▿

PubMed Central

Létant, Sonia E.; Murphy, Gloria A.; Alfaro, Teneile M.; Avila, Julie R.; Kane, Staci R.; Raber, Ellen; Bunt, Thomas M.; Shah, Sanjiv R.

2011-01-01

In the event of a biothreat agent release, hundreds of samples would need to be rapidly processed to characterize the extent of contamination and determine the efficacy of remediation activities. Current biological agent identification and viability determination methods are both labor- and time-intensive such that turnaround time for confirmed results is typically several days. In order to alleviate this issue, automated, high-throughput sample processing methods were developed in which real-time PCR analysis is conducted on samples before and after incubation. The method, referred to as rapid-viability (RV)-PCR, uses the change in cycle threshold after incubation to detect the presence of live organisms. In this article, we report a novel RV-PCR method for detection of live, virulent Bacillus anthracis, in which the incubation time was reduced from 14 h to 9 h, bringing the total turnaround time for results below 15 h. The method incorporates a magnetic bead-based DNA extraction and purification step prior to PCR analysis, as well as specific real-time PCR assays for the B. anthracis chromosome and pXO1 and pXO2 plasmids. A single laboratory verification of the optimized method applied to the detection of virulent B. anthracis in environmental samples was conducted and showed a detection level of 10 to 99 CFU/sample with both manual and automated RV-PCR methods in the presence of various challenges. Experiments exploring the relationship between the incubation time and the limit of detection suggest that the method could be further shortened by an additional 2 to 3 h for relatively clean samples. PMID:21764960

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

Automated detection of pain from facial expressions: a rule-based approach using AAM

NASA Astrophysics Data System (ADS)

Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

2012-02-01

In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

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.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

PubMed

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

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

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.

Automated classification of dolphin echolocation click types from the Gulf of Mexico.

PubMed

Frasier, Kaitlin E; Roch, Marie A; Soldevilla, Melissa S; Wiggins, Sean M; Garrison, Lance P; Hildebrand, John A

2017-12-01

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso's dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.

Automated classification of dolphin echolocation click types from the Gulf of Mexico

PubMed Central

Roch, Marie A.; Soldevilla, Melissa S.; Wiggins, Sean M.; Garrison, Lance P.; Hildebrand, John A.

2017-01-01

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso’s dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori. PMID:29216184

A PCR procedure for the detection of Giardia intestinalis cysts and Escherichia coli in lettuce.

PubMed

Ramirez-Martinez, M L; Olmos-Ortiz, L M; Barajas-Mendiola, M A; Giono Cerezo, S; Avila, E E; Cuellar-Mata, P

2015-06-01

Giardia intestinalis is a pathogen associated with foodborne outbreaks and Escherichia coli is commonly used as a marker of faecal contamination. Implementation of routine identification methods of G. intestinalis is difficult for the analysis of vegetables and the microbiological detection of E. coli requires several days. This study proposes a PCR-based assay for the detection of E. coli and G. intestinalis cysts using crude DNA isolated from artificially contaminated lettuce. The G. intestinalis and E. coli PCR assays targeted the β-giardin and uidA genes, respectively, and were 100% specific. Forty lettuces from local markets were analysed by both PCR and light microscopy and no cysts were detected, the calculated detection limit was 20 cysts per gram of lettuce; however, by PCR, E. coli was detected in eight of ten randomly selected samples of lettuce. These data highlight the need to validate procedures for routine quality assurance. These PCR-based assays can be employed as alternative methods for the detection of G. intestinalis and E. coli and have the potential to allow for the automation and simultaneous detection of protozoa and bacterial pathogens in multiple samples. Significance and impact of the study: There are few studies for Giardia intestinalis detection in food because methods for its identification are difficult for routine implementation. Here, we developed a PCR-based method as an alternative to the direct observation of cysts in lettuce by light microscopy. Additionally, Escherichia coli was detected by PCR and the sanitary quality of lettuce was evaluated using molecular and standard microbiological methods. Using PCR, the detection probability of Giardia cysts inoculated onto samples of lettuce was improved compared to light microscopy, with the advantage of easy automation. These methods may be employed to perform timely and affordable detection of foodborne pathogens. © 2015 The Society for Applied Microbiology.

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.

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy

PubMed Central

Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-01-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum. The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. PMID:27707942

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy.

PubMed

Jun, Zhou; Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-12-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. Copyright © 2016 Jun et al.

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

USGS Publications Warehouse

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

2017-01-01

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

Isolation of circulating tumor cells from pancreatic cancer by automated filtration

PubMed Central

Brychta, Nora; Drosch, Michael; Driemel, Christiane; Fischer, Johannes C.; Neves, Rui P.; Esposito, Irene; Knoefel, Wolfram; Möhlendick, Birte; Hille, Claudia; Stresemann, Antje; Krahn, Thomas; Kassack, Matthias U.; Stoecklein, Nikolas H.; von Ahsen, Oliver

2017-01-01

It is now widely recognized that the isolation of circulating tumor cells based on cell surface markers might be hindered by variability in their protein expression. Especially in pancreatic cancer, isolation based only on EpCAM expression has produced very diverse results. Methods that are independent of surface markers and therefore independent of phenotypical changes in the circulating cells might increase CTC recovery also in pancreatic cancer. We compared an EpCAM-dependent (IsoFlux) and a size-dependent (automated Siemens Healthineers filtration device) isolation method for the enrichment of pancreatic cancer CTCs. The recovery rate of the filtration based approach is dramatically superior to the EpCAM-dependent approach especially for cells with low EpCAM-expression (filtration: 52%, EpCAM-dependent: 1%). As storage and shipment of clinical samples is important for centralized analyses, we also evaluated the use of frozen diagnostic leukapheresis (DLA) as source for isolating CTCs and subsequent genetic analysis such as KRAS mutation detection analysis. Using frozen DLA samples of pancreatic cancer patients we detected CTCs in 42% of the samples by automated filtration. PMID:29156783

Isolation of circulating tumor cells from pancreatic cancer by automated filtration.

PubMed

Brychta, Nora; Drosch, Michael; Driemel, Christiane; Fischer, Johannes C; Neves, Rui P; Esposito, Irene; Knoefel, Wolfram; Möhlendick, Birte; Hille, Claudia; Stresemann, Antje; Krahn, Thomas; Kassack, Matthias U; Stoecklein, Nikolas H; von Ahsen, Oliver

2017-10-17

It is now widely recognized that the isolation of circulating tumor cells based on cell surface markers might be hindered by variability in their protein expression. Especially in pancreatic cancer, isolation based only on EpCAM expression has produced very diverse results. Methods that are independent of surface markers and therefore independent of phenotypical changes in the circulating cells might increase CTC recovery also in pancreatic cancer. We compared an EpCAM-dependent (IsoFlux) and a size-dependent (automated Siemens Healthineers filtration device) isolation method for the enrichment of pancreatic cancer CTCs. The recovery rate of the filtration based approach is dramatically superior to the EpCAM-dependent approach especially for cells with low EpCAM-expression (filtration: 52%, EpCAM-dependent: 1%). As storage and shipment of clinical samples is important for centralized analyses, we also evaluated the use of frozen diagnostic leukapheresis (DLA) as source for isolating CTCs and subsequent genetic analysis such as KRAS mutation detection analysis. Using frozen DLA samples of pancreatic cancer patients we detected CTCs in 42% of the samples by automated filtration.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography.

PubMed

Zang, Pengxiao; Gao, Simon S; Hwang, Thomas S; Flaxel, Christina J; Wilson, David J; Morrison, John C; Huang, David; Li, Dengwang; Jia, Yali

2017-03-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

PubMed Central

Zang, Pengxiao; Gao, Simon S.; Hwang, Thomas S.; Flaxel, Christina J.; Wilson, David J.; Morrison, John C.; Huang, David; Li, Dengwang; Jia, Yali

2017-01-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch’s membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm). PMID:28663830

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

PubMed

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

2014-01-01

In this work, an application of an enzymatic reaction for the determination of the highly hydrophobic drug propofol in emulsion dosage form is presented. Emulsions represent a complex and therefore challenging matrix for analysis. Ethanol was used for breakage of a lipid emulsion, which enabled optical detection. A fully automated method based on Sequential Injection Analysis was developed, allowing propofol determination without the requirement of tedious sample pre-treatment. The method was based on spectrophotometric detection after the enzymatic oxidation catalysed by horseradish peroxidase and subsequent coupling with 4-aminoantipyrine leading to a coloured product with an absorbance maximum at 485 nm. This procedure was compared with a simple fluorimetric method, which was based on the direct selective fluorescence emission of propofol in ethanol at 347 nm. Both methods provide comparable validation parameters with linear working ranges of 0.005-0.100 mg mL(-1) and 0.004-0.243 mg mL(-1) for the spectrophotometric and fluorimetric methods, respectively. The detection and quantitation limits achieved with the spectrophotometric method were 0.0016 and 0.0053 mg mL(-1), respectively. The fluorimetric method provided the detection limit of 0.0013 mg mL(-1) and limit of quantitation of 0.0043 mg mL(-1). The RSD did not exceed 5% and 2% (n=10), correspondingly. A sample throughput of approx. 14 h(-1) for the spectrophotometric and 68 h(-1) for the fluorimetric detection was achieved. Both methods proved to be suitable for the determination of propofol in pharmaceutical formulation with average recovery values of 98.1 and 98.5%. © 2013 Elsevier B.V. All rights reserved.

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

PubMed

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Background estimation and player detection in badminton video clips using histogram of pixel values along temporal dimension

NASA Astrophysics Data System (ADS)

Peng, Yahui; Ma, Xiao; Gao, Xinyu; Zhou, Fangxu

2015-12-01

Computer vision is an important tool for sports video processing. However, its application in badminton match analysis is very limited. In this study, we proposed a straightforward but robust histogram-based background estimation and player detection methods for badminton video clips, and compared the results with the naive averaging method and the mixture of Gaussians methods, respectively. The proposed method yielded better background estimation results than the naive averaging method and more accurate player detection results than the mixture of Gaussians player detection method. The preliminary results indicated that the proposed histogram-based method could estimate the background and extract the players accurately. We conclude that the proposed method can be used for badminton player tracking and further studies are warranted for automated match analysis.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

PubMed Central

Pour, Elias Khalili; Pourreza, Hamidreza; Zamani, Kambiz Ameli; Mahmoudi, Alireza; Sadeghi, Arash Mir Mohammad; Shadravan, Mahla; Karkhaneh, Reza; Pour, Ramak Rouhi

2017-01-01

Purpose To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. Methods Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. Results Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively. Conclusions The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field. PMID:29022295

The Changing Role of the Clinical Microbiology Laboratory in Defining Resistance in Gram-negatives.

PubMed

Endimiani, Andrea; Jacobs, Michael R

2016-06-01

The evolution of resistance in Gram-negatives has challenged the clinical microbiology laboratory to implement new methods for their detection. Multidrug-resistant strains present major challenges to conventional and new detection methods. More rapid pathogen identification and antimicrobial susceptibility testing have been developed for use directly on specimens, including fluorescence in situ hybridization tests, automated polymerase chain reaction systems, microarrays, mass spectroscopy, next-generation sequencing, and microfluidics. Review of these methods shows the advances that have been made in rapid detection of resistance in cultures, but limited progress in direct detection from specimens. Copyright © 2016 Elsevier Inc. All rights reserved.

A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry

PubMed Central

Reggiannini, Brian; Sheinkopf, Stephen J.; Silverman, Harvey F.; Li, Xiaoxue; Lester, Barry M.

2015-01-01

Purpose In this article, the authors describe and validate the performance of a modern acoustic analyzer specifically designed for infant cry analysis. Method Utilizing known algorithms, the authors developed a method to extract acoustic parameters describing infant cries from standard digital audio files. They used a frame rate of 25 ms with a frame advance of 12.5 ms. Cepstral-based acoustic analysis proceeded in 2 phases, computing frame-level data and then organizing and summarizing this information within cry utterances. Using signal detection methods, the authors evaluated the accuracy of the automated system to determine voicing and to detect fundamental frequency (F0) as compared to voiced segments and pitch periods manually coded from spectrogram displays. Results The system detected F0 with 88% to 95% accuracy, depending on tolerances set at 10 to 20 Hz. Receiver operating characteristic analyses demonstrated very high accuracy at detecting voicing characteristics in the cry samples. Conclusions This article describes an automated infant cry analyzer with high accuracy to detect important acoustic features of cry. A unique and important aspect of this work is the rigorous testing of the system’s accuracy as compared to ground-truth manual coding. The resulting system has implications for basic and applied research on infant cry development. PMID:23785178

A new automated turbidimetric immunoassay for the measurement of canine C-reactive protein.

PubMed

Piñeiro, Matilde; Pato, Raquel; Soler, Lourdes; Peña, Raquel; García, Natalia; Torrente, Carlos; Saco, Yolanda; Lampreave, Fermín; Bassols, Anna; Canalias, Francesca

2018-03-01

In dogs, as in humans, C-reactive protein (CRP) is a major acute phase protein that is rapidly and prominently increased after exposure to inflammatory stimuli. CRP measurements are used in the diagnosis and monitoring of infectious and inflammatory diseases. The study aim was to develop and validate a turbidimetric immunoassay for the quantification of canine CRP (cCRP), using canine-specific reagents and standards. A particle-enhanced turbidimetric immunoassay was developed. The assay was set up in a fully automated analyzer, and studies of imprecision, limits of linearity, limits of detection, prozone effects, and interferences were carried out. The new method was compared with 2 other commercially available automated immunoassays for cCRP: one turbidimetric immunoassay (Gentian CRP) and one point-of-care assay based on magnetic permeability (Life Assays CRP). The within-run and between-day imprecision were <1.7% and 4.2%, respectively. The assay quantified CRP proportionally in an analytic range up to 150 mg/L, with a prozone effect appearing at cCRP concentrations >320 mg/L. No interference from hemoglobin (20 g/L), triglycerides (10 g/L), or bilirubin (150 mg/L) was detected. Good agreement was observed between the results obtained with the new method and the Gentian cCRP turbidimetric immunoassay. The new turbidimetric immunoassay (Turbovet canine CRP, Acuvet Biotech) is a rapid, robust, precise, and accurate method for the quantification of cCRP. The method can be easily set up in automated analyzers, providing a suitable tool for routine clinical use. © 2018 American Society for Veterinary Clinical Pathology.

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Enhanced versus automated urinalysis for screening of urinary tract infections in children in the emergency department.

PubMed

Shah, Ami P; Cobb, Benjamin T; Lower, Darla R; Shaikh, Nader; Rasmussen, Jayne; Hoberman, Alejandro; Wald, Ellen R; Rosendorff, Adam; Hickey, Robert W

2014-03-01

Urinary tract infections (UTI) are the most common serious bacterial infection in febrile infants. Urinalysis (UA) is a screening test for preliminary diagnosis of UTI. UA can be performed manually or using automated techniques. We sought to compare manual versus automated UA for urine specimens obtained via catheterization in the pediatric emergency department. In this prospective study, we processed catheterized urine samples from infants with suspected UTI by both the manual method (enhanced UA) and the automated method. We defined a positive enhanced UA as ≥ 10 white blood cells per cubic millimeter and presence of any bacteria per 10 oil immersion fields on a Gram-stained smear. We defined a positive automated UA as ≥ 2 white blood cells per high-powered field and presence of any bacteria using the IRIS iQ200 ELITE. We defined a positive urine culture as growth of ≥ 50,000 colony-forming units per milliliter of a single uropathogen. We analyzed data using SPSS software. A total of 703 specimens were analyzed. Prevalence of UTI was 7%. For pyuria, the sensitivity and positive predictive value (PPV) of the enhanced UA in predicting positive urine culture were 83.6% and 52.5%, respectively; corresponding values for the automated UA were 79.5% and 37.5%, respectively. For bacteriuria, the sensitivity and PPV of a Gram-stained smear (enhanced UA) were 83.6% and 59.4%, respectively; corresponding values for the automated UA were 73.4%, and 26.2%, respectively. Using criteria of both pyuria and bacteriuria for the enhanced UA resulted in a sensitivity of 77.5% and a PPV of 84.4%; corresponding values for the automated UA were 63.2% and 51.6%, respectively. Combining automated pyuria (≥ 2 white blood cells/high-powered microscopic field) with a Gram-stained smear resulted in a sensitivity of 75.5% and a PPV of 84%. Automated UA is comparable with manual UA for detection of pyuria in young children with suspected UTI. Bacteriuria detected by automated UA is less sensitive and specific for UTI when compared with a Gram-stained smear. We recommend using either manual or automated measurement of pyuria in combination with Gram-stained smear as the preferred technique for UA of catheterized specimens obtained from children in an acute care setting.

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance.

PubMed

Xing, Jian; Burkom, Howard; Tokars, Jerome

2011-12-01

Automated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models. We used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008-4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1-19, 20-49, ≥50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods. Among the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6-14% improvement), and in two categories the time series method had higher sensitivity. When automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data. Published by Elsevier Inc.

An automated tool joint inspection device for the drill string

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

Moyer, M.C.; Dale, B.A.; Kusenberger, F.N.

1983-02-01

This paper discusses the development of an automated tool joint inspection device (i.e., the Fatigue Crack Detector), which is capable of detecting defects in the threaded region of drill pipe and drill collars. On the basis of inspection tests conducted at a research test facility and at drilling rig sites, this device is capable of detecting both simulated defects (saw slots and drilled holes) and service-induced defects, such as fatigue cracks, pin stretch (plastic deformation), mashed threads, and corrosion pitting. The system employs an electromagnetic flux-leakage principle and has several advantages over the conventional method of magnetic particle inspection.

An Automated Mouse Tail Vascular Access System by Vision and Pressure Feedback.

PubMed

Chang, Yen-Chi; Berry-Pusey, Brittany; Yasin, Rashid; Vu, Nam; Maraglia, Brandon; Chatziioannou, Arion X; Tsao, Tsu-Chin

2015-08-01

This paper develops an automated vascular access system (A-VAS) with novel vision-based vein and needle detection methods and real-time pressure feedback for murine drug delivery. Mouse tail vein injection is a routine but critical step for preclinical imaging applications. Due to the small vein diameter and external disturbances such as tail hair, pigmentation, and scales, identifying vein location is difficult and manual injections usually result in poor repeatability. To improve the injection accuracy, consistency, safety, and processing time, A-VAS was developed to overcome difficulties in vein detection noise rejection, robustness in needle tracking, and visual servoing integration with the mechatronics system.

Kepler Planet Detection Metrics: Automatic Detection of Background Objects Using the Centroid Robovetter

NASA Technical Reports Server (NTRS)

Mullally, Fergal

2017-01-01

We present an automated method of identifying background eclipsing binaries masquerading as planet candidates in the Kepler planet candidate catalogs. We codify the manual vetting process for Kepler Objects of Interest (KOIs) described in Bryson et al. (2013) with a series of measurements and tests that can be performed algorithmically. We compare our automated results with a sample of manually vetted KOIs from the catalog of Burke et al. (2014) and find excellent agreement. We test the performance on a set of simulated transits and find our algorithm correctly identifies simulated false positives approximately 50 of the time, and correctly identifies 99 of simulated planet candidates.

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.

Residual bovine serum albumin (BSA) quantitation in vaccines using automated Capillary Western technology.

PubMed

Loughney, John W; Lancaster, Catherine; Ha, Sha; Rustandi, Richard R

2014-09-15

Bovine serum albumin (BSA) is a major component of fetal bovine serum (FBS), which is commonly used as a culture medium during vaccine production. Because BSA can cause allergic reactions in humans the World Health Organization (WHO) has set a guidance of 50 ng or less residual BSA per vaccine dose. Vaccine manufacturers are expected to develop sensitive assays to detect residual BSA. Generally, sandwich enzyme-linked immunosorbent assays (ELISA) are used in the industry to detect these low levels of BSA. We report the development of a new improved method for residual BSA detection using the SimpleWestern technology to analyze residual BSA in an attenuated virus vaccine. The method is based on automated Capillary Western and has linearity of two logs, >80% spike recovery (accuracy), intermediate precision of CV <15%, and LOQ of 5.2 ng/ml. The final method was applied to analyze BSA in four lots of bulk vaccine products and was used to monitor BSA clearance during vaccine process purification. Copyright © 2014 Elsevier Inc. All rights reserved.

Microfluidic approaches to malaria detection

PubMed Central

Gascoyne, Peter; Satayavivad, Jutamaad; Ruchirawat, Mathuros

2009-01-01

Microfluidic systems are under development to address a variety of medical problems. Key advantages of micrototal analysis systems based on microfluidic technology are the promise of small size and the integration of sample handling and measurement functions within a single, automated device having low mass-production costs. Here, we review the spectrum of methods currently used to detect malaria, consider their advantages and disadvantages, and discuss their adaptability towards integration into small, automated micro total analysis systems. Molecular amplification methods emerge as leading candidates for chip-based systems because they offer extremely high sensitivity, the ability to recognize malaria species and strain, and they will be adaptable to the detection of new genotypic signatures that will emerge from current genomic-based research of the disease. Current approaches to the development of chip-based molecular amplification are considered with special emphasis on flow-through PCR, and we present for the first time the method of malaria specimen preparation by dielectrophoretic field-flow-fractionation. Although many challenges must be addressed to realize a micrototal analysis system for malaria diagnosis, it is concluded that the potential benefits of the approach are well worth pursuing. PMID:14744562

Cross-checking of Large Evaluated and Experimental Nuclear Reaction Databases

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

Zeydina, O.; Koning, A.J.; Soppera, N.

2014-06-15

Automated methods are presented for the verification of large experimental and evaluated nuclear reaction databases (e.g. EXFOR, JEFF, TENDL). These methods allow an assessment of the overall consistency of the data and detect aberrant values in both evaluated and experimental databases.

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.

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 dynamic detection of position enabling automated quantification of timing with millisecond accuracy.« less

Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach

NASA Astrophysics Data System (ADS)

Vasuki, Yathunanthan; Holden, Eun-Jung; Kovesi, Peter; Micklethwaite, Steven

2014-08-01

Recent advances in data acquisition technologies, such as Unmanned Aerial Vehicles (UAVs), have led to a growing interest in capturing high-resolution rock surface images. However, due to the large volumes of data that can be captured in a short flight, efficient analysis of this data brings new challenges, especially the time it takes to digitise maps and extract orientation data. We outline a semi-automated method that allows efficient mapping of geological faults using photogrammetric data of rock surfaces, which was generated from aerial photographs collected by a UAV. Our method harnesses advanced automated image analysis techniques and human data interaction to rapidly map structures and then calculate their dip and dip directions. Geological structures (faults, joints and fractures) are first detected from the primary photographic dataset and the equivalent three dimensional (3D) structures are then identified within a 3D surface model generated by structure from motion (SfM). From this information the location, dip and dip direction of the geological structures are calculated. A structure map generated by our semi-automated method obtained a recall rate of 79.8% when compared against a fault map produced using expert manual digitising and interpretation methods. The semi-automated structure map was produced in 10 min whereas the manual method took approximately 7 h. In addition, the dip and dip direction calculation, using our automated method, shows a mean±standard error of 1.9°±2.2° and 4.4°±2.6° respectively with field measurements. This shows the potential of using our semi-automated method for accurate and efficient mapping of geological structures, particularly from remote, inaccessible or hazardous sites.

Automated synovium segmentation in doppler ultrasound images for rheumatoid arthritis assessment

NASA Astrophysics Data System (ADS)

Yeung, Pak-Hei; Tan, York-Kiat; Xu, Shuoyu

2018-02-01

We need better clinical tools to improve monitoring of synovitis, synovial inflammation in the joints, in rheumatoid arthritis (RA) assessment. Given its economical, safe and fast characteristics, ultrasound (US) especially Doppler ultrasound is frequently used. However, manual scoring of synovitis in US images is subjective and prone to observer variations. In this study, we propose a new and robust method for automated synovium segmentation in the commonly affected joints, i.e. metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints, which would facilitate automation in quantitative RA assessment. The bone contour in the US image is firstly detected based on a modified dynamic programming method, incorporating angular information for detecting curved bone surface and using image fuzzification to identify missing bone structure. K-means clustering is then performed to initialize potential synovium areas by utilizing the identified bone contour as boundary reference. After excluding invalid candidate regions, the final segmented synovium is identified by reconnecting remaining candidate regions using level set evolution. 15 MCP and 15 MTP US images were analyzed in this study. For each image, segmentations by our proposed method as well as two sets of annotations performed by an experienced clinician at different time-points were acquired. Dice's coefficient is 0.77+/-0.12 between the two sets of annotations. Similar Dice's coefficients are achieved between automated segmentation and either the first set of annotations (0.76+/-0.12) or the second set of annotations (0.75+/-0.11), with no significant difference (P = 0.77). These results verify that the accuracy of segmentation by our proposed method and by clinician is comparable. Therefore, reliable synovium identification can be made by our proposed method.

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 detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

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

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

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.

WE-G-204-07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection

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

Wells, J; Zhang, L; Samei, E

Purpose: To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] Methods: Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c-means (FCM) clustering, and a clinically-derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale- and neighborhood-based pixelmore » classification probabilities which are weighted by the clinically-derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left-right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity-based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient-based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel{sup 2} ROIs with and 1920 ROIs without hardware were manually selected. Results: Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively. Conclusion: Updated segmentation and centerline estimation methods in addition to new gradient-based hardware detection software provide improved data integrity control and error-checking for automated clinical chest image quality characterization across multiple radiography systems.« less

Automated Online Solid-Phase Derivatization for Sensitive Quantification of Endogenous S-Nitrosoglutathione and Rapid Capture of Other Low-Molecular-Mass S-Nitrosothiols.

PubMed

Wang, Xin; Garcia, Carlos T; Gong, Guanyu; Wishnok, John S; Tannenbaum, Steven R

2018-02-06

S-Nitrosothiols (RSNOs) constitute a circulating endogenous reservoir of nitric oxide and have important biological activities. In this study, an online coupling of solid-phase derivatization (SPD) with liquid chromatography-mass spectrometry (LC-MS) was developed and applied in the analysis of low-molecular-mass RSNOs. A derivatizing-reagent-modified polymer monolithic column was prepared and adapted for online SPD-LC-MS. Analytes from the LC autosampler flowed through the monolithic column for derivatization and then directly into the LC-MS for analysis. This integration of the online derivatization, LC separation, and MS detection facilitated system automation, allowing rapid, laborsaving, and sensitive detection of RSNOs. S-Nitrosoglutathione (GSNO) was quantified using this automated online method with good linearity (R 2 = 0.9994); the limit of detection was 0.015 nM. The online SPD-LC-MS method has been used to determine GSNO levels in mouse samples, 138 ± 13.2 nM of endogenous GSNO was detected in mouse plasma. Besides, the GSNO concentrations in liver (64.8 ± 11.3 pmol/mg protein), kidney (47.2 ± 6.1 pmol/mg protein), heart (8.9 ± 1.8 pmol/mg protein), muscle (1.9 ± 0.3 pmol/mg protein), hippocampus (5.3 ± 0.9 pmol/mg protein), striatum (6.7 ± 0.6 pmol/mg protein), cerebellum (31.4 ± 6.5 pmol/mg protein), and cortex (47.9 ± 4.6 pmol/mg protein) were also successfully quantified. When the derivatization was performed within 8 min, followed by LC-MS detection, samples could be rapidly analyzed compared with the offline manual method. Other low-molecular-mass RSNOs, such as S-nitrosocysteine and S-nitrosocysteinylglycine, were captured by rapid precursor-ion scanning, showing that the proposed method is a potentially powerful tool for capture, identification, and quantification of RSNOs in biological samples.

Genomic Data Quality Impacts Automated Detection of Lateral Gene Transfer in Fungi

PubMed Central

Dupont, Pierre-Yves; Cox, Murray P.

2017-01-01

Lateral gene transfer (LGT, also known as horizontal gene transfer), an atypical mechanism of transferring genes between species, has almost become the default explanation for genes that display an unexpected composition or phylogeny. Numerous methods of detecting LGT events all rely on two fundamental strategies: primary structure composition or gene tree/species tree comparisons. Discouragingly, the results of these different approaches rarely coincide. With the wealth of genome data now available, detection of laterally transferred genes is increasingly being attempted in large uncurated eukaryotic datasets. However, detection methods depend greatly on the quality of the underlying genomic data, which are typically complex for eukaryotes. Furthermore, given the automated nature of genomic data collection, it is typically impractical to manually verify all protein or gene models, orthology predictions, and multiple sequence alignments, requiring researchers to accept a substantial margin of error in their datasets. Using a test case comprising plant-associated genomes across the fungal kingdom, this study reveals that composition- and phylogeny-based methods have little statistical power to detect laterally transferred genes. In particular, phylogenetic methods reveal extreme levels of topological variation in fungal gene trees, the vast majority of which show departures from the canonical species tree. Therefore, it is inherently challenging to detect LGT events in typical eukaryotic genomes. This finding is in striking contrast to the large number of claims for laterally transferred genes in eukaryotic species that routinely appear in the literature, and questions how many of these proposed examples are statistically well supported. PMID:28235827

Automated homogeneous liposome immunoassay systems for anticonvulsant drugs.

PubMed

Kubotsu, K; Goto, S; Fujita, M; Tuchiya, H; Kida, M; Takano, S; Matsuura, S; Sakurabayashi, I

1992-06-01

We developed automated homogeneous immunoassays, based on immunolysis of liposomes, for measuring phenytoin, phenobarbital, and carbamazepine from serum. Liposome lysis was detected spectrophotometrically from entrapped glucose-6-phosphate dehydrogenase activity. The procedure was fully automated on a routine automated clinical analyzer. Within-run, between-run, dilution, and recovery tests showed good accuracies and reproducibilities. Bilirubin, hemoglobin, triglycerides, and Intrafat did not affect assay results. The results obtained by liposome immunoassays for phenytoin, phenobarbital, and carbamazepine correlated well with those obtained by enzyme-multiplied immunoassay (Syva EMIT) kits (r = 0.995, 0.986, and 0.988, respectively) and fluorescence polarization immunoassay (Abbott TDx) kits (r = 0.990, 0.991, and 0.975, respectively). The proposed method should be useful for monitoring anticonvulsant drug concentrations in blood.

Flaw Detection and Evaluation of Composite Cylinders Using Laser Speckle Interferometry and Holography

DTIC Science & Technology

1979-11-23

Entered) ACKNOWLEDGMENTS The author hereby expresses his appreciation to Mr. J. A. Schaeffel Jr. for his guidance on interferometry and the computer...were collected by an automated laser speckle interferometry displacement contour analyzer developed by John A. Schaeffel , Jr. [3]. The new method of 10...Fringe Patterns, US Army Missile Command, Redstone Arsenal, Alabama, Technical Report RL-76-18, 20 April 1976. 3. Schaeffel , J. A., Automated Laser

An automated approach to detecting signals in electroantennogram data

Treesearch

D.H. Slone; B.T. Sullivan

2007-01-01

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

DETERMINATION OF CARBENDAZIM IN WATER BY HIGH-PERFORMANCE IMMUNOAFFINITY CHROMATOGRAPHY ON-LINE WITH HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY WITH DIODE-ARRAY OR MASS SPECTROMETRIC DETECTION

EPA Science Inventory

An automated method for the determination of carbendazim in water that combines high-performance immunoaffinity chromatography (HPIAC), high-performance liquid chromatography (HPLC) in the reversed-phase mode, and detection by either UV-Vis diode array detector (DAD) spectroscopy...

Spore-forming organisms in platelet concentrates: a challenge in transfusion bacterial safety.

PubMed

Störmer, M; Vollmer, T; Kleesiek, K; Dreier, J

2008-12-01

Bacterial detection and pathogen reduction are widely used methods of minimizing the risk of transfusion-transmitted bacterial infection. But, bacterial spores are highly resistant to chemical and physical agents. In this study, we assessed the bacterial proliferation of spore-forming organisms seeded into platelet concentrates (PCs) to demonstrate that spores can enter the vegetative state in PCs during storage. In the in vitro study, PCs were inoculated with 1-10 spores mL(-1)of Bacillus cereus (n = 1), Bacillus subtilis (n = 2) and Clostridium sporogenes (n = 2). Sampling was performed during 6-day aerobic storage at 22 degrees C. The presence of bacteria was assessed by plating culture, automated culture and real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Spores of the C. sporogenes do not enter the vegetative phase under PC storage conditions, whereas B. subtilis and B. cereus showed growth in the PC and could be detected using RT-PCR and automated culture. Depending on the species and inoculums, bacterial spores may enter the vegetative phase during PC storage and can be detected by bacterial detection methods.

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

PubMed Central

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

2014-01-01

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

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

Performance of Kiestra Total Laboratory Automation Combined with MS in Clinical Microbiology Practice

PubMed Central

Hodiamont, Caspar J.; de Jong, Menno D.; Overmeijer, Hendri P. J.; van den Boogaard, Mandy; Visser, Caroline E.

2014-01-01

Background Microbiological laboratories seek technologically innovative solutions to cope with large numbers of samples and limited personnel and financial resources. One platform that has recently become available is the Kiestra Total Laboratory Automation (TLA) system (BD Kiestra B.V., the Netherlands). This fully automated sample processing system, equipped with digital imaging technology, allows superior detection of microbial growth. Combining this approach with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MS) (Bruker Daltonik, Germany) is expected to enable more rapid identification of pathogens. Methods Early growth detection by digital imaging using Kiestra TLA combined with MS was compared to conventional methods (CM) of detection. Accuracy and time taken for microbial identification were evaluated for the two methods in 219 clinical blood culture isolates. The possible clinical impact of earlier microbial identification was assessed according to antibiotic treatment prescription. Results Pathogen identification using Kiestra TLA combined with MS resulted in a 30.6 hr time gain per isolate compared to CM. Pathogens were successfully identified in 98.4% (249/253) of all tested isolates. Early microbial identification without susceptibility testing led to an adjustment of antibiotic regimen in 12% (24/200) of patients. Conclusions The requisite 24 hr incubation time for microbial pathogens to reach sufficient growth for susceptibility testing and identification would be shortened by the implementation of Kiestra TLA in combination with MS, compared to the use of CM. Not only can this method optimize workflow and reduce costs, but it can allow potentially life-saving switches in antibiotic regimen to be initiated sooner. PMID:24624346

Evaluation of automated sample preparation, retention time locked gas chromatography-mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species.

PubMed

Gu, Qun; David, Frank; Lynen, Frédéric; Rumpel, Klaus; Dugardeyn, Jasper; Van Der Straeten, Dominique; Xu, Guowang; Sandra, Pat

2011-05-27

In this paper, automated sample preparation, retention time locked gas chromatography-mass spectrometry (GC-MS) and data analysis methods for the metabolomics study were evaluated. A miniaturized and automated derivatisation method using sequential oximation and silylation was applied to a polar extract of 4 types (2 types×2 ages) of Arabidopsis thaliana, a popular model organism often used in plant sciences and genetics. Automation of the derivatisation process offers excellent repeatability, and the time between sample preparation and analysis was short and constant, reducing artifact formation. Retention time locked (RTL) gas chromatography-mass spectrometry was used, resulting in reproducible retention times and GC-MS profiles. Two approaches were used for data analysis. XCMS followed by principal component analysis (approach 1) and AMDIS deconvolution combined with a commercially available program (Mass Profiler Professional) followed by principal component analysis (approach 2) were compared. Several features that were up- or down-regulated in the different types were detected. Copyright © 2011 Elsevier B.V. All rights reserved.

Electrochemical Detection in Stacked Paper Networks.

PubMed

Liu, Xiyuan; Lillehoj, Peter B

2015-08-01

Paper-based electrochemical biosensors are a promising technology that enables rapid, quantitative measurements on an inexpensive platform. However, the control of liquids in paper networks is generally limited to a single sample delivery step. Here, we propose a simple method to automate the loading and delivery of liquid samples to sensing electrodes on paper networks by stacking multiple layers of paper. Using these stacked paper devices (SPDs), we demonstrate a unique strategy to fully immerse planar electrodes by aqueous liquids via capillary flow. Amperometric measurements of xanthine oxidase revealed that electrochemical sensors on four-layer SPDs generated detection signals up to 75% higher compared with those on single-layer paper devices. Furthermore, measurements could be performed with minimal user involvement and completed within 30 min. Due to its simplicity, enhanced automation, and capability for quantitative measurements, stacked paper electrochemical biosensors can be useful tools for point-of-care testing in resource-limited settings. © 2015 Society for Laboratory Automation and Screening.

Acoustic Methods to Monitor Protein Crystallization and to Detect Protein Crystals in Suspensions of Agarose and Lipidic Cubic Phase.

PubMed

Ericson, Daniel L; Yin, Xingyu; Scalia, Alexander; Samara, Yasmin N; Stearns, Richard; Vlahos, Harry; Ellson, Richard; Sweet, Robert M; Soares, Alexei S

2016-02-01

Improvements needed for automated crystallography include crystal detection and crystal harvesting. A technique that uses acoustic droplet ejection to harvest crystals was previously reported. Here a method is described for using the same acoustic instrument to detect protein crystals and to monitor crystal growth. Acoustic pulses were used to monitor the progress of crystallization trials and to detect the presence and location of protein crystals. Crystals were detected, and crystallization was monitored in aqueous solutions and in lipidic cubic phase. Using a commercially available acoustic instrument, crystals measuring ~150 µm or larger were readily detected. Simple laboratory techniques were used to increase the sensitivity to 50 µm by suspending the crystals away from the plastic surface of the crystallization plate. This increased the sensitivity by separating the strong signal generated by the plate bottom that can mask the signal from small protein crystals. It is possible to further boost the acoustic reflection from small crystals by reducing the wavelength of the incident sound pulse, but our current instrumentation does not allow this option. In the future, commercially available sound-emitting transducers with a characteristic frequency near 300 MHz should detect and monitor the growth of individual 3 µm crystals. © 2015 Society for Laboratory Automation and Screening.

Comparative Evaluation of Multiplex PCR and Routine Laboratory Phenotypic Methods for Detection of Carbapenemases among Gram Negative Bacilli.

PubMed

Solanki, Rachana; Vanjari, Lavanya; Subramanian, Sreevidya; B, Aparna; E, Nagapriyanka; Lakshmi, Vemu

2014-12-01

Carbapenem resistant pathogens cause infections associated with significant morbidity and mortality. This study evaluates the use of Multiplex PCR for rapid detection of carbapenemase genes among carbapenem resistant Gram negative bacteria in comparison with the existing phenotypic methods like modified Hodge test (MHT), combined disc test (CDT) and automated methods. A total of 100 Carbapenem resistant clinical isolates, [Escherichia coli (25), Klebsiella pneumoniae (35) P. aeruginosa (18) and Acinetobacter baumannii (22)] were screened for the presence of carbapenemases (bla NDM-1, bla VIM , blaIMP and blaKPC genes) by phenotype methods such as the modified Hodge test (MHT) and combined disc test (CDT) and the molecular methods such as Multiplex PCR. Seventy of the 100 isolates were MHT positive while, 65 isolates were positive by CDT. All the CDT positive isolates with EDTA and APB were Metallo betalactamase (MBL) and K. pneumoniae carbapenemase (KPC) producers respectively. bla NDM-1 was present as a lone gene in 44 isolates. In 14 isolates bla NDM-1 gene was present with blaKPC gene, and in one isolate bla NDM-1 gene was present with blaVIM , gene. Only one E. coli isolate had a lone blaKPC gene. We didn't find bla IMP gene in any of the isolates. Neither of the genes could be detected in 35 isolates. Accurate detection of the genes related with carbapenemase production by Molecular methods like Multiplex PCR overcome the limitations of the phenotypic methods and Automated systems.

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 attribution of remotely-sensed ecological disturbances using spatial and temporal characteristics of common disturbance classes.

NASA Astrophysics Data System (ADS)

Cooper, L. A.; Ballantyne, A.

2017-12-01

Forest disturbances are critical components of ecosystems. Knowledge of their prevalence and impacts is necessary to accurately describe forest health and ecosystem services through time. While there are currently several methods available to identify and describe forest disturbances, especially those which occur in North America, the process remains inefficient and inaccessible in many parts of the world. Here, we introduce a preliminary approach to streamline and automate both the detection and attribution of forest disturbances. We use a combination of the Breaks for Additive Season and Trend (BFAST) detection algorithm to detect disturbances in combination with supervised and unsupervised classification algorithms to attribute the detections to disturbance classes. Both spatial and temporal disturbance characteristics are derived and utilized for the goal of automating the disturbance attribution process. The resulting preliminary algorithm is applied to up-scaled (100m) Landsat data for several different ecosystems in North America, with varying success. Our results indicate that supervised classification is more reliable than unsupervised classification, but that limited training data are required for a region. Future work will improve the algorithm through refining and validating at sites within North America before applying this approach globally.

Task-oriented situation recognition

NASA Astrophysics Data System (ADS)

Bauer, Alexander; Fischer, Yvonne

2010-04-01

From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building.

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.

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

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.

Rapid, automated, parallel quantitative immunoassays using highly integrated microfluidics and AlphaLISA

PubMed Central

Tak For Yu, Zeta; Guan, Huijiao; Ki Cheung, Mei; McHugh, Walker M.; Cornell, Timothy T.; Shanley, Thomas P.; Kurabayashi, Katsuo; Fu, Jianping

2015-01-01

Immunoassays represent one of the most popular analytical methods for detection and quantification of biomolecules. However, conventional immunoassays such as ELISA and flow cytometry, even though providing high sensitivity and specificity and multiplexing capability, can be labor-intensive and prone to human error, making them unsuitable for standardized clinical diagnoses. Using a commercialized no-wash, homogeneous immunoassay technology (‘AlphaLISA’) in conjunction with integrated microfluidics, herein we developed a microfluidic immunoassay chip capable of rapid, automated, parallel immunoassays of microliter quantities of samples. Operation of the microfluidic immunoassay chip entailed rapid mixing and conjugation of AlphaLISA components with target analytes before quantitative imaging for analyte detections in up to eight samples simultaneously. Aspects such as fluid handling and operation, surface passivation, imaging uniformity, and detection sensitivity of the microfluidic immunoassay chip using AlphaLISA were investigated. The microfluidic immunoassay chip could detect one target analyte simultaneously for up to eight samples in 45 min with a limit of detection down to 10 pg mL−1. The microfluidic immunoassay chip was further utilized for functional immunophenotyping to examine cytokine secretion from human immune cells stimulated ex vivo. Together, the microfluidic immunoassay chip provides a promising high-throughput, high-content platform for rapid, automated, parallel quantitative immunosensing applications. PMID:26074253

Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

NASA Astrophysics Data System (ADS)

Janugani, S.; Jayaram, V.; Cabrera, S. D.; Rosiles, J. G.; Gill, T. E.; Rivera Rivera, N.

2009-05-01

In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.

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.

Using Satellite Data to Characterize the Temporal Thermal Behavior of an Active Volcano: Mount St. Helens, WA

NASA Technical Reports Server (NTRS)

Vaughan, R. Greg; Hook, Simon J.

2006-01-01

ASTER thermal infrared data over Mt. St Helens were used to characterize its thermal behavior from Jun 2000 to Feb 2006. Prior to the Oct 2004 eruption, the average crater temperature varied seasonally between -12 and 6 C. After the eruption, maximum single-pixel temperature increased from 10 C (Oct 2004) to 96 C (Aug 2005), then showed a decrease to Feb 2006. The initial increase in temperature was correlated with dome morphology and growth rate and the subsequent decrease was interpreted to relate to both seasonal trends and a decreased growth rate/increased cooling rate, possibly suggesting a significant change in the volcanic system. A single-pixel ASTER thermal anomaly first appeared on Oct 1, 2004, eleven hours after the first eruption - 10 days before new lava was exposed at the surface. By contrast, an automated algorithm for detecting thermal anomalies in MODIS data did not trigger an alert until Dec 18. However, a single-pixel thermal anomaly first appeared in MODIS channel 23 (4 um) on Oct 13, 12 days after the first eruption - 2 days after lava was exposed. The earlier thermal anomaly detected with ASTER data is attributed to the higher spatial resolution (90 m) compared with MODIS (1 m) and the earlier visual observation of anomalous pixels compared to the automated detection method suggests that local spatial statistics and background radiance data could improve automated detection methods.

A novel flow injection chemiluminescence method for automated and miniaturized determination of phenols in smoked food samples.

PubMed

Vakh, Christina; Evdokimova, Ekaterina; Pochivalov, Aleksei; Moskvin, Leonid; Bulatov, Andrey

2017-12-15

An easily performed fully automated and miniaturized flow injection chemiluminescence (CL) method for determination of phenols in smoked food samples has been proposed. This method includes the ultrasound assisted solid-liquid extraction coupled with gas-diffusion separation of phenols from smoked food sample and analytes absorption into a NaOH solution in a specially designed gas-diffusion cell. The flow system was designed to focus on automation and miniaturization with minimal sample and reagent consumption by inexpensive instrumentation. The luminol - N-bromosuccinimide system in an alkaline medium was used for the CL determination of phenols. The limit of detection of the proposed procedure was 3·10 -8 ·molL -1 (0.01mgkg -1 ) in terms of phenol. The presented method demonstrated to be a good tool for easy, rapid and cost-effective point-of-need screening phenols in smoked food samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images.

PubMed

Rangel-Fonseca, Piero; Gómez-Vieyra, Armando; Malacara-Hernández, Daniel; Wilson, Mario C; Williams, David R; Rossi, Ethan A

2013-12-01

Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.

A Novel Automated Method for Analyzing Cylindrical Computed Tomography Data

NASA Technical Reports Server (NTRS)

Roth, D. J.; Burke, E. R.; Rauser, R. W.; Martin, R. E.

2011-01-01

A novel software method is presented that is applicable for analyzing cylindrical and partially cylindrical objects inspected using computed tomography. This method involves unwrapping and re-slicing data so that the CT data from the cylindrical object can be viewed as a series of 2-D sheets in the vertical direction in addition to volume rendering and normal plane views provided by traditional CT software. The method is based on interior and exterior surface edge detection and under proper conditions, is FULLY AUTOMATED and requires no input from the user except the correct voxel dimension from the CT scan. The software is available from NASA in 32- and 64-bit versions that can be applied to gigabyte-sized data sets, processing data either in random access memory or primarily on the computer hard drive. Please inquire with the presenting author if further interested. This software differentiates itself in total from other possible re-slicing software solutions due to complete automation and advanced processing and analysis capabilities.

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

PubMed Central

Choudhry, Priya

2016-01-01

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays. PMID:26848849

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.

Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data.

PubMed

Raith, Stefan; Vogel, Eric Per; Anees, Naeema; Keul, Christine; Güth, Jan-Frederik; Edelhoff, Daniel; Fischer, Horst

2017-01-01

Chairside manufacturing based on digital image acquisition is gainingincreasing importance in dentistry. For the standardized application of these methods, it is paramount to have highly automated digital workflows that can process acquired 3D image data of dental surfaces. Artificial Neural Networks (ANNs) arenumerical methods primarily used to mimic the complex networks of neural connections in the natural brain. Our hypothesis is that an ANNcan be developed that is capable of classifying dental cusps with sufficient accuracy. This bears enormous potential for an application in chairside manufacturing workflows in the dental field, as it closes the gap between digital acquisition of dental geometries and modern computer-aided manufacturing techniques.Three-dimensional surface scans of dental casts representing natural full dental arches were transformed to range image data. These data were processed using an automated algorithm to detect candidates for tooth cusps according to salient geometrical features. These candidates were classified following common dental terminology and used as training data for a tailored ANN.For the actual cusp feature description, two different approaches were developed and applied to the available data: The first uses the relative location of the detected cusps as input data and the second method directly takes the image information given in the range images. In addition, a combination of both was implemented and investigated.Both approaches showed high performance with correct classifications of 93.3% and 93.5%, respectively, with improvements by the combination shown to be minor.This article presents for the first time a fully automated method for the classification of teeththat could be confirmed to work with sufficient precision to exhibit the potential for its use in clinical practice,which is a prerequisite for automated computer-aided planning of prosthetic treatments with subsequent automated chairside manufacturing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hot spot detection, segmentation, and identification in PET images

NASA Astrophysics Data System (ADS)

Blaffert, Thomas; Meetz, Kirsten

2006-03-01

Positron Emission Tomography (PET) images provide functional or metabolic information from areas of high concentration of [18F]fluorodeoxyglucose (FDG) tracer, the "hot spots". These hot spots can be easily detected by the eye, but delineation and size determination required e.g. for diagnosis and staging of cancer is a tedious task that demands for automation. The approach for such an automated hot spot segmentation described in this paper comprises three steps: A region of interest detection by the watershed transform, a heart identification by an evaluation of scan lines, and the final segmentation of hot spot areas by a local threshold. The region of interest detection is the essential step, since it localizes the hot spot identification and the final segmentation. The heart identification is an example of how to differentiate between hot spots. Finally, we demonstrate the combination of PET and CT data. Our method is applicable to other techniques like SPECT.

An iterative method for airway segmentation using multiscale leakage detection

NASA Astrophysics Data System (ADS)

Nadeem, Syed Ahmed; Jin, Dakai; Hoffman, Eric A.; Saha, Punam K.

2017-02-01

There are growing applications of quantitative computed tomography for assessment of pulmonary diseases by characterizing lung parenchyma as well as the bronchial tree. Many large multi-center studies incorporating lung imaging as a study component are interested in phenotypes relating airway branching patterns, wall-thickness, and other morphological measures. To our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. Even when there are failures in a small fraction of segmentation results, the airway tree masks must be manually reviewed for all results which is laborious considering that several thousands of image data sets are evaluated in large studies. In this paper, we present a CT-based novel airway tree segmentation algorithm using iterative multi-scale leakage detection, freezing, and active seed detection. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity based connectivity and a new leakage detection algorithm to iteratively grow an airway tree starting from an initial seed inside the trachea. It begins with a conservative threshold and then, iteratively shifts toward generous values. The method was applied on chest CT scans of ten non-smoking subjects at total lung capacity and ten at functional residual capacity. Airway segmentation results were compared to an expert's manually edited segmentations. Branch level accuracy of the new segmentation method was examined along five standardized segmental airway paths (RB1, RB4, RB10, LB1, LB10) and two generations beyond these branches. The method successfully detected all branches up to two generations beyond these segmental bronchi with no visual leakages.

Detection of longitudinal ulcer using roughness value for computer aided diagnosis of Crohn's disease

NASA Astrophysics Data System (ADS)

Oda, Masahiro; Kitasaka, Takayuki; Furukawa, Kazuhiro; Watanabe, Osamu; Ando, Takafumi; Goto, Hidemi; Mori, Kensaku

2011-03-01

The purpose of this paper is to present a new method to detect ulcers, which is one of the symptoms of Crohn's disease, from CT images. Crohn's disease is an inflammatory disease of the digestive tract. Crohn's disease commonly affects the small intestine. An optical or a capsule endoscope is used for small intestine examinations. However, these endoscopes cannot pass through intestinal stenosis parts in some cases. A CT image based diagnosis allows a physician to observe whole intestine even if intestinal stenosis exists. However, because of the complicated shape of the small and large intestines, understanding of shapes of the intestines and lesion positions are difficult in the CT image based diagnosis. Computer-aided diagnosis system for Crohn's disease having automated lesion detection is required for efficient diagnosis. We propose an automated method to detect ulcers from CT images. Longitudinal ulcers make rough surface of the small and large intestinal wall. The rough surface consists of combination of convex and concave parts on the intestinal wall. We detect convex and concave parts on the intestinal wall by a blob and an inverse-blob structure enhancement filters. A lot of convex and concave parts concentrate on roughed parts. We introduce a roughness value to differentiate convex and concave parts concentrated on the roughed parts from the other on the intestinal wall. The roughness value effectively reduces false positives of ulcer detection. Experimental results showed that the proposed method can detect convex and concave parts on the ulcers.

Automation for Air Traffic Control: The Rise of a New Discipline

NASA Technical Reports Server (NTRS)

Erzberger, Heinz; Tobias, Leonard (Technical Monitor)

1997-01-01

The current debate over the concept of Free Flight has renewed interest in automated conflict detection and resolution in the enroute airspace. An essential requirement for effective conflict detection is accurate prediction of trajectories. Trajectory prediction is, however, an inexact process which accumulates errors that grow in proportion to the length of the prediction time interval. Using a model of prediction errors for the trajectory predictor incorporated in the Center-TRACON Automation System (CTAS), a computationally fast algorithm for computing conflict probability has been derived. Furthermore, a method of conflict resolution has been formulated that minimizes the average cost of resolution, when cost is defined as the increment in airline operating costs incurred in flying the resolution maneuver. The method optimizes the trade off between early resolution at lower maneuver costs but higher prediction error on the one hand and late resolution with higher maneuver costs but lower prediction errors on the other. The method determines both the time to initiate the resolution maneuver as well as the characteristics of the resolution trajectory so as to minimize the cost of the resolution. Several computational examples relevant to the design of a conflict probe that can support user-preferred trajectories in the enroute airspace will be presented.

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.

Tackling the x-ray cargo inspection challenge using machine learning

NASA Astrophysics Data System (ADS)

Jaccard, Nicolas; Rogers, Thomas W.; Morton, Edward J.; Griffin, Lewis D.

2016-05-01

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography.

PubMed

Hadjiiski, Lubomir; Liu, Jordan; Chan, Heang-Ping; Zhou, Chuan; Wei, Jun; Chughtai, Aamer; Kuriakose, Jean; Agarwal, Prachi; Kazerooni, Ella

2016-01-01

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

PubMed

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

PubMed

Burlina, Philippe M; Joshi, Neil; Pekala, Michael; Pacheco, Katia D; Freund, David E; Bressler, Neil M

2017-11-01

Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Accuracy, receiver operating characteristics and area under the curve, and kappa score. The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

Automated detection of insect-damaged sunflower seeds by X-ray imaging

USDA-ARS?s Scientific Manuscript database

The development of insect-resistant sunflowers is hindered by the lack of a quick and effective method for scoring samples in terms of insect damage. The current method for scoring insect damage, which involves manual inspection of seeds for holes bored into the shell, is tedious, requiring approxi...

Electronic drop sensing in microfluidic devices: automated operation of a nanoliter viscometer

PubMed Central

Srivastava, Nimisha; Burns, Mark A.

2007-01-01

We describe three droplet sensing techniques: a digital electrode, an analog electrode, and a thermal method. All three techniques use a single layer of metal lines that is easy to microfabricate and an electronic signal can be produced using low DC voltages. While the electrode methods utilize changes in electrical conductivity when the air/liquid interface of the droplet passes over a pair of electrodes, the thermal method is based on convective heat loss from a locally heated region. For the electrode method, the analog technique is able to detect 25 nL droplets while the digital technique is capable of detecting droplets as small as 100 pL. For thermal sensing, temperature profiles in the range of 36 °C and higher were used. Finally, we have used the digital electrode method and an array of electrodes located at preset distances to automate the operation of a previously described microfluidic viscometer. The viscometer is completely controlled by a laptop computer, and the total time for operation including setup, calibration, sample addition and viscosity calculation is approximately 4 minutes. PMID:16738725

Automated position control of a surface array relative to a liquid microjunction surface sampler

DOEpatents

Van Berkel, Gary J.; Kertesz, Vilmos; Ford, Michael James

2007-11-13

A system and method utilizes an image analysis approach for controlling the probe-to-surface distance of a liquid junction-based surface sampling system for use with mass spectrometric detection. Such an approach enables a hands-free formation of the liquid microjunction used to sample solution composition from the surface and for re-optimization, as necessary, of the microjunction thickness during a surface scan to achieve a fully automated surface sampling system.

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

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

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images.

PubMed

van 't Klooster, Ronald; de Koning, Patrick J H; Dehnavi, Reza Alizadeh; Tamsma, Jouke T; de Roos, Albert; Reiber, Johan H C; van der Geest, Rob J

2012-01-01

To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery. A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared. Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster. This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies. Copyright © 2011 Wiley Periodicals, Inc.

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 53.1% on the arms.This suggests that image analysis is a feasible avenue of investigation for overcoming variability in clinical assessment. Future studies should focus on more sophisticated features to improve sensitivity for actinic keratoses without erythema and limit false positives associated with the anatomical structures on the face. PMID:25615930

Automated X-ray image analysis for cargo security: Critical review and future promise.

PubMed

Rogers, Thomas W; Jaccard, Nicolas; Morton, Edward J; Griffin, Lewis D

2017-01-01

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

PubMed

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Grain quality inspection system

NASA Technical Reports Server (NTRS)

Flood, C. A., Jr.; Singletow, D. P.; James, S. N.

1979-01-01

A review of grain quality indicators and measurement methods was conducted in order to assess the feasibility of using remote sensing technology to develop a continuous monitoring system for use during grain transfer operations. Most detection methods were found to be too slow or too expensive to be incorporated into the normal inspection procedure of a grain elevator on a continuous basis. Two indicators, moisture content and broken corn and foreign material, show potential for automation and are of an economic value. A microprocessor based system which utilizes commercially available electronic moisture meter was developed and tested. A method for automating BCFM measurement is described. A complete system description is presented along with performance test results.

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 CRE, whereas additional tests are required for the Pheonix 100 system. Our study provides a useful guideline for using automated identification systems for CRE identification.

Quantifying Standing Dead Tree Volume and Structural Loss with Voxelized Terrestrial Lidar Data

NASA Astrophysics Data System (ADS)

Popescu, S. C.; Putman, E.

2017-12-01

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The overall objective of this study was to provide automated aboveground volume estimates of SDTs and automated procedures to detect, quantify, and characterize structural losses over time with terrestrial lidar data. The specific objectives of this study were: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned in dense forests; 2) develop an automated change detection methodology to accurately detect and quantify SDT structural loss between subsequent terrestrial lidar observations; and 3) characterize the structural loss rates of pine and oak SDTs in southeastern Texas. A voxel-based volume estimation algorithm, "TreeVolX", was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models (RTMs). TreeVolX estimated large and small branch volume with an RMSE of 7.3% and 13.8%, respectively. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 - 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in this study.

Automated immunomagnetic separation for the detection of Escherichia coli O157:H7 from spinach

USDA-ARS?s Scientific Manuscript database

Escherichia coli O157:H7 is a major cause of foodborne illness and methods for rapid and sensitive detection of this deadly pathogen are needed to protect consumers. The use of immunomagnetic separation (IMS) for the capture and concentration of foodborne pathogens has been gaining popularity, in p...

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 matching of multiple terrestrial laser scans for stem mapping without the use of artificial references

NASA Astrophysics Data System (ADS)

Liu, Jingbin; Liang, Xinlian; Hyyppä, Juha; Yu, Xiaowei; Lehtomäki, Matti; Pyörälä, Jiri; Zhu, Lingli; Wang, Yunsheng; Chen, Ruizhi

2017-04-01

Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency.

Rapid and sensitive detection of hepatitis A virus in representative food matrices.

PubMed

Papafragkou, Efstathia; Plante, Michelle; Mattison, Kirsten; Bidawid, Sabah; Karthikeyan, Kalavethi; Farber, Jeffrey M; Jaykus, Lee-Ann

2008-01-01

Hepatitis A virus (HAV) is an important cause of foodborne disease worldwide. The detection of this virus in naturally contaminated food products is complicated by the absence of a reliable culture method, low levels of contamination, and the presence of matrix-associated compounds which inhibit molecular detection. In this study, we report a novel method to concentrate HAV from foods prior to the application of reverse transcription-PCR (RT-PCR) for detection. Specifically, we used cationically charged magnetic particles with an automated capture system (Pathatrix) to concentrate the virus from 25 g samples of artificially contaminated lettuce, strawberries, green onions, deli-turkey, oysters, and cake with frosting. Detection limits varied according to the product but in most cases, the virus could be consistently detected at input levels corresponding to 10(2)PFU/25 g food sample. For some products, detection was possible at levels as low as 10(-1)PFU/25 g. The assay was applied by a second independent laboratory and was also used to confirm viral contamination of produce items associated with a recent HAV outbreak. Parallel infectivity assays demonstrated that the cationically charged particles bound approximately 50% of the input virus. This is the first application of the automated magnetic capture technology to the concentration of viruses from foods, and it offers promise for facilitating the rapid detection of HAV from naturally contaminated products.

Computational methods for evaluation of cell-based data assessment--Bioconductor.

PubMed

Le Meur, Nolwenn

2013-02-01

Recent advances in miniaturization and automation of technologies have enabled cell-based assay high-throughput screening, bringing along new challenges in data analysis. Automation, standardization, reproducibility have become requirements for qualitative research. The Bioconductor community has worked in that direction proposing several R packages to handle high-throughput data including flow cytometry (FCM) experiment. Altogether, these packages cover the main steps of a FCM analysis workflow, that is, data management, quality assessment, normalization, outlier detection, automated gating, cluster labeling, and feature extraction. Additionally, the open-source philosophy of R and Bioconductor, which offers room for new development, continuously drives research and improvement of theses analysis methods, especially in the field of clustering and data mining. This review presents the principal FCM packages currently available in R and Bioconductor, their advantages and their limits. Copyright © 2012 Elsevier Ltd. All rights reserved.

Retina Image Analysis and Ocular Telehealth: The Oak Ridge National Laboratory-Hamilton Eye Institute Case Study

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

Karnowski, Thomas Paul; Giancardo, Luca; Li, Yaquin

2013-01-01

Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to howmore » validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.« less

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 seizures could be monitored continuously using relatively inexpensive and non-invasive video techniques that supplement direct observation by nursery personnel. This would represent a major advance in seizure surveillance and offers the possibility for earlier identification of potential neurological problems and subsequent intervention.

A Flexible Workflow for Automated Bioluminescent Kinase Selectivity Profiling.

PubMed

Worzella, Tracy; Butzler, Matt; Hennek, Jacquelyn; Hanson, Seth; Simdon, Laura; Goueli, Said; Cowan, Cris; Zegzouti, Hicham

2017-04-01

Kinase profiling during drug discovery is a necessary process to confirm inhibitor selectivity and assess off-target activities. However, cost and logistical limitations prevent profiling activities from being performed in-house. We describe the development of an automated and flexible kinase profiling workflow that combines ready-to-use kinase enzymes and substrates in convenient eight-tube strips, a bench-top liquid handling device, ADP-Glo Kinase Assay (Promega, Madison, WI) technology to quantify enzyme activity, and a multimode detection instrument. Automated methods were developed for kinase reactions and quantification reactions to be assembled on a Gilson (Middleton, WI) PIPETMAX, following standardized plate layouts for single- and multidose compound profiling. Pipetting protocols were customized at runtime based on user-provided information, including compound number, increment for compound titrations, and number of kinase families to use. After the automated liquid handling procedures, a GloMax Discover (Promega) microplate reader preloaded with SMART protocols was used for luminescence detection and automatic data analysis. The functionality of the automated workflow was evaluated with several compound-kinase combinations in single-dose or dose-response profiling formats. Known target-specific inhibitions were confirmed. Novel small molecule-kinase interactions, including off-target inhibitions, were identified and confirmed in secondary studies. By adopting this streamlined profiling process, researchers can quickly and efficiently profile compounds of interest on site.

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

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.

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.

System for evaluating weld quality using eddy currents

DOEpatents

Todorov, Evgueni I.; Hay, Jacob

2017-12-12

Electromagnetic and eddy current techniques for fast automated real-time and near real-time inspection and monitoring systems for high production rate joining processes. An eddy current system, array and method for the fast examination of welds to detect anomalies such as missed seam (MS) and lack of penetration (LOP) the system, array and methods capable of detecting and sizing surface and slightly subsurface flaws at various orientations in connection with at least the first and second weld pass.

PathFinder: reconstruction and dynamic visualization of metabolic pathways.

PubMed

Goesmann, Alexander; Haubrock, Martin; Meyer, Folker; Kalinowski, Jörn; Giegerich, Robert

2002-01-01

Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.

Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

NASA Astrophysics Data System (ADS)

Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

2018-04-01

The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

PubMed Central

Al-Mulla, Mohamed R.; Sepulveda, Francisco; Colley, Martin

2011-01-01

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results. PMID:22163810

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.

Color-coded automated signal intensity curves for detection and characterization of breast lesions: preliminary evaluation of a new software package for integrated magnetic resonance-based breast imaging.

PubMed

Pediconi, Federica; Catalano, Carlo; Venditti, Fiammetta; Ercolani, Mauro; Carotenuto, Luigi; Padula, Simona; Moriconi, Enrica; Roselli, Antonella; Giacomelli, Laura; Kirchin, Miles A; Passariello, Roberto

2005-07-01

The objective of this study was to evaluate the value of a color-coded automated signal intensity curve software package for contrast-enhanced magnetic resonance mammography (CE-MRM) in patients with suspected breast cancer. Thirty-six women with suspected breast cancer based on mammographic and sonographic examinations were preoperatively evaluated on CE-MRM. CE-MRM was performed on a 1.5-T magnet using a 2D Flash dynamic T1-weighted sequence. A dosage of 0.1 mmol/kg of Gd-BOPTA was administered at a flow rate of 2 mL/s followed by 10 mL of saline. Images were analyzed with the new software package and separately with a standard display method. Statistical comparison was performed of the confidence for lesion detection and characterization with the 2 methods and of the diagnostic accuracy for characterization compared with histopathologic findings. At pathology, 54 malignant lesions and 14 benign lesions were evaluated. All 68 (100%) lesions were detected with both methods and good correlation with histopathologic specimens was obtained. Confidence for both detection and characterization was significantly (P < or = 0.025) better with the color-coded method, although no difference (P > 0.05) between the methods was noted in terms of the sensitivity, specificity, and overall accuracy for lesion characterization. Excellent agreement between the 2 methods was noted for both the determination of lesion size (kappa = 0.77) and determination of SI/T curves (kappa = 0.85). The novel color-coded signal intensity curve software allows lesions to be visualized as false color maps that correspond to conventional signal intensity time curves. Detection and characterization of breast lesions with this method is quick and easily interpretable.

Automated brainstem co-registration (ABC) for MRI.

PubMed

Napadow, Vitaly; Dhond, Rupali; Kennedy, David; Hui, Kathleen K S; Makris, Nikos

2006-09-01

Group data analysis in brainstem neuroimaging is predicated on accurate co-registration of anatomy. As the brainstem is comprised of many functionally heterogeneous nuclei densely situated adjacent to one another, relatively small errors in co-registration can manifest in increased variance or decreased sensitivity (or significance) in detecting activations. We have devised a 2-stage automated, reference mask guided registration technique (Automated Brainstem Co-registration, or ABC) for improved brainstem co-registration. Our approach utilized a brainstem mask dataset to weight an automated co-registration cost function. Our method was validated through measurement of RMS error at 12 manually defined landmarks. These landmarks were also used as guides for a secondary manual co-registration option, intended for outlier individuals that may not adequately co-register with our automated method. Our methodology was tested on 10 healthy human subjects and compared to traditional co-registration techniques (Talairach transform and automated affine transform to the MNI-152 template). We found that ABC had a significantly lower mean RMS error (1.22 +/- 0.39 mm) than Talairach transform (2.88 +/- 1.22 mm, mu +/- sigma) and the global affine (3.26 +/- 0.81 mm) method. Improved accuracy was also found for our manual-landmark-guided option (1.51 +/- 0.43 mm). Visualizing individual brainstem borders demonstrated more consistent and uniform overlap for ABC compared to traditional global co-registration techniques. Improved robustness (lower susceptibility to outliers) was demonstrated with ABC through lower inter-subject RMS error variance compared with traditional co-registration methods. The use of easily available and validated tools (AFNI and FSL) for this method should ease adoption by other investigators interested in brainstem data group analysis.

An automated online turboflow cleanup LC/MS/MS method for the determination of 11 plasticizers in beverages and milk.

PubMed

Ates, Ebru; Mittendorf, Klaus; Senyuva, Hamide

2013-01-01

An automated sample preparation technique involving cleanup and analytical separation in a single operation using an online coupled TurboFlow (RP-LC system) is reported. This method eliminates time-consuming sample preparation steps that can be potential sources for cross-contamination in the analysis of plasticizers. Using TurboFlow chromatography, liquid samples were injected directly into the automated system without previous extraction or cleanup. Special cleanup columns enabled specific binding of target compounds; higher MW compounds, i.e., fats and proteins, and other matrix interferences with different chemical properties were removed to waste, prior to LC/MS/MS. Systematic stepwise method development using this new technology in the food safety area is described. Selection of optimum columns and mobile phases for loading onto the cleanup column followed by transfer onto the analytical column and MS detection are critical method parameters. The method was optimized for the assay of 10 phthalates (dimethyl, diethyl, dipropyl, butyl benzyl, diisobutyl, dicyclohexyl, dihexyl, diethylhexyl, diisononyl, and diisododecyl) and one adipate (diethylhexyl) in beverages and milk.

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

PubMed

Failmezger, Henrik; Fröhlich, Holger; Tresch, Achim

2013-10-04

Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.

Automated segmentation reveals silent radiographic progression in adult-onset vanishing white-matter disease.

PubMed

Huber, Thomas; Herwerth, Marina; Alberts, Esther; Kirschke, Jan S; Zimmer, Claus; Ilg, Ruediger

2017-02-01

Adult-onset vanishing white-matter disease (VWM) is a rare autosomal recessive disease with neurological symptoms such as ataxia and paraparesis, showing extensive white-matter hyperintensities (WMH) on magnetic resonance (MR) imaging. Besides symptom-specific scores like the International Cooperative Ataxia Rating Scale (ICARS), there is no established tool to monitor disease progression. Because of extensive WMH, visual comparison of MR images is challenging. Here, we report the results of an automated method of segmentation to detect alterations in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) sequences in a one-year follow-up study of a clinically stable patient with genetically diagnosed VWM. Signal alterations in MR imaging were quantified with a recently published WMH segmentation method by means of extreme value distribution (EVD). Our analysis revealed progressive FLAIR alterations of 5.84% in the course of one year, whereas no significant WMH change could be detected in a stable multiple sclerosis (MS) control group. This result demonstrates that automated EVD-based segmentation allows a precise and rapid quantification of extensive FLAIR alterations like in VWM and might be a powerful tool for the clinical and scientific monitoring of degenerative white-matter diseases and potential therapeutic interventions.

Understanding the Effect of Workload on Automation Use for Younger and Older Adults

PubMed Central

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

2018-01-01

Objective 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. Background 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. Method 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. Results 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. Conclusion 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. Application 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. PMID:22235529

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

PubMed Central

2017-01-01

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects. PMID:28984823

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data.

PubMed

Falque, Raphael; Vidal-Calleja, Teresa; Miro, Jaime Valls

2017-10-06

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

A semi-automated magnetic capture probe based DNA extraction and real-time PCR method applied in the Swedish surveillance of Echinococcus multilocularis in red fox (Vulpes vulpes) faecal samples.

PubMed

Isaksson, Mats; Hagström, Åsa; Armua-Fernandez, Maria Teresa; Wahlström, Helene; Ågren, Erik Olof; Miller, Andrea; Holmberg, Anders; Lukacs, Morten; Casulli, Adriano; Deplazes, Peter; Juremalm, Mikael

2014-12-19

Following the first finding of Echinococcus multilocularis in Sweden in 2011, 2985 red foxes (Vulpes vulpes) were analysed by the segmental sedimentation and counting technique. This is a labour intensive method and requires handling of the whole carcass of the fox, resulting in a costly analysis. In an effort to reduce the cost of labour and sample handling, an alternative method has been developed. The method is sensitive and partially automated for detection of E. multilocularis in faecal samples. The method has been used in the Swedish E. multilocularis monitoring program for 2012-2013 on more than 2000 faecal samples. We describe a new semi-automated magnetic capture probe DNA extraction method and real time hydrolysis probe polymerase chain reaction assay (MC-PCR) for the detection of E. multilocularis DNA in faecal samples from red fox. The diagnostic sensitivity was determined by validating the new method against the sedimentation and counting technique in fox samples collected in Switzerland where E. multilocularis is highly endemic. Of 177 foxes analysed by the sedimentation and counting technique, E. multilocularis was detected in 93 animals. Eighty-two (88%, 95% C.I 79.8-93.9) of these were positive in the MC-PCR. In foxes with more than 100 worms, the MC-PCR was positive in 44 out of 46 (95.7%) cases. The two MC-PCR negative samples originated from foxes with only immature E. multilocularis worms. In foxes with 100 worms or less, (n = 47), 38 (80.9%) were positive in the MC-PCR. The diagnostic specificity of the MC-PCR was evaluated using fox scats collected within the Swedish screening. Of 2158 samples analysed, two were positive. This implies that the specificity is at least 99.9% (C.I. = 99.7-100). The MC-PCR proved to have a high sensitivity and a very high specificity. The test is partially automated but also possible to perform manually if desired. The test is well suited for nationwide E. multilocularis surveillance programs where sampling of fox scats is done to reduce the costs for sampling and where a test with a high sensitivity and a very high specificity is needed.

Analysis of nitrosamines in water by automated SPE and isotope dilution GC/HRMS Occurrence in the different steps of a drinking water treatment plant, and in chlorinated samples from a reservoir and a sewage treatment plant effluent.

PubMed

Planas, Carles; Palacios, Oscar; Ventura, Francesc; Rivera, Josep; Caixach, Josep

2008-08-15

A method based on automated solid-phase extraction (SPE) and isotope dilution gas chromatography/high resolution mass spectrometry (GC/HRMS) has been developed for the analysis of nine nitrosamines in water samples. The combination of automated SPE and GC/HRMS for the analysis of nitrosamines has not been reported previously. The method shows as advantages the selectivity and sensitivity of GC/HRMS analysis and the high efficiency of automated SPE with coconut charcoal EPA 521 cartridges. Low method detection limits (MDLs) were achieved, along with a greater facility of the procedure and less dependence on the operator with regard to the methods based on manual SPE. Quality requirements for isotope dilution-based methods were accomplished for most analysed nitrosamines, regarding to trueness (80-120%), method precision (<15%) and MDLs (0.08-1.7 ng/L). Nineteen water samples (16 samples from a drinking water treatment plant {DWTP}, 2 chlorinated samples from a sewage treatment plant {STP} effluent, and 1 chlorinated sample from a reservoir) were analysed. Concentrations of nitrosamines in the STP effluent were 309.4 and 730.2 ng/L, being higher when higher doses of chlorine were applied. N-Nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) were the main compounds identified in the STP effluent, and NDEA was detected above 200 ng/L, regulatory level for NDMA in effluents stated in Ontario (Canada). Lower concentrations of nitrosamines were found in the reservoir (20.3 ng/L) and in the DWTP samples (n.d. -28.6 ng/L). NDMA and NDEA were respectively found in the reservoir and in treated and highly chlorinated DWTP samples at concentrations above 10 ng/L (guide value established in different countries). The highest concentrations of nitrosamines were found after chlorination and ozonation processes (ozonated, treated and highly chlorinated water) in DWTP samples.

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: this also suggests that these areas must be carefully considered when analysing erosion and accretion processes. Ultimately, we have shown that automatic detection of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

Automated Quantitative Nuclear Cardiology Methods

PubMed Central

Motwani, Manish; Berman, Daniel S.; Germano, Guido; Slomka, Piotr J.

2016-01-01

Quantitative analysis of SPECT and PET has become a major part of nuclear cardiology practice. Current software tools can automatically segment the left ventricle, quantify function, establish myocardial perfusion maps and estimate global and local measures of stress/rest perfusion – all with minimal user input. State-of-the-art automated techniques have been shown to offer high diagnostic accuracy for detecting coronary artery disease, as well as predict prognostic outcomes. This chapter briefly reviews these techniques, highlights several challenges and discusses the latest developments. PMID:26590779

Neural network expert system for X-ray analysis of welded joints

NASA Astrophysics Data System (ADS)

Kozlov, V. V.; Lapik, N. V.; Popova, N. V.

2018-03-01

The use of intelligent technologies for the automated analysis of product quality is one of the main trends in modern machine building. At the same time, rapid development in various spheres of human activity is experienced by methods associated with the use of artificial neural networks, as the basis for building automated intelligent diagnostic systems. Technologies of machine vision allow one to effectively detect the presence of certain regularities in the analyzed designation, including defects of welded joints according to radiography data.

Interpretation of Blood Microbiology Results - Function of the Clinical Microbiologist.

PubMed

Kristóf, Katalin; Pongrácz, Júlia

2016-04-01

The proper use and interpretation of blood microbiology results may be one of the most challenging and one of the most important functions of clinical microbiology laboratories. Effective implementation of this function requires careful consideration of specimen collection and processing, pathogen detection techniques, and prompt and precise reporting of identification and susceptibility results. The responsibility of the treating physician is proper formulation of the analytical request and to provide the laboratory with complete and precise patient information, which are inevitable prerequisites of a proper testing and interpretation. The clinical microbiologist can offer advice concerning the differential diagnosis, sampling techniques and detection methods to facilitate diagnosis. Rapid detection methods are essential, since the sooner a pathogen is detected, the better chance the patient has of getting cured. Besides the gold-standard blood culture technique, microbiologic methods that decrease the time in obtaining a relevant result are more and more utilized today. In the case of certain pathogens, the pathogen can be identified directly from the blood culture bottle after propagation with serological or automated/semi-automated systems or molecular methods or with MALDI-TOF MS (matrix-assisted laser desorption-ionization time of flight mass spectrometry). Molecular biology methods are also suitable for the rapid detection and identification of pathogens from aseptically collected blood samples. Another important duty of the microbiology laboratory is to notify the treating physician immediately about all relevant information if a positive sample is detected. The clinical microbiologist may provide important guidance regarding the clinical significance of blood isolates, since one-third to one-half of blood culture isolates are contaminants or isolates of unknown clinical significance. To fully exploit the benefits of blood culture and other (non- culture based) diagnoses, the microbiologist and the clinician should interact directly.

Detection of cardiac activity using a 5.8 GHz radio frequency sensor.

PubMed

Vasu, V; Fox, N; Brabetz, T; Wren, M; Heneghan, C; Sezer, S

2009-01-01

A 5.8-GHz ISM-Band radio-frequency sensor has been developed for non-contact measurement of respiration and heart rate from stationary and semi-stationary subjects at a distance of 0.5 to 1.5 meters. We report on the accuracy of the heart rate measurements obtained using two algorithmic approaches, as compared to a reference heart rate obtained using a pulse oximeter. Simultaneous Photoplethysmograph (PPG) and non-contact sensor recordings were recorded over fifteen minute periods for ten healthy subjects (8M/2F, ages 29.6 + or - 5.6 yrs) One algorithm is based on automated detection of individual peaks associated with each cardiac cycle; a second algorithm extracts a heart rate over a 60-second period using spectral analysis. Peaks were also extracted manually for comparison with the automated method. The peak-detection methods were less accurate than the spectral methods, but suggest the possibility of acquiring beat by beat data; the spectral algorithms measured heart rate to within + or -10% for the ten subjects chosen. Non-contact measurement of heart rate will be useful in chronic disease monitoring for conditions such as heart failure and cardiovascular disease.

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM.

PubMed

Ding, Huiyang; Shi, Chaoyang; Ma, Li; Yang, Zhan; Wang, Mingyu; Wang, Yaqiong; Chen, Tao; Sun, Lining; Toshio, Fukuda

2018-04-08

The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system's capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks.

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM

PubMed Central

Ding, Huiyang; Shi, Chaoyang; Ma, Li; Yang, Zhan; Wang, Mingyu; Wang, Yaqiong; Chen, Tao; Sun, Lining; Toshio, Fukuda

2018-01-01

The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system’s capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks. PMID:29642495

Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images.

PubMed

Dabbah, M A; Graham, J; Petropoulos, I; Tavakoli, M; Malik, R A

2010-01-01

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

Glaucoma progression detection: agreement, sensitivity, and specificity of expert visual field evaluation, event analysis, and trend analysis.

PubMed

Antón, Alfonso; Pazos, Marta; Martín, Belén; Navero, José Manuel; Ayala, Miriam Eleonora; Castany, Marta; Martínez, Patricia; Bardavío, Javier

2013-01-01

To assess sensitivity, specificity, and agreement among automated event analysis, automated trend analysis, and expert evaluation to detect glaucoma progression. This was a prospective study that included 37 eyes with a follow-up of 36 months. All had glaucomatous disks and fields and performed reliable visual fields every 6 months. Each series of fields was assessed with 3 different methods: subjective assessment by 2 independent teams of glaucoma experts, glaucoma/guided progression analysis (GPA) event analysis, and GPA (visual field index-based) trend analysis. Kappa agreement coefficient between methods and sensitivity and specificity for each method using expert opinion as gold standard were calculated. The incidence of glaucoma progression was 16% to 18% in 3 years but only 3 cases showed progression with all 3 methods. Kappa agreement coefficient was high (k=0.82) between subjective expert assessment and GPA event analysis, and only moderate between these two and GPA trend analysis (k=0.57). Sensitivity and specificity for GPA event and GPA trend analysis were 71% and 96%, and 57% and 93%, respectively. The 3 methods detected similar numbers of progressing cases. The GPA event analysis and expert subjective assessment showed high agreement between them and moderate agreement with GPA trend analysis. In a period of 3 years, both methods of GPA analysis offered high specificity, event analysis showed 83% sensitivity, and trend analysis had a 66% sensitivity.

Speciation analysis of arsenic in biological matrices by automated hydride generation-cryotrapping-atomic absorption spectrometry with multiple microflame quartz tube atomizer (multiatomizer)

PubMed Central

Hernández-Zavala, Araceli; Matoušek, Tomáš; Drobná, Zuzana; Paul, David S.; Walton, Felecia; Adair, Blakely M.; Jiří, Dědina; Thomas, David J.

2008-01-01

Analyses of arsenic (As) species in tissues and body fluids of individuals chronically exposed to inorganic arsenic (iAs) provide essential information about the exposure level and pattern of iAs metabolism. We have previously described an oxidation state-specific analysis of As species in biological matrices by hydride-generation atomic absorption spectrometry (HG-AAS), using cryotrapping (CT) for preconcentration and separation of arsines. To improve performance and detection limits of the method, HG and CT steps are automated and a conventional flame-in-tube atomizer replaced with a recently developed multiple microflame quartz tube atomizer (multiatomizer). In this system, arsines from AsIII-species are generated in a mixture of Tris-HCl (pH 6) and sodium borohydride. For generation of arsines from both AsIII- and AsV-species, samples are pretreated with L-cysteine. Under these conditions, dimethylthioarsinic acid, a newly described metabolite of iAs, does not interfere significantly with detection and quantification of methylated trivalent arsenicals. Analytical performance of the automated HG-CT-AAS was characterized by analyses of cultured cells and mouse tissues that contained mono- and dimethylated metabolites of iAs. The capacity to detect methylated AsIII- and AsV-species was verified, using an in vitro methylation system containing recombinant rat arsenic (+3 oxidation state) methyltransferase and cultured rat hepatocytes treated with iAs. Compared with the previous HG-CT-AAS design, detection limits for iAs and its metabolites have improved significantly with the current system, ranging from 8 to 20 pg. Recoveries of As were between 78 and 117%. The precision of the method was better than 5% for all biological matrices examined. Thus, the automated HG-CT-AAS system provides an effective and sensitive tool for analysis of all major human metabolites of iAs in complex biological matrices. PMID:18677417

[Automated analyser of organ cultured corneal endothelial mosaic].

PubMed

Gain, P; Thuret, G; Chiquet, C; Gavet, Y; Turc, P H; Théillère, C; Acquart, S; Le Petit, J C; Maugery, J; Campos, L

2002-05-01

Until now, organ-cultured corneal endothelial mosaic has been assessed in France by cell counting using a calibrated graticule, or by drawing cells on a computerized image. The former method is unsatisfactory because it is characterized by a lack of objective evaluation of the cell surface and hexagonality and it requires an experienced technician. The latter method is time-consuming and requires careful attention. We aimed to make an efficient, fast and easy to use, automated digital analyzer of video images of the corneal endothelium. The hardware included a PC Pentium III ((R)) 800 MHz-Ram 256, a Data Translation 3155 acquisition card, a Sony SC 75 CE CCD camera, and a 22-inch screen. Special functions for automated cell boundary determination consisted of Plug-in programs included in the ImageTool software. Calibration was performed using a calibrated micrometer. Cell densities of 40 organ-cultured corneas measured by both manual and automated counting were compared using parametric tests (Student's t test for paired variables and the Pearson correlation coefficient). All steps were considered more ergonomic i.e., endothelial image capture, image selection, thresholding of multiple areas of interest, automated cell count, automated detection of errors in cell boundary drawing, presentation of the results in an HTML file including the number of counted cells, cell density, coefficient of variation of cell area, cell surface histogram and cell hexagonality. The device was efficient because the global process lasted on average 7 minutes and did not require an experienced technician. The correlation between cell densities obtained with both methods was high (r=+0.84, p<0.001). The results showed an under-estimation using manual counting (2191+/-322 vs. 2273+/-457 cell/mm(2), p=0.046), compared with the automated method. Our automated endothelial cell analyzer is efficient and gives reliable results quickly and easily. A multicentric validation would allow us to standardize cell counts among cornea banks in our country.

Sub-micron accurate track navigation method ``Navi'' for the analysis of Nuclear Emulsion

NASA Astrophysics Data System (ADS)

Yoshioka, T.; Yoshida, J.; Kodama, K.

2011-03-01

Sub-micron accurate track navigation in Nuclear Emulsion is realized by using low energy signals detected by automated Nuclear Emulsion read-out systems. Using those much dense ``noise'', about 104 times larger than the real tracks, the accuracy of the track position navigation reaches to be sub micron only by using the information of a microscope field of view, 200 micron times 200 micron. This method is applied to OPERA analysis in Japan, i.e. support of human eye checks of the candidate tracks, confirmation of neutrino interaction vertexes and to embed missing track segments to the track data read-out by automated systems.

A Novel Method for Pulsometry Based on Traditional Iranian Medicine

PubMed Central

Yousefipoor, Farzane; Nafisi, Vahidreza

2015-01-01

Arterial pulse measurement is one of the most important methods for evaluation of healthy conditions. In traditional Iranian medicine (TIM), physician may detect radial pulse by holding four fingers on the patient's wrist. By using this method, under standard condition, the detected pulses are subjective and erroneous, in case of weak and/or abnormal pulses, the ambiguity of diagnosis may rise. In this paper, we present an equipment which is designed and implemented for automation of traditional pulse detection method. By this novel system, the developed noninvasive diagnostic method and database based on the TIM are way forward to apply traditional medicine and diagnose patients with present technology. The accuracy for period measuring is 76% and systolic peak is 72%. PMID:26955566

Method for phosphorothioate antisense DNA sequencing by capillary electrophoresis with UV detection.

PubMed

Froim, D; Hopkins, C E; Belenky, A; Cohen, A S

1997-11-01

The progress of antisense DNA therapy demands development of reliable and convenient methods for sequencing short single-stranded oligonucleotides. A method of phosphorothioate antisense DNA sequencing analysis using UV detection coupled to capillary electrophoresis (CE) has been developed based on a modified chain termination sequencing method. The proposed method reduces the sequencing cost since it uses affordable CE-UV instrumentation and requires no labeling with minimal sample processing before analysis. Cycle sequencing with ThermoSequenase generates quantities of sequencing products that are readily detectable by UV. Discrimination of undesired components from sequencing products in the reaction mixture, previously accomplished by fluorescent or radioactive labeling, is now achieved by bringing concentrations of undesired components below the UV detection range which yields a 'clean', well defined sequence. UV detection coupled with CE offers additional conveniences for sequencing since it can be accomplished with commercially available CE-UV equipment and is readily amenable to automation.

Method for phosphorothioate antisense DNA sequencing by capillary electrophoresis with UV detection.

PubMed Central

Froim, D; Hopkins, C E; Belenky, A; Cohen, A S

1997-01-01

The progress of antisense DNA therapy demands development of reliable and convenient methods for sequencing short single-stranded oligonucleotides. A method of phosphorothioate antisense DNA sequencing analysis using UV detection coupled to capillary electrophoresis (CE) has been developed based on a modified chain termination sequencing method. The proposed method reduces the sequencing cost since it uses affordable CE-UV instrumentation and requires no labeling with minimal sample processing before analysis. Cycle sequencing with ThermoSequenase generates quantities of sequencing products that are readily detectable by UV. Discrimination of undesired components from sequencing products in the reaction mixture, previously accomplished by fluorescent or radioactive labeling, is now achieved by bringing concentrations of undesired components below the UV detection range which yields a 'clean', well defined sequence. UV detection coupled with CE offers additional conveniences for sequencing since it can be accomplished with commercially available CE-UV equipment and is readily amenable to automation. PMID:9336449

Automated Test Case Generator for Phishing Prevention Using Generative Grammars and Discriminative Methods

ERIC Educational Resources Information Center

Palka, Sean

2015-01-01

This research details a methodology designed for creating content in support of various phishing prevention tasks including live exercises and detection algorithm research. Our system uses probabilistic context-free grammars (PCFG) and variable interpolation as part of a multi-pass method to create diverse and consistent phishing email content on…

Automated segmentation of linear time-frequency representations of marine-mammal sounds.

PubMed

Dadouchi, Florian; Gervaise, Cedric; Ioana, Cornel; Huillery, Julien; Mars, Jérôme I

2013-09-01

Many marine mammals produce highly nonlinear frequency modulations. Determining the time-frequency support of these sounds offers various applications, which include recognition, localization, and density estimation. This study introduces a low parameterized automated spectrogram segmentation method that is based on a theoretical probabilistic framework. In the first step, the background noise in the spectrogram is fitted with a Chi-squared distribution and thresholded using a Neyman-Pearson approach. In the second step, the number of false detections in time-frequency regions is modeled as a binomial distribution, and then through a Neyman-Pearson strategy, the time-frequency bins are gathered into regions of interest. The proposed method is validated on real data of large sequences of whistles from common dolphins, collected in the Bay of Biscay (France). The proposed method is also compared with two alternative approaches: the first is smoothing and thresholding of the spectrogram; the second is thresholding of the spectrogram followed by the use of morphological operators to gather the time-frequency bins and to remove false positives. This method is shown to increase the probability of detection for the same probability of false alarms.

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

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

Egorov, Oleg; O'Hara, Matthew J.; Grate, Jay W.

An automated fluidic instrument is described that rapidly determines the total 99Tc content of aged nuclear waste samples, where the matrix is chemically and radiologically complex and the existing speciation of the 99Tc is variable. The monitor links microwave-assisted sample preparation with an automated anion exchange column separation and detection using a flow-through solid scintillator detector. The sample preparation steps acidify the sample, decompose organics, and convert all Tc species to the pertechnetate anion. The column-based anion exchange procedure separates the pertechnetate from the complex sample matrix, so that radiometric detection can provide accurate measurement of 99Tc. We developed amore » preprogrammed spike addition procedure to automatically determine matrix-matched calibration. The overall measurement efficiency that is determined simultaneously provides a self-diagnostic parameter for the radiochemical separation and overall instrument function. Continuous, automated operation was demonstrated over the course of 54 h, which resulted in the analysis of 215 samples plus 54 hly spike-addition samples, with consistent overall measurement efficiency for the operation of the monitor. A sample can be processed and measured automatically in just 12.5 min with a detection limit of 23.5 Bq/mL of 99Tc in low activity waste (0.495 mL sample volume), with better than 10% RSD precision at concentrations above the quantification limit. This rapid automated analysis method was developed to support nuclear waste processing operations planned for the Hanford nuclear site.« less

Automation of ⁹⁹Tc extraction by LOV prior ICP-MS detection: application to environmental samples.

PubMed

Rodríguez, Rogelio; Leal, Luz; Miranda, Silvia; Ferrer, Laura; Avivar, Jessica; García, Ariel; Cerdà, Víctor

2015-02-01

A new, fast, automated and inexpensive sample pre-treatment method for (99)Tc determination by inductively coupled plasma-mass spectrometry (ICP-MS) detection is presented. The miniaturized approach is based on a lab-on-valve (LOV) system, allowing automatic separation and preconcentration of (99)Tc. Selectivity is provided by the solid phase extraction system used (TEVA resin) which retains selectively pertechnetate ion in diluted nitric acid solution. The proposed system has some advantages such as minimization of sample handling, reduction of reagents volume, improvement of intermediate precision and sample throughput, offering a significant decrease of both time and cost per analysis in comparison to other flow techniques and batch methods. The proposed LOV system has been successfully applied to different samples of environmental interest (water and soil) with satisfactory recoveries, between 94% and 98%. The detection limit (LOD) of the developed method is 0.005 ng. The high durability of the resin and its low amount (32 mg), its good intermediate precision (RSD 3.8%) and repeatability (RSD 2%) and its high extraction frequency (up to 5 h(-1)) makes this method an inexpensive, high precision and fast tool for monitoring (99)Tc in environmental samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

PubMed

Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

2015-01-01

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse. Published by Elsevier B.V.

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

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

[A study of biomechanical method for urine test based on color difference estimation].

PubMed

Wang, Chunhong; Zhou, Yue; Zhao, Hongxia; Zhou, Fengkun

2008-02-01

The biochemical analysis of urine is an important inspection and diagnosis method in hospitals. The conventional method of urine analysis covers mainly colorimetric visual appraisement and automation detection, in which the colorimetric visual appraisement technique has been superseded basically, and the automation detection method is adopted in hospital; moreover, the price of urine biochemical analyzer on market is around twenty thousand RMB yuan (Y), which is hard to enter into ordinary families. It is known that computer vision system is not subject to the physiological and psychological influence of person, its appraisement standard is objective and steady. Therefore, according to the color theory, we have established a computer vision system, which can carry through collection, management, display, and appraisement of color difference between the color of standard threshold value and the color of urine test paper after reaction with urine liquid, and then the level of an illness can be judged accurately. In this paper, we introduce the Urine Test Biochemical Analysis method, which is new and can be popularized in families. Experimental result shows that this test method is easy-to-use and cost-effective. It can realize the monitoring of a whole course and can find extensive applications.

Automated railroad reconstruction from remote sensing image based on texture filter

NASA Astrophysics Data System (ADS)

Xiao, Jie; Lu, Kaixia

2018-03-01

Techniques of remote sensing have been improved incredibly in recent years and very accurate results and high resolution images can be acquired. There exist possible ways to use such data to reconstruct railroads. In this paper, an automated railroad reconstruction method from remote sensing images based on Gabor filter was proposed. The method is divided in three steps. Firstly, the edge-oriented railroad characteristics (such as line features) in a remote sensing image are detected using Gabor filter. Secondly, two response images with the filtering orientations perpendicular to each other are fused to suppress the noise and acquire a long stripe smooth region of railroads. Thirdly, a set of smooth regions can be extracted by firstly computing global threshold for the previous result image using Otsu's method and then converting it to a binary image based on the previous threshold. This workflow is tested on a set of remote sensing images and was found to deliver very accurate results in a quickly and highly automated manner.

Automation, Miniature Robotics and Sensors for Nondestructive Testing and Evaluation, Volume 4

NASA Technical Reports Server (NTRS)

Bar-Cohen, Y.; Baumgartner, E.; Backes, P.; Sherrit, S.; Bao, X.; Leary, S.; Kennedy, B.; Mavroidis, C.; Pfeiffer, C.; Culbert, C.;

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.

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.

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.

Detection of Orbital Debris Collision Risks for the Automated Transfer Vehicle

NASA Technical Reports Server (NTRS)

Peret, L.; Legendre, P.; Delavault, S.; Martin, T.

2007-01-01

In this paper, we present a general collision risk assessment method, which has been applied through numerical simulations to the Automated Transfer Vehicle (ATV) case. During ATV ascent towards the International Space Station, close approaches between the ATV and objects of the USSTRACOM catalog will be monitored through collision rosk assessment. Usually, collision risk assessment relies on an exclusion volume or a probability threshold method. Probability methods are more effective than exclusion volumes but require accurate covariance data. In this work, we propose to use a criterion defined by an adaptive exclusion area. This criterion does not require any probability calculation but is more effective than exclusion volume methods as demonstrated by our numerical experiments. The results of these studies, when confirmed and finalized, will be used for the ATV operations.

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.

Creating an automated chiller fault detection and diagnostics tool using a data fault library.

PubMed

Bailey, Margaret B; Kreider, Jan F

2003-07-01

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.

Automated Nucleic Acid Extraction Systems for Detecting Cytomegalovirus and Epstein-Barr Virus Using Real-Time PCR: A Comparison Study Between the QIAsymphony RGQ and QIAcube Systems.

PubMed

Kim, Hanah; Hur, Mina; Kim, Ji Young; Moon, Hee Won; Yun, Yeo Min; Cho, Hyun Chan

2017-03-01

Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are increasingly important in immunocompromised patients. Nucleic acid extraction methods could affect the results of viral nucleic acid amplification tests. We compared two automated nucleic acid extraction systems for detecting CMV and EBV using real-time PCR assays. One hundred and fifty-three whole blood (WB) samples were tested for CMV detection, and 117 WB samples were tested for EBV detection. Viral nucleic acid was extracted in parallel by using QIAsymphony RGQ and QIAcube (Qiagen GmbH, Germany), and real-time PCR assays for CMV and EBV were performed with a Rotor-Gene Q real-time PCR cycler (Qiagen). Detection rates for CMV and EBV were compared, and agreements between the two systems were analyzed. The detection rate of CMV and EBV differed significantly between the QIAsymphony RGQ and QIAcube systems (CMV, 59.5% [91/153] vs 43.8% [67/153], P=0.0005; EBV, 59.0% [69/117] vs 42.7% [50/117], P=0.0008). The two systems showed moderate agreement for CMV and EBV detection (kappa=0.43 and 0.52, respectively). QIAsymphony RGQ showed a negligible correlation with QIAcube for quantitative EBV detection. QIAcube exhibited EBV PCR inhibition in 23.9% (28/117) of samples. Automated nucleic acid extraction systems have different performances and significantly affect the detection of viral pathogens. The QIAsymphony RGQ system appears to be superior to the QIAcube system for detecting CMV and EBV. A suitable sample preparation system should be considered for optimized nucleic acid amplification in clinical laboratories.

Use of noncrystallographic symmetry for automated model building at medium to low resolution.

PubMed

Wiegels, Tim; Lamzin, Victor S

2012-04-01

A novel method is presented for the automatic detection of noncrystallographic symmetry (NCS) in macromolecular crystal structure determination which does not require the derivation of molecular masks or the segmentation of density. It was found that throughout structure determination the NCS-related parts may be differently pronounced in the electron density. This often results in the modelling of molecular fragments of variable length and accuracy, especially during automated model-building procedures. These fragments were used to identify NCS relations in order to aid automated model building and refinement. In a number of test cases higher completeness and greater accuracy of the obtained structures were achieved, specifically at a crystallographic resolution of 2.3 Å or poorer. In the best case, the method allowed the building of up to 15% more residues automatically and a tripling of the average length of the built fragments.

In situ detection of a PCR-synthesized human pancentromeric DNA hybridization probe by color pigment immunostaining: application for dicentric assay automation.

PubMed

Kolanko, C J; Pyle, M D; Nath, J; Prasanna, P G; Loats, H; Blakely, W F

2000-03-01

We report a low cost and efficient method for synthesizing a human pancentromeric DNA probe by the polymerase chain reaction (PRC) and an optimized protocol for in situ detection using color pigment immunostaining. The DNA template used in the PCR was a 2.4 kb insert containing human alphoid repeated sequences of pancentromeric DNA subcloned into pUC9 (Miller et al. 1988) and the primers hybridized to internal sequences of the 172 bp consensus tandem repeat associated with human centromeres. PCR was performed in the presence of biotin-11-dUTP, and the product was used for in situ hybridization to detect the pancentromeric region of human chromosomes in metaphase spreads. Detection of pancentromeric probe was achieved by immunoenzymatic color pigment painting to yield a permanent image detected at high resolution by bright field microscopy. The ability to synthesize the centromeric probe rapidly and to detect it with color pigment immunostaining will lead to enhanced identification and eventually to automation of various chromosome aberration assays.

Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.

PubMed

Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

2016-05-01

Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.

Rapid test for the detection of hazardous microbiological material

NASA Astrophysics Data System (ADS)

Mordmueller, Mario; Bohling, Christian; John, Andreas; Schade, Wolfgang

2009-09-01

After attacks with anthrax pathogens have been committed since 2001 all over the world the fast detection and determination of biological samples has attracted interest. A very promising method for a rapid test is Laser Induced Breakdown Spectroscopy (LIBS). LIBS is an optical method which uses time-resolved or time-integrated spectral analysis of optical plasma emission after pulsed laser excitation. Even though LIBS is well established for the determination of metals and other inorganic materials the analysis of microbiological organisms is difficult due to their very similar stoichiometric composition. To analyze similar LIBS-spectra computer assisted chemometrics is a very useful approach. In this paper we report on first results of developing a compact and fully automated rapid test for the detection of hazardous microbiological material. Experiments have been carried out with two setups: A bulky one which is composed of standard laboratory components and a compact one consisting of miniaturized industrial components. Both setups work at an excitation wavelength of λ=1064nm (Nd:YAG). Data analysis is done by Principal Component Analysis (PCA) with an adjacent neural network for fully automated sample identification.

Determination of nitrite and nitrate in water samples by an automated hydrodynamic sequential injection method.

PubMed

Somnam, Sarawut; Jakmunee, Jaroon; Grudpan, Kate; Lenghor, Narong; Motomizu, Shoji

2008-12-01

An automated hydrodynamic sequential injection (HSI) system with spectrophotometric detection was developed. Thanks to the hydrodynamic injection principle, simple devices can be used for introducing reproducible microliter volumes of both sample and reagent into the flow channel to form stacked zones in a similar fashion to those in a sequential injection system. The zones were then pushed to the detector and a peak profile was recorded. The determination of nitrite and nitrate in water samples by employing the Griess reaction was chosen as a model. Calibration graphs with linearity in the range of 0.7 - 40 muM were obtained for both nitrite and nitrate. Detection limits were found to be 0.3 muM NO(2)(-) and 0.4 muM NO(3)(-), respectively, with a sample throughput of 20 h(-1) for consecutive determination of both the species. The developed system was successfully applied to the analysis of water samples, employing simple and cost-effective instrumentation and offering higher degrees of automation and low chemical consumption.

Automated diagnosis of fetal alcohol syndrome using 3D facial image analysis

PubMed Central

Fang, Shiaofen; McLaughlin, Jason; Fang, Jiandong; Huang, Jeffrey; Autti-Rämö, Ilona; Fagerlund, Åse; Jacobson, Sandra W.; Robinson, Luther K.; Hoyme, H. Eugene; Mattson, Sarah N.; Riley, Edward; Zhou, Feng; Ward, Richard; Moore, Elizabeth S.; Foroud, Tatiana

2012-01-01

Objectives Use three-dimensional (3D) facial laser scanned images from children with fetal alcohol syndrome (FAS) and controls to develop an automated diagnosis technique that can reliably and accurately identify individuals prenatally exposed to alcohol. Methods A detailed dysmorphology evaluation, history of prenatal alcohol exposure, and 3D facial laser scans were obtained from 149 individuals (86 FAS; 63 Control) recruited from two study sites (Cape Town, South Africa and Helsinki, Finland). Computer graphics, machine learning, and pattern recognition techniques were used to automatically identify a set of facial features that best discriminated individuals with FAS from controls in each sample. Results An automated feature detection and analysis technique was developed and applied to the two study populations. A unique set of facial regions and features were identified for each population that accurately discriminated FAS and control faces without any human intervention. Conclusion Our results demonstrate that computer algorithms can be used to automatically detect facial features that can discriminate FAS and control faces. PMID:18713153

High-speed autoverifying technology for printed wiring boards

NASA Astrophysics Data System (ADS)

Ando, Moritoshi; Oka, Hiroshi; Okada, Hideo; Sakashita, Yorihiro; Shibutani, Nobumi

1996-10-01

We have developed an automated pattern verification technique. The output of an automated optical inspection system contains many false alarms. Verification is needed to distinguish between minor irregularities and serious defects. In the past, this verification was usually done manually, which led to unsatisfactory product quality. The goal of our new automated verification system is to detect pattern features on surface mount technology boards. In our system, we employ a new illumination method, which uses multiple colors and multiple direction illumination. Images are captured with a CCD camera. We have developed a new algorithm that uses CAD data for both pattern matching and pattern structure determination. This helps to search for patterns around a defect and to examine defect definition rules. These are processed with a high speed workstation and a hard-wired circuits. The system can verify a defect within 1.5 seconds. The verification system was tested in a factory. It verified 1,500 defective samples and detected all significant defects with only a 0.1 percent of error rate (false alarm).

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

NASA Astrophysics Data System (ADS)

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A.; Purdie, Thomas G.

2017-08-01

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

Analysis of HER2 status in breast carcinoma by fully automated HER2 fluorescence in situ hybridization (FISH): comparison of two immunohistochemical tests and manual FISH.

PubMed

Yoon, Nara; Do, In-Gu; Cho, Eun Yoon

2014-09-01

Easy and accurate HER2 testing is essential when considering the prognostic and predictive significance of HER2 in breast cancer. The use of a fully automated, quantitative FISH assay would be helpful to detect HER2 amplification in breast cancer tissue specimens with reduced inter-laboratory variability. We compared the concordance of HER2 status as assessed by an automated FISH staining system to manual FISH testing. Using 60 formalin-fixed paraffin-embedded breast carcinoma specimens, we assessed HER2 immunoexpression with two antibodies (DAKO HercepTest and CB11). In addition, HER2 status was evaluated with automated FISH using the Leica FISH System for BOND and a manual FISH using the Abbott PathVysion DNA Probe Kit. All but one specimen were successfully stained using both FISH methods. When the data were divided into two groups according to HER2/CEP17 ratio, positive and negative, the results from both the automated and manual FISH techniques were identical for all 59 evaluable specimens. The HER2 and CEP17 copy numbers and HER2/CEP17 ratio showed great agreement between both FISH methods. The automated FISH technique was interpretable with signal intensity similar to those of the manual FISH technique. In contrast with manual FISH, the automated FISH technique showed well-preserved architecture due to low membrane digestion. HER2 immunohistochemistry and FISH results showed substantial significant agreement (κ = 1.0, p < 0.001). HER2 status can be reliably determined using a fully automated HER2 FISH system with high concordance to the well-established manual FISH method. Because of stable signal intensity and high staining quality, the automated FISH technique may be more appropriate than manual FISH for routine applications. © 2013 APMIS. Published by John Wiley & Sons Ltd.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.

PubMed

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A; Purdie, Thomas G

2017-07-06

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

Automated cell counts on CSF samples: A multicenter performance evaluation of the GloCyte system.

PubMed

Hod, E A; Brugnara, C; Pilichowska, M; Sandhaus, L M; Luu, H S; Forest, S K; Netterwald, J C; Reynafarje, G M; Kratz, A

2018-02-01

Automated cell counters have replaced manual enumeration of cells in blood and most body fluids. However, due to the unreliability of automated methods at very low cell counts, most laboratories continue to perform labor-intensive manual counts on many or all cerebrospinal fluid (CSF) samples. This multicenter clinical trial investigated if the GloCyte System (Advanced Instruments, Norwood, MA), a recently FDA-approved automated cell counter, which concentrates and enumerates red blood cells (RBCs) and total nucleated cells (TNCs), is sufficiently accurate and precise at very low cell counts to replace all manual CSF counts. The GloCyte System concentrates CSF and stains RBCs with fluorochrome-labeled antibodies and TNCs with nucleic acid dyes. RBCs and TNCs are then counted by digital image analysis. Residual adult and pediatric CSF samples obtained for clinical analysis at five different medical centers were used for the study. Cell counts were performed by the manual hemocytometer method and with the GloCyte System following the same protocol at all sites. The limits of the blank, detection, and quantitation, as well as precision and accuracy of the GloCyte, were determined. The GloCyte detected as few as 1 TNC/μL and 1 RBC/μL, and reliably counted as low as 3 TNCs/μL and 2 RBCs/μL. The total coefficient of variation was less than 20%. Comparison with cell counts obtained with a hemocytometer showed good correlation (>97%) between the GloCyte and the hemocytometer, including at very low cell counts. The GloCyte instrument is a precise, accurate, and stable system to obtain red cell and nucleated cell counts in CSF samples. It allows for the automated enumeration of even very low cell numbers, which is crucial for CSF analysis. These results suggest that GloCyte is an acceptable alternative to the manual method for all CSF samples, including those with normal cell counts. © 2017 John Wiley & Sons Ltd.

Environment Monitor

NASA Technical Reports Server (NTRS)

1988-01-01

Viking landers touched down on Mars equipped with a variety of systems to conduct automated research, each carrying a compact but highly sophisticated instrument for analyzing Martian soil and atmosphere. Instrument called a Gas Chromatography/Mass Spectrometer (GC/MS) had to be small, lightweight, shock resistant, highly automated and extremely sensitive, yet require minimal electrical power. Viking Instruments Corporation commercialized this technology and targeted their primary market as environmental monitoring, especially toxic and hazardous waste site monitoring. Waste sites often contain chemicals in complex mixtures, and the conventional method of site characterization, taking samples on-site and sending them to a laboratory for analysis is time consuming and expensive. Other terrestrial applications are explosive detection in airports, drug detection, industrial air monitoring, medical metabolic monitoring and for military, chemical warfare agents.

Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts

NASA Astrophysics Data System (ADS)

Lorsakul, Auranuch; Andersson, Emilia; Vega Harring, Suzana; Sade, Hadassah; Grimm, Oliver; Bredno, Joerg

2017-03-01

Multiplex-brightfield immunohistochemistry (IHC) staining and quantitative measurement of multiple biomarkers can support therapeutic targeting of carcinoma-associated fibroblasts (CAF). This paper presents an automated digitalpathology solution to simultaneously analyze multiple biomarker expressions within a single tissue section stained with an IHC duplex assay. Our method was verified against ground truth provided by expert pathologists. In the first stage, the automated method quantified epithelial-carcinoma cells expressing cytokeratin (CK) using robust nucleus detection and supervised cell-by-cell classification algorithms with a combination of nucleus and contextual features. Using fibroblast activation protein (FAP) as biomarker for CAFs, the algorithm was trained, based on ground truth obtained from pathologists, to automatically identify tumor-associated stroma using a supervised-generation rule. The algorithm reported distance to nearest neighbor in the populations of tumor cells and activated-stromal fibroblasts as a wholeslide measure of spatial relationships. A total of 45 slides from six indications (breast, pancreatic, colorectal, lung, ovarian, and head-and-neck cancers) were included for training and verification. CK-positive cells detected by the algorithm were verified by a pathologist with good agreement (R2=0.98) to ground-truth count. For the area occupied by FAP-positive cells, the inter-observer agreement between two sets of ground-truth measurements was R2=0.93 whereas the algorithm reproduced the pathologists' areas with R2=0.96. The proposed methodology enables automated image analysis to measure spatial relationships of cells stained in an IHC-multiplex assay. Our proof-of-concept results show an automated algorithm can be trained to reproduce the expert assessment and provide quantitative readouts that potentially support a cutoff determination in hypothesis testing related to CAF-targeting-therapy decisions.

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.

Automated Gait Analysis Through Hues and Areas (AGATHA): a method to characterize the spatiotemporal pattern of rat gait

PubMed Central

Kloefkorn, Heidi E.; Pettengill, Travis R.; Turner, Sara M. F.; Streeter, Kristi A.; Gonzalez-Rothi, Elisa J.; Fuller, David D.; Allen, Kyle D.

2016-01-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns. PMID:27554674

Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait.

PubMed

Kloefkorn, Heidi E; Pettengill, Travis R; Turner, Sara M F; Streeter, Kristi A; Gonzalez-Rothi, Elisa J; Fuller, David D; Allen, Kyle D

2017-03-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

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 search techniques of spectral image based small target detection. It offers evidence of the functionality of the NNI visualization and also provides evidence that the increased spectral dimensionality of the 8-band Worldview-2 datasets provides noteworthy improvement in results over traditional 4-band multispectral datasets. The final experiment presents the results from a prototype fully automated target detection scheme in support of the overarching premise. This work establishes the analytic sweet spot as the optimum threshold defined as the point where error detection rate curves -- false detections vs. missing detections -- cross. At this point the errors are minimized while the detection rate is maximized. It then demonstrates that taking the first moment statistic of the histogram of calculated target detection values from a detection search with test threshold set arbitrarily high will estimate the analytic sweet spot for that image. It also demonstrates that directed search techniques -- when utilized with appropriate scene-specific modeled signatures and atmospheric compensations -- perform at least as well as in-scene search techniques 88% of the time and grossly under-performing only 11% of the time; the in-scene only performs as well or better 50% of the time. It further demonstrates the clear advantage increased multispectral dimensionality brings to detection searches improving performance in 50% of the cases while performing at least as well 72% of the time. Lastly, it presents evidence that a fully automated prototype performs as anticipated laying the groundwork for further research into fully automated processes for small target detection.

Detection of Respiratory Viruses in Sputum from Adults by Use of Automated Multiplex PCR

PubMed Central

Walsh, Edward E.; Formica, Maria A.; Falsey, Ann R.

2014-01-01

Respiratory tract infections (RTI) frequently cause hospital admissions among adults. Diagnostic viral reverse transcriptase PCR (RT-PCR) of nose and throat swabs (NTS) is useful for patient care by informing antiviral use and appropriate isolation. However, automated RT-PCR systems are not amenable to utilizing sputum due to its viscosity. We evaluated a simple method of processing sputum samples in a fully automated respiratory viral panel RT-PCR assay (FilmArray). Archived sputum and NTS samples collected in 2008-2012 from hospitalized adults with RTI were evaluated. A subset of sputum samples positive for 10 common viruses by a uniplex RT-PCR was selected. A sterile cotton-tip swab was dunked in sputum, swirled in 700 μL of sterile water (dunk and swirl method) and tested by the FilmArray assay. Quantitative RT-PCR was performed on “dunked” sputum and NTS samples for influenza A (Flu A), respiratory syncytial virus (RSV), coronavirus OC43 (OC43), and human metapneumovirus (HMPV). Viruses were identified in 31% of 965 illnesses using a uniplex RT-PCR. The sputum sample was the only sample positive for 105 subjects, including 35% (22/64) of influenza cases and significantly increased the diagnostic yield of NTS alone (302/965 [31%] versus 197/965 [20%]; P = 0.0001). Of 108 sputum samples evaluated by the FilmArray assay using the dunk and swirl method, 99 (92%) were positive. Quantitative RT-PCR revealed higher mean viral loads in dunked sputum samples compared to NTS samples for Flu A, RSV, and HMPV (P = 0.0001, P = 0.006, and P = 0.011, respectively). The dunk and swirl method is a simple and practical method for reliably processing sputum samples in a fully automated PCR system. The higher viral loads in sputa may increase detection over NTS testing alone. PMID:25056335

Robotic CCD microscope for enhanced crystal recognition

DOEpatents

Segelke, Brent W.; Toppani, Dominique

2007-11-06

A robotic CCD microscope and procedures to automate crystal recognition. The robotic CCD microscope and procedures enables more accurate crystal recognition, leading to fewer false negative and fewer false positives, and enable detection of smaller crystals compared to other methods available today.

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.

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

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

PubMed

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing.

PubMed

Matheny, Michael E; Normand, Sharon-Lise T; Gross, Thomas P; Marinac-Dabic, Danica; Loyo-Berrios, Nilsa; Vidi, Venkatesan D; Donnelly, Sharon; Resnic, Frederic S

2011-12-14

Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery. A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year. We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80). The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Image processing occupancy sensor

DOEpatents

Brackney, Larry J.

2016-09-27

A system and method of detecting occupants in a building automation system environment using image based occupancy detection and position determinations. In one example, the system includes an image processing occupancy sensor that detects the number and position of occupants within a space that has controllable building elements such as lighting and ventilation diffusers. Based on the position and location of the occupants, the system can finely control the elements to optimize conditions for the occupants, optimize energy usage, among other advantages.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

PubMed Central

Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

2012-01-01

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

Lab-on-a-disc agglutination assay for protein detection by optomagnetic readout and optical imaging using nano- and micro-sized magnetic beads.

PubMed

Uddin, Rokon; Burger, Robert; Donolato, Marco; Fock, Jeppe; Creagh, Michael; Hansen, Mikkel Fougt; Boisen, Anja

2016-11-15

We present a biosensing platform for the detection of proteins based on agglutination of aptamer coated magnetic nano- or microbeads. The assay, from sample to answer, is integrated on an automated, low-cost microfluidic disc platform. This ensures fast and reliable results due to a minimum of manual steps involved. The detection of the target protein was achieved in two ways: (1) optomagnetic readout using magnetic nanobeads (MNBs); (2) optical imaging using magnetic microbeads (MMBs). The optomagnetic readout of agglutination is based on optical measurement of the dynamics of MNB aggregates whereas the imaging method is based on direct visualization and quantification of the average size of MMB aggregates. By enhancing magnetic particle agglutination via application of strong magnetic field pulses, we obtained identical limits of detection of 25pM with the same sample-to-answer time (15min 30s) using the two differently sized beads for the two detection methods. In both cases a sample volume of only 10µl is required. The demonstrated automation, low sample-to-answer time and portability of both detection instruments as well as integration of the assay on a low-cost disc are important steps for the implementation of these as portable tools in an out-of-lab setting. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

PubMed

Tripathy, R K; Dandapat, S

2016-06-01

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

A Comparative Study of Automated Infrasound Detectors - PMCC and AFD with Analyst Review.

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

Park, Junghyun; Hayward, Chris; Zeiler, Cleat

Automated detections calculated by the progressive multi-channel correlation (PMCC) method (Cansi, 1995) and the adaptive F detector (AFD) (Arrowsmith et al., 2009) are compared to the signals identified by five independent analysts. Each detector was applied to a four-hour time sequence recorded by the Korean infrasound array CHNAR. This array was used because it is composed of both small (<100 m) and large (~1000 m) aperture element spacing. The four hour time sequence contained a number of easily identified signals under noise conditions that have average RMS amplitudes varied from 1.2 to 4.5 mPa (1 to 5 Hz), estimated withmore » running five-minute window. The effectiveness of the detectors was estimated for the small aperture, large aperture, small aperture combined with the large aperture, and full array. The full and combined arrays performed the best for AFD under all noise conditions while the large aperture array had the poorest performance for both detectors. PMCC produced similar results as AFD under the lower noise conditions, but did not produce as dramatic an increase in detections using the full and combined arrays. Both automated detectors and the analysts produced a decrease in detections under the higher noise conditions. Comparing the detection probabilities with Estimated Receiver Operating Characteristic (EROC) curves we found that the smaller value of consistency for PMCC and the larger p-value for AFD had the highest detection probability. These parameters produced greater changes in detection probability than estimates of the false alarm rate. The detection probability was impacted the most by noise level, with low noise (average RMS amplitude of 1.7 mPa) having an average detection probability of ~40% and high noise (average RMS amplitude of 2.9 mPa) average detection probability of ~23%.« less

Automated daily quality control analysis for mammography in a multi-unit imaging center.

PubMed

Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

2018-01-01

Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

The Objective Identification and Quantification of Interstitial Lung Abnormalities in Smokers.

PubMed

Ash, Samuel Y; Harmouche, Rola; Ross, James C; Diaz, Alejandro A; Hunninghake, Gary M; Putman, Rachel K; Onieva, Jorge; Martinez, Fernando J; Choi, Augustine M; Lynch, David A; Hatabu, Hiroto; Rosas, Ivan O; Estepar, Raul San Jose; Washko, George R

2017-08-01

Previous investigation suggests that visually detected interstitial changes in the lung parenchyma of smokers are highly clinically relevant and predict outcomes, including death. Visual subjective analysis to detect these changes is time-consuming, insensitive to subtle changes, and requires training to enhance reproducibility. Objective detection of such changes could provide a method of disease identification without these limitations. The goal of this study was to develop and test a fully automated image processing tool to objectively identify radiographic features associated with interstitial abnormalities in the computed tomography scans of a large cohort of smokers. An automated tool that uses local histogram analysis combined with distance from the pleural surface was used to detect radiographic features consistent with interstitial lung abnormalities in computed tomography scans from 2257 individuals from the Genetic Epidemiology of COPD study, a longitudinal observational study of smokers. The sensitivity and specificity of this tool was determined based on its ability to detect the visually identified presence of these abnormalities. The tool had a sensitivity of 87.8% and a specificity of 57.5% for the detection of interstitial lung abnormalities, with a c-statistic of 0.82, and was 100% sensitive and 56.7% specific for the detection of the visual subtype of interstitial abnormalities called fibrotic parenchymal abnormalities, with a c-statistic of 0.89. In smokers, a fully automated image processing tool is able to identify those individuals who have interstitial lung abnormalities with moderate sensitivity and specificity. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Automated and sensitive method for the determination of formoterol in human plasma by high-performance liquid chromatography and electrochemical detection.

PubMed

Campestrini, J; Lecaillon, J B; Godbillon, J

1997-12-19

An automated high-performance liquid chromatography (HPLC) method for the determination of formoterol in human plasma with improved sensitivity has been developed and validated. Formoterol and CGP 47086, the internal standard, were extracted from plasma (1 ml) using a cation-exchange solid-phase extraction (SPE) cartridge. The compounds were eluted with pH 6 buffer solution-methanol (70:30, v/v) and the eluate was further diluted with water. An aliquot of the extract solution was injected and analyzed by HPLC. The extraction, dilution, injection and chromatographic analysis were combined and automated using the automate (ASPEC) system. The chromatographic separations were achieved on a 5 microm, Hypersil ODS analytical column (200 mm x 3 mm I.D.), using (pH 6 phosphate buffer, 0.035 M + 20 mg/l EDTA)-MeOH-CH3CN (70:25:5, v/v/v) as the mobile phase at a flow-rate of 0.4 ml/min. The analytes were detected with electrochemical detection at an operating potential of +0.63 V. Intra-day accuracy and precision were assessed from the relative recoveries of calibration/quality control plasma samples in the concentration range of 7.14 to 238 pmol/l of formoterol base. The accuracy over the entire concentration range varied from 81 to 105%, and the precision (C.V.) ranged from 3 to 14%. Inter-day accuracy and precision were assessed in the concentration range of 11.9 to 238 pmol/l of formoterol base in plasma. The accuracy over the entire concentration range varied from 98 to 109%, and precision ranged from 8 to 19%. At the limit of quantitation (LOQ) of 11.9 pmol/l for inter-day measurements, the recovery value was 109% and C.V. was 19%. As shown from intra-day accuracy and precision results, favorable conditions (a newly used column, a newly washed detector cell and moderate residual cell current level) allowed us to reach a LOQ of 7.14 pmol/l of formoterol base (3 pg/ml of formoterol fumarate dihydrate). Improvement of the limit of detection by a factor of about 10 was reached as compared to the previously described methods. The method has been applied for quantifying formoterol in plasma after 120 microg drug inhalation to volunteers. Formoterol was still measurable at 24 h post-dosing in most subjects and a slow elimination of formoterol from plasma beyond 6-8 h after inhalation was demonstrated for the first time thanks to the sensitivity of the method.

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

Hyperspectral laser-induced autofluorescence imaging of dental caries

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-01-01

Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.

Feasibility of Using Low-Cost Motion Capture for Automated Screening of Shoulder Motion Limitation after Breast Cancer Surgery.

PubMed

Gritsenko, Valeriya; Dailey, Eric; Kyle, Nicholas; Taylor, Matt; Whittacre, Sean; Swisher, Anne K

2015-01-01

To determine if a low-cost, automated motion analysis system using Microsoft Kinect could accurately measure shoulder motion and detect motion impairments in women following breast cancer surgery. Descriptive study of motion measured via 2 methods. Academic cancer center oncology clinic. 20 women (mean age = 60 yrs) were assessed for active and passive shoulder motions during a routine post-operative clinic visit (mean = 18 days after surgery) following mastectomy (n = 4) or lumpectomy (n = 16) for breast cancer. Participants performed 3 repetitions of active and passive shoulder motions on the side of the breast surgery. Arm motion was recorded using motion capture by Kinect for Windows sensor and on video. Goniometric values were determined from video recordings, while motion capture data were transformed to joint angles using 2 methods (body angle and projection angle). Correlation of motion capture with goniometry and detection of motion limitation. Active shoulder motion measured with low-cost motion capture agreed well with goniometry (r = 0.70-0.80), while passive shoulder motion measurements did not correlate well. Using motion capture, it was possible to reliably identify participants whose range of shoulder motion was reduced by 40% or more. Low-cost, automated motion analysis may be acceptable to screen for moderate to severe motion impairments in active shoulder motion. Automatic detection of motion limitation may allow quick screening to be performed in an oncologist's office and trigger timely referrals for rehabilitation.

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

Algorithms for Autonomous Plume Detection on Outer Planet Satellites

NASA Astrophysics Data System (ADS)

Lin, Y.; Bunte, M. K.; Saripalli, S.; Greeley, R.

2011-12-01

We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to increase effectiveness. We have demonstrated an ability to detect transient events above the planetary limb and on the surface and to distinguish feature classes in spacecraft images.

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

NASA Technical Reports Server (NTRS)

R.Neigh, Christopher S.; Bolton, Douglas K.; Williams, Jennifer J.; Diabate, Mouhamad

2014-01-01

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

NASA Astrophysics Data System (ADS)

Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L.; Assad, Albert; Abramson, Richard G.; Landman, Bennett A.

2017-02-01

Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≍1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly.

PubMed

Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L; Assad, Albert; Abramson, Richard G; Landman, Bennett A

2017-02-11

Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

[Comparative research into sensitivity and specificity of immune-enzyme analysis with chemiluminescence and colorimetric detection for detecting antigens and antibodies to avian influenza viruses and newcastle disease].

PubMed

Vitkova, O N; Kapustina, T P; Mikhailova, V V; Safonov, G A; Vlasova, N N; Belousova, R V

2015-01-01

The goal of this work was to demonstrate the results of the development of the enzyme-linked immunosorbent tests with chemiluminescence detection and colorimetric detection of specific viral antigens and antibodies for identifying the avian influenza and the Newcastle disease viruses: high sensitivity and specificity of the immuno- chemiluminescence assay, which are 10-50 times higher than those of the ELISA colorimetric method. The high effectiveness of the results and the automation of the process of laboratory testing (using a luminometer) allow these methods to be recommended for including in primary screening tests for these infectious diseases.

Using Thermal Radiation in Detection of Negative Obstacles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.

2009-01-01

A method of automated detection of negative obstacles (potholes, ditches, and the like) ahead of ground vehicles at night involves processing of imagery from thermal-infrared cameras aimed at the terrain ahead of the vehicles. The method is being developed as part of an overall obstacle-avoidance scheme for autonomous and semi-autonomous offroad robotic vehicles. The method could also be applied to help human drivers of cars and trucks avoid negative obstacles -- a development that may entail only modest additional cost inasmuch as some commercially available passenger cars are already equipped with infrared cameras as aids for nighttime operation.

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

PubMed

Jin, Bo; Krishnan, Balu; Adler, Sophie; Wagstyl, Konrad; Hu, Wenhan; Jones, Stephen; Najm, Imad; Alexopoulos, Andreas; Zhang, Kai; Zhang, Jianguo; Ding, Meiping; Wang, Shuang; Wang, Zhong Irene

2018-05-01

Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation. The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation

NASA Technical Reports Server (NTRS)

Haley, D. C.; Almand, B. J.; Thomas, M. M.; Krauze, L. D.; Gremban, K. D.; Sanborn, J. C.; Kelly, J. H.; Depkovich, T. M.

1984-01-01

A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation.

Automated Conflict Resolution For Air Traffic Control

NASA Technical Reports Server (NTRS)

Erzberger, Heinz

2005-01-01

The ability to detect and resolve conflicts automatically is considered to be an essential requirement for the next generation air traffic control system. While systems for automated conflict detection have been used operationally by controllers for more than 20 years, automated resolution systems have so far not reached the level of maturity required for operational deployment. Analytical models and algorithms for automated resolution have been traffic conditions to demonstrate that they can handle the complete spectrum of conflict situations encountered in actual operations. The resolution algorithm described in this paper was formulated to meet the performance requirements of the Automated Airspace Concept (AAC). The AAC, which was described in a recent paper [1], is a candidate for the next generation air traffic control system. The AAC's performance objectives are to increase safety and airspace capacity and to accommodate user preferences in flight operations to the greatest extent possible. In the AAC, resolution trajectories are generated by an automation system on the ground and sent to the aircraft autonomously via data link .The algorithm generating the trajectories must take into account the performance characteristics of the aircraft, the route structure of the airway system, and be capable of resolving all types of conflicts for properly equipped aircraft without requiring supervision and approval by a controller. Furthermore, the resolution trajectories should be compatible with the clearances, vectors and flight plan amendments that controllers customarily issue to pilots in resolving conflicts. The algorithm described herein, although formulated specifically to meet the needs of the AAC, provides a generic engine for resolving conflicts. Thus, it can be incorporated into any operational concept that requires a method for automated resolution, including concepts for autonomous air to air resolution.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

PubMed

Sweeney, Elizabeth M; Shinohara, Russell T; Shiee, Navid; Mateen, Farrah J; Chudgar, Avni A; Cuzzocreo, Jennifer L; Calabresi, Peter A; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

2013-01-01

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Automated detection of red lesions from digital colour fundus photographs.

PubMed

Jaafar, Hussain F; Nandi, Asoke K; Al-Nuaimy, Waleed

2011-01-01

Earliest signs of diabetic retinopathy, the major cause of vision loss, are damage to the blood vessels and the formation of lesions in the retina. Early detection of diabetic retinopathy is essential for the prevention of blindness. In this paper we present a computer-aided system to automatically identify red lesions from retinal fundus photographs. After pre-processing, a morphological technique was used to segment red lesion candidates from the background and other retinal structures. Then a rule-based classifier was used to discriminate actual red lesions from artifacts. A novel method for blood vessel detection is also proposed to refine the detection of red lesions. For a standarised test set of 219 images, the proposed method can detect red lesions with a sensitivity of 89.7% and a specificity of 98.6% (at lesion level). The performance of the proposed method shows considerable promise for detection of red lesions as well as other types of lesions.

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

PubMed Central

Bashar, Md. Khayrul; Yamagata, Kazuo; Kobayashi, Tetsuya J.

2014-01-01

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels. PMID:25020042

A method for the automated long-term monitoring of three-spined stickleback Gasterosteus aculeatus shoal dynamics.

PubMed

Kleinhappel, T K; Al-Zoubi, A; Al-Diri, B; Burman, O; Dickinson, P; John, L; Wilkinson, A; Pike, T W

2014-04-01

This paper describes and evaluates a flexible, non-invasive tagging system for the automated identification and long-term monitoring of individual three-spined sticklebacks Gasterosteus aculeatus. The system is based on barcoded tags, which can be reliably and robustly detected and decoded to provide information on an individual's identity and location. Because large numbers of fish can be individually tagged, it can be used to monitor individual- and group-level dynamics within fish shoals. © 2014 The Fisheries Society of the British Isles.

Automated breast segmentation in ultrasound computer tomography SAFT images

NASA Astrophysics Data System (ADS)

Hopp, T.; You, W.; Zapf, M.; Tan, W. Y.; Gemmeke, H.; Ruiter, N. V.

2017-03-01

Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data

NASA Astrophysics Data System (ADS)

Sawall, Mathias; von Harbou, Erik; Moog, Annekathrin; Behrens, Richard; Schröder, Henning; Simoneau, Joël; Steimers, Ellen; Neymeyr, Klaus

2018-04-01

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

PubMed

Timp, Sheila; Karssemeijer, Nico

2004-05-01

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

Automation of the linear array HPV genotyping test and its application for routine typing of human papillomaviruses in cervical specimens of women without cytological abnormalities in Switzerland.

PubMed

Dobec, Marinko; Bannwart, Fridolin; Kaeppeli, Franz; Cassinotti, Pascal

2009-05-01

There is a need for reliable, automated high throughput HPV detection and genotyping methods for pre- and post-prophylactic vaccine intervention analyses. To optimize the linear array (LA) HPV genotyping test (Roche Diagnostics, Rotkreuz) in regard to possible automation steps for the routine laboratory diagnosis of HPV infections and to analyze the HPV genotype distribution in cervical specimens of women without cytological abnormalities in Switzerland. 680 cervical cell specimens with normal cytology, obtained from women undergoing routine cervical screening by liquid-based Pap smear, were analyzed by the LA HPV genotyping test for HPV-DNA. The automation of the LA HPV genotyping test resulted in a total hands-on time reduction of 255 min (from 480 to 225 min; 53%). Any of 37 HPV genotypes were detected in 117 (17.2%) and high-risk (HR) HPV in 55 (8.1%) of 680 women with normal cytology. The highest prevalence of any HPV (28.1%) and HR-HPV (15.1%) was observed in age-group 21-30 and showed a continuous decrease in older age-groups. The most common HR-HPV genotypes were HPV-16 (12%), HPV-31 (9.4%), HPV-52 (6%), HPV-51 (5.1%), HPV-45 (4.3%), HPV-58 (4.3%) and HPV-59 (4.3%). The optimization and automation of the LA HPV genotyping test makes it suited for high throughput HPV detection and typing. The epidemiological data provides information about distribution of HPV genotypes in women without cytological abnormalities in Switzerland and may be important for determining the future impact of vaccines and potential changes in the country's epidemiological HPV profile.

Confocal nanoscanning, bead picking (CONA): PickoScreen microscopes for automated and quantitative screening of one-bead one-compound libraries.

PubMed

Hintersteiner, Martin; Buehler, Christof; Uhl, Volker; Schmied, Mario; Müller, Jürgen; Kottig, Karsten; Auer, Manfred

2009-01-01

Solid phase combinatorial chemistry provides fast and cost-effective access to large bead based libraries with compound numbers easily exceeding tens of thousands of compounds. Incubating one-bead one-compound library beads with fluorescently labeled target proteins and identifying and isolating the beads which contain a bound target protein, potentially represents one of the most powerful generic primary high throughput screening formats. On-bead screening (OBS) based on this detection principle can be carried out with limited automation. Often hit bead detection, i.e. recognizing beads with a fluorescently labeled protein bound to the compound on the bead, relies on eye-inspection under a wide-field microscope. Using low resolution detection techniques, the identification of hit beads and their ranking is limited by a low fluorescence signal intensity and varying levels of the library beads' autofluorescence. To exploit the full potential of an OBS process, reliable methods for both automated quantitative detection of hit beads and their subsequent isolation are needed. In a joint collaborative effort with Evotec Technologies (now Perkin-Elmer Cellular Technologies Germany GmbH), we have built two confocal bead scanner and picker platforms PS02 and a high-speed variant PS04 dedicated to automated high resolution OBS. The PS0X instruments combine fully automated confocal large area scanning of a bead monolayer at the bottom of standard MTP plates with semiautomated isolation of individual hit beads via hydraulic-driven picker capillaries. The quantification of fluorescence intensities with high spatial resolution in the equatorial plane of each bead allows for a reliable discrimination between entirely bright autofluorescent beads and real hit beads which exhibit an increased fluorescence signal at the outer few micrometers of the bead. The achieved screening speed of up to 200,000 bead assayed in less than 7 h and the picking time of approximately 1 bead/min allow exploitation of one-bead one-compound libraries with high sensitivity, accuracy, and speed.

Rapid quantitative detection of glucose content in glucose injection by reaction headspace gas chromatography.

PubMed

Xie, Wei-Qi; Gong, Yi-Xian; Yu, Kong-Xian

2017-10-20

This work investigates an automated technique for rapid detecting the glucose content in glucose injection by reaction headspace gas chromatography (HS-GC). This method is based on the oxidation reaction of glucose in glucose injection with potassium dichromate. The carbon dioxide (CO 2 ) formed from the oxidation reaction can be quantitatively detected by GC. The results show that the relative standard deviation (RSD) of the present method was within 2.91%, and the measured glucose contents in glucose injection closely match those quantified by the reference method (relative differences <6.45%). The new HS-GC technique is rapid, practical and can be used to the batch detection of the glucose content in glucose injection related applications. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-11-01

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

Advanced in In Situ Inspection of Automated Fiber Placement Systems

NASA Technical Reports Server (NTRS)

Juarez, Peter D.; Cramer, K. Elliott; Seebo, Jeffrey P.

2016-01-01

Automated Fiber Placement (AFP) systems have been developed to help take advantage of the tailorability of composite structures in aerospace applications. AFP systems allow the repeatable placement of uncured, spool fed, preimpregnated carbon fiber tape (tows) onto substrates in desired thicknesses and orientations. This automated process can incur defects, such as overlapping tow lines, which can severely undermine the structural integrity of the part. Current defect detection and abatement methods are very labor intensive, and still mostly rely on human manual inspection. Proposed is a thermographic in situ inspection technique which monitors tow placement with an on board thermal camera using the preheated substrate as a through transmission heat source. An investigation of the concept is conducted, and preliminary laboratory results are presented. Also included will be a brief overview of other emerging technologies that tackle the same issue. Keywords: Automated Fiber Placement, Manufacturing defects, Thermography

The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

PubMed

Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

2013-01-01

This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

Evaluation of an antibody avidity index method for detecting recent human immunodeficiency virus type 1 infection using an automated chemiluminescence immunoassay.

PubMed

Fernández, Gema; Manzardo, Christian; Montoliu, Alexandra; Campbell, Colin; Fernández, Gregorio; Casabona, Jordi; Miró, José Maria; Matas, Lurdes; Rivaya, Belén; González, Victoria

2015-04-01

Recent infection testing algorithms (RITAs) are used in public health surveillance to estimate the incidence of recently acquired HIV-1 infection. Our aims were (i) to evaluate the precision of the VITROS® Anti-HIV 1+2 automated antibody avidity assay for qualitative detection of antibodies to HIV 1+2 virus; (ii) to validate the accuracy of an automated guanidine-based antibody avidity assay to discriminate between recent and long standing infections using the VITROS 3600 platform; (iii) to compare this method with BED-CEIA assay; and (iv) to evaluate the occurrence of false recent misclassifications by the VITROS antibody avidity assay in patients with a CD4 count <200 cells/μL and in patients on combination antiretroviral therapy (cART). The VITROS® antibody avidity assay is highly reproducible. The ROC curve analysis of the accuracy of this assay, optimized for sensitivity and specificity, had an AI cut off of ≤0.51, with sensitivity and specificity values of 86.67% (95% CI: 72.51-94.46) and 86.24% (95% CI: 78.00-91.84), respectively. The agreement between VITROS antibody avidity and BED-CEIA assays was good. Misclassifications of long standing infections as recent infection occurred in 8.2% of patients with CD4 <200 cell/μL and 8.7% in patients on combination antiretroviral therapy. The VITROS antibody avidity assay is a reliable serological method to detect recent HIV-1 infections and it could be incorporated into a RITA to estimate HIV incidence. Copyright © 2014 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Real-time biscuit tile image segmentation method based on edge detection.

PubMed

Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

2018-05-01

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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 simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

A new automated multiple allergen simultaneous test-chemiluminescent assay (MAST-CLA) using an AP720S analyzer.

PubMed

Lee, Sungsil; Lim, Hwan Sub; Park, Jungyong; Kim, Hyon Suk

2009-04-01

In the diagnosis of atopic diseases, allergen detection is a crucial step. Multiple allergen simultaneous test-chemiluminescent assay (MAST-CLA) is a simple and noninvasive method for in vitro screening of allergen-specific IgE antibodies. The Korean Inhalant Panel test on 20 patients and Food Panel test on 19 patients were performed using the conventional manual MAST-CLA kit and the new automated MAST-CLA method (automated AP720S system for the Optigen Assay; Hitachi Chemical Diagnostics, Inc., USA) simultaneously. The results were evaluated for positive reactivity and concordance. The results of inhalant panel gave a relatively higher class level result than the food panel. The 8 patients out of 20 (40%) of the inhalation panel, and 9 patients out of 18 (47.4%) of the food panel showed 100% concordance between the 2 systems. Eighteen patients (90%) of the Inhalation Panel and sixteen patients (84.2%) of the Food Panel showed more than 91% concordance. These results suggest that the MAST-CLA assay using the new, automated AP720S analyzer performs well, showing a high concordance rate with conventional MAST-CLA. Compared to manual MAST-CLA, the automated AP720S system has a shorter assay time and uses a smaller serum volume (500 microl) along with other conveniences.

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.

System and method for automated object detection in an image

DOEpatents

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

2015-10-06

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

Study on the Automatic Detection Method and System of Multifunctional Hydrocephalus Shunt

NASA Astrophysics Data System (ADS)

Sun, Xuan; Wang, Guangzhen; Dong, Quancheng; Li, Yuzhong

2017-07-01

Aiming to the difficulty of micro pressure detection and the difficulty of micro flow control in the testing process of hydrocephalus shunt, the principle of the shunt performance detection was analyzed.In this study, the author analyzed the principle of several items of shunt performance detection,and used advanced micro pressure sensor and micro flow peristaltic pump to overcome the micro pressure detection and micro flow control technology.At the same time,This study also puted many common experimental projects integrated, and successfully developed the automatic detection system for a shunt performance detection function, to achieve a test with high precision, high efficiency and automation.

A new automated colorimetric method for measuring total oxidant status.

PubMed

Erel, Ozcan

2005-12-01

To develop a new, colorimetric and automated method for measuring total oxidation status (TOS). The assay is based on the oxidation of ferrous ion to ferric ion in the presence of various oxidant species in acidic medium and the measurement of the ferric ion by xylenol orange. The oxidation reaction of the assay was enhanced and precipitation of proteins was prevented. In addition, autoxidation of ferrous ion present in the reagent was prevented during storage. The method was applied to an automated analyzer, which was calibrated with hydrogen peroxide and the analytical performance characteristics of the assay were determined. There were important correlations with hydrogen peroxide, tert-butyl hydroperoxide and cumene hydroperoxide solutions (r=0.99, P<0.001 for all). In addition, the new assay presented a typical sigmoidal reaction pattern in copper-induced lipoprotein autoxidation. The novel assay is linear up to 200 micromol H2O2 Equiv./L and its precision value is lower than 3%. The lower detection limit is 1.13 micromol H2O2 Equiv./L. The reagents are stable for at least 6 months on the automated analyzer. Serum TOS level was significantly higher in patients with osteoarthritis (21.23+/-3.11 micromol H2O2 Equiv./L) than in healthy subjects (14.19+/-3.16 micromol H2O2 Equiv./L, P<0.001) and the results showed a significant negative correlation with total antioxidant capacity (TAC) (r=-0.66 P<0.01). This easy, stable, reliable, sensitive, inexpensive and fully automated method that is described can be used to measure total oxidant status.

A new method of edge detection for object recognition

USGS Publications Warehouse

Maddox, Brian G.; Rhew, Benjamin

2004-01-01

Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated object recognition systems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an object recognition system where the edge data would also contain information as to what type of feature it bordered.

Effects of Automation Types on Air Traffic Controller Situation Awareness and Performance

NASA Technical Reports Server (NTRS)

Sethumadhavan, A.

2009-01-01

The Joint Planning and Development Office has proposed the introduction of automated systems to help air traffic controllers handle the increasing volume of air traffic in the next two decades (JPDO, 2007). Because fully automated systems leave operators out of the decision-making loop (e.g., Billings, 1991), it is important to determine the right level and type of automation that will keep air traffic controllers in the loop. This study examined the differences in the situation awareness (SA) and collision detection performance of individuals when they worked with information acquisition, information analysis, decision and action selection and action implementation automation to control air traffic (Parasuraman, Sheridan, & Wickens, 2000). When the automation was unreliable, the time taken to detect an upcoming collision was significantly longer for all the automation types compared with the information acquisition automation. This poor performance following automation failure was mediated by SA, with lower SA yielding poor performance. Thus, the costs associated with automation failure are greater when automation is applied to higher order stages of information processing. Results have practical implications for automation design and development of SA training programs.

Validation of a digital mammographic unit model for an objective and highly automated clinical image quality assessment.

PubMed

Perez-Ponce, Hector; Daul, Christian; Wolf, Didier; Noel, Alain

2013-08-01

In mammography, image quality assessment has to be directly related to breast cancer indicator (e.g. microcalcifications) detectability. Recently, we proposed an X-ray source/digital detector (XRS/DD) model leading to such an assessment. This model simulates very realistic contrast-detail phantom (CDMAM) images leading to gold disc (representing microcalcifications) detectability thresholds that are very close to those of real images taken under the simulated acquisition conditions. The detection step was performed with a mathematical observer. The aim of this contribution is to include human observers into the disc detection process in real and virtual images to validate the simulation framework based on the XRS/DD model. Mathematical criteria (contrast-detail curves, image quality factor, etc.) are used to assess and to compare, from the statistical point of view, the cancer indicator detectability in real and virtual images. The quantitative results given in this paper show that the images simulated by the XRS/DD model are useful for image quality assessment in the case of all studied exposure conditions using either human or automated scoring. Also, this paper confirms that with the XRS/DD model the image quality assessment can be automated and the whole time of the procedure can be drastically reduced. Compared to standard quality assessment methods, the number of images to be acquired is divided by a factor of eight. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

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.

Re-refinement from deposited X-ray data can deliver improved models for most PDB entries

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

Joosten, Robbie P.; Womack, Thomas; Vriend, Gert, E-mail: vriend@cmbi.ru.nl

2009-02-01

An evaluation of validation and real-space intervention possibilities for improving existing automated (re-)refinement methods. The deposition of X-ray data along with the customary structural models defining PDB entries makes it possible to apply large-scale re-refinement protocols to these entries, thus giving users the benefit of improvements in X-ray methods that have occurred since the structure was deposited. Automated gradient refinement is an effective method to achieve this goal, but real-space intervention is most often required in order to adequately address problems detected by structure-validation software. In order to improve the existing protocol, automated re-refinement was combined with structure validation andmore » difference-density peak analysis to produce a catalogue of problems in PDB entries that are amenable to automatic correction. It is shown that re-refinement can be effective in producing improvements, which are often associated with the systematic use of the TLS parameterization of B factors, even for relatively new and high-resolution PDB entries, while the accompanying manual or semi-manual map analysis and fitting steps show good prospects for eventual automation. It is proposed that the potential for simultaneous improvements in methods and in re-refinement results be further encouraged by broadening the scope of depositions to include refinement metadata and ultimately primary rather than reduced X-ray data.« less

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

NASA Technical Reports Server (NTRS)

Kennedy, Kellie D.; Stephens, Chad L.; Williams, Ralph A.; Schutte, Paul C.

2017-01-01

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This paper explored the relationship between automation and inattentional blindness (IB) occurrences in a repeated induction paradigm using two types of runway incursions. The critical stimuli for both runway incursions were directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation conditions: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70 percent (42 of 60) detection failure rate with those in the PA condition significantly more likely to detect the incursion compared to the FA condition or the NA condition. The second induction yielded a 50 percent detection failure rate. Although detection improved (detection failure rates declined) in all conditions, those in the FA condition demonstrated the greatest improvement with doubled detection rates. The detection behavior in the first trial did not preclude a failed detection in the second induction. Group membership (IB vs. Detection) in the FA condition showed a greater improvement than those in the NA condition and rated the Mental Demand and Effort subscales of the NASA-TLX (NASA Task Load Index) significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to improve task performance whereas those in the NA condition did not, indicating the availability and reallocation of attentional resources in the FA condition. These findings support the role of engagement in operational attention detriment and the consideration of attentional failure causation to determine appropriate mitigation strategies.

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

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.

Apparatus for responding to an anomalous change in downhole pressure

DOEpatents

Hall, David R.; Fox, Joe; Wilde, Tyson; Barlow, Jonathan S.

2010-04-13

A method of responding to an anomalous change in downhole pressure in a bore hole comprises detecting the anomalous change in downhole pressure, sending a signal along the segmented electromagnetic transmission path, receiving the signal, and performing a automated response. The anomalous change in downhole pressure is detected at a first location along a segmented electromagnetic transmission path, and the segmented electromagnetic transmission path is integrated into the tool string. The signal is received by at least one receiver in communication with the segmented electromagnetic transmission path. The automated response is performed along the tool string. Disclosed is an apparatus for responding to an anomalous change in downhole pressure in a downhole tool string, comprising a segmented electromagnetic transmission path connecting one or more receivers and at least one pressure sensor.

Automated Corrosion Detection Program

DTIC Science & Technology

2001-10-01

More detailed explanations of the methodology development can be found in Hidden Corrosion Detection Technology Assessment, a paper presented at...Detection Program, a paper presented at the Fourth Joint DoD/FAA/NASA Conference on Aging Aircraft, 2000. AS&M PULSE. The PULSE system, developed...selection can be found in The Evaluation of Hidden Corrosion Detection Technologies on the Automated Corrosion Detection Program, a paper presented

Searching for Life with Rovers: Exploration Methods & Science Results from the 2004 Field Campaign of the "Life in the Atacama" Project and Applications to Future Mars Missions

NASA Technical Reports Server (NTRS)

Cabrol, N. A.a; Wettergreen, D. S.; Whittaker, R.; Grin, E. A.; Moersch, J.; Diaz, G. Chong; Cockell, C.; Coppin, P.; Dohm, J. M.; Fisher, G.

2005-01-01

The Life In The Atacama (LITA) project develops and field tests a long-range, solarpowered, automated rover platform (Zo ) and a science payload assembled to search for microbial life in the Atacama desert. Life is barely detectable over most of the driest desert on Earth. Its unique geological, climatic, and biological evolution have created a unique training site for designing and testing exploration strategies and life detection methods for the robotic search for life on Mars.

Semi-automated non-invasive diagnostics method for melanoma differentiation from nevi and pigmented basal cell carcinomas

NASA Astrophysics Data System (ADS)

Lihacova, I.; Bolocko, K.; Lihachev, A.

2017-12-01

The incidence of skin cancer is still increasing mostly in in industrialized countries with light- skinned people. Late tumour detection is the main reason of the high mortality associated with skin cancer. The accessibility of early diagnostics of skin cancer in Latvia is limited by several factors, such as high cost of dermatology services, long queues on state funded oncologist examinations, as well as inaccessibility of oncologists in the countryside regions - this is an actual clinical problem. The new strategies and guidelines for skin cancer early detection and post-surgical follow-up intend to realize the full body examination (FBE) by primary care physicians (general practitioners, interns) in combination with classical dermoscopy. To implement this approach, a semi- automated method was established. Developed software analyses the combination of 3 optical density images at 540 nm, 650 nm, and 950 nm from pigmented skin malformations and classifies them into three groups- nevi, pigmented basal cell carcinoma or melanoma.

"SmartMonitor"--an intelligent security system for the protection of individuals and small properties with the possibility of home automation.

PubMed

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

2014-06-05

"SmartMonitor" is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the "SmartMonitor" system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons.

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

PubMed

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

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Automated detection of Lagrangian eddies and coherent transport of heat and salinity in the Agulhas leakage

NASA Astrophysics Data System (ADS)

Huhn, Florian; Haller, George

2014-05-01

Haller and Beron-Vera(2013) have recently introduced a new objective method to detect coherent Lagrangian eddies in turbulence. They find that closed null-geodesics of a generalized Green-Lagrange strain tensor act as coherent Lagrangian eddy boundaries, showing near-zero and uniform material stretching. We make use of this method to develop an automated detection procedure for coherent Lagrangian eddies in large-scale ocean data. We apply our results to a recent 3D general circulation model, the Southern Ocean State Estimate (SOSE), with focus on the South Atlantic Ocean and the inter-ocean exchange between the Indian and Atlantic ocean. We detect a large number of coherent Lagrangian eddies and present statistics of their properties. The largest and most circular eddy boundaries represent Lagrangian Agulhas rings. Circular regions inside these rings with higher temperature and salinity than the surrounding waters can be explained by the coherent eddy boundaries that enclose and isolate the eddy interiors. We compare eddy boundaries at different depths with eddy boundaries obtained from geostrophic velocities derived from the model's sea surface height (SSH). The transport of mass, heat and salinity enclosed by coherent eddies through a section in the Cape basin is quantified and compared to the non-coherent transport by the background flow.

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Revie, Crawford W.; Sanchez, Javier

2013-01-01

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel. PMID:23576782

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

PubMed

Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Revie, Crawford W; Sanchez, Javier

2013-06-06

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

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

PubMed Central

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

2014-01-01

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

Quantification of vocal fold motion using echography: application to recurrent nerve paralysis detection

NASA Astrophysics Data System (ADS)

Cohen, Mike-Ely; Lefort, Muriel; Bergeret-Cassagne, Héloïse; Hachi, Siham; Li, Ang; Russ, Gilles; Lazard, Diane; Menegaux, Fabrice; Leenhardt, Laurence; Trésallet, Christophe; Frouin, Frédérique

2015-03-01

Recurrent nerve paralysis (RP) is one of the most frequent complications of thyroid surgery. It reduces vocal fold mobility. Nasal endoscopy, a mini-invasive procedure, is the conventional way to detect RP. We suggest a new approach based on laryngeal ultrasound and a specific data analysis was designed to help with the automated detection of RP. Ten subjects were enrolled for this feasibility study: four controls, three patients with RP and three patients without RP according to nasal endoscopy. The ultrasound protocol was based on a ten seconds B-mode acquisition in a coronal plane during normal breathing. Image processing included three steps: 1) automated detection of two consecutive closing and opening images, corresponding to extreme positions of vocal folds in the sequence of B-mode images, using principal component analysis of the image sequence; 2) positioning of three landmarks and robust tracking of these points using a multi-pyramidal refined optical flow approach; 3) estimation of quantitative parameters indicating left and right fractions of mobility, and motion symmetry. Results provided by automated image processing were compared to those obtained by an expert. Detection of extreme images was accurate; tracking of landmarks was reliable in 80% of cases. Motion symmetry indices showed similar values for controls and patients without RP. Fraction of mobility was reduced in cases of RP. Thus, our CAD system helped in the detection of RP. Laryngeal ultrasound combined with appropriate image processing helped in the diagnosis of recurrent nerve paralysis and could be proposed as a first-line method.

Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

PubMed

Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

2016-06-01

Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors. © 2016. Published by The Company of Biologists Ltd.

Topological leakage detection and freeze-and-grow propagation for improved CT-based airway segmentation

NASA Astrophysics Data System (ADS)

Nadeem, Syed Ahmed; Hoffman, Eric A.; Sieren, Jered P.; Saha, Punam K.

2018-03-01

Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.

Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning.

PubMed

Levitt, Joshua; Nitenson, Adam; Koyama, Suguru; Heijmans, Lonne; Curry, James; Ross, Jason T; Kamerling, Steven; Saab, Carl Y

2018-06-23

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. Copyright © 2018. Published by Elsevier B.V.

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Automated drumlin shape and volume estimation using high resolution LiDAR imagery (Curvature Based Relief Separation): A test from the Wadena Drumlin Field, Minnesota

NASA Astrophysics Data System (ADS)

Yu, Peter; Eyles, Nick; Sookhan, Shane

2015-10-01

Resolving the origin(s) of drumlins and related megaridges in areas of megascale glacial lineations (MSGL) left by paleo-ice sheets is critical to understanding how ancient ice sheets interacted with their sediment beds. MSGL is now linked with fast-flowing ice streams but there is a broad range of erosional and depositional models. Further progress is reliant on constraining fluxes of subglacial sediment at the ice sheet base which in turn is dependent on morphological data such as landform shape and elongation and most importantly landform volume. Past practice in determining shape has employed a broad range of geomorphological methods from strictly visualisation techniques to more complex semi-automated and automated drumlin extraction methods. This paper reviews and builds on currently available visualisation, semi-automated and automated extraction methods and presents a new, Curvature Based Relief Separation (CBRS) technique; for drumlin mapping. This uses curvature analysis to generate a base level from which topography can be normalized and drumlin volume can be derived. This methodology is tested using a high resolution (3 m) LiDAR elevation dataset from the Wadena Drumlin Field, Minnesota, USA, which was constructed by the Wadena Lobe of the Laurentide Ice Sheet ca. 20,000 years ago and which as a whole contains 2000 drumlins across an area of 7500 km2. This analysis demonstrates that CBRS provides an objective and robust procedure for automated drumlin extraction. There is strong agreement with manually selected landforms but the method is also capable of resolving features that were not detectable manually thereby considerably expanding the known population of streamlined landforms. CBRS provides an effective automatic method for visualisation of large areas of the streamlined beds of former ice sheets and for modelling sediment fluxes below ice sheets.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

PubMed Central

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-01-01

Purpose Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Methods Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. Results There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). Conclusions MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics. PMID:28873173

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.

Automated multi-plug filtration cleanup for liquid chromatographic-tandem mass spectrometric pesticide multi-residue analysis in representative crop commodities.

PubMed

Qin, Yuhong; Zhang, Jingru; Zhang, Yuan; Li, Fangbing; Han, Yongtao; Zou, Nan; Xu, Haowei; Qian, Meiyuan; Pan, Canping

2016-09-02

An automated multi-plug filtration cleanup (m-PFC) method on modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) extracts was developed. The automatic device was aimed to reduce labor-consuming manual operation workload in the cleanup steps. It could control the volume and the speed of pulling and pushing cycles accurately. In this work, m-PFC was based on multi-walled carbon nanotubes (MWCNTs) mixed with other sorbents and anhydrous magnesium sulfate (MgSO4) in a packed tip for analysis of pesticide multi-residues in crop commodities followed by liquid chromatography with tandem mass spectrometric (LC-MS/MS) detection. It was validated by analyzing 25 pesticides in six representative matrices spiked at two concentration levels of 10 and 100μg/kg. Salts, sorbents, m-PFC procedure, automated pulling and pushing volume, automated pulling speed, and pushing speed for each matrix were optimized. After optimization, two general automated m-PFC methods were introduced to relatively simple (apple, citrus fruit, peanut) and relatively complex (spinach, leek, green tea) matrices. Spike recoveries were within 83 and 108% and 1-14% RSD for most analytes in the tested matrices. Matrix-matched calibrations were performed with the coefficients of determination >0.997 between concentration levels of 10 and 1000μg/kg. The developed method was successfully applied to the determination of pesticide residues in market samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

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

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

DOE PAGES

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei; ...

2017-12-22

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

NASA Astrophysics Data System (ADS)

Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John

2017-04-01

Focal mechanisms are important for understanding seismotectonics of a region, and they serve as a basic input for seismic hazard assessment. Usually, the point source approximation and the moment tensor (MT) are used. We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances and high signal-to-noise are rejected, and full-waveform inversion in a space-time grid around a provided hypocenter. The method is innovative in the following aspects: (i) The CMT inversion is fully automated, no user interaction is required, although the details of the process can be visually inspected latter on many figures which are automatically plotted.(ii) The automated process includes detection of disturbances based on MouseTrap code, so disturbed recordings do not affect inversion.(iii) A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequencies.(iv) Bayesian approach is used, so not only the best solution is obtained, but also the posterior probability density function.(v) A space-time grid search effectively combined with the least-squares inversion of moment tensor components speeds up the inversion and allows to obtain more accurate results compared to stochastic methods. The method has been tested on synthetic and observed data. It has been tested by comparison with manually processed moment tensors of all events greater than M≥3 in the Swiss catalogue over 16 years using data available at the Swiss data center (http://arclink.ethz.ch). The quality of the results of the presented automated process is comparable with careful manual processing of data. The software package programmed in Python has been designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large previously existing earthquake catalogues and data sets.

Evaluation of an alternative extraction procedure for enterotoxin determination in dairy products.

PubMed

Meyrand, A; Atrache, V; Bavai, C; Montet, M P; Vernozy-Rozand, C

1999-06-01

A concentration protocol based on trichloroacetic acid precipitation was evaluated and compared with the reference method using dialysis concentration. Different quantities of purified staphylococcal enterotoxins were added to pasteurized Camembert-type cheeses. Detection of enterotoxins in these cheeses was performed using an automated detection system. Raw goat milk Camembert-type cheeses involved in a staphylococcal food poisoning were also tested. Both enterotoxin extraction methods allowed detection of the lowest enterotoxin concentration level used in this study (0.5 ng g-1). Compared with the dialysis concentration method, TCA precipitation of staphylococcal enterotoxins was 'user-friendly' and less time-consuming. These results suggest that TCA precipitation is a rapid (1 h), simple and reliable method of extracting enterotoxin from food which gives excellent recovery from dairy products.

Real-time heart rate measurement for multi-people using compressive tracking

NASA Astrophysics Data System (ADS)

Liu, Lingling; Zhao, Yuejin; Liu, Ming; Kong, Lingqin; Dong, Liquan; Ma, Feilong; Pang, Zongguang; Cai, Zhi; Zhang, Yachu; Hua, Peng; Yuan, Ruifeng

2017-09-01

The rise of aging population has created a demand for inexpensive, unobtrusive, automated health care solutions. Image PhotoPlethysmoGraphy(IPPG) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. However, the main deficiencies of the recent IPPG methods are non-automated, non-real-time and susceptible to motion artifacts(MA). In this paper, a real-time heart rate(HR) detection method for multiple subjects simultaneously was proposed and realized using the open computer vision(openCV) library, which consists of getting multiple subjects' facial video automatically through a Webcam, detecting the region of interest (ROI) in the video, reducing the false detection rate by our improved Adaboost algorithm, reducing the MA by our improved compress tracking(CT) algorithm, wavelet noise-suppression algorithm for denoising and multi-threads for higher detection speed. For comparison, HR was measured simultaneously using a medical pulse oximetry device for every subject during all sessions. Experimental results on a data set of 30 subjects show that the max average absolute error of heart rate estimation is less than 8 beats per minute (BPM), and the processing speed of every frame has almost reached real-time: the experiments with video recordings of ten subjects under the condition of the pixel resolution of 600× 800 pixels show that the average HR detection time of 10 subjects was about 17 frames per second (fps).

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Continuous Grading of Early Fibrosis in NAFLD Using Label-Free Imaging: A Proof-of-Concept Study

PubMed Central

Pirhonen, Juho; Arola, Johanna; Sädevirta, Sanja; Luukkonen, Panu; Karppinen, Sanna-Maria; Pihlajaniemi, Taina; Isomäki, Antti; Hukkanen, Mika

2016-01-01

Background and Aims Early detection of fibrosis is important in identifying individuals at risk for advanced liver disease in non-alcoholic fatty liver disease (NAFLD). We tested whether second-harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy, detecting fibrillar collagen and fat in a label-free manner, might allow automated and sensitive quantification of early fibrosis in NAFLD. Methods We analyzed 32 surgical biopsies from patients covering histological fibrosis stages 0–4, using multimodal label-free microscopy. Native samples were visualized by SHG and CARS imaging for detecting fibrillar collagen and fat. Furthermore, we developed a method for quantitative assessment of early fibrosis using automated analysis of SHG signals. Results We found that the SHG mean signal intensity correlated well with fibrosis stage and the mean CARS signal intensity with liver fat. Little overlap in SHG signal intensities between fibrosis stages 0 and 1 was observed. A specific fibrillar SHG signal was detected in the liver parenchyma outside portal areas in all samples histologically classified as having no fibrosis. This signal correlated with immunohistochemical location of fibrillar collagens I and III. Conclusions This study demonstrates that label-free SHG imaging detects fibrillar collagen deposition in NAFLD more sensitively than routine histological staging and enables observer-independent quantification of early fibrosis in NAFLD with continuous grading. PMID:26808140

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.