WorldWideScience

Sample records for automated fall detection

  1. User-based motion sensing and fuzzy logic for automated fall detection in older adults

    DEFF Research Database (Denmark)

    Boissy, Patrice; Choquette, Stéphane; Hamel, Mathieu;

    2007-01-01

    More than one third of community-dwelling older adults and up to 60% of nursing home residents fall each year, with 10-15% of fallers sustaining a serious injury. Reliable automated fall detection can increase confidence in people with fear of falling, promote active safe living for older adults,...... detection of fall events based on user-based motion sensing and fuzzy logic shows promising results. Additional rules and optimization of the algorithm will be needed to decrease the false-positive rate....

  2. Fall Detection Using Smartphone Audio Features.

    Science.gov (United States)

    Cheffena, Michael

    2016-07-01

    An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

  3. Fall Detection Sensor System for the Elderly

    Directory of Open Access Journals (Sweden)

    Alicia Y.C. Tang

    2015-06-01

    Full Text Available Many elderly people are living alone in their homes. If the elderly fall down, it may be difficult for them to request for help. The main objective of this work is to design an android-based fall detection sensor system at affordable cost for the elderly in Malaysia. This paper describes the design of the android-based fall detection sensor system. The system is able to acknowledge a falling incident to the contact person such that the incident can be reported to the ambulance department soonest possible, and to provide necessary medical treatments for the injured elderly. The design and implementation combines both hardware and software that work seamlessly in detecting and reporting a fall at home. The hardware part consists of the falling detection sensor that detects the body position of the user whether it is on a falling mode while the software side consists of some formulas that detect the fallings and triggers the alarm.

  4. FALL DETECTION SYSTEM DESIGN BY SMART PHONE

    Directory of Open Access Journals (Sweden)

    Yung-Gi Wu

    2014-12-01

    Full Text Available Fall detection is one of the major issues in health care filed. Falls can cause serious injury both in physiology and psychology, especially to the old people. A reliable fall detector can provide rapid emergency medical care for the fallen down people. Thus, a reliable and effectively fall detection system is necessary. In this paper, we propose a system which utilizing mobile phones as a detector to detect the falling. When fall accident occurs, the system has three response procedures for help. The first procedure is transmitting the emergency message to the related people for help. The second procedure shows the user’s status and location on the map of webpage, according to user’s GPS location and status. The third procedure makes the alarm sound; its purpose is to let the person who nearby the user can be noticed that the user needs help. First, using a waist-mounted mobile phone to capture accelerometer of the human body and adopt the DCT (Discrete Cosine Transform to analyze the value of accelerometer to distinguish the activities of daily living (ADL and falls. ADL consist of walking, standing and sitting. We utilized a tri-axial accelerometer in mobile phone to capture the signal and transmit it to the server by way of Internet. We adapt two judgments achieved in Server, first judgment is based on an adaptive threshold for detecting the energy by DCT; the setting of adaptive threshold include height, weight and gender. The second judgment is according to the tilt of smart phone. Experimental results show that this method can detect the falls effectively; in addition, it is more portable than other devices as well.

  5. Detecting Falls with Wearable Sensors Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Ahmet Turan Özdemir

    2014-06-01

    Full Text Available Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects’ body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass. Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs, resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers: the k-nearest neighbor (k-NN classifier, least squares method (LSM, support vector machines (SVM, Bayesian decision making (BDM, dynamic time warping (DTW, and artificial neural networks (ANNs. We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

  6. Elderly fall detection using SIFT hybrid features

    Science.gov (United States)

    Wang, Xiaoxiao; Gao, Chao; Guo, Yongcai

    2015-10-01

    With the tendency of aging society, countries all over the world are dealing with the demographic change. Fall had been proven to be of the highest fatality rate among the elderly. To realize the elderly fall detection, the proposed algorithm used the hybrid feature. Based on the rate of centroid change, the algorithm adopted VEI to offer the posture feature, this combined motion feature with posture feature. The algorithm also took advantage of SIFT descriptor of VEI(V-SIFT) to show more details of behaviors with occlusion. An improved motion detection method was proposed to improve the accuracy of front-view motion detection. The experimental results on CASIA database and self-built database showed that the proposed approach has high efficiency and strong robustness which effectively improved the accuracy of fall detection.

  7. Fall Down Detection Under Smart Home System.

    Science.gov (United States)

    Juang, Li-Hong; Wu, Ming-Ni

    2015-10-01

    Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used. PMID:26276014

  8. Fall Down Detection Under Smart Home System.

    Science.gov (United States)

    Juang, Li-Hong; Wu, Ming-Ni

    2015-10-01

    Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.

  9. A Wavelet-Based Approach to Fall Detection

    OpenAIRE

    Luca Palmerini; Fabio Bagalà; Andrea Zanetti; Jochen Klenk; Clemens Becker; Angelo Cappello

    2015-01-01

    Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea ...

  10. A wavelet-based approach to fall detection.

    Science.gov (United States)

    Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo

    2015-01-01

    Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the "prototype fall".In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. PMID:26007719

  11. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

  12. Skin-contact sensor for automatic fall detection.

    Science.gov (United States)

    Narasimhan, Ravi

    2012-01-01

    This paper describes an adhesive sensor system worn on the skin that automatically detects human falls. The sensor, which consists of a tri-axial accelerometer, a microcon-troller and a Bluetooth Low Energy transceiver, can be worn anywhere on a subject's torso and in any orientation. In order to distinguish easily between falls and activities of daily living (ADL), a possible fall is detected only if an impact is detected and if the subject is horizontal shortly afterwards. As an additional criterion to reduce false positives, a fall is confirmed if the user activity level several seconds after a possible fall is below a threshold. Intentional falls onto a gymnastics mat were performed by 10 volunteers (total of 297 falls); ADL were performed by 15 elderly volunteers (total of 315 ADL). The fall detection algorithm provided a sensitivity of 99% and a specificity of 100%. PMID:23366814

  13. Comparison and characterization of Android-based fall detection systems.

    Science.gov (United States)

    Luque, Rafael; Casilari, Eduardo; Morón, María-José; Redondo, Gema

    2014-10-08

    Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.

  14. Comparison and Characterization of Android-Based Fall Detection Systems

    Directory of Open Access Journals (Sweden)

    Rafael Luque

    2014-10-01

    Full Text Available Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed and false positives (conventional movements that are erroneously classified as falls. In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.

  15. Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

    NARCIS (Netherlands)

    Bagala, Fabio; Becker, Clemens; Cappello, Angelo; Chiari, Lorenzo; Aminian, Kamiar; Hausdorff, Jeffrey M.; Zijlstra, Wiebren; Klenk, Jochen

    2012-01-01

    Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elders. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing s

  16. Automated early detection of diabetic retinopathy

    NARCIS (Netherlands)

    Abràmoff, M.D.; Reinhardt, J.M.; Russell, S.R.; Folk, J.C.; Mahajan, V.B.; Niemeijer, M.; Quellec, G.

    2010-01-01

    Purpose To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, the Challenge2009, against that of the one currently used in EyeCheck, a large computer-aided early DR detection project. Desig

  17. Development of a Wearable-Sensor-Based Fall Detection System

    Directory of Open Access Journals (Sweden)

    Falin Wu

    2015-01-01

    Full Text Available Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location.

  18. Automated Detection of Solar Eruptions

    CERN Document Server

    Hurlburt, Neal

    2015-01-01

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

  19. Analysis of Android Device-Based Solutions for Fall Detection.

    Science.gov (United States)

    Casilari, Eduardo; Luque, Rafael; Morón, María-José

    2015-01-01

    Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions. PMID:26213928

  20. Analysis of Android Device-Based Solutions for Fall Detection.

    Science.gov (United States)

    Casilari, Eduardo; Luque, Rafael; Morón, María-José

    2015-07-23

    Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

  1. Analysis of Android Device-Based Solutions for Fall Detection

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    2015-07-01

    Full Text Available Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources to fall detection solutions.

  2. Automated macromolecular crystal detection system and method

    Science.gov (United States)

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

    2007-06-05

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

  3. Judge Rules Plagiarism-Detection Tool Falls under "Fair Use"

    Science.gov (United States)

    Young, Jeffrey R.

    2008-01-01

    Judge Claude M. Hilton, of the U.S. District Court in Alexandria, Virginia, in March found that scanning the student papers for the purpose of detecting plagiarism is a "highly transformative" use that falls under the fair-use provision of copyright law. He ruled that the company "makes no use of any work's particular expressive or creative…

  4. A system for improving fall detection performance using critical phase fall signal and a neural network

    Directory of Open Access Journals (Sweden)

    Patimakorn Jantaraprim

    2012-12-01

    Full Text Available We present a system for improving fall detection performance using a short time min-max feature based on the specificsignatures of critical phase fall signal and a neural network as a classifier. Two subject groups were tested: Group A involvingfalls and activities by young subjects; Group B testing falls by young and activities by elderly subjects. The performance wasevaluated by comparing the short time min-max with a maximum peak feature using a feed-forward backpropagation networkwith two-fold cross validation. The results, obtained from 672 sequences, show that the proposed method offers a betterperformance for both subject groups. Group B’s performance is higher than Group A’s. The best performances are 98.2%sensitivity and 99.3% specificity for Group A, and 99.4% sensitivity and 100% specificity for Group B. The proposed systemuses one sensor for a body’s position, without a fixed threshold for 100% sensitivity or specificity and without additionalprocessing of posture after a fall.

  5. Sunglint Detection for Unmanned and Automated Platforms

    Directory of Open Access Journals (Sweden)

    Oliver Zielinski

    2012-09-01

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

  6. Hardware Design of the Energy Efficient Fall Detection Device

    Science.gov (United States)

    Skorodumovs, A.; Avots, E.; Hofmanis, J.; Korāts, G.

    2016-04-01

    Health issues for elderly people may lead to different injuries obtained during simple activities of daily living. Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. In the project "Wireless Sensor Systems in Telecare Application for Elderly People", we have developed a robust fall detection algorithm for a wearable wireless sensor. To optimise the algorithm for hardware performance and test it in field, we have designed an accelerometer based wireless fall detector. Our main considerations were: a) functionality - so that the algorithm can be applied to the chosen hardware, and b) power efficiency - so that it can run for a very long time. We have picked and tested the parts, built a prototype, optimised the firmware for lowest consumption, tested the performance and measured the consumption parameters. In this paper, we discuss our design choices and present the results of our work.

  7. Automated detection of Antarctic blue whale calls.

    Science.gov (United States)

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

    2015-11-01

    This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate. PMID:26627784

  8. Automated object detection for astronomical images

    Science.gov (United States)

    Orellana, Sonny; Zhao, Lei; Boussalis, Helen; Liu, Charles; Rad, Khosrow; Dong, Jane

    2005-10-01

    Sponsored by the National Aeronautical Space Association (NASA), the Synergetic Education and Research in Enabling NASA-centered Academic Development of Engineers and Space Scientists (SERENADES) Laboratory was established at California State University, Los Angeles (CSULA). An important on-going research activity in this lab is to develop an easy-to-use image analysis software with the capability of automated object detection to facilitate astronomical research. This paper presented a fast object detection algorithm based on the characteristics of astronomical images. This algorithm consists of three steps. First, the foreground and background are separated using histogram-based approach. Second, connectivity analysis is conducted to extract individual object. The final step is post processing which refines the detection results. To improve the detection accuracy when some objects are blocked by clouds, top-hat transform is employed to split the sky into cloudy region and non-cloudy region. A multi-level thresholding algorithm is developed to select the optimal threshold for different regions. Experimental results show that our proposed approach can successfully detect the blocked objects by clouds.

  9. Automated Detection of Events of Scientific Interest

    Science.gov (United States)

    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.

  10. Automating Vendor Fraud Detection in Enterprise Systems

    Directory of Open Access Journals (Sweden)

    Kishore Singh

    2013-06-01

    Full Text Available Fraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i a model for proactive fraud detection, ii methods for visualising user activities in transaction data, iii a stand-alone MCL-based prototype.

  11. Automated Detection System for SQL Injection Attacks

    Directory of Open Access Journals (Sweden)

    Dr K.V.N.Sunitha

    2010-10-01

    Full Text Available Many software systems have evolved to include a Web-based component that makes them available to the public via the Internet and can expose them to a variety of Web-based attacks. One of these attacks is SQL Injection vulnerability (SQLIV, which can give attackers unrestricted access to the databases that underlie Web applications and has become increasingly frequent and serious. The intent is that Web applications will limit the kinds of queries that can be generated to a safe subset of all possible queries, regardless of what input users provide. SQL Injection attacks are possible due to the design drawbacks of the web sites, which interact with back-end databases. Successful attacks may damage more. We introduce a system that deals with new automated technique for preventing SQLIA based on the novel concept of regular expressions is to detect SQL Injection attacks. The proposed system can detect the attacks that are from Internet and Insider Attacks, by analyzing the packets of the network servers.

  12. An Automated Flying-Insect Detection System

    Science.gov (United States)

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

    2007-01-01

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

  13. Fall

    OpenAIRE

    Odundo, Magdalene

    2008-01-01

    The monoprint Fall, created in the artist-in-residence studio at Dartmouth College, Hanover, New England, represents a transient yet vivid memory of the season spent walking and re-walking a trail I took to the studio on a daily basis. The work arose spontaneously from a direct and instinctive wish to replicate the ghost imprints left on the trail by the wet and dry weather of that autumn. It also represented a sensationally hopeful political transition of what seemed to be the growth of hope...

  14. High-resolution time-frequency distributions for fall detection

    Science.gov (United States)

    Amin, Moeness G.; Zhang, Yimin D.; Boashash, Boualem

    2015-05-01

    In this paper, we examine the role of high-resolution time-frequency distributions (TFDs) of radar micro-Doppler signatures for fall detection. The work supports the recent and rising interest in using emerging radar technology for elderly care and assisted living. Spectrograms have been the de facto joint-variable signal representation, depicting the signal power in both time and frequency. Although there have been major advances in designing quadratic TFDs which are superior to spectrograms in terms of detailing the local signal behavior, the contributions of these distributions in the area of human motion classifications and their offerings in enhanced feature extractions have not yet been properly evaluated. The main purpose of this paper is to show the effect of using high-resolution TFD kernels, in lieu of spectrogram, on fall detection. We focus on the extended modified B-distribution (EMBD) and exploit the level of details it provides as compared with the coarse and smoothed time-frequency signatures offered by spectrograms.

  15. Improvement of acoustic fall detection using Kinect depth sensing.

    Science.gov (United States)

    Li, Yun; Banerjee, Tanvi; Popescu, Mihail; Skubic, Marjorie

    2013-01-01

    The latest acoustic fall detection system (acoustic FADE) has achieved encouraging results on real-world dataset. However, the acoustic FADE device is difficult to be deployed in real environment due to its large size. In addition, the estimation accuracy of sound source localization (SSL) and direction of arrival (DOA) becomes much lower in multi-interference environment, which will potentially result in the distortion of the source signal using beamforming (BF). Microsoft Kinect is used in this paper to address these issues by measuring source position using the depth sensor. We employ robust minimum variance distortionless response (MVDR) adaptive BF (ABF) to take advantage of well-estimated source position for acoustic FADE. A significant reduction of false alarms and improvement of detection rate are both achieved using the proposed fusion strategy on real-world data.

  16. Fusion of Color and Depth Camera Data for Robust Fall Detection

    NARCIS (Netherlands)

    W. Josemans; G. Englebienne; B. Kröse

    2013-01-01

    The availability of cheap imaging sensors makes it possible to increase the robustness of vision-based alarm systems. This paper explores the benefit of data fusion in the application of fall detection. Falls are a common source of injury for elderly people and automatic fall detection is, therefore

  17. Mobile Phone Based Falling Detection Sensor and Computer-Aided Algorithm for Elderly People

    Directory of Open Access Journals (Sweden)

    Lee Jong-Ha

    2016-01-01

    Full Text Available Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.

  18. Fall detection using an address-event temporal contrast vision sensor

    OpenAIRE

    Fu, Z.; Culurciello, E; Lichtsteiner, P.; Delbruck, T.

    2008-01-01

    In this paper we describe an address-event vision system designed to detect accidental falls in elderly home care applications. The system raises an alarm when a fall hazard is detected. We use an asynchronous temporal contrast vision sensor which features sub-millisecond temporal resolution. A lightweight algorithm computes an instantaneous motion vector and reports fall events. We are able to distinguish fall events from normal human behavior, such as walking, crouching down, and sitting do...

  19. Fall-detection solution for mobile platforms using accelerometer and gyroscope data.

    Science.gov (United States)

    De Cillisy, Francesca; De Simioy, Francesca; Guidoy, Floriana; Incalzi, Raffaele Antonelli; Setolay, Roberto

    2015-08-01

    Falls are a major health risk that diminish the quality of life among elderly people. Apart from falls themselves, most dramatic consequences are usually related with long lying periods that can cause serious side effects. These findings call for pervasive long-term fall detection systems able to automatically detect falls. In this paper, we propose an effective fall detection algorithm for mobile platforms. Using data retrieved from wearable sensors, such as Inertial Measurements Units (IMUs) and/or SmartPhones (SPs), our algorithm is able to detect falls using features extracted from accelerometer and gyroscope. While mostly of the mobile-based solutions for fall management deal only with accelerometer data, in the proposed approach we combine the instantaneous acceleration magnitude vector with changes of the user's heading in a Threshold Based Algorithm (TBA). In such a way, we were able to handle falls detection with minimal computational load, increasing the overall system accuracy with respect to traditional fall management methods. Experimental results show the strong detection performance of the proposed solution in discriminating between falls and typical Activities of Daily Living (ADLs) presenting fall-like acceleration patterns. PMID:26737103

  20. Fall-detection solution for mobile platforms using accelerometer and gyroscope data.

    Science.gov (United States)

    De Cillisy, Francesca; De Simioy, Francesca; Guidoy, Floriana; Incalzi, Raffaele Antonelli; Setolay, Roberto

    2015-08-01

    Falls are a major health risk that diminish the quality of life among elderly people. Apart from falls themselves, most dramatic consequences are usually related with long lying periods that can cause serious side effects. These findings call for pervasive long-term fall detection systems able to automatically detect falls. In this paper, we propose an effective fall detection algorithm for mobile platforms. Using data retrieved from wearable sensors, such as Inertial Measurements Units (IMUs) and/or SmartPhones (SPs), our algorithm is able to detect falls using features extracted from accelerometer and gyroscope. While mostly of the mobile-based solutions for fall management deal only with accelerometer data, in the proposed approach we combine the instantaneous acceleration magnitude vector with changes of the user's heading in a Threshold Based Algorithm (TBA). In such a way, we were able to handle falls detection with minimal computational load, increasing the overall system accuracy with respect to traditional fall management methods. Experimental results show the strong detection performance of the proposed solution in discriminating between falls and typical Activities of Daily Living (ADLs) presenting fall-like acceleration patterns.

  1. Fall detection with body-worn sensors : A systematic review

    NARCIS (Netherlands)

    Schwickert, L.; Becker, C.; Lindemann, U.; Marechal, C.; Bourke, A.; Chiari, L.; Helbostad, J. L.; Zijlstra, Wiebren; Aminian, K.; Todd, C.; Bandinelli, S.; Klenk, J.

    2013-01-01

    Background and aims. Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information avail

  2. A ZigBee-based location-aware fall detection system for improving elderly telecare.

    Science.gov (United States)

    Huang, Chih-Ning; Chan, Chia-Tai

    2014-04-01

    Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person. PMID:24743841

  3. A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

    Directory of Open Access Journals (Sweden)

    Chih-Ning Huang

    2014-04-01

    Full Text Available Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person.

  4. Automated detection of exudates for diabetic retinopathy screening

    Energy Technology Data Exchange (ETDEWEB)

    Fleming, Alan D [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Philip, Sam [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Goatman, Keith A [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Williams, Graeme J [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Olson, John A [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Sharp, Peter F [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom)

    2007-12-21

    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.

  5. Automated detection of exudates for diabetic retinopathy screening

    International Nuclear Information System (INIS)

    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

  6. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-07-26

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.

  7. It Is Always on Your Mind: Experiences and Perceptions of Falling of Older People and Their Carers and the Potential of a Mobile Falls Detection Device

    Directory of Open Access Journals (Sweden)

    Veronika Williams

    2013-01-01

    Full Text Available Background. Falls and fear of falling present a major risk to older people as both can affect their quality of life and independence. Mobile assistive technologies (AT fall detection devices may maximise the potential for older people to live independently for as long as possible within their own homes by facilitating early detection of falls. Aims. To explore the experiences and perceptions of older people and their carers as to the potential of a mobile falls detection AT device. Methods. Nine focus groups with 47 participants including both older people with a range of health conditions and their carers. Interviews were audio recorded, transcribed verbatim, and thematically analysed. Results. Four key themes were identified relating to participants’ experiences and perceptions of falling and the potential impact of a mobile falls detector: cause of falling, falling as everyday vulnerability, the environmental context of falling, and regaining confidence and independence by having a mobile falls detector. Conclusion. The perceived benefits of a mobile falls detector may differ between older people and their carers. The experience of falling has to be taken into account when designing mobile assistive technology devices as these may influence perceptions of such devices and how older people utilise them.

  8. Automated fetal spine detection in ultrasound images

    Science.gov (United States)

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

    2009-02-01

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

  9. Automated detection and association of surface waves

    Directory of Open Access Journals (Sweden)

    C. R. D. Woodgold

    1994-06-01

    Full Text Available An algorithm for the automatic detection and association of surface waves has been developed and tested over an 18 month interval on broad band data from the Yellowknife array (YKA. The detection algorithm uses a conventional STA/LTA scheme on data that have been narrow band filtered at 20 s periods and a test is then applied to identify dispersion. An average of 9 surface waves are detected daily using this technique. Beamforming is applied to determine the arrival azimuth; at a nonarray station this could be provided by poIarization analysis. The detected surface waves are associated daily with the events located by the short period array at Yellowknife, and later with the events listed in the USGS NEIC Monthly Summaries. Association requires matching both arrival time and azimuth of the Rayleigh waves. Regional calibration of group velocity and azimuth is required. . Large variations in both group velocity and azimuth corrections were found, as an example, signals from events in Fiji Tonga arrive with apparent group velocities of 2.9 3.5 krn/s and azimuths from 5 to + 40 degrees clockwise from true (great circle azimuth, whereas signals from Kuriles Kamchatka have velocities of 2.4 2.9 km/s and azimuths off by 35 to 0 degrees. After applying the regional corrections, surface waves are considered associated if the arrival time matches to within 0.25 km/s in apparent group velocity and the azimuth is within 30 degrees of the median expected. Over the 18 month period studied, 32% of the automatically detected surface waves were associated with events located by the Yellowknife short period array, and 34% (1591 with NEIC events; there is about 70% overlap between the two sets of events. Had the automatic detections been reported to the USGS, YKA would have ranked second (after LZH in terms of numbers of associated surface waves for the study period of April 1991 to September 1992.

  10. Wearable technology and ECG processing for fall risk assessment, prevention and detection.

    Science.gov (United States)

    Melillo, Paolo; Castaldo, Rossana; Sannino, Giovanna; Orrico, Ada; de Pietro, Giuseppe; Pecchia, Leandro

    2015-08-01

    Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens. PMID:26738086

  11. RFI detection by automated feature extraction and statistical analysis

    OpenAIRE

    Winkel, Benjamin; Kerp, Juergen; Stanko, Stephan

    2006-01-01

    In this paper we present an interference detection toolbox consisting of a high dynamic range Digital Fast-Fourier-Transform spectrometer (DFFT, based on FPGA-technology) and data analysis software for automated radio frequency interference (RFI) detection. The DFFT spectrometer allows high speed data storage of spectra on time scales of less than a second. The high dynamic range of the device assures constant calibration even during extremely powerful RFI events. The software uses an algorit...

  12. After the Fall: The Use of Surplus Capacity in an Academic Library Automation System.

    Science.gov (United States)

    Wright, A. J.

    The possible uses of excess central processing unit capacity in an integrated academic library automation system discussed in this draft proposal include (1) in-house services such as word processing, electronic mail, management decision support using PERT/CPM techniques, and control of physical plant operation; (2) public services such as the…

  13. Automated baseline change detection phase I. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-12-01

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

  14. AUTOMATED EDGE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Mohamed A. El-Sayed

    2013-11-01

    Full Text Available The edge detection on the images is so important for image processing. It is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness, but require excessive post-processing. Proposed CNN technique is used to realize edge detection task it takes the advantage of momentum features extraction, it can process any input image of any size with no more training required, the results are very promising when compared to both classical methods and other ANN based methods

  15. (Automated) software modularization using community detection

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius; Manikas, Konstantinos

    2015-01-01

    detection algorithm of Reichardt and Bornholdt improved community quality for all systems, that coupling decreased for 99 of the systems, and that coherence increased for 102 of the systems. Furthermore, the modularity function correlates with existing metrics for coupling and coherence.......The modularity of a software system is known to have an effect on, among other, development effort, change impact, and technical debt. Modularizing a specific system and evaluating this modularization is, however, challenging. In this paper, we apply community detection methods to the graph...... of class dependencies in software systems to find optimal modularizations through communities. We evaluate this approach through a study of 111 Java systems contained in the Qualitas Corpus. We found that using the modularity function of Newman with an Erdős-Rényi null-model and using the community...

  16. Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

    Directory of Open Access Journals (Sweden)

    Mohammad Ashfak Habib

    2014-04-01

    Full Text Available This paper presents a state-of-the-art survey of smartphone (SP-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers’ interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.

  17. Automated detection of geomagnetic storms with heightened risk of GIC

    Science.gov (United States)

    Bailey, Rachel L.; Leonhardt, Roman

    2016-06-01

    Automated detection of geomagnetic storms is of growing importance to operators of technical infrastructure (e.g., power grids, satellites), which is susceptible to damage caused by the consequences of geomagnetic storms. In this study, we compare three methods for automated geomagnetic storm detection: a method analyzing the first derivative of the geomagnetic variations, another looking at the Akaike information criterion, and a third using multi-resolution analysis of the maximal overlap discrete wavelet transform of the variations. These detection methods are used in combination with an algorithm for the detection of coronal mass ejection shock fronts in ACE solar wind data prior to the storm arrival on Earth as an additional constraint for possible storm detection. The maximal overlap discrete wavelet transform is found to be the most accurate of the detection methods. The final storm detection software, implementing analysis of both satellite solar wind and geomagnetic ground data, detects 14 of 15 more powerful geomagnetic storms over a period of 2 years.

  18. Method and automated apparatus for detecting coliform organisms

    Science.gov (United States)

    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.

  19. Detecting inpatient falls by using natural language processing of electronic medical records

    Directory of Open Access Journals (Sweden)

    Toyabe Shin-ichi

    2012-12-01

    Full Text Available Abstract Background Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose. Methods We tried to detect adverse events from narrative text data of electronic medical records by using a natural language processing method. We made syntactic category decision rules to detect inpatient falls from text data in electronic medical records. We compared how often the true fall events were recorded in various sources of data including progress notes, discharge summaries, image order entries and incident reports. We applied the rules to these data sources and compared F-measures to detect falls between these data sources with reference to the results of a manual chart review. The lag time between event occurrence and data submission and the degree of injury were compared. Results We made 170 syntactic rules to detect inpatient falls by using a natural language processing method. Information on true fall events was most frequently recorded in progress notes (100%, incident reports (65.0% and image order entries (12.5%. However, F-measure to detect falls using the rules was poor when using progress notes (0.12 and discharge summaries (0.24 compared with that when using incident reports (1.00 and image order entries (0.91. Since the results suggested that incident reports and image order entries were possible data sources for prompt detection of serious falls, we focused on a comparison of falls found by incident reports and image order entries. Injury caused by falls found by image order entries was significantly more severe than falls detected by

  20. Automated detection, characterization, and tracking of filaments from SDO data

    Science.gov (United States)

    Buchlin, Eric; Vial, Jean-Claude; Mercier, Claude

    2016-07-01

    Thanks to the cadence and continuity of AIA and HMI observations, SDO offers unique data for detecting, characterizing, and tracking solar filaments, until their eruptions, which are often associated with coronal mass ejections. Because of the requirement of short latency when aiming at space weather applications, and because of the important data volume, only an automated detection can be worked out. We present the code "FILaments, Eruptions, and Activations detected from Space" (FILEAS) that we have developed for the automated detection and tracking of filaments. Detections are based on the analysis of AIA 30.4 nm He II images and on the magnetic polarity inversion lines derived from HMI. Following the tracking of filaments as they rotate with the Sun, filament characteristics are computed and a database of filaments parameters is built. We present the algorithms and performances of the code, and we compare its results with the filaments detected in Hα and already present in the Heliophysics Events Knowledgebase. We finally discuss the possibility of using such a code to detect eruptions in real time.

  1. Towards an Automated Acoustic Detection System for Free Ranging Elephants

    OpenAIRE

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

    2015-01-01

    The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In t...

  2. A Survey on Automatic Fall Detection in the Context of Ambient Assisted Living Systems

    Directory of Open Access Journals (Sweden)

    Velislava Spasova

    2014-03-01

    Full Text Available Ambient Assisted Living (AAL systems are a relatively new and expanding area of research. Due to current demographic trends towards gentrification of the population AAL systems are bound to become more important in todays and near future’s societies. Fall detection is an important component of AAL systems which could provide better safety and higher independency of the elderly. This paper presents a survey on automatic fall detection in the context of AAL systems.

  3. MEMS-based sensing and algorithm development for fall detection and gait analysis

    Science.gov (United States)

    Gupta, Piyush; Ramirez, Gabriel; Lie, Donald Y. C.; Dallas, Tim; Banister, Ron E.; Dentino, Andrew

    2010-02-01

    Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.

  4. Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system.

    Science.gov (United States)

    Shieh, Wann-Yun; Huang, Ju-Chin

    2012-09-01

    For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. PMID:22154761

  5. Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Quoc T. Huynh

    2015-01-01

    Full Text Available Falling is a common and significant cause of injury in elderly adults (>65 yrs old, often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling. Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

  6. Fully automated period detection from variable stars' time series data

    CERN Document Server

    Shaju, K Y; Thayyullathil, Ramesh Babu

    2010-01-01

    We propose a fully automated method of period determination for the time series data of variable stars. For convenience the discussions in this paper are done in terms of frequency instead of period. Relying on the SigSpec technique (Reegen 2007), it employs a statistically unbiased treatment of frequency-domain noise and avoids spurious (i. e. noise induced) and alias peaks to the highest possible extent without any human intervention. From the output file produced by SigSpec, the frequency with maximum significance is chosen as the genuine frequency. We present tests on ASAS data and the results show that SigSpec can be effectively used for fully automated frequency detection from variable stars' time series data.

  7. Fully Automated Lipid Pool Detection Using Near Infrared Spectroscopy.

    Science.gov (United States)

    Pociask, Elżbieta; Jaworek-Korjakowska, Joanna; Malinowski, Krzysztof Piotr; Roleder, Tomasz; Wojakowski, Wojciech

    2016-01-01

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

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

    Science.gov (United States)

    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

  9. Automated detection of multiple sclerosis lesions in serial brain MRI

    Energy Technology Data Exchange (ETDEWEB)

    Llado, Xavier; Ganiler, Onur; Oliver, Arnau; Marti, Robert; Freixenet, Jordi [University of Girona, Computer Vision and Robotics Group, Girona (Spain); Valls, Laia [Dr. Josep Trueta University Hospital, Department of Radiology, Girona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Ramio-Torrenta, Lluis [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Multiple Sclerosis and Neuroimmunology Unit, Girona (Spain); Rovira, Alex [Vall d' Hebron University Hospital, Magnetic Resonance Unit, Department of Radiology, Barcelona (Spain)

    2012-08-15

    Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

  10. Automated detection of multiple sclerosis lesions in serial brain MRI

    International Nuclear Information System (INIS)

    Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

  11. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

    OpenAIRE

    Miguel FELGUEIRAS; Luis MADURO; Felisberto, Filipe; Pereira, António

    2013-01-01

    In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to ...

  12. Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

    OpenAIRE

    Huynh, Quoc T.; Nguyen, Uyen D; Irazabal, Lucia B.; Nazanin Ghassemian; Tran, Binh Q.

    2015-01-01

    Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of va...

  13. 一种人体跌倒检测方法%Method of Human Fall Detection

    Institute of Scientific and Technical Information of China (English)

    茅莉磊; 高强

    2016-01-01

    As the problem of population aging is becoming more and more serious, a method of human fall detection based on wearable device is proposed to solve the social problem that the elderly are prone to fall. Different from the majority of fall detection methods which detect fall events after falling to the ground, the features of acceleration and angle are considered and support vector machine (SVM) is used as the classification algorithm to detect fall events before falling to the ground. The experiment results show that the fall event is recognized with a 99.2% recognition rate and the recognition rate of the activity of daily living is 96%. The average lead-time is 273ms.%随着人口老龄化问题日趋严重,针对老年人容易跌倒的社会问题,进行跌倒检测方法的研究.采用基于穿戴式设备的跌倒检测方法,不同于绝大多数的跌倒事后检测方法,结合加速度特征和角度特征,采用支持向量机算法作为分类算法,进行人体跌倒的事前检测.通过实验发现,跌倒行为的检测率达到99.2%,日常活动行为的检测率达到96%,跌倒检测的平均前置时间为273ms.

  14. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry......Traditionally, different manual, labour intensive and hence costly methods have been used for change detection. Conducting field inspections, comparing the map contents with the real world "on location" is one method. In another method two neighbouring images from a flight campaign are used...... and a stereo model generated. The digital map database is superimposed (in 3D) on the stereo model, and a stereo-operator locates the differences. Automating the update process for a topographic map database is, however, non-trivial, as it involves the comparison of the existing (vector based) map database...

  15. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry......Traditionally, different manual, labour intensive and hence costly methods have been used for change detection. Conducting field inspections, comparing the map contents with the real world "on location" is onemethod. In another method two neighbouring images from a flight campaign are used...... and a stereo model generated. The digital map database is superimposed (in3D) on the stereo model, and a stereo-operator locates the differences. Automating the update process for a topographic map database is, however, non-trivial, as itinvolves the comparison of the existing (vector based) map database...

  16. Automated Detection and Tracking of Solar Magnetic Bright Points

    CERN Document Server

    Crockett, P J; Mathioudakis, M; Keenan, F P

    2009-01-01

    Magnetic Bright Points (MBPs) in the internetwork are among the smallest objects in the solar photosphere and appear bright against the ambient environment. An algorithm is presented that can be used for the automated detection of the MBPs in the spatial and temporal domains. The algorithm works by mapping the lanes through intensity thresholding. A compass search, combined with a study of the intensity gradient across the detected objects, allows the disentanglement of MBPs from bright pixels within the granules. Object growing is implemented to account for any pixels that might have been removed when mapping the lanes. The images are stabilized by locating long-lived objects that may have been missed due to variable light levels and seeing quality. Tests of the algorithm employing data taken with the Swedish Solar Telescope (SST), reveal that ~90% of MBPs within a 75"x 75" field of view are detected.

  17. Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study.

    Science.gov (United States)

    Leone, Alessandro; Diraco, Giovanni; Siciliano, Pietro

    2011-07-01

    In recent years several world-wide ambient assisted living (AAL) programs have been activated in order to improve the quality of life of older people, and to strengthen the industrial base through the use of information and communication technologies. An important issue is extending the time that older people can live in their home environment, by increasing their autonomy and helping them to carry out activities of daily living (ADLs). Research in the automatic detection of falls has received a lot of attention, with the object of enhancing safety, emergency response and independence of the elderly, at the same time comparing the social and economic costs related to fall accidents. In this work, an algorithmic framework to detect falls by using a 3D time-of-flight vision technology is presented. The proposed system presented complementary working requirements with respect to traditional worn and non-worn fall-detection devices. The vision system used a state-of-the-art 3D range camera for elderly movement measurement and detection of critical events, such as falls. The depth images provided by the active sensor allowed reliable segmentation and tracking of elderly movements, by using well-established imaging methods. Moreover, the range camera provided 3D metric information in all illumination conditions (even night vision), allowing the overcoming of some typical limitations of passive vision (shadows, camouflage, occlusions, brightness fluctuations, perspective ambiguity). A self-calibration algorithm guarantees different setup mountings of the range camera by non-technical users. A large dataset of simulated fall events and ADLs in real dwellings was collected and the proposed fall-detection system demonstrated high performance in terms of sensitivity and specificity.

  18. Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

    Science.gov (United States)

    Skofronick-Jackson, Gail; Johnson, Benjamin T.; Munchak, S. Joseph

    2012-01-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the

  19. Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-fall Detection

    Directory of Open Access Journals (Sweden)

    Tong Liu

    2014-03-01

    Full Text Available This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The l1 norm minimizations with linear programming (LP and orthogonal matching pursuit (OMP are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

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

    Science.gov (United States)

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

    2011-03-01

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

  1. The 5th Umpire: Automating Cricket's Edge Detection System

    Directory of Open Access Journals (Sweden)

    R. Rock

    2013-02-01

    Full Text Available The game of cricket and the use of technology in the sport have grown rapidly over the past decade. However, technology-based systems introduced to adjudicate decisions such as run outs, stumpings, boundary infringements and close catches are still prone to human error, and thus their acceptance has not been fully embraced by cricketing administrators. In particular, technology is not employed for bat-pad decisions. Although the snickometer may assist in adjudicating such decisions it depends heavily on human interpretation. The aim of this study is to investigate the use of Wavelets in developing an edgedetection adjudication system for the game of cricket. Artificial Intelligence (AI tools, namely Neural Networks, will be employed to automate this edge detection process. Live audio samples of ball-on-bat and ball-on-pad events from a cricket match will be recorded. DSP analysis, feature extraction and neural network classification will then be employed on these samples. Results will show the ability of the neural network to differentiate between these key events. This is crucial to developing a fully automated edge detection system.

  2. Automated microaneurysm detection in diabetic retinopathy using curvelet transform.

    Science.gov (United States)

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

    2016-10-01

    Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies. PMID:26868326

  3. Automated calibration methods for robotic multisensor landmine detection

    Science.gov (United States)

    Keranen, Joe G.; Miller, Jonathan; Schultz, Gregory; Topolosky, Zeke

    2007-04-01

    Both force protection and humanitarian demining missions require efficient and reliable detection and discrimination of buried anti-tank and anti-personnel landmines. Widely varying surface and subsurface conditions, mine types and placement, as well as environmental regimes challenge the robustness of the automatic target recognition process. In this paper we present applications created for the U.S. Army Nemesis detection platform. Nemesis is an unmanned rubber-tracked vehicle-based system designed to eradicate a wide variety of anti-tank and anti-personnel landmines for humanitarian demining missions. The detection system integrates advanced ground penetrating synthetic aperture radar (GPSAR) and electromagnetic induction (EMI) arrays, highly accurate global and local positioning, and on-board target detection/classification software on the front loader of a semi-autonomous UGV. An automated procedure is developed to estimate the soil's dielectric constant using surface reflections from the ground penetrating radar. The results have implications not only for calibration of system data acquisition parameters, but also for user awareness and tuning of automatic target recognition detection and discrimination algorithms.

  4. Digital tripwire: a small automated human detection system

    Science.gov (United States)

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

    2009-05-01

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

  5. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

    Directory of Open Access Journals (Sweden)

    Yuwono Mitchell

    2012-02-01

    Full Text Available Abstract Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.

  6. Automated Detection of Client-State Manipulation Vulnerabilities

    DEFF Research Database (Denmark)

    Møller, Anders; Schwarz, Mathias

    2012-01-01

    Web application programmers must be aware of a wide range of potential security risks. Although the most common pitfalls are well described and categorized in the literature, it remains a challenging task to ensure that all guidelines are followed. For this reason, it is desirable to construct...... produced by the tool help the application programmer identify vulnerabilities. Moreover, the inferred information can be applied to configure a security filter that automatically guards against attacks. Experiments on a collection of open source web applications indicate that the static analysis is able...... automated tools that can assist the programmers in the application development process by detecting weaknesses. Many vulnerabilities are related to web application code that stores references to application state in the generated HTML documents to work around the statelessness of the HTTP protocol...

  7. Automated Structure Detection in HRTEM Images: An Example with Graphene

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm;

    analysis. Single-layer graphene with its regular honeycomb lattice is a perfect model structure to apply automated structure detection. By utilizing Fourier analysis the initial perfect hexagonal structure can easily be recognized. The recorded hexagonal tessellation reflects the unperturbed structure...... challenging to interpret. In order to increase the signal-to-noise ratio of the images two routes can be pursued: 1) the exposure time can be increased; or 2) acquiring series of images and summarize them after alignment. Both methods have the disadvantage of summing images acquired over a certain period...... in the image. The centers of the C-hexagons are displayed as nodes. To segment the image into “pure” and “impure” regions, like areas with residual amorphous contamination or defects e.g. holes, a sliding window approach is used. The magnitude of the Fourier transformation within a window is compared...

  8. Automated detection of open magnetic field regions in EUV images

    Science.gov (United States)

    Krista, Larisza Diana; Reinard, Alysha

    2016-05-01

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

  9. Automated detection of retinal whitening in malarial retinopathy

    Science.gov (United States)

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

    2016-03-01

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

  10. [Design of Fall Detection System for Elderly People Based on MPU6050 Sensor].

    Science.gov (United States)

    Liu, Li; Zheng, Dongxue; Liu, Xiaojun

    2015-09-01

    This paper proposes a falling detection system based on MPU6050 senor. The system consists of a MPU6050 sensor, a STM32 MCU and a set of Bluetooth 4.0 device: collecting and parsing the falling data, transferring the result to a smartphone, the smartphone: receiving the result, alarming the elder's family and hospital. This paper presentes a new judging algorithm based on the threshold of three-axis acceleration and angle deviation of body, in order to differentiate AF and normal daily activity. The result proves that the accuracy of the system is higher than 95%, which strongly highlight the robustness and reliability. PMID:26904872

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Michał Grega

    2016-01-01

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

  13. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  14. Automated analysis for detecting beams in laser wakefield simulations

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-07-03

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

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

    Science.gov (United States)

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

    2014-01-01

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

  16. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Miguel FELGUEIRAS

    2013-07-01

    Full Text Available In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%. 

  17. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    António PEREIRA

    2012-07-01

    Full Text Available In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.  

  18. Privacy Preserving Fall Detection Based on Simple Human Silhouette Extraction and a Linear Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Velislava Spasova

    2016-06-01

    Full Text Available The paper presents a novel fast, real-time and privacy protecting algorithm for fall detection based on geometric properties of the human silhouette and a linear support vector machine. The algorithm uses infrared and visible light imagery in order to detect the human. A simple real-time human silhouette extraction algorithm has been developed and used to extract features for training of the support vector machine. The achieved sensitivity and specificity of the proposed approach are over 97% which match state of the art research in the area of fall detection. The developed solution uses low-cost hardware components and open source software library and is suitable for usage in assistive systems for the home or nursing homes.

  19. Smartphone-based solutions for fall detection and prevention: the FARSEEING approach.

    Science.gov (United States)

    Mellone, S; Tacconi, C; Schwickert, L; Klenk, J; Becker, C; Chiari, L

    2012-12-01

    Falls are not an inevitable consequence of aging. The risk and rate of falls can be reduced. Recent improvements in smartphone technology enable implementation of a wide variety of services and applications, thus making the smartphone more of a digital companion than simply a communication tool. This paper presents the results obtained by the FARSEEING project where smartphones are one example of intervention in a population-based scenario. The applications developed take advantage of the smartphone-embedded inertial sensors and require that subjects wear the smartphone by means of a waist belt. The uFall Android application has been developed for monitoring the user's motor activities at home. The application does not require any direct interaction with the user and it is also capable of running a real-time fall-detection algorithm. uTUG is a stand-alone application for instrumenting the Timed Up and Go test, which is a test often included in fall risk assessment protocols. The application acts like a pocket-sized motion laboratory, since it is capable not only of recording the trial but also of processing the data and immediately displaying the results. uTUG is designed to be self-administrable at home. PMID:23184298

  20. Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

    Directory of Open Access Journals (Sweden)

    Stefan Wiehle

    2015-01-01

    Full Text Available We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

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

    International Nuclear Information System (INIS)

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Byler, E.

    1997-10-31

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

  3. A practical automated polyp detection scheme for CT colonography

    Science.gov (United States)

    Li, Hong; Santago, Pete

    2004-05-01

    A fully automated computerized polyp detection (CPD) system is presented that takes DICOM images from CT scanners and provides a list of detected polyps. The system comprises three stages, segmentation, polyp candidate generation (PCG), and false positive reduction (FPR). Employing computer tomographic colonography (CTC), both supine and prone scans are used for improving detection sensitivity. We developed a novel and efficient segmentation scheme. Major shape features, e.g., the mean curvature and Gaussian curvature, together with a connectivity test efficiently produce polyp candidates. We select six shape features and introduce a multi-plane linear discriminant function (MLDF) classifier in our system for FPR. The classifier parameters are empirically assigned with respect to the geometric meanings of a specific feature. We have tested the system on 68 real subjects, 20 positive and 48 negative for 6 mm and larger polyps from colonoscopy results. Using a patient-based criterion, 95% accuracy and 31% specificity were achieved when 6 mm was used as the cutoff size, implying that 15 out of 48 healthy subjects could avoid OC. One 11 mm polyp was missed by CPD but was also not reported by the radiologist. With a complete polyp database, we anticipate that a maximum a posteriori probability (MAP) classifier tuned by supervised training will improve the detection performance. The execution time for both scans is about 10-15 minutes using a 1 GHz PC running Linux. The system may be used standalone, but is envisioned more as a part of a computer-aided CTC screening that can address the problems with a fully automatic approach and a fully physician approach.

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

    Energy Technology Data Exchange (ETDEWEB)

    E. Bean

    2006-11-30

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

  5. A heuristic approach to automated nipple detection in digital mammograms.

    Science.gov (United States)

    Jas, Mainak; Mukhopadhyay, Sudipta; Chakraborty, Jayasree; Sadhu, Anup; Khandelwal, Niranjan

    2013-10-01

    In this paper, a heuristic approach to automated nipple detection in digital mammograms is presented. A multithresholding algorithm is first applied to segment the mammogram and separate the breast region from the background region. Next, the problem is considered separately for craniocaudal (CC) and mediolateral-oblique (MLO) views. In the simplified algorithm, a search is performed on the segmented image along a band around the centroid and in a direction perpendicular to the pectoral muscle edge in the MLO view image. The direction defaults to the horizontal (perpendicular to the thoracic wall) in case of CC view images. The farthest pixel from the base found in this direction can be approximated as the nipple point. Further, an improved version of the simplified algorithm is proposed which can be considered as a subclass of the Branch and Bound algorithms. The mean Euclidean distance between the ground truth and calculated nipple position for 500 mammograms from the Digital Database for Screening Mammography (DDSM) database was found to be 11.03 mm and the average total time taken by the algorithm was 0.79 s. Results of the proposed algorithm demonstrate that even simple heuristics can achieve the desired result in nipple detection thus reducing the time and computational complexity.

  6. Context Awareness in Communication around Fall Handling with PERS

    OpenAIRE

    VAN DEN BERGH, Jan; Elprama, Shirley A.; Decancq, Jasmien; Jacobs, An; Coninx, Karin

    2015-01-01

    Assuring timely intervention after falls is important to enable older adults to live independently for a longer time. There are two strategies where technology could assist timely intervention: 1) automated fall detection and 2) handling of falls - the process of sending help to a fall victim - using a personal emergency response system (PERS). This paper presents first insights on using sensors not only on the patient’s side but also on the caregiver side. We present the results of two st...

  7. Automated motion detection from space in sea surveilliance

    Science.gov (United States)

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

    2015-06-01

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

  8. Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic.

    Science.gov (United States)

    Anderson, Derek; Luke, Robert H; Keller, James M; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

    2009-01-01

    In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person's states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.

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

    Science.gov (United States)

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

    2016-05-01

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

  10. An exploration study to detect different factors influencing on inefficiency of office automation systems

    OpenAIRE

    Azam Roostaee; Jamshid Salehi Sadaghiani

    2013-01-01

    Office automation systems play important role on increasing productivity and efficiency of organizations. An automated system is capable of improving required communications, speed up the process of tasks and removes unnecessary activities. This paper presents an empirical investigation to detect important factors influencing on inefficiency of office automation systems in ministry of science, research and technology of Iran. The proposed study of this paper designs a questionnaire and distri...

  11. Automated Ground Penetrating Radar hyperbola detection in complex environment

    Science.gov (United States)

    Mertens, Laurence; Lambot, Sébastien

    2015-04-01

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

  12. Comparison of Machine Learning Methods for the Purpose Of Human Fall Detection

    Directory of Open Access Journals (Sweden)

    Strémy Maximilián

    2014-12-01

    Full Text Available According to several studies, the European population is rapidly aging far over last years. It is therefore important to ensure that aging population is able to live independently without the support of working-age population. In accordance with the studies, fall is the most dangerous and frequent accident in the everyday life of aging population. In our paper, we present a system to track the human fall by a visual detection, i.e. using no wearable equipment. For this purpose, we used a Kinect sensor, which provides the human body position in the Cartesian coordinates. It is possible to directly capture a human body because the Kinect sensor has a depth and also an infrared camera. The first step in our research was to detect postures and classify the fall accident. We experimented and compared the selected machine learning methods including Naive Bayes, decision trees and SVM method to compare the performance in recognizing the human postures (standing, sitting and lying. The highest classification accuracy of over 93.3% was achieved by the decision tree method.

  13. Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

    Institute of Scientific and Technical Information of China (English)

    LIU Liping; Qin XU; Pengfei ZHANG; Shun LIU

    2008-01-01

    In mountain areas,radar observations are often contaminated(1)by echoes from high-speed moving vehicles and(2)by point-wise ground clutter under either normal propagation(NP)or anomalous propa-gation(AP)conditions.Level II data are collected from KMTX(Salt Lake City,Utah)radar to analyze these two types of contamination in the mountain area around the Great Salt Lake.Human experts provide the"ground truth"for possible contamination of either type on each individual pixel.Common features are then extracted for contaminated pixels of each type.For example,pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width.Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways.These contaminated pixels are only seen in areas of large terrain gradient(in the radial direction along the radar beam).The same is true for the second type of contamination-point-wise ground clutters.Six quality control(QC)parameters are selected to quantify the extracted features.Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels.Based on the computed histograms,a fuzzy logical algorithm is developed for automated detection of contaminated pixels.The algorithm is tested with KMTX radar data under different(clear and rainy)weather conditions.

  14. Automated weed detection in the field - possibilities and limits

    Directory of Open Access Journals (Sweden)

    Pflanz, Michael

    2016-02-01

    Full Text Available Unmanned Aerial Vehicles (UAV have become omnipresent and adequate tools to generate high-resolution spatial data of agricultural cropland. Their implementation into remote sensing approaches of weeds provides suitable applications for a site-specific herbicide management. In general, an increasingly use of innovative technologies gradually leads from agricultural research into the practical application. This requires an evaluation of possibilities and limits of UAV-based remote sensing procedures. While spectrals from UAVs are being used already for mapping needs of nutrient or water, the image supported weed detection is much more complex and at the moment not relevant in practice. In this regard, there is a lack of weed and crop differentiation through spectral analyses and object-based approaches separate different plants not species-specific or are not adapted to morphologic changes of the growth. Moreover, there is a need for alternative positioning techniques without GPS, as it is required for a precise optical imaging analysis at low altitudes. To evaluate the possibilities and limitations of automated weed identification regarding the optical and sampling requirements, flights were carried out with a hexacopter at an altitude of 5 m over agricultural crop land with variable weed patches. The altitude was controlled by the GPS-autopilot. Images were captured at geo-referenced points and the number of different weed species was simultaneously determined by manually counting. The required optical resolution on the ground was estimated by comparing the number of weeds between image analysis on the PC and with the field rating data.

  15. A posture recognition based fall detection system for monitoring an elderly person in a smart home environment.

    Science.gov (United States)

    Yu, Miao; Rhuma, Adel; Naqvi, Syed Mohsen; Wang, Liang; Chambers, Jonathon

    2012-11-01

    We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

  16. Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network

    Directory of Open Access Journals (Sweden)

    Mineichi Kudo

    2012-12-01

    Full Text Available An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the “pixel values” as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc. performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value, the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.

  17. Automated image capture and defects detection by cavity inspection camera

    International Nuclear Information System (INIS)

    The defects as pit and scar make electric/magnetic field enhance and it cause field emission and quench in superconducting cavities. We used inspection camera to find these defects, but the current system which operated by human often mistake file naming and require long acquisition time. This study aims to solve these problems with introduction of cavity driving automation and defect inspection. We used rs232c of serial communication to drive of motor and camera for the automation of the inspection camera, and we used defect inspection software with defects reference images and pattern match software with the OpenCV lib. By the automation, we cut down the acquisition time from 8 hours to 2 hours, however defect inspection software is under preparation. The defect inspection software has a problem of complexity of image back ground. (author)

  18. Sludge settleability detection using automated SV30 measurement and its application to a field WWTP.

    Science.gov (United States)

    Kim, Y J; Choi, S J; Bae, H; Kim, C W

    2011-01-01

    The need for automation & measurement technologies to detect the process state has been a driving force in the development of various measurements at wastewater treatment plants. While the number of applications of automation & measurement technologies to the field is increasing, there have only been a few cases where they have been applied to the area of sludge settling. This is because it is not easy to develop an automated operation support system for the detection of sludge settleability due to its site-specific characteristics. To automate the human operator's daily test and diagnosis works on sludge settling, an on-line SV30 measurement was developed and an automated detection algorithm on settleability was developed that imitated heuristics to detect settleability faults. The automated SV30 measurement is based on automatic pumping with a predefined schedule, the image capture of the settling test with a digital camera, and an analysis of the images to detect the settled sludge height. A sludge settleability detection method was developed and its applicability was investigated by field application. PMID:22335120

  19. FPGA-Based Real-Time Motion Detection for Automated Video Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Sanjay Singh

    2016-03-01

    Full Text Available Design of automated video surveillance systems is one of the exigent missions in computer vision community because of their ability to automatically select frames of interest in incoming video streams based on motion detection. This research paper focuses on the real-time hardware implementation of a motion detection algorithm for such vision based automated surveillance systems. A dedicated VLSI architecture has been proposed and designed for clustering-based motion detection scheme. The working prototype of a complete standalone automated video surveillance system, including input camera interface, designed motion detection VLSI architecture, and output display interface, with real-time relevant motion detection capabilities, has been implemented on Xilinx ML510 (Virtex-5 FX130T FPGA platform. The prototyped system robustly detects the relevant motion in real-time in live PAL (720 × 576 resolution video streams directly coming from the camera.

  20. Automated detection of test fixture strategies and smells

    NARCIS (Netherlands)

    Greiler, M.S.; Van Deursen, A.; Storey, M.-A.

    2013-01-01

    Paper accepted for publication in the Proceedings of the Sixth International Conference on Software Testing, Verification and Validation, IEEE Computer Society, 18-22 March 2013, ISBN 978-1-4673-5961-0, doi: 10.1109/ICST.2013.45 Designing automated tests is a challenging task. One important concern

  1. Fully Autonomous Multiplet Event Detection: Application to Local-Distance Monitoring of Blood Falls Seismicity

    Energy Technology Data Exchange (ETDEWEB)

    Carmichael, Joshua Daniel [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Carr, Christina [Univ. of Alaska, Fairbanks, AK (United States); Pettit, Erin C. [Univ. of Alaska, Fairbanks, AK (United States)

    2015-06-18

    We apply a fully autonomous icequake detection methodology to a single day of high-sample rate (200 Hz) seismic network data recorded from the terminus of Taylor Glacier, ANT that temporally coincided with a brine release episode near Blood Falls (May 13, 2014). We demonstrate a statistically validated procedure to assemble waveforms triggered by icequakes into populations of clusters linked by intra-event waveform similarity. Our processing methodology implements a noise-adaptive power detector coupled with a complete-linkage clustering algorithm and noise-adaptive correlation detector. This detector-chain reveals a population of 20 multiplet sequences that includes ~150 icequakes and produces zero false alarms on the concurrent, diurnally variable noise. Our results are very promising for identifying changes in background seismicity associated with the presence or absence of brine release episodes. We thereby suggest that our methodology could be applied to longer time periods to establish a brine-release monitoring program for Blood Falls that is based on icequake detections.

  2. An exploration study to detect different factors influencing on inefficiency of office automation systems

    Directory of Open Access Journals (Sweden)

    Azam Roostaee

    2013-06-01

    Full Text Available Office automation systems play important role on increasing productivity and efficiency of organizations. An automated system is capable of improving required communications, speed up the process of tasks and removes unnecessary activities. This paper presents an empirical investigation to detect important factors influencing on inefficiency of office automation systems in ministry of science, research and technology of Iran. The proposed study of this paper designs a questionnaire and distributes it among management team who work for this organization. The results of our investigation indicate that two factors, lack of necessary infrastructure for participating in office automation activities as well as lack of management support, play important role on reaching desirable results. In addition, educational background and work experience also influence office automation systems’ applicability.

  3. An Intelligent Alarm and Messaging Based Surveillance System for Fall Detection and Absence Recognition of Unaccompanied Child

    Directory of Open Access Journals (Sweden)

    Ali Javed

    2013-03-01

    Full Text Available Video analytics refers to process the videos intelligently. Video analytics has its most important usage in the field of the surveillance. Surveillance has been used in various areas and one of them is the detection of unintentional fall of patients, senior citizens and children which can cause serious injuries and health threats to children as well as to old persons. Developed countries are progressing in the Surveillance and activity monitoring. But there are limitation and facing problems under certain circumstances. Advancement in the field of computer vision and the prominent decrease in the prices of digital cameras assisted and motivated researchers to propose very useful algorithms for fall detection. The proposed research work is based on the combination of motion history images and eclipse centroid calculation to detect the fall efficiently. The proposed system provides very effective and efficient results on the video sequences of simulated falls.

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

    Science.gov (United States)

    Chodera, John D

    2016-04-12

    Molecular simulations intended to compute equilibrium properties are often initiated from configurations that are highly atypical of equilibrium samples, a practice which can generate a distinct initial transient in mechanical observables computed from the simulation trajectory. Traditional practice in simulation data analysis recommends this initial portion be discarded to equilibration, but no simple, general, and automated procedure for this process exists. Here, we suggest a conceptually simple automated procedure that does not make strict assumptions about the distribution of the observable of interest in which the equilibration time is chosen to maximize the number of effectively uncorrelated samples in the production timespan used to compute equilibrium averages. We present a simple Python reference implementation of this procedure and demonstrate its utility on typical molecular simulation data. PMID:26771390

  5. Automated prostate tissue referencing for cancer detection and diagnosis

    OpenAIRE

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

    2016-01-01

    Background The current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics. Results The system includes a database o...

  6. Computer automated movement detection for the analysis of behavior

    OpenAIRE

    Ramazani, Roseanna B.; Harish R Krishnan; BERGESON, SUSAN E.; Atkinson, Nigel S.

    2007-01-01

    Currently, measuring ethanol behaviors in flies depends on expensive image analysis software or time intensive experimenter observation. We have designed an automated system for the collection and analysis of locomotor behavior data, using the IEEE 1394 acquisition program dvgrab, the image toolkit ImageMagick and the programming language Perl. In the proposed method, flies are placed in a clear container and a computer-controlled camera takes pictures at regular intervals. Digital subtractio...

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

    Science.gov (United States)

    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.

  8. Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm.

    Directory of Open Access Journals (Sweden)

    Dario Martelli

    Full Text Available The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT was 351±123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. Results support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions.

  9. Automated Detection of Soma Location and Morphology in Neuronal Network Cultures

    OpenAIRE

    Burcin Ozcan; Pooran Negi; Fernanda Laezza; Manos Papadakis; Demetrio Labate

    2015-01-01

    Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons ...

  10. From drafting guideline to error detection: Automating style checking for legislative texts

    OpenAIRE

    Höfler, Stefan; Sugisaki, Kyoko

    2012-01-01

    This paper reports on the development of methods for the automated detection of violations of style guidelines for legislative texts, and their implementation in a prototypical tool. To this aim, the approach of error modelling employed in automated style checkers for technical writing is enhanced to meet the requirements of legislative editing. The paper identifies and discusses the two main sets of challenges that have to be tackled in this process: (i) the provision of domain-specific NLP ...

  11. Automated Sputum Cytometry for Detection of Intraepithelial Neoplasias in the Lung

    Directory of Open Access Journals (Sweden)

    Gerald Li

    2012-01-01

    Full Text Available Background: Despite the benefits of early lung cancer detection, no effective strategy for early screening and treatment exists, partly due to a lack of effective surrogate biomarkers. Our novel sputum biomarker, the Combined Score (CS, uses automated image cytometric analysis of ploidy and nuclear morphology to detect subtle intraepithelial changes that often precede lung tumours.

  12. Cell-Detection Technique for Automated Patch Clamping

    Science.gov (United States)

    McDowell, Mark; Gray, Elizabeth

    2008-01-01

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

  13. Automated Contour Detection for Intravascular Ultrasound Image Sequences Based on Fast Active Contour Algorithm

    Institute of Scientific and Technical Information of China (English)

    DONG Hai-yan; WANG Hui-nan

    2006-01-01

    Intravascular ultrasound can provide high-resolution real-time crosssectional images about lumen, plaque and tissue. Traditionally, the luminal border and medial-adventitial border are traced manually. This process is extremely timeconsuming and the subjective difference would be large. In this paper, a new automated contour detection method is introduced based on fast active contour model.Experimental results found that lumen and vessel area measurements after automated detection showed good agreement with manual tracings with high correlation coefficients (0.94 and 0.95, respectively) and small system difference ( -0.32 and 0.56, respectively). So it can be a reliable and accurate diagnostic tool.

  14. A Multi-Wavelength Analysis of Active Regions and Sunspots by Comparison of Automated Detection Algorithms

    OpenAIRE

    Verbeeck, Cis; Higgins, Paul A.; Colak, Tufan; Watson, Fraser T.; Delouille, Veronique; Mampaey, Benjamin; Qahwaji, Rami

    2011-01-01

    Since the Solar Dynamics Observatory (SDO) began recording ~ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction...

  15. Automated Detection and Evaluation of Swallowing Using a Combined EMG/Bioimpedance Measurement System

    OpenAIRE

    2014-01-01

    Introduction. Developing an automated diagnostic and therapeutic instrument for treating swallowing disorders requires procedures able to reliably detect and evaluate a swallow. We tested a two-stage detection procedure based on a combined electromyography/bioimpedance (EMBI) measurement system. EMBI is able to detect swallows and distinguish them from similar movements in healthy test subjects. Study Design. The study was planned and conducted as a case-control study (EA 1/019/10, and EA1/16...

  16. A Multi-stage System for the Automated Detection of Epileptic Seizures in Neonatal EEG

    OpenAIRE

    Mitra, Joyeeta; Glover, John R.; Ktonas, Periklis Y.; Thitai Kumar, Arun; Mukherjee, Amit; Karayiannis, Nicolaos B.; Frost, James D.; Hrachovy, Richard A.; Mizrahi, Eli M.

    2009-01-01

    This paper describes the design and test results of a 3-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage, and identifies overlapping 5-s segments of suspected seizure activity in each EEG channel. In Stage II, the detected segments from Stage I are spatiotemporally clustered to produce multi-channel candidate seizures. In Stage III, the candidate seizures are processed further using measures of quality and context-based rules to e...

  17. Automated Detection and Evaluation of Swallowing Using a Combined EMG/Bioimpedance Measurement System

    Directory of Open Access Journals (Sweden)

    Corinna Schultheiss

    2014-01-01

    Full Text Available Introduction. Developing an automated diagnostic and therapeutic instrument for treating swallowing disorders requires procedures able to reliably detect and evaluate a swallow. We tested a two-stage detection procedure based on a combined electromyography/bioimpedance (EMBI measurement system. EMBI is able to detect swallows and distinguish them from similar movements in healthy test subjects. Study Design. The study was planned and conducted as a case-control study (EA 1/019/10, and EA1/160/09, EA1/161/09. Method. The study looked at differences in swallowing parameters in general and in the event of penetration during swallows in healthy subjects and in patients with an oropharyngeal swallowing disorder. A two-stage automated swallow detection procedure which used electromyography (EMG and bioimpedance (BI to reliably detect swallows was developed. Results. Statistically significant differences between healthy subjects and patients with a swallowing disorder were found in swallowing parameters previously used to distinguish between swallowing and head movements. Our two-stage algorithm was able to reliably detect swallows (sensitivity = 96.1%, specificity = 97.1% on the basis of these differences. Discussion. Using a two-stage detection procedure, the EMBI measurement procedure is able to detect and evaluate swallows automatically and reliably. The two procedures (EMBI + swallow detection could in future form the basis for automated diagnosis and treatment (stimulation of swallowing disorders.

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

    Science.gov (United States)

    Ippoliti, Dennis

    2014-01-01

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

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

    DEFF Research Database (Denmark)

    Carl, Jesper; Nielsen, Henning; Nielsen, Jane;

    2006-01-01

    of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection...... algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study....... Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7  mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67...

  20. Automated Signature Creator for a Signature Based Intrusion Detection System with Network Attack Detection Capabilities (Pancakes

    Directory of Open Access Journals (Sweden)

    Frances Bernadette C. De Ocampo

    2015-05-01

    Full Text Available Signature-based Intrusion Detection System (IDS helps in maintaining the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created. If the signature database of the Signature-based IDS is not updated, network attacks just pass through this type of IDS without being noticed. To avoid this, an Anomaly-based IDS is used in order to countercheck if a network traffic that is not detected by Signature-based IDS is a true malicious traffic or not. In doing so, the Anomaly-based IDS might come up with several numbers of logs containing numerous network attacks which could possibly be a false positive. This is the reason why the Anomaly-based IDS is not perfect, it would readily alarm the system that a network traffic is an attack just because it is not on its baseline. In order to resolve the problem between these two IDSs, the goal is to correlate data between the logs of the Anomaly-based IDS and the packet that has been captured in order to determine if a network traffic is really malicious or not. With the supervision of a security expert, the malicious network traffic would be verified as malicious. Using machine learning, the researchers can identify which algorithm is better than the other algorithms in classifying if a certain network traffic is really malicious. Upon doing so, the creation of signatures would follow by basing the automated creation of signatures from the detected malicious traffic.

  1. New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

    Science.gov (United States)

    Yang, Lei; Ren, Yanyun; Hu, Huosheng; Tian, Bo

    2015-01-01

    In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity. PMID:26378540

  2. Scalable Automated Detection of Spiral Galaxy Arm Segments

    CERN Document Server

    Davis, Darren R

    2014-01-01

    Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy and then automatically extracts structural information about the spiral arms. For each arm segment found, we list the pixels in that segment and perform a least-squares fit of a logarithmic spiral arc to the pixels in the segment. The algorithm takes about 1 minute per galaxy, and can easily be scaled using parallelism. We have run it on all ~644,000 Sloan objects classified as "galaxy" and large enough to observe some structure. Our algorithm is stable in the sense that the statistics across a large sample of galaxies vary smoothly based on algorithmic parameters, although results for individual galaxies can sometimes vary in a non-smooth but easily understood manner. We find a very good correlation between our quantitative description of spiral structure and the qualitative description provided by humans via Galaxy Zoo. In addition, we find that pitch angle often varie...

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

    Science.gov (United States)

    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.

  4. ASTRiDE: Automated Streak Detection for Astronomical Images

    Science.gov (United States)

    Kim, Dae-Won

    2016-05-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

  6. Automated detection of coherent Lagrangian vortices in two-dimensional unsteady flows

    CERN Document Server

    Karrasch, Daniel; Haller, George

    2014-01-01

    Coherent boundaries of Lagrangian vortices in fluid flows have recently been identified as closed orbits of line fields associated with the Cauchy-Green strain tensor. Here we develop a fully automated procedure for the detection of such closed orbits in large-scale velocity data sets. We illustrate the power of our method on ocean surface velocities derived from satellite altimetry.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

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

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

    Science.gov (United States)

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

    2015-03-01

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

  10. Automated Detection of Lupus White Matter Lesions in MRI.

    Science.gov (United States)

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

    2016-01-01

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

  11. Automated Detection of Lupus White Matter Lesions in MRI

    Science.gov (United States)

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

    2016-01-01

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

  12. Using Akka Platform in Unidentified Falling Object Detection on the LHC.

    CERN Document Server

    Motesnitsalis, Evangelos

    2013-01-01

    During my participation in the CERN Summer Student Program 2013, I worked under the Technology Department of CERN and, more specifically, in the Machine Protection and Electrical Integrity (MPE) Group. The MPE Group supports LHC operation and maintains state‐of‐the art technology for magnet circuit protection and interlock systems for the present and future accelerators, magnet test facilities and CERN hosted experiments. Within this context, we developed an application that parallelizes the Unidentified Falling Object Detection Algorithm on the LHC Operational Data Analysis Software. For this reason, we used a JVM-based toolkit, named Akka, which parallelizes the execution by creating a number of actors that run simultaneously. The results of the new approach are presented on the last part of this report. They tend to be quite interesting and promising as we managed to reduce the execution time of the analysis by a factor of 10 on a local machine and the first attempts to execute the program on a cluster...

  13. A comparison of Bayesian and evidence-based fusion methods for automated building detection in aerial data

    NARCIS (Netherlands)

    Khoshelham, K.; Nedkov, S.; Nardinocchi, C.

    2008-01-01

    Automated approaches to building detection are of great importance in a number of different applications including map updating and monitoring of informal settlements. With the availability of multi-source aerial data in recent years, data fusion approaches to automated building detection have becom

  14. An automated computer misuse detection system for UNICOS

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-09-27

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Chikkagoudar, Satish

    2013-11-12

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

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

    Directory of Open Access Journals (Sweden)

    Mohendra Roy

    2016-05-01

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

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

    Science.gov (United States)

    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, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

  18. Automated Detection of Elementary Calcium Release Events Using the À Trous Wavelet Transform

    OpenAIRE

    Wegner, F. v.; Both, M.; Fink, R H A

    2005-01-01

    We developed an algorithm for the automated detection and analysis of elementary Ca2+ release events (ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the “à trous” algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectra...

  19. Hand, foot and mouth disease in China: evaluating an automated system for the detection of outbreaks

    OpenAIRE

    2014-01-01

    Abstract Objective To evaluate the performance of China’s infectious disease automated alert and response system in the detection of outbreaks of hand, foot and mouth (HFM) disease. Methods We estimated size, duration and delay in reporting HFM disease outbreaks from cases notified between 1 May 2008 and 30 April 2010 and between 1 May 2010 and 30 April 2012, before and after automatic alert and response included HFM disease. Sensitivity, specificity and timeliness of detection of aberrations...

  20. Automated Detection of Conformational Epitopes Using Phage Display Peptide Sequences

    Directory of Open Access Journals (Sweden)

    Surendra S Negi

    2009-01-01

    Full Text Available Background: Precise determination of conformational epitopes of neutralizing antibodies represents a key step in the rational design of novel vaccines. A powerful experimental method to gain insights on the physical chemical nature of conformational epitopes is the selection of linear peptides that bind with high affinities to a monoclonal antibody of interest by phage display technology. However, the structural characterization of conformational epitopes from these mimotopes is not straightforward, and in the past the interpretation of peptide sequences from phage display experiments focused on linear sequence analysis to find a consensus sequence or common sequence motifs.Results: We present a fully automated search method, EpiSearch that predicts the possible location of conformational epitopes on the surface of an antigen. The algorithm uses peptide sequences from phage display experiments as input, and ranks all surface exposed patches according to the frequency distribution of similar residues in the peptides and in the patch. We have tested the performance of the EpiSearch algorithm for six experimental data sets of phage display experiments, the human epidermal growth factor receptor-2 (HER-2/neu, the antibody mAb Bo2C11 targeting the C2 domain of FVIII, antibodies mAb 17b and mAb b12 of the HIV envelope protein gp120, mAb 13b5 targeting HIV-1 capsid protein and 80R of the SARS coronavirus spike protein. In all these examples the conformational epitopes as determined by the X-ray crystal structures of the antibody-antigen complexes, were found within the highest scoring patches of EpiSearch, covering in most cases more than 50% residues of experimental observed conformational epitopes. Input options of the program include mapping of a single peptide or a set of peptides on the antigen structure, and the results of the calculation can be visualized on our interactive web server.Availability: Users can access the EpiSearch from our web

  1. Automated Detection of Objects Based on Sérsic Profiles

    Science.gov (United States)

    Cabrera, Guillermo; Miller, C.; Harrison, C.; Vera, E.; Asahi, T.

    2011-01-01

    We present the results of a new astronomical object detection and deblending algorithm when applied to Sloan Digital Sky Survey data. Our algorithm fits PSF-convolved Sérsic profiles to elliptical isophotes of source candidates. The main advantage of our method is that it minimizes the amount and complexity of real-time user input relative to many commonly used source detection algorithms. Our results are compared with 1D radial profile Sérsic fits. Our long-term goal is to use these techniques in a mixture-model environment to leverage the speed and advantages of machine learning. This approach will have a great impact when re-processing large data-sets and data-streams from next generation telescopes, such as the LSST and the E-ELT.

  2. Automated detection of diabetic retinopathy in retinal images

    Directory of Open Access Journals (Sweden)

    Carmen Valverde

    2016-01-01

    Full Text Available Diabetic retinopathy (DR is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.

  3. Automated muscle wrapping using finite element contact detection.

    Science.gov (United States)

    Favre, Philippe; Gerber, Christian; Snedeker, Jess G

    2010-07-20

    Realistic muscle path representation is essential to musculoskeletal modeling of joint function. Algorithms predicting these muscle paths typically rely on a labor intensive predefinition of via points or underlying geometries to guide wrapping for given joint positions. While muscle wrapping using anatomically precise three-dimensional (3D) finite element (FE) models of bone and muscle has been achieved, computational expense and pre-processing associated with this approach exclude its use in applications such as subject-specific modeling. With the intention of combining advantageous features of both approaches, an intermediate technique relying on contact detection capabilities of commercial FE packages is presented. We applied the approach to the glenohumeral joint, and validated the method by comparison against existing experimental data. Individual muscles were modeled as a straight series of deformable beam elements and bones as anatomically precise 3D rigid bodies. Only the attachment locations and a default orientation of the undeformed muscle segment were pre-defined. The joint was then oriented in a static position of interest. The muscle segment free end was then moved along the shortest Euclidean path to its origin on the scapula, wrapping the muscle along bone surfaces by relying on software contact detection. After wrapping for a given position, the resulting moment arm was computed as the perpendicular distance from the line of action vector to the humeral head center of rotation. This approach reasonably predicted muscle length and moment arm for 27 muscle segments when compared to experimental measurements over a wide range of shoulder motion. Artificial via points or underlying contact geometries were avoided, contact detection and multiobject wrapping on the bone surfaces were automatic, and low computational cost permitted wrapping of individual muscles within seconds on a standard desktop PC. These advantages may be valuable for both general

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

    OpenAIRE

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

    2016-01-01

    Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The ef...

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

    Science.gov (United States)

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

    2001-01-01

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

  6. Automated detection of meteors in observed image sequence

    Science.gov (United States)

    Šimberová, Stanislava; Suk, Tomáš

    2015-12-01

    We propose a new detection technique based on statistical characteristics of images in the video sequence. These characteristics displayed in time enable to catch any bright track during the whole sequence. We applied our method to the image datacubes that are created from camera pictures of the night sky. Meteor flying through the Earth's atmosphere leaves a light trail lasting a few seconds on the sky background. We developed a special technique to recognize this event automatically in the complete observed video sequence. For further analysis leading to the precise recognition of object we suggest to apply Fourier and Hough transformations.

  7. Cooperative Automated Worm Response and Detection Immune Algorithm

    CERN Document Server

    Kim, Jungwon; Aickelin, Uwe; McLeod, Julie

    2010-01-01

    The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

  8. An automated detection of glaucoma using histogram features

    Institute of Scientific and Technical Information of China (English)

    Karthikeyan; Sakthivel; Rengarajan; Narayanan

    2015-01-01

    Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.

  9. A Smartwatch-Based Assistance System for the Elderly Performing Fall Detection, Unusual Inactivity Recognition and Medication Reminding.

    Science.gov (United States)

    Deutsch, Markus; Burgsteiner, Harald

    2016-01-01

    The growing number of elderly people in our society makes it increasingly important to help them live an independent and self-determined life up until a high age. A smartwatch-based assistance system should be implemented that is capable of automatically detecting emergencies and helping elderly people to adhere to their medical therapy. Using the acceleration data of a widely available smartwatch, we implemented fall detection and inactivity recognition based on a smartphone connected via Bluetooth. The resulting system is capable of performing fall detection, inactivity recognition, issuing medication reminders and alerting relatives upon manual activation. Though some challenges, like the dependence on a smartphone remain, the resulting system is a promising approach to help elderly people as well as their relatives to live independently and with a feeling of safety. PMID:27139412

  10. Preprocessing: A Step in Automating Early Detection of Cervical Cancer

    CERN Document Server

    Das, Abhishek; Bhattacharyya, Debasis

    2011-01-01

    Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms.

  11. Preprocessing for Automating Early Detection of Cervical Cancer

    CERN Document Server

    Das, Abhishek; Bhattacharyya, Debasis

    2011-01-01

    Uterine Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms.

  12. Hypothesis Testing for Automated Community Detection in Networks

    CERN Document Server

    Bickel, Peter J

    2013-01-01

    Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web. The problem of finding communities or clusters in a network has received much attention from statistics, physics and computer science. However, most clustering algorithms assume knowledge of the number of clusters k. In this paper we propose to automatically determine k in a graph generated from a Stochastic Blockmodel. Our main contribution is twofold; first, we theoretically establish the limiting distribution of the principal eigenvalue of the suitably centered and scaled adjacency matrix, and use that distribution for our hypothesis test. Secondly, we use this test to design a recursive bipartitioning algorithm. Using quantifiable classification tasks on real world networks with ground truth, we show that our algorithm outperforms existing probabilistic models for learning overlapping clust...

  13. Automated Selection of Uniform Regions for CT Image Quality Detection

    CERN Document Server

    Naeemi, Maitham D; Roychodhury, Sohini

    2016-01-01

    CT images are widely used in pathology detection and follow-up treatment procedures. Accurate identification of pathological features requires diagnostic quality CT images with minimal noise and artifact variation. In this work, a novel Fourier-transform based metric for image quality (IQ) estimation is presented that correlates to additive CT image noise. In the proposed method, two windowed CT image subset regions are analyzed together to identify the extent of variation in the corresponding Fourier-domain spectrum. The two square windows are chosen such that their center pixels coincide and one window is a subset of the other. The Fourier-domain spectral difference between these two sub-sampled windows is then used to isolate spatial regions-of-interest (ROI) with low signal variation (ROI-LV) and high signal variation (ROI-HV), respectively. Finally, the spatial variance ($var$), standard deviation ($std$), coefficient of variance ($cov$) and the fraction of abdominal ROI pixels in ROI-LV ($\

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

    Science.gov (United States)

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

    2012-02-01

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

  15. An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

    Directory of Open Access Journals (Sweden)

    Ashwin Belle

    2012-01-01

    Full Text Available This research proposes to develop a monitoring system which uses Electrocardiograph (ECG as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

  16. An automated approach to detecting signals in electroantennogram data

    Science.gov (United States)

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

    2007-01-01

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

  17. Automated extraction improves multiplex molecular detection of infection in septic patients.

    Directory of Open Access Journals (Sweden)

    Benito J Regueiro

    Full Text Available Sepsis is one of the leading causes of morbidity and mortality in hospitalized patients worldwide. Molecular technologies for rapid detection of microorganisms in patients with sepsis have only recently become available. LightCycler SeptiFast test M(grade (Roche Diagnostics GmbH is a multiplex PCR analysis able to detect DNA of the 25 most frequent pathogens in bloodstream infections. The time and labor saved while avoiding excessive laboratory manipulation is the rationale for selecting the automated MagNA Pure compact nucleic acid isolation kit-I (Roche Applied Science, GmbH as an alternative to conventional SeptiFast extraction. For the purposes of this study, we evaluate extraction in order to demonstrate the feasibility of automation. Finally, a prospective observational study was done using 106 clinical samples obtained from 76 patients in our ICU. Both extraction methods were used in parallel to test the samples. When molecular detection test results using both manual and automated extraction were compared with the data from blood cultures obtained at the same time, the results show that SeptiFast with the alternative MagNA Pure compact extraction not only shortens the complete workflow to 3.57 hrs., but also increases sensitivity of the molecular assay for detecting infection as defined by positive blood culture confirmation.

  18. Automating dicentric chromosome detection from cytogenetic biodosimetry data.

    Science.gov (United States)

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

    2014-06-01

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

  19. Automating dicentric chromosome detection from cytogenetic bio-dosimetry data

    International Nuclear Information System (INIS)

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

  20. Automating dicentric chromosome detection from cytogenetic biodosimetry data.

    Science.gov (United States)

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

    2014-06-01

    We present a prototype software system with sufficient capacity and speed to estimate radiation exposures in a mass casualty event by counting dicentric chromosomes (DCs) in metaphase cells from many individuals. Top-ranked metaphase cell images are segmented by classifying and defining chromosomes with an active contour gradient vector field (GVF) and by determining centromere locations along the centreline. The centreline is extracted by discrete curve evolution (DCE) skeleton branch pruning and curve interpolation. Centromere detection minimises the global width and DAPI-staining intensity profiles along the centreline. A second centromere is identified by reapplying this procedure after masking the first. Dicentrics can be identified from features that capture width and intensity profile characteristics as well as local shape features of the object contour at candidate pixel locations. The correct location of the centromere is also refined in chromosomes with sister chromatid separation. The overall algorithm has both high sensitivity (85 %) and specificity (94 %). Results are independent of the shape and structure of chromosomes in different cells, or the laboratory preparation protocol followed. The prototype software was recoded in C++/OpenCV; image processing was accelerated by data and task parallelisation with Message Passaging Interface and Intel Threading Building Blocks and an asynchronous non-blocking I/O strategy. Relative to a serial process, metaphase ranking, GVF and DCE are, respectively, 100 and 300-fold faster on an 8-core desktop and 64-core cluster computers. The software was then ported to a 1024-core supercomputer, which processed 200 metaphase images each from 1025 specimens in 1.4 h. PMID:24757176

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

    OpenAIRE

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

    2014-01-01

    Background Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. Methods We used a set of complex detection rules to take account of the patient’s clinical and biological context and th...

  2. Development and Evaluation of an Automated, Home-Based, Electronic Questionnaire for Detecting COPD Exacerbations

    Directory of Open Access Journals (Sweden)

    Francisco de B. Velazquez-Peña

    2015-01-01

    Full Text Available Collaboration between patients and their medical and technical experts enabled the development of an automated questionnaire for the early detection of COPD exacerbations (AQCE. The questionnaire consisted of fourteen questions and was implemented on a computer system for use by patients at home in an un-supervised environment. Psychometric evaluation was conducted after a 6-month field trial. Fifty-two patients were involved in the development of the questionnaire. Reproducibility was studied using 19 patients (ICC = 0.94. Sixteen out of the 19 subjects started the 6 month-field trial with the computer application. Cronbach’s alpha of 0.81 was achieved. In the concurrent validity analysis, a correlation of 0.80 (p = 0.002 with the CCQ was reported. The results suggest that AQCE is a valid and reliable questionnaire, showing that an automated home-based electronic questionnaire may enable early detection of exacerbations of COPD.

  3. Effect of Using Automated Auditing Tools on Detecting Compliance Failures in Unmanaged Processes

    Science.gov (United States)

    Doganata, Yurdaer; Curbera, Francisco

    The effect of using automated auditing tools to detect compliance failures in unmanaged business processes is investigated. In the absence of a process execution engine, compliance of an unmanaged business process is tracked by using an auditing tool developed based on business provenance technology or employing auditors. Since budget constraints limit employing auditors to evaluate all process instances, a methodology is devised to use both expert opinion on a limited set of process instances and the results produced by fallible automated audit machines on all process instances. An improvement factor is defined based on the average number of non-compliant process instances detected and it is shown that the improvement depends on the prevalence of non-compliance in the process as well as the sensitivity and the specificity of the audit machine.

  4. Automated Region of Interest Detection of Fluorescent Neurons for Optogenetic Stimulation

    Science.gov (United States)

    Mishler, Jonathan; Plenz, Dietmar

    With the emergence of optogenetics, light has been used to simultaneously stimulate and image neural clusters in vivofor the purpose of understanding neural dynamics. Spatial light modulators (SLMs) have become the choice method for the targeted stimulation of neural clusters, offering unprecedented spatio-temporal resolution. By first imaging, and subsequently selecting the desired neurons for stimulation, SLMs can reliably stimulate those regions of interest (ROIs). However, as the cluster size grows, manually selecting the neurons becomes cumbersome and inefficient. Automated ROI detectors for this purpose have been developed, but rely on neural fluorescent spiking for detection, requiring several thousand imaging frames. To overcome this limitation, we present an automated ROI detection algorithm utilizing neural geometry and stationary information from a few hundred imaging frames that can be adjusted for sensitivity.

  5. A Multi-Wavelength Analysis of Active Regions and Sunspots by Comparison of Automated Detection Algorithms

    CERN Document Server

    Verbeeck, Cis; Colak, Tufan; Watson, Fraser T; Delouille, Veronique; Mampaey, Benjamin; Qahwaji, Rami

    2011-01-01

    Since the Solar Dynamics Observatory (SDO) began recording ~ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH) and quiet Sun (QS). One month of data from the SOHO/MDI and SOHO/EIT instruments during 12 May - 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oc...

  6. Detection of virus-specific intrathecally synthesised immunoglobulin G with a fully automated enzyme immunoassay system

    Directory of Open Access Journals (Sweden)

    Weissbrich Benedikt

    2007-05-01

    Full Text Available Abstract Background The determination of virus-specific immunoglobulin G (IgG antibodies in cerebrospinal fluid (CSF is useful for the diagnosis of virus associated diseases of the central nervous system (CNS and for the detection of a polyspecific intrathecal immune response in patients with multiple sclerosis. Quantification of virus-specific IgG in the CSF is frequently performed by calculation of a virus-specific antibody index (AI. Determination of the AI is a demanding and labour-intensive technique and therefore automation is desirable. We evaluated the precision and the diagnostic value of a fully automated enzyme immunoassay for the detection of virus-specific IgG in serum and CSF using the analyser BEP2000 (Dade Behring. Methods The AI for measles, rubella, varicella-zoster, and herpes simplex virus IgG was determined from pairs of serum and CSF samples of patients with viral CNS infections, multiple sclerosis and of control patients. CSF and serum samples were tested simultaneously with reference to a standard curve. Starting dilutions were 1:6 and 1:36 for CSF and 1:1386 and 1:8316 for serum samples. Results The interassay coefficient of variation was below 10% for all parameters tested. There was good agreement between AIs obtained with the BEP2000 and AIs derived from the semi-automated reference method. Conclusion Determination of virus-specific IgG in serum-CSF-pairs for calculation of AI has been successfully automated on the BEP2000. Current limitations of the assay layout imposed by the analyser software should be solved in future versions to offer more convenience in comparison to manual or semi-automated methods.

  7. A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

    Science.gov (United States)

    Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

    2014-03-01

    As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

  8. Automated type specific ELISA probe detection of amplified NS3 gene products of dengue viruses.

    OpenAIRE

    Chow, V T; Yong, R Y; Ngoh, B L; Chan, Y. C.

    1997-01-01

    AIM: To apply an automated system of nucleic acid hybridisation coupled with the enzyme linked immunosorbent assay (ELISA) for the type specific detection of amplification products of dengue viruses. METHODS: Non-structural 3 (NS3) gene targets of reference strains of all four dengue and other flaviviruses, as well as dengue patient viraemic sera, were subjected to reverse transcription and polymerase chain reaction using consensus and dengue type specific primers and digoxigenin-11-dUTP labe...

  9. Power Utility Automation Cybersecurity: IEC 61850 Specification of an Intrusion Detection Function

    OpenAIRE

    Kabir-Querrec, Maëlle; Mocanu, Stéphane; Thiriet, Jean-Marc; Savary, Eric

    2015-01-01

    International audience The IEC 61850 standard defines a global framework for designing power utility automation systems. The main goal of IEC 61850 being interoperability, it brings information and tools for both system modelling and communication architecture. But cybersecurity measures and propositions are scarce. They should be a priority. To help fill this lack of cybersecurity, we specify a fully IEC 61850-compatible intrusion detection function. This paper explains the procedure of d...

  10. Automated detection and analysis of volcanic thermal anomalies through the combined use of SEVIRI and MODIS

    OpenAIRE

    Ganci, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Del Negro, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Vicari, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Fortuna, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia

    2010-01-01

    Multispectral infrared observations carried out by the spacecrafts have shown that spaceborne remote sensing of high-temperature volcanic features is feasible and robust enough to turn into volcano monitoring. Especially meteorological satellites have proven a powerful instrument to detect and monitor dynamic phenomena, such as volcanic processes, allowing very high temporal resolution despite of their low spatial resolution. An automated system that uses both EOS-MODIS and ...

  11. Rapid detection of significant bacteriuria by use of an automated Limulus amoebocyte lysate assay.

    OpenAIRE

    Jorgensen, J H; Alexander, G A

    1982-01-01

    Previous studies have demonstrated that significant gram-negative bacteriuria can be detected by using the Limulus amoebocyte lysate test. A series of 580 urine specimens were tested in parallel with the automated MS-2 (Abbott Laboratories) assay and with quantitative urine bacterial cultures. The overall ability of the MS-2 Limulus amoebocyte lysate test to correctly classify urine specimens as containing either greater than or equal to 10(5) organisms or less than 10(5) organisms per ml dur...

  12. An automated algorithm for online detection of fragmented QRS and identification of its various morphologies

    OpenAIRE

    Maheshwari, Sidharth; Acharyya, Amit; Puddu, Paolo Emilio; Mazomenos, Evangelos B.; Leekha, Gourav; Maharatna, Koushik; Schiariti, Michele

    2013-01-01

    Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detect...

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

    Science.gov (United States)

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

    2014-03-01

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

  14. Automated detection and classification of cryptographic algorithms in binary programs through machine learning

    OpenAIRE

    Hosfelt, Diane Duros

    2015-01-01

    Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats proliferate too quickly for the time-consuming traditional methods of binary analysis to be effective. By automating detection and classification of cryptographic algorithms, we can speed program analysis and more efficiently combat malware. This thesis wil...

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

    Directory of Open Access Journals (Sweden)

    Shaun S Lodder

    Full Text Available 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 1 took 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.

  16. Molecular Detection of Bladder Cancer by Fluorescence Microsatellite Analysis and an Automated Genetic Analyzing System

    Directory of Open Access Journals (Sweden)

    Sarel Halachmi

    2007-01-01

    Full Text Available To investigate the ability of an automated fluorescent analyzing system to detect microsatellite alterations, in patients with bladder cancer. We investigated 11 with pathology proven bladder Transitional Cell Carcinoma (TCC for microsatellite alterations in blood, urine, and tumor biopsies. DNA was prepared by standard methods from blood, urine and resected tumor specimens, and was used for microsatellite analysis. After the primers were fluorescent labeled, amplification of the DNA was performed with PCR. The PCR products were placed into the automated genetic analyser (ABI Prism 310, Perkin Elmer, USA and were subjected to fluorescent scanning with argon ion laser beams. The fluorescent signal intensity measured by the genetic analyzer measured the product size in terms of base pairs. We found loss of heterozygocity (LOH or microsatellite alterations (a loss or gain of nucleotides, which alter the original normal locus size in all the patients by using fluorescent microsatellite analysis and an automated analyzing system. In each case the genetic changes found in urine samples were identical to those found in the resected tumor sample. The studies demonstrated the ability to detect bladder tumor non-invasively by fluorescent microsatellite analysis of urine samples. Our study supports the worldwide trend for the search of non-invasive methods to detect bladder cancer. We have overcome major obstacles that prevented the clinical use of an experimental system. With our new tested system microsatellite analysis can be done cheaper, faster, easier and with higher scientific accuracy.

  17. A nationwide web-based automated system for early outbreak detection and rapid response in China

    Directory of Open Access Journals (Sweden)

    Yilan Liao

    2011-03-01

    Full Text Available Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

  18. Home Automation

    OpenAIRE

    Ahmed, Zeeshan

    2010-01-01

    In this paper I briefly discuss the importance of home automation system. Going in to the details I briefly present a real time designed and implemented software and hardware oriented house automation research project, capable of automating house's electricity and providing a security system to detect the presence of unexpected behavior.

  19. Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

    Science.gov (United States)

    Bogaarts, J G; Gommer, E D; Hilkman, D M W; van Kranen-Mastenbroek, V H J M; Reulen, J P H

    2016-08-01

    Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age

  20. Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods

    DEFF Research Database (Denmark)

    Warby, Simon C.; Wendt, Sabrina Lyngbye; Welinder, Peter;

    2014-01-01

    to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance...... that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects....

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

    Science.gov (United States)

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

    2012-06-01

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

  2. An Automated Framework with Application to Study Url Based Online Advertisements Detection

    Directory of Open Access Journals (Sweden)

    Szczepański Piotr L.

    2013-05-01

    Full Text Available A rapid growth of online advertisements results in unsolicited bulk of data being downloaded during web surfing. To tackle this problem a fast mechanism detecting adverts is required. In this paper we present the usefulness of URL based web-pages classification in the process of online advertisements detection. Our experiments are performed on seven popular classifiers using the real-life dataset obtained by human agents browsing the internet. We introduce a general and fully automated framework that allows us to do a comprehensive analysis by performing simultaneously hundreds of experiments. This study results in solution with 0.987 accuracy and 0.822 F-measure.

  3. Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database

    Energy Technology Data Exchange (ETDEWEB)

    Ibrahim, Norhayati; Fujita, Hiroshi; Hara, Takeshi [Department of Information Science, Faculty of Engineering, Gifu University, Yanagido, Gifu 501-11 (Japan); Endo, Tokiko [Department of Radiology, Nagoya National Hospital, Naka-ku, Nagoya 460 (Japan)

    1997-12-01

    To investigate the detection performance of our automated detection scheme for clustered microcalcifications on mammograms, we applied our computer-aided diagnosis (CAD) system to the database of the Mammographic Image Analysis Society (MIAS) in the UK. Forty-three mammograms from this database were used in this study. In our scheme, the breast regions were firstly extracted by determining the skinline. Histograms of the original images were used to extract the high-density area within the breast region as the segmentation from the fatty area around the skinline. Then the contrast correction technique was employed. Gradient vectors of the image density were calculated on the contrast corrected images. To extract the specific features of the pattern of the microcalcifications, triple-ring filter analysis was employed. A variable-ring filter was used for more accurate detection after the triple-ring filter. The features of the detected candidate areas were then characterized by feature analysis. The areas which satisfied the characteristics and specific terms were classified and displayed as clusters. As a result, the sensitivity was 95.8% with the false-positive rate at 1.8 clusters per image. This demonstrates that the automated detection of clustered microcalcifications in our CAD system is reliable as an aid to radiologists. (author)

  4. Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease

    Science.gov (United States)

    Wu, Jing; Montuoro, Alessio; Gerendas, Bianca S.; Langs, Georg

    2016-01-01

    In macular spectral domain optical coherence tomography (SD-OCT) volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3 µm in cystoid macular edema and 262 µm in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in “big data.” Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.

  5. Applications of Wireless Sensor Networks in Fall Detection for Senior People

    OpenAIRE

    Wernhuar Tarng; Chia-Hwei Lin; Hsin-Hun Liou

    2012-01-01

    Due to the advance of modern medical technology, the average age of global population increasescontinuously in recent years. In 1993, the society in Taiwan had already reached the criterion of agingpopulation, bringing the country an important issue of looking after old people which could not be ignoredby its society. In this study, a wireless sensor device is developed to monitor the activities of old people athome and report fall events in real time. It can decrease the mental and physical ...

  6. Automated detection of extended sources in radio maps: progress from the SCORPIO survey

    CERN Document Server

    Riggi, S; Leto, P; Cavallaro, F; Bufano, F; Schillirò, F; Trigilio, C; Umana, G; Buemi, C S; Norris, R P

    2016-01-01

    Automated source extraction and parameterization 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, dubbed 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 parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also 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 ...

  7. Automated Anomaly Detection in Distribution Grids Using $\\mu$PMU Measurements

    CERN Document Server

    Jamei, Mahdi; Roberts, Ciaran; Stewart, Emma; Peisert, Sean; McParland, Chuck; McEachern, Alex

    2016-01-01

    The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators has been widely documented. Micro-PMUs ($\\mu$PMUs) are an emerging sensing technology that can provide similar benefits to Distribution System Operators (DSOs), enabling a level of visibility into the distribution grid that was previously unattainable. In order to support the deployment of these high resolution sensors, the automation of data analysis and prioritizing communication to the DSO becomes crucial. In this paper, we explore the use of $\\mu$PMUs to detect anomalies on the distribution grid. Our methodology is motivated by growing concern about failures and attacks to distribution automation equipment. The effectiveness of our approach is demonstrated through both real and simulated data.

  8. AUTOMATED POLICY COMPLIANCE AND CHANGE DETECTION MANAGED SERVICE IN DATA NETWORKS

    Directory of Open Access Journals (Sweden)

    Saeed M. Agbariah

    2013-11-01

    Full Text Available As networks continue to grow in size, speed and complexity, as well as in the diversification of their services, they require many ad-hoc configuration changes. Such changes may lead to potential configuration errors, policy violations, inefficiencies, and vulnerable states. The current Network Management landscape is in a dire need for an automated process to prioritize and manage risk, audit configurations against internal policies or external best practices, and provide centralize reporting for monitoring and regulatory purposes in real time. This paper defines a framework for automated configuration process with a policy compliance and change detection system, which performs automatic and intelligent network configuration audits by using pre-defined configuration templates and library of rules that encompass industry standards for various routing and security related guidelines.System administrators and change initiators will have a real time feedback if any of their configuration changes violate any of the policies set for any given device.

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

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

  11. Automated Detection of Coronal Mass Ejections in STEREO Heliospheric Imager data

    CERN Document Server

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2014-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Drukker, Karen, E-mail: kdrukker@uchicago.edu; Sennett, Charlene A.; Giger, Maryellen L. [Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

    2014-01-15

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

  14. Automated Detection of coronal mass ejections in three-dimensions using multi-viewpoint observations

    Science.gov (United States)

    Hutton, Joseph; Morgan, Huw

    2016-10-01

    A new, automated method of detecting Solar Wind transients such as Coronal Mass Ejections (CMEs) in three dimensions for the LASCO C2 and STEREO COR2 coronagraphs is presented. By triangulating isolated CME signal from the three coronagraphs over a sliding window of five hours, the most likely region through which CMEs pass at 5 solar radii is identified. The centre and size of the region gives the most likely direction of propagation and angular extent. The Automated CME Triangulation (ACT) method is tested extensively using a series of synthetic CME images created using a flux rope density model, and on a sample of real coronagraph data; including Halo CMEs. The accuracy of the detection remains acceptable regardless of CME position relative to the observer, the relative separation of the three observers, and even through the loss of one coronagraph. By comparing the detection results with the input parameters of the synthetic CMEs, and the low coronal sources of the real CMEs, it is found that the detection is on average accurate to within 7.14 degrees. All current CME catalogues (CDAW, CACTus, SEEDS, ARTEMIS and CORIMP) rely on plane-of-sky measurements for key parameters such as height and velocity. Estimating the true geometry using the new method gains considerable accuracy for kinematics and mass/density. The results of the new method will be incorporated into the CORIMP database in the near future, enabling improved space weather diagnostics and forecasting.

  15. Development of automated high throughput single molecular microfluidic detection platform for signal transduction analysis

    Science.gov (United States)

    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.

  16. Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Mohamed L Seghier

    Full Text Available 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.

  18. How can one detect the rotation of the Earth "around the Moon"? Part 2: Ultra-slow fall

    CERN Document Server

    Roehner, Bertrand M

    2011-01-01

    The paper proposes an alternative to the Foucault pendulum for detecting various movements of rotation of the Earth. Calculations suggest that if the duration of a "free" fall becomes longer the eastward deflection will be amplified in proportion with the increased duration. Instead of 20 micrometers for a one-meter fall, one can expect deflections more than 1,000 times larger when the fall lasts a few minutes. The method proposed in this paper consists in using the buoyancy of a (non viscous) liquid in order to work in reduced gravity. Not surprisingly, as in many astronomical observations, the main challenge is to minimize the level of "noise". Possible sources of noise are discussed and remedies are proposed. In principle, the experiment should be done in superfluid helium. However, a preliminary experiment done in water gave encouraging results in spite of a fairly high level of noise. In forthcoming experiments the main objective will be to identify and eliminate the main sources of noise. This experimen...

  19. Reconstruction of the Magnetkoepfl rockfall event - Detecting rock fall release zones using terrestrial laser scanning, Hohe Tauern, Austria

    Science.gov (United States)

    Hartmeyer, I.; Keuschnig, M.; Delleske, R.; Schrott, L.

    2012-04-01

    Instability of rock faces in high mountain areas is an important risk factor for man and infrastructure, particularly within the context of climate change. Numerous rock fall events in the European Alps suggest an increasing occurrence of mass movements due to rising temperatures in recent years. Within the MOREXPERT project ('Monitoring Expert System for Hazardous Rock Walls') a new long-term monitoring site for mass movement and permafrost interaction has been initiated in the Austrian Alps. The study area is located at the Kitzsteinhorn (Hohe Tauern), a particularly interesting site for the investigation of glacier retreat and potential permafrost degradation and their respective consequences for the stability of alpine rock faces. To detect and quantify changes occurring at the terrain surface an extensive terrestrial laser scanning (TLS) monitoring campaign was started in 2011. TLS creates three-dimensional high-resolution images of the scanned area allowing precise quantification of changes in geometry and volume in steep rock faces over distances of up to several hundreds of meters. Within the TLS monitoring campaign at the Kitzsteinhorn a large number of differently dimensioned rock faces is examined (varying size, slope inclination etc.). Scanned areas include the Kitzsteinhorn northwest and south face, the Magnetkoepfl east face as well as a couple of small rock faces in the vicinity of the summit station. During the night from August 27th to August 28th 2011 a rock fall event was documented by employees of the cable car company. The release zone could not immediately be detected. The east face of the Magnetkoepfl covers approximately 70 meters in height and about 200 meters in width. It is made up of calcareous mica-schist and displays an abundance of well-developed joint sets with large joint apertures. Meteorological data from a weather station located at the same altitude (2.950m) and just 500m away from the release zone show that the rock fall event

  20. Automation of Optimized Gabor Filter Parameter Selection for Road Cracks Detection

    Directory of Open Access Journals (Sweden)

    Haris Ahmad Khan

    2016-03-01

    Full Text Available Automated systems for road crack detection are extremely important in road maintenance for vehicle safety and traveler’s comfort. Emerging cracks in roads need to be detected and accordingly repaired as early as possible to avoid further damage thus reducing rehabilitation cost. In this paper, a robust method for Gabor filter parameters optimization for automatic road crack detection is discussed. Gabor filter has been used in previous literature for similar applications. However, there is a need for automatic selection of optimized Gabor filter parameters due to variation in texture of roads and cracks. The problem of change of background, which in fact is road texture, is addressed through a learning process by using synthetic road crack generation for Gabor filter parameter tuning. Tuned parameters are then tested on real cracks and a thorough quantitative analysis is performed for performance evaluation.

  1. Primer effect in the detection of mitochondrial DNA point heteroplasmy by automated sequencing.

    Science.gov (United States)

    Calatayud, Marta; Ramos, Amanda; Santos, Cristina; Aluja, Maria Pilar

    2013-06-01

    The correct detection of mitochondrial DNA (mtDNA) heteroplasmy by automated sequencing presents methodological constraints. The main goals of this study are to investigate the effect of sense and distance of primers in heteroplasmy detection and to test if there are differences in the accurate determination of heteroplasmy involving transitions or transversions. A gradient of the heteroplasmy levels was generated for mtDNA positions 9477 (transition G/A) and 15,452 (transversion C/A). Amplification and subsequent sequencing with forward and reverse primers, situated at 550 and 150 bp from the heteroplasmic positions, were performed. Our data provide evidence that there is a significant difference between the use of forward and reverse primers. The forward primer is the primer that seems to give a better approximation to the real proportion of the variants. No significant differences were found concerning the distance at which the sequencing primers were placed neither between the analysis of transitions and transversions. The data collected in this study are a starting point that allows to glimpse the importance of the sequencing primers in the accurate detection of point heteroplasmy, providing additional insight into the overall automated sequencing strategy.

  2. An automated system for lung nodule detection in low-dose computed tomography

    OpenAIRE

    Gori, I.; Fantacci, M. E.; Martinez, A. Preite; Retico, A.

    2007-01-01

    A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a n...

  3. Procedure for Automated Eddy Current Crack Detection in Thin Titanium Plates

    Science.gov (United States)

    Wincheski, Russell A.

    2012-01-01

    This procedure provides the detailed instructions for conducting Eddy Current (EC) inspections of thin (5-30 mils) titanium membranes with thickness and material properties typical of the development of Ultra-Lightweight diaphragm Tanks Technology (ULTT). The inspection focuses on the detection of part-through, surface breaking fatigue cracks with depths between approximately 0.002" and 0.007" and aspect ratios (a/c) of 0.2-1.0 using an automated eddy current scanning and image processing technique.

  4. Fast detection of Noroviruses using a real-time PCR assay and automated sample preparation

    Directory of Open Access Journals (Sweden)

    Schmid Michael

    2004-06-01

    Full Text Available Abstract Background Noroviruses (NoV have become one of the most commonly reported causative agents of large outbreaks of non-bacterial acute gastroenteritis worldwide as well as sporadic gastroenteritis in the community. Currently, reverse transcriptase polymerase chain reaction (RT-PCR assays have been implemented in NoV diagnosis, but improvements that simplify and standardize sample preparation, amplification, and detection will be further needed. The combination of automated sample preparation and real-time PCR offers such refinements. Methods We have designed a new real-time RT-PCR assay on the LightCycler (LC with SYBR Green detection and melting curve analysis (Tm to detect NoV RNA in patient stool samples. The performance of the real-time PCR assay was compared with that obtained in parallel with a commercially available enzyme immunoassay (ELISA for antigen detection by testing a panel of 52 stool samples. Additionally, in a collaborative study with the Baden-Wuerttemberg State Health office, Stuttgart (Germany the real-time PCR results were blindly assessed using a previously well-established nested PCR (nPCR as the reference method, since PCR-based techniques are now considered as the "gold standard" for NoV detection in stool specimens. Results Analysis of 52 clinical stool samples by real-time PCR yielded results that were consistent with reference nPCR results, while marked differences between the two PCR-based methods and antigen ELISA were observed. Our results indicate that PCR-based procedures are more sensitive and specific than antigen ELISA for detecting NoV in stool specimens. Conclusions The combination of automated sample preparation and real-time PCR provided reliable diagnostic results in less time than conventional RT-PCR assays. These benefits make it a valuable tool for routine laboratory practice especially in terms of rapid and appropriate outbreak-control measures in health-care facilities and other settings.

  5. Development of an Automated Microfluidic System for DNA Collection, Amplification, and Detection of Pathogens

    Energy Technology Data Exchange (ETDEWEB)

    Hagan, Bethany S.; Bruckner-Lea, Cynthia J.

    2002-12-01

    This project was focused on developing and testing automated routines for a microfluidic Pathogen Detection System. The basic pathogen detection routine has three primary components; cell concentration, DNA amplification, and detection. In cell concentration, magnetic beads are held in a flow cell by an electromagnet. Sample liquid is passed through the flow cell and bacterial cells attach to the beads. These beads are then released into a small volume of fluid and delivered to the peltier device for cell lysis and DNA amplification. The cells are lysed during initial heating in the peltier device, and the released DNA is amplified using polymerase chain reaction (PCR) or strand displacement amplification (SDA). Once amplified, the DNA is then delivered to a laser induced fluorescence detection unit in which the sample is detected. These three components create a flexible platform that can be used for pathogen detection in liquid and sediment samples. Future developments of the system will include on-line DNA detection during DNA amplification and improved capture and release methods for the magnetic beads during cell concentration.

  6. Early detection of radioactive fall-out by gamma-spectrometry

    DEFF Research Database (Denmark)

    Aage, Helle Karina; Korsbech, Uffe C C; Bargholz, K.

    2003-01-01

    Radioactive fallout should be detected as early as possible. A new and efficient method for detection of low-level irradiation from manmade radioactivity is developed. Radiation abnormalities are detectable down to air kerma rate, of 0.5 to 1.0 nGy h(-1) for Cs-137 and even lower for I-131...... For multi-gamma energy radioactivity the detection level is 2.6-3.5 nGy h(-1). A standard NaI detector and a 512-channel analyser are used together with noise adjusted singular value decomposition (NASVD). Statistical noise is removed and the measured spectra are reproduced using spectral components...

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

    Science.gov (United States)

    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.

  8. Automated thermochemolysis reactor for detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Li, Dan [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States); Rands, Anthony D.; Losee, Scott C. [Torion Technologies, American Fork, UT 84003 (United States); Holt, Brian C. [Department of Statistics, Brigham Young University, Provo, UT 84602 (United States); Williams, John R. [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States); Lammert, Stephen A. [Torion Technologies, American Fork, UT 84003 (United States); Robison, Richard A. [Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602 (United States); Tolley, H. Dennis [Department of Statistics, Brigham Young University, Provo, UT 84602 (United States); Lee, Milton L., E-mail: milton_lee@byu.edu [Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602 (United States)

    2013-05-02

    Graphical abstract: -- Highlights: •An automated sample preparation system for Bacillus anthracis endospores was developed. •A thermochemolysis method was applied to produce and derivatize biomarkers for Bacillus anthracis detection. •The autoreactor controlled the precise delivery of reagents, and TCM reaction times and temperatures. •Solid phase microextraction was used to extract biomarkers, and GC–MS was used for final identification. •This autoreactor was successfully applied to the identification of Bacillus anthracis endospores. -- Abstract: An automated sample preparation system was developed and tested for the rapid detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry (GC–MS) for eventual use in the field. This reactor is capable of automatically processing suspected bio-threat agents to release and derivatize unique chemical biomarkers by thermochemolysis (TCM). The system automatically controls the movement of sample vials from one position to another, crimping of septum caps onto the vials, precise delivery of reagents, and TCM reaction times and temperatures. The specific operations of introduction of sample vials, solid phase microextraction (SPME) sampling, injection into the GC–MS system, and ejection of used vials from the system were performed manually in this study, although they can be integrated into the automated system. Manual SPME sampling is performed by following visual and audible signal prompts for inserting the fiber into and retracting it from the sampling port. A rotating carousel design allows for simultaneous sample collection, reaction, biomarker extraction and analysis of sequential samples. Dipicolinic acid methyl ester (DPAME), 3-methyl-2-butenoic acid methyl ester (a fragment of anthrose) and two methylated sugars were used to compare the performance of the autoreactor with manual TCM. Statistical algorithms were used to construct reliable bacterial endospore signatures, and 24

  9. Automated thermochemolysis reactor for detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry

    International Nuclear Information System (INIS)

    Graphical abstract: -- Highlights: •An automated sample preparation system for Bacillus anthracis endospores was developed. •A thermochemolysis method was applied to produce and derivatize biomarkers for Bacillus anthracis detection. •The autoreactor controlled the precise delivery of reagents, and TCM reaction times and temperatures. •Solid phase microextraction was used to extract biomarkers, and GC–MS was used for final identification. •This autoreactor was successfully applied to the identification of Bacillus anthracis endospores. -- Abstract: An automated sample preparation system was developed and tested for the rapid detection of Bacillus anthracis endospores by gas chromatography–mass spectrometry (GC–MS) for eventual use in the field. This reactor is capable of automatically processing suspected bio-threat agents to release and derivatize unique chemical biomarkers by thermochemolysis (TCM). The system automatically controls the movement of sample vials from one position to another, crimping of septum caps onto the vials, precise delivery of reagents, and TCM reaction times and temperatures. The specific operations of introduction of sample vials, solid phase microextraction (SPME) sampling, injection into the GC–MS system, and ejection of used vials from the system were performed manually in this study, although they can be integrated into the automated system. Manual SPME sampling is performed by following visual and audible signal prompts for inserting the fiber into and retracting it from the sampling port. A rotating carousel design allows for simultaneous sample collection, reaction, biomarker extraction and analysis of sequential samples. Dipicolinic acid methyl ester (DPAME), 3-methyl-2-butenoic acid methyl ester (a fragment of anthrose) and two methylated sugars were used to compare the performance of the autoreactor with manual TCM. Statistical algorithms were used to construct reliable bacterial endospore signatures, and 24

  10. An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-07-25

    Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications.

  11. An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-01-01

    Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications. PMID:27463719

  12. An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-01-01

    Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications. PMID:27463719

  13. Macrothrombocytopenia in North India: Role of Automated Platelet Data in the Detection of an Under Diagnosed Entity

    OpenAIRE

    Kakkar, Naveen; John, M. Joseph; Mathew, Amrith

    2014-01-01

    Congenital macrothrombocytopenia is being increasingly recognised because of the increasing availability of automated platelet counts during routine complete blood count. If not recognised, these patients may be unnecessarily investigated or treated. The study was done to assess the occurrence of macrothrombocytopenia in the North Indian population and the role of automated platelet parameters in its detection. This prospective study was done on patients whose blood samples were sent for CBC ...

  14. Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

    OpenAIRE

    Stefan Wiehle; Susanne Lehner

    2015-01-01

    We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Du...

  15. Automated Aflatoxin Analysis Using Inline Reusable Immunoaffinity Column Cleanup and LC-Fluorescence Detection.

    Science.gov (United States)

    Rhemrev, Ria; Pazdanska, Monika; Marley, Elaine; Biselli, Scarlett; Staiger, Simone

    2015-01-01

    A novel reusable immunoaffinity cartridge containing monoclonal antibodies to aflatoxins coupled to a pressure resistant polymer has been developed. The cartridge is used in conjunction with a handling system inline to LC with fluorescence detection to provide fully automated aflatoxin analysis for routine monitoring of a variety of food matrixes. The handling system selects an immunoaffinity cartridge from a tray and automatically applies the sample extract. The cartridge is washed, then aflatoxins B1, B2, G1, and G2 are eluted and transferred inline to the LC system for quantitative analysis using fluorescence detection with postcolumn derivatization using a KOBRA® cell. Each immunoaffinity cartridge can be used up to 15 times without loss in performance, offering increased sample throughput and reduced costs compared to conventional manual sample preparation and cleanup. The system was validated in two independent laboratories using samples of peanuts and maize spiked at 2, 8, and 40 μg/kg total aflatoxins, and paprika, nutmeg, and dried figs spiked at 5, 20, and 100 μg/kg total aflatoxins. Recoveries exceeded 80% for both aflatoxin B1 and total aflatoxins. The between-day repeatability ranged from 2.1 to 9.6% for aflatoxin B1 for the six levels and five matrixes. Satisfactory Z-scores were obtained with this automated system when used for participation in proficiency testing (FAPAS®) for samples of chilli powder and hazelnut paste containing aflatoxins. PMID:26651571

  16. Automated detection of extended sources in radio maps: progress from the SCORPIO survey

    Science.gov (United States)

    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.

  17. An automated and integrated framework for dust storm detection based on ogc web processing services

    Science.gov (United States)

    Xiao, F.; Shea, G. Y. K.; Wong, M. S.; Campbell, J.

    2014-11-01

    Dust storms are known to have adverse effects on public health. Atmospheric dust loading is also one of the major uncertainties in global climatic modelling as it is known to have a significant impact on the radiation budget and atmospheric stability. The complexity of building scientific dust storm models is coupled with the scientific computation advancement, ongoing computing platform development, and the development of heterogeneous Earth Observation (EO) networks. It is a challenging task to develop an integrated and automated scheme for dust storm detection that combines Geo-Processing frameworks, scientific models and EO data together to enable the dust storm detection and tracking processes in a dynamic and timely manner. This study develops an automated and integrated framework for dust storm detection and tracking based on the Web Processing Services (WPS) initiated by Open Geospatial Consortium (OGC). The presented WPS framework consists of EO data retrieval components, dust storm detecting and tracking component, and service chain orchestration engine. The EO data processing component is implemented based on OPeNDAP standard. The dust storm detecting and tracking component combines three earth scientific models, which are SBDART model (for computing aerosol optical depth (AOT) of dust particles), WRF model (for simulating meteorological parameters) and HYSPLIT model (for simulating the dust storm transport processes). The service chain orchestration engine is implemented based on Business Process Execution Language for Web Service (BPEL4WS) using open-source software. The output results, including horizontal and vertical AOT distribution of dust particles as well as their transport paths, were represented using KML/XML and displayed in Google Earth. A serious dust storm, which occurred over East Asia from 26 to 28 Apr 2012, is used to test the applicability of the proposed WPS framework. Our aim here is to solve a specific instance of a complex EO data

  18. An Automated System for the Detection of Stratified Squamous Epithelial Cancer Cell Using Image Processing Techniques

    Directory of Open Access Journals (Sweden)

    Ram Krishna Kumar

    2013-06-01

    Full Text Available Early detection of cancer disease is a difficult problem and if it is not detected in starting phase the cancer can be fatal. Current medical procedures which are used to diagnose the cancer in body partsare time taking and more laboratory work is required for them. This work is an endeavor to possible recognition of cancer cells in the body part. The process consists of image taken of the affected area and digital image processing of the images to get a morphological pattern which differentiate normal cell to cancer cell. The technique is different than visual inspection and biopsy process. Image processing enables the visualization of cellular structure with substantial resolution. The aim of the work is to exploit differences in cellular organization between cancerous and normal tissue using image processing technique, thus allowing for automated, fast and accurate diagnosis.

  19. Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya

    DEFF Research Database (Denmark)

    Juul Bøgelund Hansen, Morten; Abramoff, M. D.; Folk, J. C.;

    2015-01-01

    Objective Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased...... workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields...... gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. Conclusions In this epidemiological sample, the IDP's grading was comparable...

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

    Science.gov (United States)

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

    2013-05-01

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

  1. Radiologists' Performance for Detecting Lesions and the Interobserver Variability of Automated Whole Breast Ultrasound

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Hun; Kang, Bong Joo; Choi, Byung Gil; Choi, Jae Jung; Lee, Ji Hye [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Song, Byung Joo; Choe, Byung Joo [Department of General Surgery, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Park, Sarah [Department of Internal Medicine, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of); Kim, Hyunbin [CMC Clinical Research Coordinating Center, College of Medicine, The Catholic University of Korea, Seoul 137-701 (Korea, Republic of)

    2013-07-01

    To compare the detection performance of the automated whole breast ultrasound (AWUS) with that of the hand-held breast ultrasound (HHUS) and to evaluate the interobserver variability in the interpretation of the AWUS. AWUS was performed in 38 breast cancer patients. A total of 66 lesions were included: 38 breast cancers, 12 additional malignancies and 16 benign lesions. Three breast radiologists independently reviewed the AWUS data and analyzed the breast lesions according to the BI-RADS classification. The detection rate of malignancies was 98.0% for HHUS and 90.0%, 88.0% and 96.0% for the three readers of the AWUS. The sensitivity and the specificity were 98.0% and 62.5% in HHUS, 90.0% and 87.5% for reader 1, 88.0% and 81.3% for reader 2, and 96.0% and 93.8% for reader 3, in AWUS. There was no significant difference in the radiologists' detection performance, sensitivity and specificity (p > 0.05) between the two modalities. The interobserver agreement was fair to good for the ultrasonographic features, categorization, size, and the location of breast masses. AWUS is thought to be useful for detecting breast lesions. In comparison with HHUS, AWUS shows no significant difference in the detection rate, sensitivity and the specificity, with high degrees of interobserver agreement.

  2. Automated detection of submerged navigational obstructions in freshwater impoundments with hull mounted sidescan sonar

    Science.gov (United States)

    Morris, Phillip A.

    The prevalence of low-cost side scanning sonar systems mounted on small recreational vessels has created improved opportunities to identify and map submerged navigational hazards in freshwater impoundments. However, these economical sensors also present unique challenges for automated techniques. This research explores related literature in automated sonar imagery processing and mapping technology, proposes and implements a framework derived from these sources, and evaluates the approach with video collected from a recreational grade sonar system. Image analysis techniques including optical character recognition and an unsupervised computer automated detection (CAD) algorithm are employed to extract the transducer GPS coordinates and slant range distance of objects protruding from the lake bottom. The retrieved information is formatted for inclusion into a spatial mapping model. Specific attributes of the sonar sensors are modeled such that probability profiles may be projected onto a three dimensional gridded map. These profiles are computed from multiple points of view as sonar traces crisscross or come near each other. As lake levels fluctuate over time so do the elevation points of view. With each sonar record, the probability of a hazard existing at certain elevations at the respective grid points is updated with Bayesian mechanics. As reinforcing data is collected, the confidence of the map improves. Given a lake's current elevation and a vessel draft, a final generated map can identify areas of the lake that have a high probability of containing hazards that threaten navigation. The approach is implemented in C/C++ utilizing OpenCV, Tesseract OCR, and QGIS open source software and evaluated in a designated test area at Lake Lavon, Collin County, Texas.

  3. Observing social signals in scaffolding interactions: how to detect when a helping intention risks falling short.

    Science.gov (United States)

    Leone, Giovanna

    2012-10-01

    In face-to-face interactions, some social signals are aimed at regulating scaffolding processes, by which more knowledgeable people try to help less knowledgeable ones, to enable them to learn new concepts or skills (Vygotsky 1978). Observing face-to-face scaffolding interactions might not only allow us to grasp a large variety of these highly interesting social signals but may also be useful for the sake of scaffolding processes themselves. It often happens, in fact, that the empowering intentions implicit in these processes end up falling short, if the social signals regulating this specific kind of face-to-face interaction are misunderstood. Interestingly, many of these misunderstood aspects are related to the recipient's role. Indeed, attention is usually focused on the behavior of those imparting the knowledge, while skills already mastered by the learners, as well as their feedback, tend not to be taken as much into account. For the purpose of exploring the often very subtly nuanced social signals regulating on-going scaffolding processes in real-life interactions, an example of a methodological tool is presented: one already used to observe the interactions of dyads of Italian primary school teachers and their pupils, and mothers and their children. The article leads to two main conclusions: that the results of instances of scaffolding may be predicted as to their success or otherwise simply by telescoping crucial social signals during the scaffolding's initial phases, and that when helpers disregard these signals the effects of their actions may be detrimental or even humiliating for the receivers, notwithstanding the helper's intentions. PMID:22009169

  4. Validation of a simple automated movement detection system for formalin test in rats

    Institute of Scientific and Technical Information of China (English)

    Yu-feng XIE; Jing WANG; Fu-quan HUO; Hong JIA; Jing-shi TANG

    2005-01-01

    Aim: To investigate the validity and sensitivity of an automatic movement detection system developed by our laboratory for the formalin test in rats. Methods:The effects of systemic morphine and local anesthetic lidocaine on the nociceptive behaviors induced by formalin subcutaneously injected into the hindpaw were examined by using an automated movement detection system and manual measuring methods. Results: Formalin subcutaneously injected into the hindpaw produced typical biphasic nociceptive behaviors (agitation). The mean agitation event rate during a 60-min observation period increased linearly following increases in the formalin concentration (0.0%, 0.5%, 1.5%, 2.5%, and 5%, 50 μL).Systemic application of morphine of different doses (1, 2, and 5 mg/kg) 10-min prior to formalin injection depressed the agitation responses induced by formalin injection in a dose-dependent manner, and the antinociceptive effect induced by the largest dose (5 mg/kg) of morphine was significantly antagonized by systemic application of the opioid receptor antagonist naloxone (1.25 mg/kg). Local anesthetic lidocaine (20 mg/kg) injected into the ipsilateral ankle subskin 5-min prior to formalin completely blocked the agitation response to formalin injection. These results were comparable to those obtained from manual measure of the incidence of flinching or the duration time of licking/biting of the injected paw. Conclusion:These data suggest that this automated movement detection system for formalin test is a simple, validated measure with good pharmacological sensitivity suitable for discovering novel analgesics or investigating central pain mechanisms.

  5. An Architecture for Automated Fire Detection Early Warning System Based on Geoprocessing Service Composition

    Science.gov (United States)

    Samadzadegan, F.; Saber, M.; Zahmatkesh, H.; Joze Ghazi Khanlou, H.

    2013-09-01

    Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in a Service Oriented Architecture (SOA) platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

  6. AN ARCHITECTURE FOR AUTOMATED FIRE DETECTION EARLY WARNING SYSTEM BASED ON GEOPROCESSING SERVICE COMPOSITION

    Directory of Open Access Journals (Sweden)

    F. Samadzadegan

    2013-09-01

    Full Text Available Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS has guided geospatial data processing in a Service Oriented Architecture (SOA platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

  7. Automated coronary artery calcification detection on low-dose chest CT images

    Science.gov (United States)

    Xie, Yiting; Cham, Matthew D.; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

    2014-03-01

    Coronary artery calcification (CAC) measurement from low-dose CT images can be used to assess the risk of coronary artery disease. A fully automatic algorithm to detect and measure CAC from low-dose non-contrast, non-ECG-gated chest CT scans is presented. Based on the automatically detected CAC, the Agatston score (AS), mass score and volume score were computed. These were compared with scores obtained manually from standard-dose ECG-gated scans and low-dose un-gated scans of the same patient. The automatic algorithm segments the heart region based on other pre-segmented organs to provide a coronary region mask. The mitral valve and aortic valve calcification is identified and excluded. All remaining voxels greater than 180HU within the mask region are considered as CAC candidates. The heart segmentation algorithm was evaluated on 400 non-contrast cases with both low-dose and regular dose CT scans. By visual inspection, 371 (92.8%) of the segmentations were acceptable. The automated CAC detection algorithm was evaluated on 41 low-dose non-contrast CT scans. Manual markings were performed on both low-dose and standard-dose scans for these cases. Using linear regression, the correlation of the automatic AS with the standard-dose manual scores was 0.86; with the low-dose manual scores the correlation was 0.91. Standard risk categories were also computed. The automated method risk category agreed with manual markings of gated scans for 24 cases while 15 cases were 1 category off. For low-dose scans, the automatic method agreed with 33 cases while 7 cases were 1 category off.

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

    Directory of Open Access Journals (Sweden)

    Wu Bai-Lin

    2009-10-01

    Full Text Available Abstract Background One of the most common and efficient methods for detecting mutations in genes is PCR amplification followed by direct sequencing. Until recently, the process of designing PCR assays has been to focus on individual assay parameters rather than concentrating on matching conditions for a set of assays. Primers for each individual assay were selected based on location and sequence concerns. The two primer sequences were then iteratively adjusted to make the individual assays work properly. This generally resulted in groups of assays with different annealing temperatures that required the use of multiple thermal cyclers or multiple passes in a single thermal cycler making diagnostic testing time-consuming, laborious and expensive. These factors have severely hampered diagnostic testing services, leaving many families without an answer for the exact cause of a familial genetic disease. A search of GeneTests for sequencing analysis of the entire coding sequence for genes that are known to cause muscular dystrophies returns only a small list of laboratories that perform comprehensive gene panels. The hypothesis for the study was that a complete set of universal assays can be designed to amplify and sequence any gene or family of genes using computer aided design tools. If true, this would allow automation and optimization of the mutation detection process resulting in reduced cost and increased throughput. Results An automated process has been developed for the detection of deletions, duplications/insertions and point mutations in any gene or family of genes and has been applied to ten genes known to bear mutations that cause muscular dystrophy: DMD; CAV3; CAPN3; FKRP; TRIM32; LMNA; SGCA; SGCB; SGCG; SGCD. Using this process, mutations have been found in five DMD patients and four LGMD patients (one in the FKRP gene, one in the CAV3 gene, and two likely causative heterozygous pairs of variations in the CAPN3 gene of two other

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

    Science.gov (United States)

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

    2012-06-01

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

  10. Detection of cut-off point for rapid automized naming test in good readers and dyslexics

    Directory of Open Access Journals (Sweden)

    Zahra Soleymani

    2014-01-01

    Full Text Available Background and Aim: Rapid automized naming test is an appropriate tool to diagnose learning disability even before teaching reading. This study aimed to detect the cut-off point of this test for good readers and dyslexics.Methods: The test has 4 parts including: objects, colors, numbers and letters. 5 items are repeated on cards randomly for 10 times. Children were asked to name items rapidly. We studied 18 dyslexic students and 18 age-matched good readers between 7 and 8 years of age at second and third grades of elementary school; they were recruited by non-randomize sampling into 2 groups: children with developmental dyslexia from learning disabilities centers with mean age of 100 months, and normal children with mean age of 107 months from general schools in Tehran. Good readers selected from the same class of dyslexics.Results: The area under the receiver operating characteristic curve was 0.849 for letter naming, 0.892 for color naming, 0.971 for number naming, 0.887 for picture naming, and 0.965 totally. The overall sensitivity and specificity was 1 and was 0.79, respectively. The highest sensitivity and specificity were related to number naming (1 and 0.90, respectively.Conclusion: Findings showed that the rapid automized naming test could diagnose good readers from dyslexics appropriately.

  11. Automated segmentation of subretinal layers for the detection of macular edema.

    Science.gov (United States)

    Hassan, Taimur; Akram, M Usman; Hassan, Bilal; Syed, Adeel M; Bazaz, Shafaat Ahmed

    2016-01-20

    Macular edema (ME) is considered as one of the major indications of proliferative diabetic retinopathy and it is commonly caused due to diabetes. ME causes retinal swelling due to the accumulation of protein deposits within subretinal layers. Optical coherence tomography (OCT) imaging provides an early detection of ME by showing the cross-sectional view of macular pathology. Many researchers have worked on automated identification of macular edema from fundus images, but this paper proposes a fully automated method for extracting and analyzing subretinal layers from OCT images using coherent tensors. These subretinal layers are then used to predict ME from candidate images using a support vector machine (SVM) classifier. A total of 71 OCT images of 64 patients are collected locally in which 15 persons have ME and 49 persons are healthy. Our proposed system has an overall accuracy of 97.78% in correctly classifying ME patients and healthy persons. We have also tested our proposed implementation on spectral domain OCT (SD-OCT) images of the Duke dataset consisting of 109 images from 10 patients and it correctly classified all healthy and ME images in the dataset. PMID:26835917

  12. AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. O. Ok

    2015-03-01

    Full Text Available This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012. The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

  13. Use of computer and respiratory inductance plethysmography for the automated detection of swallowing in the elderly.

    Science.gov (United States)

    Moreau-Gaudry, Alexandre; Sabil, Abdelkebir; Baconnier, Pierre; Benchetrit, Gila; Franco, Alain

    2005-01-01

    Deglutition disorders can occur at any age but are especially prevalent in the elderly. The resulting morbidity and mortality are being recognized as major geriatric health issues, Because of difficulties in studying swallowing in the frail elderly, a new, non-invasive, user-friendly, bedside technique has been developed. Ideally suited to such patients, this tool, an intermediary between purely instrumental and clinical methods, combines respiratory inductance plethysmography (RIP) and the computer to detect swallowing automatically, Based on an automated analysis of the airflow estimated by the RIP-derived signal, this new tool was evaluated according to its capacity to detect clinical swallowing from among the 1643 automatically detected respiratory events, This evaluation used contingency tables and Receiver Operator Characteristic (ROC) curves, Results were all significant (chi2(1,n=1643)>100, p<0.01). Considering its high accuracy in detecting swallowing (area under the ROC curve greater than 0.9), this system would be proposed to study deglutition and then deglutition disorders in the frail elderly, to set up medical supervision and to evaluate the efficiency of a swallowing disorder remedial therapeutic.

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

    Science.gov (United States)

    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.

  15. Knee x-ray image analysis method for automated detection of osteoarthritis.

    Science.gov (United States)

    Shamir, Lior; Ling, Shari M; Scott, William W; Bos, Angelo; Orlov, Nikita; Macura, Tomasz J; Eckley, D Mark; Ferrucci, Luigi; Goldberg, Ilya G

    2009-02-01

    We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) 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

  16. GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Xavier Núñez-Nieto

    2014-10-01

    Full Text Available Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting underground explosive artifacts. A GPR survey was conducted on a designed scenario that included the most commonly buried items in historic battle fields, such as mines, projectiles and mortar grenades. The buried targets were identified using both frequencies, although the higher vertical resolution provided by the 2.3-GHz antenna allowed for better recognition of the reflection patterns. The targets were also detected automatically using machine learning techniques. Neural networks and logistic regression algorithms were shown to be able to discriminate between potential targets and clutter. The neural network had the most success, with accuracies ranging from 89% to 92% for the 1-GHz and 2.3-GHz antennas, respectively.

  17. A novel fully automated molecular diagnostic system (AMDS for colorectal cancer mutation detection.

    Directory of Open Access Journals (Sweden)

    Shiro Kitano

    Full Text Available BACKGROUND: KRAS, BRAF and PIK3CA mutations are frequently observed in colorectal cancer (CRC. In particular, KRAS mutations are strong predictors for clinical outcomes of EGFR-targeted treatments such as cetuximab and panitumumab in metastatic colorectal cancer (mCRC. For mutation analysis, the current methods are time-consuming, and not readily available to all oncologists and pathologists. We have developed a novel, simple, sensitive and fully automated molecular diagnostic system (AMDS for point of care testing (POCT. Here we report the results of a comparison study between AMDS and direct sequencing (DS in the detection of KRAS, BRAF and PI3KCA somatic mutations. METHODOLOGY/PRINCIPAL FINDING: DNA was extracted from a slice of either frozen (n = 89 or formalin-fixed and paraffin-embedded (FFPE CRC tissue (n = 70, and then used for mutation analysis by AMDS and DS. All mutations (n = 41 among frozen and 27 among FFPE samples detected by DS were also successfully (100% detected by the AMDS. However, 8 frozen and 6 FFPE samples detected as wild-type in the DS analysis were shown as mutants in the AMDS analysis. By cloning-sequencing assays, these discordant samples were confirmed as true mutants. One sample had simultaneous "hot spot" mutations of KRAS and PIK3CA, and cloning assay comfirmed that E542K and E545K were not on the same allele. Genotyping call rates for DS were 100.0% (89/89 and 74.3% (52/70 in frozen and FFPE samples, respectively, for the first attempt; whereas that of AMDS was 100.0% for both sample sets. For automated DNA extraction and mutation detection by AMDS, frozen tissues (n = 41 were successfully detected all mutations within 70 minutes. CONCLUSIONS/SIGNIFICANCE: AMDS has superior sensitivity and accuracy over DS, and is much easier to execute than conventional labor intensive manual mutation analysis. AMDS has great potential for POCT equipment for mutation analysis.

  18. Development of an automated Red Light Violation Detection System (RLVDS) for Indian vehicles

    CERN Document Server

    Saha, Satadal; Nasipuri, Mita; Basu, Dipak Kumar

    2010-01-01

    Integrated Traffic Management Systems (ITMS) are now implemented in different cities in India to primarily address the concerns of road-safety and security. An automated Red Light Violation Detection System (RLVDS) is an integral part of the ITMS. In our present work we have designed and developed a complete system for generating the list of all stop-line violating vehicle images automatically from video snapshots of road-side surveillance cameras. The system first generates adaptive background images for each camera view, subtracts captured images from the corresponding background images and analyses potential occlusions over the stop-line in a traffic signal. Considering round-the-clock operations in a real-life test environment, the developed system could successfully track 92% images of vehicles with violations on the stop-line in a "Red" traffic signal.

  19. An automated system for lung nodule detection in low-dose computed tomography

    CERN Document Server

    Gori, I; Martinez, A Preite; Retico, A

    2007-01-01

    A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

  20. Automation of disbond detection in aircraft fuselage through thermal image processing

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Trujillo, Christopher J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-07-28

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

  2. Fully automated detection of the counting area in blood smears for computer aided hematology.

    Science.gov (United States)

    Rupp, Stephan; Schlarb, Timo; Hasslmeyer, Erik; Zerfass, Thorsten

    2011-01-01

    For medical diagnosis, blood is an indispensable indicator for a wide variety of diseases, i.e. hemic, parasitic and sexually transmitted diseases. A robust detection and exact segmentation of white blood cells (leukocytes) in stained blood smears of the peripheral blood provides the base for a fully automated, image based preparation of the so called differential blood cell count in the context of medical laboratory diagnostics. Especially for the localization of the blood cells and in particular for the segmentation of the cells it is necessary to detect the working area of the blood smear. In this contribution we present an approach for locating the so called counting area on stained blood smears that is the region where cells are predominantly separated and do not interfere with each other. For this multiple images of a blood smear are taken and analyzed in order to select the image corresponding to this area. The analysis involves the computation of an unimodal function from image content that serves as indicator for the corresponding image. This requires a prior segmentation of the cells that is carried out by a binarization in the HSV color space. Finally, the indicator function is derived from the number of cells and the cells' surface area. Its unimodality guarantees to find a maximum value that corresponds to the counting areas image index. By this, a fast lookup of the counting area is performed enabling a fully automated analysis of blood smears for medical diagnosis. For an evaluation the algorithm's performance on a number of blood smears was compared with the ground truth information that has been defined by an adept hematologist. PMID:22256137

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

    Science.gov (United States)

    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

  4. Automated detection and characterization of microstructural features: application to eutectic particles in single crystal Ni-based superalloys

    International Nuclear Information System (INIS)

    Serial sectioning methods continue to produce an abundant amount of image data for quantifying the three-dimensional nature of material microstructures. Here, we discuss a methodology to automate detecting and characterizing eutectic particles taken from serial images of a production turbine blade made of a heat-treated single crystal Ni-based superalloy (PWA 1484). This method includes two important steps for unassisted eutectic particle characterization: automatically identifying a seed point within each particle and segmenting the particle using a region growing algorithm with an automated stop point. Once detected, the segmented eutectic particles are used to calculate microstructural statistics for characterizing and reconstructing statistically representative synthetic microstructures for single crystal Ni-based superalloys. The significance of this work is its ability to automate characterization for analysing the 3D nature of eutectic particles

  5. Automated detection and characterization of microstructural features: application to eutectic particles in single crystal Ni-based superalloys

    Science.gov (United States)

    Tschopp, M. A.; Groeber, M. A.; Fahringer, R.; Simmons, J. P.; Rosenberger, A. H.; Woodward, C.

    2010-03-01

    Serial sectioning methods continue to produce an abundant amount of image data for quantifying the three-dimensional nature of material microstructures. Here, we discuss a methodology to automate detecting and characterizing eutectic particles taken from serial images of a production turbine blade made of a heat-treated single crystal Ni-based superalloy (PWA 1484). This method includes two important steps for unassisted eutectic particle characterization: automatically identifying a seed point within each particle and segmenting the particle using a region growing algorithm with an automated stop point. Once detected, the segmented eutectic particles are used to calculate microstructural statistics for characterizing and reconstructing statistically representative synthetic microstructures for single crystal Ni-based superalloys. The significance of this work is its ability to automate characterization for analysing the 3D nature of eutectic particles.

  6. Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software

    Directory of Open Access Journals (Sweden)

    Manh Cong Tran

    2016-01-01

    Full Text Available HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.

  7. Automated Simulation P2P Botnets Signature Detection by Rule-based Approach

    Directory of Open Access Journals (Sweden)

    Raihana Syahirah Abdullah

    2016-08-01

    Full Text Available Internet is a most salient services in communication. Thus, companies take this opportunity by putting critical resources online for effective business organization. This has given rise to activities of cyber criminals actuated by botnets. P2P networks had gained popularity through distributed applications such as file-sharing, web caching and network storage whereby it is not easy to guarantee that the file exchanged not the malicious in non-centralized authority of P2P networks. For this reason, these networks become the suitable venue for malicious software to spread. It is straightforward for attackers to target the vulnerable hosts in existing P2P networks as bot candidates and build their zombie army. They can be used to compromise a host and make it become a P2P bot. In order to detect these botnets, a complete flow analysis is necessary. In this paper, we proposed an automated P2P botnets through rule-based detection approach which currently focuses on P2P signature illumination. We consider both of synchronisation within a botnets and the malicious behaviour each bot exhibits at the host or network level to recognize the signature and activities in P2P botnets traffic. The rule-based approach have high detection accuracy and low false positive.

  8. Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury.

    Science.gov (United States)

    van den Heuvel, T L A; van der Eerden, A W; Manniesing, R; Ghafoorian, M; Tan, T; Andriessen, T M J C; Vande Vyvere, T; van den Hauwe, L; Ter Haar Romeny, B M; Goraj, B M; Platel, B

    2016-01-01

    In this paper a Computer Aided Detection (CAD) system is presented to automatically detect Cerebral Microbleeds (CMBs) in patients with Traumatic Brain Injury (TBI). It is believed that the presence of CMBs has clinical prognostic value in TBI patients. To study the contribution of CMBs in patient outcome, accurate detection of CMBs is required. Manual detection of CMBs in TBI patients is a time consuming task that is prone to errors, because CMBs are easily overlooked and are difficult to distinguish from blood vessels. This study included 33 TBI patients. Because of the laborious nature of manually annotating CMBs, only one trained expert manually annotated the CMBs in all 33 patients. A subset of ten TBI patients was annotated by six experts. Our CAD system makes use of both Susceptibility Weighted Imaging (SWI) and T1 weighted magnetic resonance images to detect CMBs. After pre-processing these images, a two-step approach was used for automated detection of CMBs. In the first step, each voxel was characterized by twelve features based on the dark and spherical nature of CMBs and a random forest classifier was used to identify CMB candidate locations. In the second step, segmentations were made from each identified candidate location. Subsequently an object-based classifier was used to remove false positive detections of the voxel classifier, by considering seven object-based features that discriminate between spherical objects (CMBs) and elongated objects (blood vessels). A guided user interface was designed for fast evaluation of the CAD system result. During this process, an expert checked each CMB detected by the CAD system. A Fleiss' kappa value of only 0.24 showed that the inter-observer variability for the TBI patients in this study was very large. An expert using the guided user interface reached an average sensitivity of 93%, which was significantly higher (p = 0.03) than the average sensitivity of 77% (sd 12.4%) that the six experts manually detected

  9. Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury.

    Science.gov (United States)

    van den Heuvel, T L A; van der Eerden, A W; Manniesing, R; Ghafoorian, M; Tan, T; Andriessen, T M J C; Vande Vyvere, T; van den Hauwe, L; Ter Haar Romeny, B M; Goraj, B M; Platel, B

    2016-01-01

    In this paper a Computer Aided Detection (CAD) system is presented to automatically detect Cerebral Microbleeds (CMBs) in patients with Traumatic Brain Injury (TBI). It is believed that the presence of CMBs has clinical prognostic value in TBI patients. To study the contribution of CMBs in patient outcome, accurate detection of CMBs is required. Manual detection of CMBs in TBI patients is a time consuming task that is prone to errors, because CMBs are easily overlooked and are difficult to distinguish from blood vessels. This study included 33 TBI patients. Because of the laborious nature of manually annotating CMBs, only one trained expert manually annotated the CMBs in all 33 patients. A subset of ten TBI patients was annotated by six experts. Our CAD system makes use of both Susceptibility Weighted Imaging (SWI) and T1 weighted magnetic resonance images to detect CMBs. After pre-processing these images, a two-step approach was used for automated detection of CMBs. In the first step, each voxel was characterized by twelve features based on the dark and spherical nature of CMBs and a random forest classifier was used to identify CMB candidate locations. In the second step, segmentations were made from each identified candidate location. Subsequently an object-based classifier was used to remove false positive detections of the voxel classifier, by considering seven object-based features that discriminate between spherical objects (CMBs) and elongated objects (blood vessels). A guided user interface was designed for fast evaluation of the CAD system result. During this process, an expert checked each CMB detected by the CAD system. A Fleiss' kappa value of only 0.24 showed that the inter-observer variability for the TBI patients in this study was very large. An expert using the guided user interface reached an average sensitivity of 93%, which was significantly higher (p = 0.03) than the average sensitivity of 77% (sd 12.4%) that the six experts manually detected

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

    Science.gov (United States)

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

    2012-02-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Carlos Figuera

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

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

    Science.gov (United States)

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

    2016-01-01

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

  14. Reproducibility of In Vivo Corneal Confocal Microscopy Using an Automated Analysis Program for Detection of Diabetic Sensorimotor Polyneuropathy.

    Directory of Open Access Journals (Sweden)

    Ilia Ostrovski

    Full Text Available In vivo Corneal Confocal Microscopy (IVCCM is a validated, non-invasive test for diabetic sensorimotor polyneuropathy (DSP detection, but its utility is limited by the image analysis time and expertise required. We aimed to determine the inter- and intra-observer reproducibility of a novel automated analysis program compared to manual analysis.In a cross-sectional diagnostic study, 20 non-diabetes controls (mean age 41.4±17.3y, HbA1c 5.5±0.4% and 26 participants with type 1 diabetes (42.8±16.9y, 8.0±1.9% underwent two separate IVCCM examinations by one observer and a third by an independent observer. Along with nerve density and branch density, corneal nerve fibre length (CNFL was obtained by manual analysis (CNFLMANUAL, a protocol in which images were manually selected for automated analysis (CNFLSEMI-AUTOMATED, and one in which selection and analysis were performed electronically (CNFLFULLY-AUTOMATED. Reproducibility of each protocol was determined using intraclass correlation coefficients (ICC and, as a secondary objective, the method of Bland and Altman was used to explore agreement between protocols.Mean CNFLManual was 16.7±4.0, 13.9±4.2 mm/mm2 for non-diabetes controls and diabetes participants, while CNFLSemi-Automated was 10.2±3.3, 8.6±3.0 mm/mm2 and CNFLFully-Automated was 12.5±2.8, 10.9 ± 2.9 mm/mm2. Inter-observer ICC and 95% confidence intervals (95%CI were 0.73(0.56, 0.84, 0.75(0.59, 0.85, and 0.78(0.63, 0.87, respectively (p = NS for all comparisons. Intra-observer ICC and 95%CI were 0.72(0.55, 0.83, 0.74(0.57, 0.85, and 0.84(0.73, 0.91, respectively (p<0.05 for CNFLFully-Automated compared to others. The other IVCCM parameters had substantially lower ICC compared to those for CNFL. CNFLSemi-Automated and CNFLFully-Automated underestimated CNFLManual by mean and 95%CI of 35.1(-4.5, 67.5% and 21.0(-21.6, 46.1%, respectively.Despite an apparent measurement (underestimation bias in comparison to the manual strategy of image

  15. Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows

    NARCIS (Netherlands)

    Mol, de R.M.; Andre, G.; Bleumer, E.J.B.; Werf, van der J.T.N.; Haas, de Y.; Reenen, van C.G.

    2013-01-01

    Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that

  16. Automated detection of malaria pigment: feasibility for malaria diagnosing in an area with seasonal malaria in northern Namibia

    NARCIS (Netherlands)

    A.J. de Langen; J. van Dillen; P. Witte; S. Mucheto; N. Nagelkerke; P. Kager

    2006-01-01

    OBJECTIVE To evaluate the feasibility of automated malaria detection with the Cell-Dyn (R) 3700 (Abbott Diagnostics, Santa Clara, CA, USA) haematology analyser for diagnosing malaria in northern Namibia. METHODS From April to June 2003, all patients with a positive blood smear result and a subset of

  17. Performance Evaluation of an Automated ELISA System for Alzheimer's Disease Detection in Clinical Routine.

    Science.gov (United States)

    Chiasserini, Davide; Biscetti, Leonardo; Farotti, Lucia; Eusebi, Paolo; Salvadori, Nicola; Lisetti, Viviana; Baschieri, Francesca; Chipi, Elena; Frattini, Giulia; Stoops, Erik; Vanderstichele, Hugo; Calabresi, Paolo; Parnetti, Lucilla

    2016-07-22

    The variability of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers undermines their full-fledged introduction into routine diagnostics and clinical trials. Automation may help to increase precision and decrease operator errors, eventually improving the diagnostic performance. Here we evaluated three new CSF immunoassays, EUROIMMUNtrademark amyloid-β 1-40 (Aβ1-40), amyloid-β 1-42 (Aβ1-42), and total tau (t-tau), in combination with automated analysis of the samples. The CSF biomarkers were measured in a cohort consisting of AD patients (n = 28), mild cognitive impairment (MCI, n = 77), and neurological controls (OND, n = 35). MCI patients were evaluated yearly and cognitive functions were assessed by Mini-Mental State Examination. The patients clinically diagnosed with AD and MCI were classified according to the CSF biomarkers profile following NIA-AA criteria and the Erlangen score. Technical evaluation of the immunoassays was performed together with the calculation of their diagnostic performance. Furthermore, the results for EUROIMMUN Aβ1-42 and t-tau were compared to standard immunoassay methods (INNOTESTtrademark). EUROIMMUN assays for Aβ1-42 and t-tau correlated with INNOTEST (r = 0.83, p < 0.001 for both) and allowed a similar interpretation of the CSF profiles. The Aβ1-42/Aβ1-40 ratio measured with EUROIMMUN was the best parameter for AD detection and improved the diagnostic accuracy of Aβ1-42 (area under the curve = 0.93). In MCI patients, the Aβ1-42/Aβ1-40 ratio was associated with cognitive decline and clinical progression to AD.The diagnostic performance of the EUROIMMUN assays with automation is comparable to other currently used methods. The variability of the method and the value of the Aβ1-42/Aβ1-40 ratio in AD diagnosis need to be validated in large multi-center studies. PMID:27447425

  18. Automated detection of soma location and morphology in neuronal network cultures.

    Directory of Open Access Journals (Sweden)

    Burcin Ozcan

    Full Text Available Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS, where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i image segmentation based on support vector machines with specially designed multiscale filters; (ii soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

  19. Short wave–automated perimetry (SWAP versus optical coherence tomography in early detection of glaucoma

    Directory of Open Access Journals (Sweden)

    Zaky AG

    2016-09-01

    Full Text Available Adel Galal Zaky,1 Ahmed Tarek Yassin,2 Saber Hamed El Sayid1 1Ophthalmology Department, Faculty of Medicine, Menoufia University, Shebin El Kom, Menoufia, Egypt; 2Ophthalmology Department, Banha Educational Hospital, Banha, El Kalyobia, Egypt Objective: To assess the role and diagnostic effectiveness of optical coherence tomography (OCT and short wave–automated perimetry (SWAP to distinguish between normal, glaucoma suspects, and surely diagnosed glaucomatous eye.Background: Changes in the optic disc and retinal nerve fiber layer (RNFL often precede the appearance of visual field defect with standard automated perimetry. Unfortunately, RNFL defect can be difficult to identify during clinical examination. Early detection of glaucoma is still controversial, whether by OCT, SWAP, or frequency-doubling technology perimetry.Patients and methods: In this randomized controlled, consecutive, prospective study, a total 70 subjects (140 eyes were included in the study, divided into three groups: Group A, 10 healthy volunteers (20 eyes; Group B, 30 patients (60 eyes with glaucoma suspect; and Group C, 30 patients (60 eyes with already diagnosed glaucomatous eyes.Results: Average RNFL thickness was 75±9.0 in the glaucoma group, 99±15.5 in the control group, and 94±12 in glaucoma suspect. The inferior quadrant was the early parameter affected. There was significant correlation between visual field parameters and RNFL thickness in both glaucoma and glaucoma suspect groups.Conclusion: Both RNFL thickness measured by OCT and SWAP indices are good discrimination tools between glaucomatous, glaucoma suspect, and normal eyes. OCT parameters tend to be more sensitive than SWAP parameters. Keywords: OCT, SWAP, glaucoma, intraocular pressure, RNFL

  20. Short wavelength automated perimetry can detect visual field changes in diabetic patients without retinopathy

    Directory of Open Access Journals (Sweden)

    Othman Ali Zico

    2014-01-01

    Full Text Available Purpose: The purpose of the following study is to compare short wave automated perimetry (SWAP versus standard automated perimetry (SAP for early detection of diabetic retinopathy (DR. Materials and Methods: A total of 40 diabetic patients, divided into group I without DR (20 patients = 40 eyes and group II with mild non-proliferative DR (20 patients = 40 eyes were included. They were tested with central 24-2 threshold test with both shortwave and SAP to compare sensitivity values and local visual field indices in both of them. A total of 20 healthy age and gender matched subjects were assessed as a control group. Results: Control group showed no differences between SWAP and SAP regarding mean deviation (MD, corrected pattern standard deviation (CPSD or short fluctuations (SF. In group I, MD showed significant more deflection in SWAP (−4.44 ± 2.02 dB compared to SAP (−0.96 ± 1.81 dB (P = 0.000002. However, CPSD and SF were not different between SWAP and SAP. In group II, MD and SF showed significantly different values in SWAP (−5.75 ± 3.11 dB and 2.0 ± 0.95 compared to SAP (−3.91 ± 2.87 dB and 2.86 ± 1.23 (P = 0.01 and 0.006 respectively. There are no differences regarding CPSD between SWAP and SAP. The SWAP technique was significantly more sensitive than SAP in patients without retinopathy (p, but no difference exists between the two techniques in patients with non-proliferative DR. Conclusion: The SWAP technique has a higher yield and efficacy to pick up abnormal findings in diabetic patients without overt retinopathy rather than patients with clinical retinopathy.

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Science.gov (United States)

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

    2016-03-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

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

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

    Science.gov (United States)

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

    2012-03-01

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

  7. Automated detection of lung nodules in low-dose computed tomography

    CERN Document Server

    Cascio, D; Chincarini, A; De Nunzio, G; Delogu, P; Fantacci, M E; Gargano, G; Gori, I; Masala, G L; Martinez, A Preite; Retico, A; Santoro, M; Spinelli, C; Tarantino, T

    2007-01-01

    A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very hig...

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

    Science.gov (United States)

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

    2014-02-01

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

  9. Preventing Falls

    Science.gov (United States)

    ... from osteoporosis. Lower-body strength exercises and balance exercises can help you prevent falls and avoid the disability that may result from falling. Here are some fall prevention tips from Go4Life : l Have your eyes and hearing tested often. Always wear your glasses when you ...

  10. Detection of early behavioral markers of Huntington's disease in R6/2 mice employing an automated social home cage

    DEFF Research Database (Denmark)

    Rudenko, Olga; Tkach, Vadim; Berezin, Vladimir;

    2009-01-01

    developed behavior screening system, the IntelliCage, allows automated testing of mouse behavior in the home cage employing individual recognition of animals living in social groups. The present study validates the ability of the IntelliCage system to detect behavioral and cognitive dysfunction in R6/2 mice......Huntington's disease (HD) is an autosomal-dominant neurodegenerative disorder, for which no known cure or effective treatment exists. To facilitate the search for new potential treatments of HD, an automated system for analyzing the behavior of transgenic HD mice is urgently needed. A recently...

  11. CSRFDtool: Automated Detection and Prevention of a Reflected Cross-Site Request Forgery

    Directory of Open Access Journals (Sweden)

    Omar A. Batarfi

    2014-10-01

    Full Text Available The number of Internet users is dramatically increased every year. Most of these users are exposed to the dangers of attackers in one way or another. The reason for this lies in the presence of many weaknesses that are not known for ordinary users. In addition, the lack of user awareness is considered as the main reason for falling into the attackers' snares. Cross Site Request Forgery (CSRF has placed in the list of the most dangerous threats to security in OWASP Top Ten for 2013. CSRF is an attack that forces the user's browser to send or perform unwanted request or action without user awareness by exploiting a valid session between the browser and the server. When CSRF attack success, it leads to many bad consequences. An attacker may reach private and personal information and modify it. This paper aims to detect and prevent a specific type of CSRF, called reflected CSRF. In a reflected CSRF, a malicious code could be injected by the attackers. This paper explores how CSRF Detection Extension prevents the reflected CSRF by checking browser specific information. Our evaluation shows that the proposed solution is successful in preventing this type of attack.

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

    Science.gov (United States)

    Prasicek, Günther; Robl, Jörg; Lang, Andreas

    2016-04-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Girolamo, D., E-mail: dgirola@ncsu.edu; Yuan, F. G. [National Institute of Aerospace, Integrated Structural Health Management Laboratory, Hampton, VA 23666 and North Carolina State University, Department of Mechanical and Aerospace Engineering, Raleigh, NC 27695 (United States); Girolamo, L. [North Carolina State University, Department of Mechanical and Aerospace Engineering, Raleigh, NC 27695 (United States)

    2015-03-31

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

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

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2009-01-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Science.gov (United States)

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

    2015-06-01

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

  18. AUTOMATED DIGITAL MAMMOGRAM SEGMENTATION FOR DETECTION OF ABNORMAL MASSES USING BINARY HOMOGENEITY ENHANCEMENT ALGORITHM

    Directory of Open Access Journals (Sweden)

    Indra Kanta Maitra

    2011-06-01

    Full Text Available Many image processing techniques have been developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breastcancer can increase the survival rate, thus making screening programmes a mandatory step for females.Radiologists have to examine a large number of images. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour and pectoral muscle allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. We need to performessential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present a fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI. We are using medio-lateral oblique (MLO view of mammograms. We have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA, followed by an innovative approach for edge detection (EDA. Then obtain the breast boundary by using our proposed Breast Boundary Detection Algorithm (BBDA. After we use our proposed Pectoral Muscle Detection Algorithm (PMDA to suppress the pectoral muscle thus obtaining the breast ROI, we use our proposed Anatomical Segmentation of Breast ROI (ASB algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Seeded Region Growing Algorithm (SRGA to isolate normal and abnormal regions in the breast tissue. If any

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

    Directory of Open Access Journals (Sweden)

    Kaplan RF

    2014-10-01

    Full Text Available Richard F Kaplan,1 Ying Wang,1 Kenneth A Loparo,1,2 Monica R Kelly,3 Richard R Bootzin3 1General Sleep Corporation, Euclid, OH, USA; 2Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA; 3Department of Psychology, University of Arizona, Tucson, AZ, USA Background: A need exists, from both a clinical and a research standpoint, for objective sleep measurement systems that are both easy to use and can accurately assess sleep and wake. This study evaluates the output of an automated sleep–wake detection algorithm (Z-ALG used in the Zmachine (a portable, single-channel, electroencephalographic [EEG] acquisition and analysis system against laboratory polysomnography (PSG using a consensus of expert visual scorers. Methods: Overnight laboratory PSG studies from 99 subjects (52 females/47 males, 18–60 years, median age 32.7 years, including both normal sleepers and those with a variety of sleep disorders, were assessed. PSG data obtained from the differential mastoids (A1–A2 were assessed by Z-ALG, which determines sleep versus wake every 30 seconds using low-frequency, intermediate-frequency, and high-frequency and time domain EEG features. PSG data were independently scored by two to four certified PSG technologists, using standard Rechtschaffen and Kales guidelines, and these score files were combined on an epoch-by-epoch basis, using a majority voting rule, to generate a single score file per subject to compare against the Z-ALG output. Both epoch-by-epoch and standard sleep indices (eg, total sleep time, sleep efficiency, latency to persistent sleep, and wake after sleep onset were compared between the Z-ALG output and the technologist consensus score files. Results: Overall, the sensitivity and specificity for detecting sleep using the Z-ALG as compared to the technologist consensus are 95.5% and 92.5%, respectively, across all subjects, and the positive predictive value and the

  20. Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study.

    Directory of Open Access Journals (Sweden)

    Susan S Huang

    2010-02-01

    previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention. CONCLUSIONS: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care. Please see later in the article for the Editors' Summary.

  1. Enhancement of Fusarium head blight detection in free-falling wheat kernels using a bichromatic pulsed LED design

    Science.gov (United States)

    Yang, I.-Chang; Delwiche, Stephen R.; Chen, Suming; Lo, Y. Martin

    2009-02-01

    Fusarium head blight is a worldwide fungal disease of small cereal grains such as wheat that affects the yield, quality, and safety of food and feed products. The current study was implemented to develop more efficient methods for optically detecting Fusarium-damaged (scabby) kernels from normal (sound) wheat kernels. Through development of a high-power pulsed LED (green and red) inspection system, it was found that Fusarium-damaged and normal wheat kernels have different reflected energy responses. Two parameters (slope and r2) from a regression analysis of the green and red responses were used as input parameters in linear discriminant analysis models. The examined factors affecting accuracy were the orientation of the optical probe, the color contrast between normal and Fusarium-damaged kernels, and the manner in which one LED's response is time-matched to the other LED. Whereas commercial high-speed optical sorters are, on average, 50% efficient at removing mold-damaged kernels, this efficiency can rise to 95% or better under more carefully controlled, kernel-at-rest conditions in the laboratory. The current research on free-falling kernels has demonstrated accuracies (>90% for wheat samples of high visual contrast) that approach those of controlled conditions, which will lead to improvements in high-speed optical sorters.

  2. Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket.

    Science.gov (United States)

    Vermeulen, Joan; Willard, Sarah; Aguiar, Bruno; De Witte, Luc P

    2015-01-01

    The objective of this study was to evaluate the sensitivity and specificity of a smartphone-based fall detection application when different smartphone models are worn on a belt or in a trouser pocket. Eight healthy adults aged between 18 and 24 years old simulated 10 different types of true falls, 5 different types of falls with recovery, and 11 daily activities, five consecutive times. Participants wore one smartphone in a pocket that was attached to their belt and another one in their trouser pocket. All smartphones were equipped with a built-in accelerometer and the fall detection application. Four participants tested the application on a Samsung S3 and four tested the application on a Samsung S3 mini. Sensitivity scores were .75 (Samsung S3 belt), .88 (Samsung S3 mini trouser pocket), and .90 (Samsung S3 mini belt/Samsung S3 trouser pocket). Specificity scores were .87 (Samsung S3 trouser pocket), .91 (Samsung S3 mini trouser pocket), .97 (Samsung S3 belt), and .99 (Samsung S3 mini belt). These results suggest that an application on a smartphone can generate valid fall alarms when worn on a belt or in a trouser pocket. However, sensitivity should be improved before implementation of the application in practice. PMID:26132221

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

    Science.gov (United States)

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

    2016-04-01

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

  4. Falling chains

    Science.gov (United States)

    Wong, Chun Wa; Yasui, Kosuke

    2006-06-01

    The one-dimensional fall of a folded chain with one end suspended from a rigid support and a chain falling from a resting heap on a table is studied. Because their Lagrangians contain no explicit time dependence, the falling chains are conservative systems. Their equations of motion are shown to contain a term that enforces energy conservation when masses are transferred between subchains. We show that Cayley's 1857 energy nonconserving solution for a chain falling from a resting heap is incorrect because it neglects the energy gained when a link leaves a subchain. The maximum chain tension measured by Calkin and March for the falling folded chain is given a simple if rough interpretation. Other aspects of the falling folded chain are briefly discussed.

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

    Science.gov (United States)

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

    2016-02-01

    After the Fukushima disaster, power generation from nuclear power plants in Japan was completely stopped and old coal-based power plants were re-commissioned to compensate for the decrease in power generation capacity. Although coal is a relatively inexpensive fuel for power generation, it contains high levels (mgkg(-1)) of selenium, which could contaminate the wastewater from thermal power plants. In this work, an automated selenium monitoring system was developed based on sequential hydride generation and chemiluminescence detection. This method could be applied to control of wastewater contamination. In this method, selenium is vaporized as H2Se, which reacts with ozone to produce chemiluminescence. However, interference from arsenic is of concern because the ozone-induced chemiluminescence intensity of H2Se is much lower than that of AsH3. This problem was successfully addressed by vaporizing arsenic and selenium individually in a sequential procedure using a syringe pump equipped with an eight-port selection valve and hot and cold reactors. Oxidative decomposition of organoselenium compounds and pre-reduction of the selenium were performed in the hot reactor, and vapor generation of arsenic and selenium were performed separately in the cold reactor. Sample transfers between the reactors were carried out by a pneumatic air operation by switching with three-way solenoid valves. The detection limit for selenium was 0.008 mg L(-1) and calibration curve was linear up to 1.0 mg L(-1), which provided suitable performance for controlling selenium in wastewater to around the allowable limit (0.1 mg L(-1)). This system consumes few chemicals and is stable for more than a month without any maintenance. Wastewater samples from thermal power plants were collected, and data obtained by the proposed method were compared with those from batchwise water treatment followed by hydride generation-atomic fluorescence spectrometry. PMID:26653491

  6. A machine learning system for automated whole-brain seizure detection

    Directory of Open Access Journals (Sweden)

    P. Fergus

    2016-01-01

    Full Text Available Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures. Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier.

  7. CT-guided automated detection of lung tumors on PET images

    Science.gov (United States)

    Cui, Yunfeng; Zhao, Binsheng; Akhurst, Timothy J.; Yan, Jiayong; Schwartz, Lawrence H.

    2008-03-01

    The calculation of standardized uptake values (SUVs) in tumors on serial [ 18F]2-fluoro-2-deoxy-D-glucose ( 18F-FDG) positron emission tomography (PET) images is often used for the assessment of therapy response. We present a computerized method that automatically detects lung tumors on 18F-FDG PET/Computed Tomography (CT) images using both anatomic and metabolic information. First, on CT images, relevant organs, including lung, bone, liver and spleen, are automatically identified and segmented based on their locations and intensity distributions. Hot spots (SUV >= 1.5) on 18F-FDG PET images are then labeled using the connected component analysis. The resultant "hot objects" (geometrically connected hot spots in three dimensions) that fall into, reside at the edges or are in the vicinity of the lungs are considered as tumor candidates. To determine true lesions, further analyses are conducted, including reduction of tumor candidates by the masking out of hot objects within CT-determined normal organs, and analysis of candidate tumors' locations, intensity distributions and shapes on both CT and PET. The method was applied to 18F-FDG-PET/CT scans from 9 patients, on which 31 target lesions had been identified by a nuclear medicine radiologist during a Phase II lung cancer clinical trial. Out of 31 target lesions, 30 (97%) were detected by the computer method. However, sensitivity and specificity were not estimated because not all lesions had been marked up in the clinical trial. The method effectively excluded the hot spots caused by mediastinum, liver, spleen, skeletal muscle and bone metastasis.

  8. Rapid Ertapenem Susceptibility Testing and Klebsiella pneumoniae Carbapenemase Phenotype Detection in Klebsiella pneumoniae Isolates by Use of Automated Microscopy of Immobilized Live Bacterial Cells

    OpenAIRE

    Burnham, Carey-Ann D.; Frobel, Rachel A.; Herrera, Monica L.; Wickes, Brian L.

    2014-01-01

    We evaluated detection of ertapenem (ETP) resistance and Klebsiella pneumoniae carbapenemase (KPC) in 47 Klebsiella pneumoniae isolates using a novel automated microscopy system. Automated microscopy correctly classified 22/23 isolates as ETP resistant and 24/24 as ETP susceptible and correctly classified 21/21 isolates as KPC positive and 26/26 as KPC negative.

  9. Impact of an Automated Surveillance to Detect Surgical-Site Infections in Patients Undergoing Total Hip and Knee Arthroplasty in Brazil.

    Science.gov (United States)

    Perdiz, Luciana B; Yokoe, Deborah S; Furtado, Guilherme H; Medeiros, Eduardo A S

    2016-08-01

    In this retrospective study, we compared automated surveillance with conventional surveillance to detect surgical site infection after primary total hip or knee arthroplasty. Automated surveillance demonstrated better efficacy than routine surveillance in SSI diagnosis, sensitivity, and predictive negative value in hip and knee arthroplasty. Infect Control Hosp Epidemiol 2016;37:991-993. PMID:27072598

  10. An improved respiratory syncytial virus neutralization assay based on the detection of green fluorescent protein expression and automated plaque counting

    OpenAIRE

    van Remmerden Yvonne; Xu Fang; van Eldik Mandy; Heldens Jacco GM; Huisman Willem; Widjojoatmodjo Myra N

    2012-01-01

    Abstract Background Virus neutralizing antibodies against respiratory syncytial virus (RSV) are considered important correlates of protection for vaccine evaluation. The established plaque reduction assay is time consuming, labor intensive and highly variable. Methods Here, a neutralization assay based on a modified RSV strain expressing the green fluorescent protein in combination with automated detection and quantification of plaques is described. Results The fluorescence plaque reduction a...

  11. Fall detecting process of negative reactivity in a nuclear power plant reactor and power plant protected against such fall. Procede de detection de la chute d'un element antireactif dans le reacteur d'une centrale nucleaire et centrale protegee contre une telle chute

    Energy Technology Data Exchange (ETDEWEB)

    Bourin, J.M.; Bruyere, M.; Rousseau, I.

    1988-08-26

    The fall of control rod in the core of a nuclear reactor is detected by using an external parameter influencing the reactor control and by monitoring variations in the power. The rod drop is detected when a rapide decrease in power is seen without a corresponding large change in the external parameter.

  12. Evaluation of a CLEIA automated assay system for the detection of a panel of tumor markers.

    Science.gov (United States)

    Falzarano, Renato; Viggiani, Valentina; Michienzi, Simona; Longo, Flavia; Tudini, Silvestra; Frati, Luigi; Anastasi, Emanuela

    2013-10-01

    Tumor markers are commonly used to detect a relapse of disease in oncologic patients during follow-up. It is important to evaluate new assay systems for a better and more precise assessment, as a standardized method is currently lacking. The aim of this study was to assess the concordance between an automated chemiluminescent enzyme immunoassay system (LUMIPULSE® G1200) and our reference methods using seven tumor markers. Serum samples from 787 subjects representing a variety of diagnoses, including oncologic, were analyzed using LUMIPULSE® G1200 and our reference methods. Serum values were measured for the following analytes: prostate-specific antigen (PSA), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), carbohydrate antigen 15-3 (CA15-3), carbohydrate antigen 19-9 (CA19-9), and cytokeratin 19 fragment (CYFRA 21-1). For the determination of CEA, AFP, and PSA, an automatic analyzer based on chemiluminescence was applied as reference method. To assess CYFRA 21-1, CA125, CA19-9, and CA15-3, an immunoradiometric manual system was employed. Method comparison by Passing-Bablok analysis resulted in slopes ranging from 0.9728 to 1.9089 and correlation coefficients from 0.9977 to 0.9335. The precision of each assay was assessed by testing six serum samples. Each sample was analyzed for all tumor biomarkers in duplicate and in three different runs. The coefficients of variation were less than 6.3 and 6.2 % for within-run and between-run variation, respectively. Our data suggest an overall good interassay agreement for all markers. The comparison with our reference methods showed good precision and reliability, highlighting its usefulness in clinical laboratory's routine.

  13. Evaluation of a CLEIA automated assay system for the detection of a panel of tumor markers.

    Science.gov (United States)

    Falzarano, Renato; Viggiani, Valentina; Michienzi, Simona; Longo, Flavia; Tudini, Silvestra; Frati, Luigi; Anastasi, Emanuela

    2013-10-01

    Tumor markers are commonly used to detect a relapse of disease in oncologic patients during follow-up. It is important to evaluate new assay systems for a better and more precise assessment, as a standardized method is currently lacking. The aim of this study was to assess the concordance between an automated chemiluminescent enzyme immunoassay system (LUMIPULSE® G1200) and our reference methods using seven tumor markers. Serum samples from 787 subjects representing a variety of diagnoses, including oncologic, were analyzed using LUMIPULSE® G1200 and our reference methods. Serum values were measured for the following analytes: prostate-specific antigen (PSA), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), carbohydrate antigen 15-3 (CA15-3), carbohydrate antigen 19-9 (CA19-9), and cytokeratin 19 fragment (CYFRA 21-1). For the determination of CEA, AFP, and PSA, an automatic analyzer based on chemiluminescence was applied as reference method. To assess CYFRA 21-1, CA125, CA19-9, and CA15-3, an immunoradiometric manual system was employed. Method comparison by Passing-Bablok analysis resulted in slopes ranging from 0.9728 to 1.9089 and correlation coefficients from 0.9977 to 0.9335. The precision of each assay was assessed by testing six serum samples. Each sample was analyzed for all tumor biomarkers in duplicate and in three different runs. The coefficients of variation were less than 6.3 and 6.2 % for within-run and between-run variation, respectively. Our data suggest an overall good interassay agreement for all markers. The comparison with our reference methods showed good precision and reliability, highlighting its usefulness in clinical laboratory's routine. PMID:23775009

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-05-21

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

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

    Science.gov (United States)

    Collins, Patrick; Bahr, Thomas

    2016-04-01

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

  17. An improved respiratory syncytial virus neutralization assay based on the detection of green fluorescent protein expression and automated plaque counting

    Directory of Open Access Journals (Sweden)

    van Remmerden Yvonne

    2012-10-01

    Full Text Available Abstract Background Virus neutralizing antibodies against respiratory syncytial virus (RSV are considered important correlates of protection for vaccine evaluation. The established plaque reduction assay is time consuming, labor intensive and highly variable. Methods Here, a neutralization assay based on a modified RSV strain expressing the green fluorescent protein in combination with automated detection and quantification of plaques is described. Results The fluorescence plaque reduction assay in microplate format requires only two days to complete and is simple and reproducible. A good correlation between visual and automated counting methods to determine RSV neutralizing serum antibody titers was observed. Conclusions The developed virus neutralization assay is suitable for high-throughput testing and can be used for both animal studies and (large scale vaccine clinical trials.

  18. Falling chains

    CERN Document Server

    Wong, C W; Wong, Chun Wa; Yasui, Kosuke

    2006-01-01

    The one-dimensional falling motion of a bungee chain suspended from a rigid support and of a chain falling from a resting heap on a table is studied. Their Lagrangians are found to contain no explicit time dependence. As a result, these falling chains are conservative systems. Each of their Lagrange's equations of motion is shown to contain a term that enforces energy conservation when masses are transferred between subchains. We show in particular that Cayley's 1857 energy nonconserving solution for a chain falling from a resting heap is incorrect because it neglects the energy gained when the transferred link is emitted by the emitting subchain. The maximum chain tension measured by Calkin and March for the falling bungee chain is given a simple if rough interpretation. In the simplified one-dimensional treatment, the kinetic energy of the center of mass of the falling bungee chain is found to be converted by the chain tension at the rigid support into the internal kinetic energy of the chain. However, as t...

  19. Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention.

    Science.gov (United States)

    Dinh, A; Teng, D; Chen, L; Ko, S B; Shi, Y; Basran, J; Del Bello-Hass, V

    2008-01-01

    Fall detection and prevention require logged physiological activity data of a patient for a long period of time. This work develops a data acquisition system to collect motion data from multiple patients and store in a data base. A wireless sensor network is built using high precision inertia sensors and low power Zigbee wireless transceivers. Testing results prove the system function properly. Researchers and physicians can now retrieve and analyze the accurate data of the patient movement with ease. PMID:19163174

  20. Macrothrombocytopenia in north India: role of automated platelet data in the detection of an under diagnosed entity.

    Science.gov (United States)

    Kakkar, Naveen; John, M Joseph; Mathew, Amrith

    2015-03-01

    Congenital macrothrombocytopenia is being increasingly recognised because of the increasing availability of automated platelet counts during routine complete blood count. If not recognised, these patients may be unnecessarily investigated or treated. The study was done to assess the occurrence of macrothrombocytopenia in the North Indian population and the role of automated platelet parameters in its detection. This prospective study was done on patients whose blood samples were sent for CBC to the hematology laboratory of a tertiary care hospital. Samples were run on Advia-120, a 5-part differential automated analyzer. Routine blood parameters including platelet count, mean platelet volume (MPV), platelet cytogram pattern and platelet flagging was studied along with peripheral blood smear examination. ANOVA was used to compare difference in mean MPV in patients with macrothrombocytopenia, and those with secondary thrombocytopenia and ITP. Seventy five (0.6 %) patients with CBC evaluation were detected to have macrothrombocytopenia, majority (96 %) of North Indian origin. The MPV (fl) in the 75 patients ranged from 10.9 to 23.3 (mean 15.1 ± 3.1 fl) with a dispersed cytogram pattern distinct from that seen in patients with normal platelet count, raised platelet count or low platelets due to secondary thrombocytopenia (MPV-10.9 ± 2.6) or ITP (10.8 ± 3.5). The difference in mean MPV in these patients was statistically significant (p blood counts. Along with a blood smear examination, automated data (MPV and platelet cytogram pattern) aids the diagnosis and can avoid unnecessary investigations and interventions for these patients. PMID:25548447

  1. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  2. Automated High-Pressure Titration System with In Situ Infrared Spectroscopic Detection

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-04-17

    A fully automated titration system with infrared detection was developed for investigating interfacial chemistry at high pressures. The apparatus consists of a high-pressure fluid generation and delivery system coupled to a high-pressure cell with infrared optics. A manifold of electronically actuated valves is used to direct pressurized fluids into the cell. Precise reagent additions to the pressurized cell are made with calibrated tubing loops that are filled with reagent and placed in-line with the cell and a syringe pump. The cell’s infrared optics facilitate both transmission and attenuated total reflection (ATR) measurements to monitor bulk-fluid composition and solid-surface phenomena such as adsorption, desorption, complexation, dissolution, and precipitation. Switching between the two measurement modes is accomplished with moveable mirrors that direct radiation from a Fourier transform infrared spectrometer into the cell along transmission or ATR light paths. The versatility of the high-pressure IR titration system is demonstrated with three case studies. First, we titrated water into supercritical CO2 (scCO2) to generate an infrared calibration curve and determine the solubility of water in CO2 at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO2 at 50 °C and 90 bar. Transmission-mode spectra were used to quantify changes in the clay’s sorbed water concentration as a function of scCO2 hydration, and ATR measurements provided insights into competitive residency of water and CO2 on the clay surface and in the interlayer. Finally, we demonstrated how time-dependent studies can be conducted with the system by monitoring the carbonation reaction of forsterite (Mg2SiO4) in water-bearing scCO2 at 50 °C and 90 bar. Immediately after water dissolved in the scCO2, a thin film of adsorbed water formed on the mineral surface, and the film thickness increased with time as the forsterite began to dissolve

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

    Directory of Open Access Journals (Sweden)

    Christoph eSchmitz

    2014-05-01

    Full Text Available 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 38–99% and false-positive rates from 3.6–82%. 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.

  4. Automated cerebellar segmentation: Validation and application to detect smaller volumes in children prenatally exposed to alcohol

    Directory of Open Access Journals (Sweden)

    Valerie A. Cardenas

    2014-01-01

    Discussion: These results demonstrate excellent reliability and validity of automated cerebellar volume and mid-sagittal area measurements, compared to manual measurements. These data also illustrate that this new technology for automatically delineating the cerebellum leads to conclusions regarding the effects of prenatal alcohol exposure on the cerebellum consistent with prior studies that used labor intensive manual delineation, even with a very small sample.

  5. 基于Kinect体感传感器的老年人跌倒自动检测%Automatic Fall Detection For the elderly Using Kinect Sensor

    Institute of Scientific and Technical Information of China (English)

    瞿畅; 孙杰; 王君泽; 朱小龙

    2016-01-01

    Falls are one of the major risks for the elderly living alone at home.In order to get information of fall quickly and efficiently,an automatic fall detection method using depth image of human body based on Kinect sensor is put forward. Using depth image technology,the foreground depth image of human body is obtained to build the 3D bounding box of the foreground depth image. By computing the length,width and height value of the 3D bound⁃ing box and the change speed of these values,the accidental falls can be determined. Meanwhile,when the human body is blocked partly by obstructions,the fall detection and determination are solved by using the fusion algorithm of occluded objects. 26 kinds of test scenarios are arranged in indoor environment,the rate of false positives in the system is 2.0%~6.0%,and the rate of false negatives in the system is 0~4.0%. Expermental results indicate that the proposed method can realize human’s fall detection with much accuracy.%跌倒是独居老人最主要的意外风险之一,为快速有效获取跌倒信息,使老年人得到及时救助,提出一种基于Kinect体感传感器的人体跌倒自动检测方法,利用Kinect深度图像技术获取人体深度图像前景图,建立前景图三维包围盒,通过实时计算的三维包围盒的长、宽、高数值以及该数值的变化速度,判断人体跌倒是否发生。利用遮挡融合算法,解决了人体躯干被障碍物部分遮挡时,跌倒事件的检测和判定。在室内居家环境下进行了26种测试场景实验,检测误报率为2.0%~6.0%,漏报率为0~4.0%。该方法可以较为准确地实现人体跌倒自动检测。

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

    Science.gov (United States)

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

    2015-07-01

    The aim of this work is to present a computer-aided automated methodology for the assessment of carious lesions, according to the International Caries Detection and Assessment System (ICDAS II), which are located on the occlusal surfaces of posterior permanent teeth from photographic color tooth images. The proposed methodology consists of two stages: (a) the detection of regions of interest and (b) the classification of the detected regions according to ICDAS ΙΙ. In the first stage, pre-processing, segmentation and post-processing mechanisms were employed. For each pixel of the detected regions, a 15×15 neighborhood is used and a set of intensity-based and texture-based features were extracted. A correlation based technique was applied to select a subset of 36 features which were given as input into the classification stage, where five classifiers (J48, Random Tree, Random Forests, Support Vector Machines and Naïve Bayes) were compared to conclude to the best one, in our case, to Random Forests. The methodology was evaluated on a set of 103 digital color images where 425 regions of interest from occlusal surfaces of extracted permanent teeth were manually segmented and classified, based on visual assessments by two experts. The methodology correctly detected 337 out of 340 regions in the detection stage with accuracy of detection 80%. For the classification stage an overall accuracy 83% is achieved. The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge. PMID:25932969

  7. High throughput detection of Coxiella burnetii by real-time PCR with internal control system and automated DNA preparation

    Directory of Open Access Journals (Sweden)

    Kramme Stefanie

    2008-05-01

    Full Text Available Abstract Background Coxiella burnetii is the causative agent of Q-fever, a widespread zoonosis. Due to its high environmental stability and infectivity it is regarded as a category B biological weapon agent. In domestic animals infection remains either asymptomatic or presents as infertility or abortion. Clinical presentation in humans can range from mild flu-like illness to acute pneumonia and hepatitis. Endocarditis represents the most common form of chronic Q-fever. In humans serology is the gold standard for diagnosis but is inadequate for early case detection. In order to serve as a diagnostic tool in an eventual biological weapon attack or in local epidemics we developed a real-time 5'nuclease based PCR assay with an internal control system. To facilitate high-throughput an automated extraction procedure was evaluated. Results To determine the minimum number of copies that are detectable at 95% chance probit analysis was used. Limit of detection in blood was 2,881 copies/ml [95%CI, 2,188–4,745 copies/ml] with a manual extraction procedure and 4,235 copies/ml [95%CI, 3,143–7,428 copies/ml] with a fully automated extraction procedure, respectively. To demonstrate clinical application a total of 72 specimens of animal origin were compared with respect to manual and automated extraction. A strong correlation between both methods was observed rendering both methods suitable. Testing of 247 follow up specimens of animal origin from a local Q-fever epidemic rendered real-time PCR more sensitive than conventional PCR. Conclusion A sensitive and thoroughly evaluated real-time PCR was established. Its high-throughput mode may show a useful approach to rapidly screen samples in local outbreaks for other organisms relevant for humans or animals. Compared to a conventional PCR assay sensitivity of real-time PCR was higher after testing samples from a local Q-fever outbreak.

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Evaluation of two automated enzyme-immunoassays for detection of thermophilic campylobacters in faecal samples from cattle and swine

    DEFF Research Database (Denmark)

    Hoorfar, Jeffrey; Nielsen, E.M.; Stryhn, H.;

    1999-01-01

    We evaluated the performance of two enzyme-immunoassays (EIA) for the detection of naturally occurring, thermophilic Campylobacter spp. found in faecal samples from cattle (n = 21 and n = 26) and swine (n = 43) relative to the standard culture method, and also assuming that none of the tests......-2 method resulted in a rather low specificity (32%). This seemed to be partially due to the isolation of nonthermophilic species. In conclusion, EIA-1 method may provide a simple and fast tool with good accuracy in cattle and swine samples for automated screening of large number of samples....

  10. Home Automation Using SSVEP & Eye-Blink Detection Based Brain-Computer Interface

    OpenAIRE

    Goel, Kratarth; Vohra, Raunaq; Kamath, Anant; Baths, Veeky

    2014-01-01

    In this paper, we present a novel brain computer interface based home automation system using two responses - Steady State Visually Evoked Potential (SSVEP) and the eye-blink artifact, which is augmented by a Bluetooth based indoor localization system, to greatly increase the number of controllable devices. The hardware implementation of this system to control a table lamp and table fan using brain signals has also been discussed and state-of-the-art results have been achieved.

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

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, Christopher J., E-mail: chris.thompson@pnnl.gov; Martin, Paul F.; Chen, Jeffrey; Schaef, Herbert T.; Rosso, Kevin M.; Felmy, Andrew R.; Loring, John S. [Pacific Northwest National Laboratory, Richland, Washington 99352 (United States); Benezeth, Pascale [Géosciences Environnement Toulouse (GET), CNRS-Université de Toulouse, 31400 Toulouse (France)

    2014-04-15

    A fully automated titration system with infrared detection was developed for investigating interfacial chemistry at high pressures. The apparatus consists of a high-pressure fluid generation and delivery system coupled to a high-pressure cell with infrared optics. A manifold of electronically actuated valves is used to direct pressurized fluids into the cell. Precise reagent additions to the pressurized cell are made with calibrated tubing loops that are filled with reagent and placed in-line with the cell and a syringe pump. The cell's infrared optics facilitate both transmission and attenuated total reflection (ATR) measurements to monitor bulk-fluid composition and solid-surface phenomena such as adsorption, desorption, complexation, dissolution, and precipitation. Switching between the two measurement modes is accomplished with moveable mirrors that direct the light path of a Fourier transform infrared spectrometer into the cell along transmission or ATR light paths. The versatility of the high-pressure IR titration system was demonstrated with three case studies. First, we titrated water into supercritical CO{sub 2} (scCO{sub 2}) to generate an infrared calibration curve and determine the solubility of water in CO{sub 2} at 50 °C and 90 bar. Next, we characterized the partitioning of water between a montmorillonite clay and scCO{sub 2} at 50 °C and 90 bar. Transmission-mode spectra were used to quantify changes in the clay's sorbed water concentration as a function of scCO{sub 2} hydration, and ATR measurements provided insights into competitive residency of water and CO{sub 2} on the clay surface and in the interlayer. Finally, we demonstrated how time-dependent studies can be conducted with the system by monitoring the carbonation reaction of forsterite (Mg{sub 2}SiO{sub 4}) in water-bearing scCO{sub 2} at 50 °C and 90 bar. Immediately after water dissolved in the scCO{sub 2}, a thin film of adsorbed water formed on the mineral surface

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

    Science.gov (United States)

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

    2016-03-01

    Microscopy is the reference method for intestinal parasite identification. The cuvette-based automated microscopy analyser, sediMAX 1, provides 15 digital images of each sediment sample. In this study, we have evaluated this fully automated instrument for detection of enteric parasites, helminths and protozoa. A total of 700 consecutively preserved samples consisting of 60 positive samples (50 protozoa, ten helminths) and 640 negative samples were analysed. Operators were blinded to each others' results. Samples were randomized and were tested both by manual microscopy and sediMAX 1 for parasite recognition. The sediMAX 1 analysis was conducted using a dilution of faecal samples, allowing determination of morphology. The data obtained using sediMAX 1 showed a specificity of 100% and a sensitivity of 100%. Some species of helminths, such as Enterobius vermicularis, Strongyloides stercolaris, the Ancylostoma duodenale/Necator americanus complex, and schistosomes were not considered in this work, because they are rare in stool specimens, are not easily detectable with microscopy analysis, and require specific recovery techniques. This study demonstrated for the first time that sediMAX 1 can be an aid in enteric parasite identification. PMID:26679923

  13. Automated text categorization in a dead language. The detection of genres in Late Egyptian

    OpenAIRE

    Gohy, Stéphanie; Martin Leon, Benjamin; Polis, Stéphane

    2013-01-01

    This paper is a first step in applying machine learning methods typical of Automated Text Catego-rization (ATC) for Automatic Genre Identification (AGI) in Late Egyptian, a language written in either hieroglyphic or hieratic scripts that is found in documents from Ancient Egypt dating from ca. 1350-700 BCE. The study is divided into three parts. After a general intro¬duction on AGI (§1), we introduce the levels of annotation that are integrated in the Ramses corpus and can be used when perfor...

  14. Automated, computer generated reminders and increased detection of gonorrhoea, chlamydia and syphilis in men who have sex with men.

    Directory of Open Access Journals (Sweden)

    Huachun Zou

    Full Text Available BACKGROUND: Guidelines recommend frequent screening of men who have sex with men (MSM for sexually transmissible infections (STIs but few interventions have demonstrated increased testing and detection of bacterial STIs among MSM in controlled studies. METHODS: We used automated text message and email reminders generated by computer assisted self-interview (CASI to remind MSM to retest for syphilis. We compared clinic visits, STI testing and detection rates over 12 month between men receiving reminders (reminder group and men not offered the reminders (concurrent control group. RESULTS: Men who chose 3-monthly reminders had more clinic visits (median 3 vs 1 and higher testing rates for pharyngeal gonorrhoea (67.0% vs 33.6%, rectal gonorrhoea (62.7% vs 31.1%, urethral chlamydia (67.3% vs 39.3%, rectal chlamydia (62.9% vs 31.3%, syphilis (67.0% vs 39.3% and HIV (64.9% vs 36.7% (all p<0.001 than concurrent controls, within 12 months after their first visit. Also, men receiving reminders had a higher combined testing rate for all the aforementioned STIs at a same visit (55.7% vs 25.5%, p<0.001 compared with concurrent controls. This association remained after adjusting for differences in characteristics between the two groups (adjusted odds ratio:1.77, 95% confidence interval:1.51-2.08. Men receiving reminders also had a higher detection rate of: rectal gonorrhoea (3.7% vs 1.2%, p = 0.001, urethral chlamydia (3.1% vs 1.4%, p = 0.027, rectal chlamydia (6.6% vs 2.8%, p<0.001, and early, latent syphilis (1.7% vs 0.4%, p = 0.008 compared with concurrent controls. CONCLUSION: This is the first study to demonstate that a fully automated reminder system using CASI was associated with increased detection of bacterial STIs among MSM.

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

    Science.gov (United States)

    Tello-Mijares, Santiago; Flores, Francisco

    2016-01-01

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

  16. Automated ultrasonic inspection for crack detection at F-111 lower wing skin fastener holes

    International Nuclear Information System (INIS)

    The failure of an F-l I I wing during a full-scale fatigue test had important implications for the structural integrity management of the RAAF F- I I I fleet. This failure was due to a fatigue crack which initiated in the bore of a fastener hole. To assure structural integrity, an automated ultrasonic inspection has been developed which will be applied to up to 1200 fastener holes in each wing. The holes are inspected from the lower surface and definitive assessments of wing serviceability must be made without removing fasteners. The inspection system uses focussed immersion probes to perform a 45-degree angle-beam shear wave inspection, utilising full-waveform capture and post-processing. This style of complex, computer-based inspection system is new to the Australian Defence Organisation and challenged many existing engineering processes for performing NDT and interpreting the results. This paper reviews the application of automated ultrasonic inspection for the F- l I I lower wing skin and outlines some of the significant challenges for both the science behind the inspection and the associated engineering processes. The lessons learnt will aid the successful integration of new technologies into existing NDT practices in the future

  17. Towards Sensor-Actuator Coupling in an Automated Order Picking System by Detecting Sealed Seams on Pouch Packed Goods

    Directory of Open Access Journals (Sweden)

    Frank Weichert

    2014-10-01

    Full Text Available In this paper, a novel concept of coupling the actuators of an automated order picking system for pouch packed goods with an embedded CCD camera sensor by means of image processing and machine learning is presented. The picking system mechanically combines the conveyance and singularization of a still-connected chain of pouch packed goods in a single machinery. The proposed algorithms perform a per-frame processing of the captured images in real-time to detect the sealed seams of the ongoing pouches. The detections are used to deduce cutting decisions in order to control the system’s actuators, namely the drive pulley for conveyance and the cutting device for the separation. Within this context, two controlling strategies are presented as well which specify the interaction of the sensor and the actuators. The detection is carried out by two different marker detection strategies: enhanced Template Matching as a heuristic and Support Vector Machines as a supervised classification based concept. Depending on the employed marker, detection rates of almost 100% with a calculation time of less than 40 ms are possible. From a logistic point of view, sealed seam widths of 20 mm prove feasible.

  18. Detection of Onchocerca volvulus in Latin American black flies for pool screening PCR using high-throughput automated DNA isolation for transmission surveillance.

    Science.gov (United States)

    Rodríguez-Pérez, Mario A; Gopal, Hemavathi; Adeleke, Monsuru Adebayo; De Luna-Santillana, Erick Jesús; Gurrola-Reyes, J Natividad; Guo, Xianwu

    2013-11-01

    The posttreatment entomological surveillance (ES) of onchocerciasis in Latin America requires quite large numbers of flies to be examined for parasite infection to prove that the control strategies have worked and that the infection is on the path of elimination. Here, we report a high-throughput automated DNA isolation of Onchocerca volvulus for PCR using a major Latin American black fly vector of onchocerciasis. The sensitivity and relative effectiveness of silica-coated paramagnetic beads was evaluated in comparison with phenol chloroform (PC) method which is known as the gold standard of DNA extraction for ES in Latin America. The automated method was optimized in the laboratory and validated in the field to detect parasite DNA in Simulium ochraceum sensu lato flies in comparison with PC. The optimization of the automated method showed that it is sensitive to detect O. volvulus with a pool size of 100 flies as compared with PC which utilizes 50 flies pool size. The validation of the automated method in comparison with PC in an endemic community showed that 5/67 and 3/134 heads pools were positive for the two methods, respectively. There was no statistical variation (P < 0.05) in the estimation of transmission indices generated by automated method when compared with PC method. The fact that the automated method is sensitive to pool size up to 100 confers advantage over PC method and can, therefore, be employed in large-scale ES of onchocerciasis transmission in endemic areas of Latin America. PMID:24030195

  19. Detection and Automated Scoring of Dicentric Chromosomes in Nonstimulated Lymphocyte Prematurely Condensed Chromosomes After Telomere and Centromere Staining

    Energy Technology Data Exchange (ETDEWEB)

    M' kacher, Radhia [Laboratoire de Radiobiologie et Oncologie, Commissariat à l' Energie Atomique, Fontenay-aux-Roses (France); El Maalouf, Elie [Laboratoire de Radiobiologie et Oncologie, Commissariat à l' Energie Atomique, Fontenay-aux-Roses (France); Laboratoire Modélisation Intelligence Processus Systèmes (MIPS)–Groupe TIIM3D, Université de Haute-Alsace, Mulhouse (France); Terzoudi, Georgia [Laboratory of Radiobiology & Biodosimetry, National Center for Scientific Research Demokritos, Athens (Greece); Ricoul, Michelle [Laboratoire de Radiobiologie et Oncologie, Commissariat à l' Energie Atomique, Fontenay-aux-Roses (France); Heidingsfelder, Leonhard [MetaSystems, Altlussheim (Germany); Karachristou, Ionna [Laboratory of Radiobiology & Biodosimetry, National Center for Scientific Research Demokritos, Athens (Greece); Laplagne, Eric [Pole Concept, Paris (France); Hempel, William M. [Laboratoire de Radiobiologie et Oncologie, Commissariat à l' Energie Atomique, Fontenay-aux-Roses (France); Colicchio, Bruno; Dieterlen, Alain [Laboratoire Modélisation Intelligence Processus Systèmes (MIPS)–Groupe TIIM3D, Université de Haute-Alsace, Mulhouse (France); Pantelias, Gabriel [Laboratory of Radiobiology & Biodosimetry, National Center for Scientific Research Demokritos, Athens (Greece); Sabatier, Laure, E-mail: laure.sabatier@cea.fr [Laboratoire de Radiobiologie et Oncologie, Commissariat à l' Energie Atomique, Fontenay-aux-Roses (France)

    2015-03-01

    Purpose: To combine telomere and centromere (TC) staining of premature chromosome condensation (PCC) fusions to identify dicentrics, centric rings, and acentric chromosomes, making possible the realization of a dose–response curve and automation of the process. Methods and Materials: Blood samples from healthy donors were exposed to {sup 60}Co irradiation at varying doses up to 8 Gy, followed by a repair period of 8 hours. Premature chromosome condensation fusions were carried out, and TC staining using peptide nucleic acid probes was performed. Chromosomal aberration (CA) scoring was carried out manually and automatically using PCC-TCScore software, developed in our laboratory. Results: We successfully optimized the hybridization conditions and image capture parameters, to increase the sensitivity and effectiveness of CA scoring. Dicentrics, centric rings, and acentric chromosomes were rapidly and accurately detected, leading to a linear-quadratic dose–response curve by manual scoring at up to 8 Gy. Using PCC-TCScore software for automatic scoring, we were able to detect 95% of dicentrics and centric rings. Conclusion: The introduction of TC staining to the PCC fusion technique has made possible the rapid scoring of unstable CAs, including dicentrics, with a level of accuracy and ease not previously possible. This new approach can be used for biological dosimetry in radiation emergency medicine, where the rapid and accurate detection of dicentrics is a high priority using automated scoring. Because there is no culture time, this new approach can also be used for the follow-up of patients treated by genotoxic therapy, creating the possibility to perform the estimation of induced chromosomal aberrations immediately after the blood draw.

  20. Detection and Automated Scoring of Dicentric Chromosomes in Nonstimulated Lymphocyte Prematurely Condensed Chromosomes After Telomere and Centromere Staining

    International Nuclear Information System (INIS)

    Purpose: To combine telomere and centromere (TC) staining of premature chromosome condensation (PCC) fusions to identify dicentrics, centric rings, and acentric chromosomes, making possible the realization of a dose–response curve and automation of the process. Methods and Materials: Blood samples from healthy donors were exposed to 60Co irradiation at varying doses up to 8 Gy, followed by a repair period of 8 hours. Premature chromosome condensation fusions were carried out, and TC staining using peptide nucleic acid probes was performed. Chromosomal aberration (CA) scoring was carried out manually and automatically using PCC-TCScore software, developed in our laboratory. Results: We successfully optimized the hybridization conditions and image capture parameters, to increase the sensitivity and effectiveness of CA scoring. Dicentrics, centric rings, and acentric chromosomes were rapidly and accurately detected, leading to a linear-quadratic dose–response curve by manual scoring at up to 8 Gy. Using PCC-TCScore software for automatic scoring, we were able to detect 95% of dicentrics and centric rings. Conclusion: The introduction of TC staining to the PCC fusion technique has made possible the rapid scoring of unstable CAs, including dicentrics, with a level of accuracy and ease not previously possible. This new approach can be used for biological dosimetry in radiation emergency medicine, where the rapid and accurate detection of dicentrics is a high priority using automated scoring. Because there is no culture time, this new approach can also be used for the follow-up of patients treated by genotoxic therapy, creating the possibility to perform the estimation of induced chromosomal aberrations immediately after the blood draw

  1. Studies of criticality Monte Carlo method convergence: use of a deterministic calculation and automated detection of the transient

    International Nuclear Information System (INIS)

    Monte Carlo criticality calculation allows to estimate the effective multiplication factor as well as local quantities such as local reaction rates. Some configurations presenting weak neutronic coupling (high burn up profile, complete reactor core,...) may induce biased estimations for keff or reaction rates. In order to improve robustness of the iterative Monte Carlo methods, a coupling with a deterministic code was studied. An adjoint flux is obtained by a deterministic calculation and then used in the Monte Carlo. The initial guess is then automated, the sampling of fission sites is modified and the random walk of neutrons is modified using splitting and russian roulette strategies. An automated convergence detection method has been developed. It locates and suppresses the transient due to the initialization in an output series, applied here to keff and Shannon entropy. It relies on modeling stationary series by an order 1 auto regressive process and applying statistical tests based on a Student Bridge statistics. This method can easily be extended to every output of an iterative Monte Carlo. Methods developed in this thesis are tested on different test cases. (author)

  2. Visual compression of workflow visualizations with automated detection of macro motifs.

    Science.gov (United States)

    Maguire, Eamonn; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Davies, Jim; Chen, Min

    2013-12-01

    This paper is concerned with the creation of 'macros' in workflow visualization as a support tool to increase the efficiency of data curation tasks. We propose computation of candidate macros based on their usage in large collections of workflows in data repositories. We describe an efficient algorithm for extracting macro motifs from workflow graphs. We discovered that the state transition information, used to identify macro candidates, characterizes the structural pattern of the macro and can be harnessed as part of the visual design of the corresponding macro glyph. This facilitates partial automation and consistency in glyph design applicable to a large set of macro glyphs. We tested this approach against a repository of biological data holding some 9,670 workflows and found that the algorithmically generated candidate macros are in keeping with domain expert expectations.

  3. Quantitative Assessment of Mouse Mammary Gland Morphology Using Automated Digital Image Processing and TEB Detection.

    Science.gov (United States)

    Blacher, Silvia; Gérard, Céline; Gallez, Anne; Foidart, Jean-Michel; Noël, Agnès; Péqueux, Christel

    2016-04-01

    The assessment of rodent mammary gland morphology is largely used to study the molecular mechanisms driving breast development and to analyze the impact of various endocrine disruptors with putative pathological implications. In this work, we propose a methodology relying on fully automated digital image analysis methods including image processing and quantification of the whole ductal tree and of the terminal end buds as well. It allows to accurately and objectively measure both growth parameters and fine morphological glandular structures. Mammary gland elongation was characterized by 2 parameters: the length and the epithelial area of the ductal tree. Ductal tree fine structures were characterized by: 1) branch end-point density, 2) branching density, and 3) branch length distribution. The proposed methodology was compared with quantification methods classically used in the literature. This procedure can be transposed to several software and thus largely used by scientists studying rodent mammary gland morphology. PMID:26910307

  4. Detection of Orbital Debris Collision Risks for the Automated Transfer Vehicle

    Science.gov (United States)

    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.

  5. Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

    Directory of Open Access Journals (Sweden)

    Jacopo Aguzzi

    2011-11-01

    Full Text Available The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina, as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp., using a camera deployed in Saanich Inlet (103 m depth. For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters with Euclidean Distances (ED on Red-Green-Blue (RGB channels. The Scale-Invariant Feature Transform (SIFT features and Fourier Descriptors (FD of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA on Mean RGB (RGBv value for each object and Fourier Descriptors (RGBv+FD matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent

  6. Automated detection of discourse segment and experimental types from the text of cancer pathway results sections

    Science.gov (United States)

    Burns, Gully A.P.C.; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H.

    2016-01-01

    Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles’ Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data’s meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide

  7. Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

    Science.gov (United States)

    Burns, Gully A P C; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H

    2016-01-01

    Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide

  8. Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

    Science.gov (United States)

    Burns, Gully A P C; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H

    2016-01-01

    Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide

  9. Fluorescence detection by intensity changes for high-performance thin-layer chromatography separation of lipids using automated multiple development.

    Science.gov (United States)

    Cebolla, Vicente L; Jarne, Carmen; Domingo, Pilar; Domínguez, Andrés; Delgado-Camón, Aránzazu; Garriga, Rosa; Galbán, Javier; Membrado, Luis; Gálvez, Eva M; Cossío, Fernando P

    2011-05-13

    Changes in emission of berberine cation, induced by non-covalent interactions with lipids on silica gel plates, can be used for detecting and quantifying lipids using fluorescence scanning densitometry in HPTLC analysis. This procedure, referred to as fluorescence detection by intensity changes (FDIC) has been used here in combination with automated multiple development (HPTLC/AMD), a gradient-based separation HPTLC technique, for separating, detecting and quantifying lipids from different families. Three different HPTLC/AMD gradient schemes have been developed for separating: neutral lipid families and steryl glycosides; different sphingolipids; and sphingosine-sphinganine mixtures. Fluorescent molar responses of studied lipids, and differences in response among different lipid families have been rationalized in the light of a previously proposed model of FDIC response, which is based on ion-induced dipole interactions between the fluorophore and the analyte. Likewise, computational calculations using molecular mechanics have also been a complementary useful tool to explain high FDIC responses of cholesteryl and steryl-derivatives, and moderate responses of sphingolipids. An explanation for the high FDIC response of cholesterol, whose limit of detection (LOD) is 5 ng, has been proposed. Advantages and limitations of FDIC application have also been discussed. PMID:21145556

  10. Online sorting of recovered wood waste by automated XRF-technology. Part I: detection of preservative-treated wood waste.

    Science.gov (United States)

    Rasem Hasan, A; Schindler, John; Solo-Gabriele, Helena M; Townsend, Timothy G

    2011-04-01

    Waste wood is frequently contaminated with wood treatment preservatives including chromated copper arsenate (CCA) and alkaline copper quat (ACQ), both of which contain metals which contaminate recycled wood products. The objective of this research was to propose a design for online automated identification of As-based and Cu-based treated wood within the recovered wood waste stream utilizing an X-ray fluorescence (XRF) system, and to evaluate the detection parameters of such system. A full-scale detection unit was used for experimentation. Two main parameters (operational threshold (OT) and measurement time) were evaluated to optimize detection efficiencies. OTs of targeted metals, As and Cu, in wood were reduced to 0.02 and 0.05, respectively. The optimum minimum measurement time of 500 ms resulted in 98%, 91%, and 97% diversion of the As, Cu and Cr mass originally contained in wood, respectively. Comparisons with other detection methods show that XRF technology can potentially fulfill the need for cost-effective processing at large facilities (>30 tons per day) which require the removal of As-based preservatives from their wood waste stream. PMID:21186117

  11. Automated Chirp Detection with Diffusion Entropy: Application to Infrasound from Sprites

    CERN Document Server

    Ignaccolo, M; Farges, T; Fullekrug, M

    2005-01-01

    We study the performance of three different methods to automatically detect a chirp in background noise. (1) The standard deviation detector uses the computation of the signal to noise ratio. (2) The spectral covariance detector is based on the recognition of the chirp in the spectrogram. (3) The CASSANDRA detector uses diffusion entropy analysis to detect periodic patterns in noise. All three detectors are applied to an infrasound recording for detecting chirps produced by sprites. The CASSANDRA detector provides the best trade off between the false alarm rate and the detection efficiency.

  12. Computer-automated caries detection in digital bitewings: consistency of a program and its influence on observer agreement.

    Science.gov (United States)

    Wenzel, A

    2001-01-01

    The aim of this study was to evaluate a decision-support, caries detection program and its influence on observer agreement in caries diagnosis. 130 patients were examined by digital bitewing radiography (RVG XL sensor, Trophy Radiologie Inc.). Fifty-four approximal surfaces (27 in premolars and 27 in molars) were selected by the author: 24 surfaces (9 in molars and 15 in premolars) scored as sound, 16 surfaces (9 in molars and 7 in premolars) scored as carious in enamel, and 14 surfaces (9 in molars and 5 in premolars) scored as carious in dentine. The Logicon Caries Detector (LCD) program (Logicon Inc., USA) was assessed by repeating the automated analysis ten times for each surface. The two most varying outcomes for lesion probability (Lp(min) and Lp(max)) were saved. Five observers scored the 54 surfaces independently as sound, caries in enamel or caries in dentine before and after the use of LCD. In more than one third of all surfaces the program indicated different lesion probability, from sound at Lp(min) to the presence of a carious lesion at Lp(max). The 5 observers changed their caries score after the use of LCD in a total of 31 surfaces (only 2 of these were in the same surface). Mean kappa value for inter-observer agreement for caries scores before the use of LCD was 0.47 (range 0. 39-0.61) and after LCD 0.48 (range 0.37-0.69). It was concluded that the automated caries detection program was not very consistent and provided different opinions on the caries status in a surface. Inter-observer agreement in caries diagnosis did not improve using the program.

  13. Automated Detection of Healthy and Diseased Aortae from Images Obtained by Contrast-Enhanced CT Scan

    Directory of Open Access Journals (Sweden)

    Michael Gayhart

    2013-01-01

    Full Text Available Purpose. We developed the next stage of our computer assisted diagnosis (CAD system to aid radiologists in evaluating CT images for aortic disease by removing innocuous images and highlighting signs of aortic disease. Materials and Methods. Segmented data of patient’s contrast-enhanced CT scan was analyzed for aortic dissection and penetrating aortic ulcer (PAU. Aortic dissection was detected by checking for an abnormal shape of the aorta using edge oriented methods. PAU was recognized through abnormally high intensities with interest point operators. Results. The aortic dissection detection process had a sensitivity of 0.8218 and a specificity of 0.9907. The PAU detection process scored a sensitivity of 0.7587 and a specificity of 0.9700. Conclusion. The aortic dissection detection process and the PAU detection process were successful in removing innocuous images, but additional methods are necessary for improving recognition of images with aortic disease.

  14. Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

    Directory of Open Access Journals (Sweden)

    Tözeren Aydin

    2007-03-01

    Full Text Available Abstract Background Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. Methods This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B, percentage occupied by stroma-like regions (P, and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. Results Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. Conclusion These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists as hundreds of tumors that are used to develop an array have typically been evaluated

  15. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study

    International Nuclear Information System (INIS)

    The aim of this research was to evaluate the performance of a new spectroscopic system in the diagnosis of melanoma. This study involves a consecutive series of 1278 patients with 1391 cutaneous pigmented lesions including 184 melanomas. In an attempt to approach the 'real world' of lesion population, a further set of 1022 not excised clinically reassuring lesions was also considered for analysis. Each lesion was imaged in vivo by a multispectral imaging system. The system operates at wavelengths between 483 and 950 nm by acquiring 15 images at equally spaced wavelength intervals. From the images, different lesion descriptors were extracted related to the colour distribution and morphology of the lesions. Data reduction techniques were applied before setting up a neural network classifier designed to perform automated diagnosis. The data set was randomly divided into three sets: train (696 lesions, including 90 melanomas) and verify (348 lesions, including 53 melanomas) for the instruction of a proper neural network, and an independent test set (347 lesions, including 41 melanomas). The neural network was able to discriminate between melanomas and non-melanoma lesions with a sensitivity of 80.4% and a specificity of 75.6% in the 1391 histologized cases data set. No major variations were found in classification scores when train, verify and test subsets were separately evaluated. Following receiver operating characteristic (ROC) analysis, the resulting area under the curve was 0.85. No significant differences were found among areas under train, verify and test set curves, supporting the good network ability to generalize for new cases. In addition, specificity and area under ROC curve increased up to 90% and 0.90, respectively, when the additional set of 1022 lesions without histology was added to the test set. Our data show that performance of an automated system is greatly population dependent, suggesting caution in the comparison with results reported in the

  16. Breast cancer detection: Radiologists' performance using mammography with and without automated whole-breast ultrasound

    Energy Technology Data Exchange (ETDEWEB)

    Kelly, Kevin M. [Hall Center, Santa Monica, CA (United States); Lee, Sung-Jae; Comulada, W.S. [University of California, Semel Institute Center for Community Health, Los Angeles, CA (United States); Dean, Judy

    2010-11-15

    Radiologist reader performance for breast cancer detection using mammography plus automated whole-breast ultrasound (AWBU) was compared with mammography alone. Screenings for non-palpable breast malignancies in women with radiographically dense breasts with contemporaneous mammograms and AWBU were reviewed by 12 radiologists blinded to the diagnoses; half the studies were abnormal. Readers first reviewed the 102 mammograms. The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BIRADS) and Digital Mammographic Imaging Screening Trial (DMIST) likelihood ratings were recorded with location information for identified abnormalities. Readers then reviewed the mammograms and AWBU with knowledge of previous mammogram-only evaluation. We compared reader performance across screening techniques using absolute callback, areas under the curve (AUC), and figure of merit (FOM). True positivity of cancer detection increased 63%, with only a 4% decrease in true negativity. Reader-averaged AUC was higher for mammography plus AWBU compared with mammography alone by BIRADS (0.808 versus 0.701) and likelihood scores (0.810 versus 0.703). Similarly, FOM was higher for mammography plus AWBU compared with mammography alone by BIRADS (0.786 versus 0.613) and likelihood scores (0.791 versus 0.614). Adding AWBU to mammography improved callback rates, accuracy of breast cancer detection, and confidence in callbacks for dense-breasted women. (orig.)

  17. Renewable Surface Fluorescence Sandwich Immunoassay Biosensor for Rapid Sensitive Botulinum Toxin Detection in an Automated Fluidic Format

    Energy Technology Data Exchange (ETDEWEB)

    Grate, Jay W.; Warner, Marvin G.; Ozanich, Richard M.; Miller, Keith D.; Colburn, Heather A.; Dockendorff, Brian P.; Antolick, Kathryn C.; Anheier, Norman C.; Lind, Michael A.; Lou, Jianlong; Marks, James D.; Bruckner-Lea, Cindy J.

    2009-03-05

    A renewable surface biosensor for rapid detection of botulinum toxin is described based on fluidic automation of a fluorescence sandwich immunoassay, using a recombinant fragment of the toxin heavy chain as a structurally valid simulant. Monoclonal antibodies AR4 and RAZ1 bind to separate epitopes of both this fragment and the holotoxin. The AR4 antibody was covalently bound to Sepharose beads and used as the capture antibody. A rotating rod flow cell was used to capture these beads delivered as a suspension by the sequential injection flow system, creating a 3.6 microliter column. After perfusing the bead column with sample and washing away the matrix, the column was perfused with Alexa 647 dye-labeled RAZ1 antibody as the reporter. Optical fibers coupled to the rotating rod flow cell at a 90 degree angle to one another delivered excitation light from a HeNe laser and collected fluorescent emission light for detection. After each measurement, the used sepharose beads are released and replaced with fresh beads. In a rapid screening approach to sample analysis, the toxin simulant was detected to concentrations of 10 pM in less than 20 minutes.

  18. OGLE-2008-BLG-510: first automated real-time detection of a weak microlensing anomaly - brown dwarf or stellar binary?

    CERN Document Server

    Bozza, V; Rattenbury, N J; Joergensen, U G; Tsapras, Y; Bramich, D M; Udalski, A; Bond, I A; Liebig, C; Cassan, A; Fouque, P; Fukui, A; Hundertmark, M; Shin, I -G; Lee, S H; Choi, J -Y; Park, S -Y; Gould, A; Allan, A; Mao, S; Wyrzykowski, L; Street, R A; Buckley, D; Nagayama, T; Mathiasen, M; Hinse, T C; Novati, S Calchi; Harpsoee, K; Mancini, L; Scarpetta, G; Anguita, T; Burgdorf, M J; Horne, K; Hornstrup, A; Kains, N; Kerins, E; Kjaergaard, P; Masi, G; Rahvar, S; Ricci, D; Snodgrass, C; Southworth, J; Steele, I A; Surdej, J; Thoene, C C; Wambsganss, J; Zub, M; Albrow, M D; Batista, V; Beaulieu, J -P; Bennett, D P; Caldwell, J A R; Cole, A; Cook, K H; Coutures, C; Dieters, S; Prester, D Dominis; Donatowicz, J; Greenhill, J; Kane, S R; Kubas, D; Marquette, J -B; Martin, R; Menzies, J; Pollard, K R; Sahu, K C; Williams, A; Szymanski, M K; Kubiak, M; Pietrzynski, G; Soszynski, I; Poleski, R; Ulaczyk, K; DePoy, D L; Dong, S; Han, C; Janczak, J; Lee, C -U; Pogge, R W; Abe, F; Furusawa, K; Hearnshaw, J B; Itow, Y; Kilmartin, P M; Korpela, A V; Lin, W; Ling, C H; Masuda, K; Matsubara, Y; Miyake, N; Muraki, Y; Ohnishi, K; Perrott, Y C; Saito, To; Skuljan, L; Sullivan, D J; Sumi, T; Suzuki, D; Sweatman, W L; Tristram, P J; Wada, K; Yock, P C M; Gulbis, A; Hashimoto, Y; Kniazev, A; Vaisanen, P

    2012-01-01

    The microlensing event OGLE-2008-BLG-510 is characterised by an evident asymmetric shape of the peak, promptly detected by the ARTEMiS system in real time. The skewness of the light curve appears to be compatible both with binary-lens and binary-source models, including the possibility that the lens system consists of an M dwarf orbited by a brown dwarf. The detection of this microlensing anomaly and our analysis demonstrates that: 1) automated real-time detection of weak microlensing anomalies with immediate feedback is feasible, efficient, and sensitive, 2) rather common weak features intrinsically come with ambiguities that are not easily resolved from photometric light curves, 3) a modelling approach that finds all features of parameter space rather than just the `favourite model' is required, and 4) the data quality is most crucial, where systematics can be confused with real features, in particular small higher-order effects such as orbital motion signatures. It moreover becomes apparent that events wit...

  19. Multicenter Evaluation of a Novel Automated Rapid Detection System of BRAF Status in Formalin-Fixed, Paraffin-Embedded Tissues.

    Science.gov (United States)

    Schiefer, Ana-Iris; Parlow, Laura; Gabler, Lisa; Mesteri, Ildiko; Koperek, Oskar; von Deimling, Andreas; Streubel, Berthold; Preusser, Matthias; Lehmann, Annika; Kellner, Udo; Pauwels, Patrick; Lambin, Suzan; Dietel, Manfred; Hummel, Michael; Klauschen, Frederick; Birner, Peter; Möbs, Markus

    2016-05-01

    The mutated BRAF oncogene represents a therapeutic target in malignant melanoma. Because BRAF mutations are also involved in the pathogenesis of other human malignancies, the use of specific BRAF inhibitors might also be extended to other diseases in the future. A prerequisite for the clinical application of BRAF inhibitors is the reliable detection of activating BRAF mutations in routine histopathological samples. In a multicenter approach, we evaluated a novel and fully automated PCR-based system (Idylla) capable of detecting BRAF V600 mutations in formalin-fixed, paraffin-embedded tissue within 90 minutes with high sensitivity. We analyzed a total of 436 samples with the Idylla system. Valid results were obtained in 421 cases (96.56%). Its performance was compared with conventional methods (pyrosequencing or Sanger sequencing). Concordant results were obtained in 406 cases (96.90%). Reanalysis of eight discordant samples by next-generation sequencing and/or pyrosequencing with newly extracted DNA and the BRAF RGQ Kit confirmed the Idylla result in seven cases, resulting in an overall agreement of 98.57%. In conclusion, the Idylla system is a highly reliable and sensitive platform for detection of BRAF V600 mutations in formalin-fixed, paraffin-embedded material, providing an efficient alternative to conventional diagnostic methods, particularly for routine diagnostics laboratories with limited experience in molecular pathology.

  20. System automation for a bacterial colony detection and identification instrument via forward scattering

    International Nuclear Information System (INIS)

    A system design and automation of a microbiological instrument that locates bacterial colonies and captures the forward-scattering signatures are presented. The proposed instrument integrates three major components: a colony locator, a forward scatterometer and a motion controller. The colony locator utilizes an off-axis light source to illuminate a Petri dish and an IEEE1394 camera to capture the diffusively scattered light to provide the number of bacterial colonies and two-dimensional coordinate information of the bacterial colonies with the help of a segmentation algorithm with region-growing. Then the Petri dish is automatically aligned with the respective centroid coordinate with a trajectory optimization method, such as the Traveling Salesman Algorithm. The forward scatterometer automatically computes the scattered laser beam from a monochromatic image sensor via quadrant intensity balancing and quantitatively determines the centeredness of the forward-scattering pattern. The final scattering signatures are stored to be analyzed to provide rapid identification and classification of the bacterial samples

  1. Automated detection of ambiguity in BI-RADS assessment categories in mammography reports.

    Science.gov (United States)

    Bozkurt, Selen; Rubin, Daniel

    2014-01-01

    An unsolved challenge in biomedical natural language processing (NLP) is detecting ambiguities in the reports that can help physicians to improve report clarity. Our goal was to develop NLP methods to tackle the challenges of identifying ambiguous descriptions of the laterality of BI-RADS Final Assessment Categories in mammography radiology reports. We developed a text processing system that uses a BI-RADS ontology we built as a knowledge source for automatic annotation of the entities in mammography reports relevant to this problem. We used the GATE NLP toolkit and developed customized processing resources for report segmentation, named entity recognition, and detection of mismatches between BI-RADS Final Assessment Categories and mammogram laterality. Our system detected 55 mismatched cases in 190 reports and the accuracy rate was 81%. We conclude that such NLP techniques can detect ambiguities in mammography reports and may reduce discrepancy and variability in reporting. PMID:24743074

  2. High-Speed Observer: Automated Streak Detection for the Aerospike Engine

    Science.gov (United States)

    Rieckhoff, T. J.; Covan, M. A.; OFarrell, J. M.

    2001-01-01

    A high-frame-rate digital video camera, installed on test stands at Stennis Space Center (SSC), has been used to capture images of the aerospike engine plume during test. These plume images are processed in real time to detect and differentiate anomalous plume events. Results indicate that the High-Speed Observer (HSO) system can detect anomalous plume streaking events that are indicative of aerospike engine malfunction.

  3. Automated microcalcification detection in mammograms using statistical variable-box-threshold filter method

    Science.gov (United States)

    Wilson, Mark; Mitra, Sunanda; Roberson, Glenn H.; Shieh, Yao-Yang

    1997-10-01

    Currently early detection of breast cancer is primarily accomplished by mammography and suspicious findings may lead to a decision for performing a biopsy. Digital enhancement and pattern recognition techniques may aid in early detection of some patterns such as microcalcification clusters indicating onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers and could be treated when detected early. These individual calcifications are hard to detect due to size and shape variability and inhomogeneous background texture. Our study addresses only early detection of microcalcifications that allows the radiologist to interpret the x-ray findings in computer-aided enhanced form easier than evaluating the x-ray film directly. We present an algorithm which locates microcalcifications based on local grayscale variability and of tissue structures and image statistics. Threshold filters with lower and upper bounds computed from the image statistics of the entire image and selected subimages were designed to enhance the entire image. This enhanced image was used as the initial image for identifying the micro-calcifications based on the variable box threshold filters at different resolutions. The test images came from the Texas Tech University Health Sciences Center and the MIAS mammographic database, which are classified into various categories including microcalcifications. Classification of other types of abnormalities in mammograms based on their characteristic features is addressed in later studies.

  4. Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.

    Science.gov (United States)

    Papp, Eszter A; Leergaard, Trygve B; Csucs, Gergely; Bjaalie, Jan G

    2016-01-01

    Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data. PMID:27148038

  5. Automated detection of semagram-laden images using adaptive neural networks

    Science.gov (United States)

    Cerkez, Paul S.; Cannady, James D.

    2012-04-01

    Digital steganography is gaining wide acceptance in the world of electronic copyright stamping. Digital media that are easy to steal, such as graphics, photos and audio files, are being tagged with both visible and invisible copyright stamps (known as digital watermarking). However, these same techniques can also be used to hide communications between actors in criminal or covert activities. An inherent difficulty in detecting steganography is overcoming the variety of methods for hiding a message and the multitude of choices of available media. Another problem in steganography defense is the issue of detection speed since the encoded data is frequently time-sensitive. When a message is visually transmitted in a non-textual format (i.e., in an image) it is referred to as a semagram. Semagrams are relatively easy to create, but very difficult to detect. While steganography can often be identified by detecting digital modifications to an image's structure, an image-based semagram is more difficult because the message is the image itself. The work presented describes the creation of a novel, computer-based application, which uses hybrid hierarchical neural network architecture to detect the likely presence of a semagram message in an image. The prototype system was used to detect semagrams containing Morse Code messages. Based on the results of these experiments our approach provides a significant advance in the detection of complex semagram patterns. Specific results of the experiments and the potential practical applications of the neural network-based technology are discussed. This presentation provides the final results of our research experiments.

  6. OGLE‐2008‐BLG‐510: first automated real‐time detection of a weak microlensing anomaly – brown dwarf or stellar binary?★

    DEFF Research Database (Denmark)

    Bozza, V.; Dominik, M.; Rattenbury, N. J.;

    2012-01-01

    The microlensing event OGLE‐2008‐BLG‐510 is characterized by an evident asymmetric shape of the peak, promptly detected by the Automated Robotic Terrestrial Exoplanet Microlensing Search (ARTEMiS) system in real time. The skewness of the light curve appears to be compatible both with binary...

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

    Science.gov (United States)

    Reeves, Sheldon L; Fleming, Kelsey E; Zhang, Lin; Scimemi, Annalisa

    2016-09-01

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

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

    Science.gov (United States)

    Zhang, Lin

    2016-01-01

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

  9. Catheter detection and classification on chest radiographs: an automated prototype computer-aided detection (CAD) system for radiologists

    Science.gov (United States)

    Ramakrishna, Bharath; Brown, Matthew; Goldin, Jonathan; Cagnon, Chris; Enzmann, Dieter

    2011-03-01

    Chest radiographs are the quickest and safest method to check placement of man-made medical devices placed in the body like catheters, stents and pacemakers etc out of which catheters are the most commonly used devices. The two most often used catheters especially in the ICU are the Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed and administer drugs. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. Incorrect tube placements can cause serious complications and can even be fatal. The task of identifying these tubes on chest radiographs is difficult for radiologists and ICU personnel given the high volume of cases. This motivates the need for an automatic detection system to aid radiologists in processing these critical cases in a timely fashion while maintaining patient safety. To-date there has been very little research in this area. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for detection and classification of catheters on chest radiographs using a combination of template matching, morphological processing and region growing. The preliminary evaluation was carried out on 25 cases. The prototype CAD system was able to detect ET and NG tubes with sensitivities of 73.7% and 76.5% respectively and with specificities of 91.3% and 84.0% respectively. The results from the prototype system show that it is feasible to automatically detect both catheters on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

  10. Robustness of time frequency distribution based features for automated neonatal EEG seizure detection.

    Science.gov (United States)

    Nagaraj, S B; Stevenson, N J; Marnane, W P; Boylan, G B; Lightbody, G

    2014-01-01

    In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93. PMID:25570580

  11. Automated flow system for sildenafil enrichment using surfactant coated solid-phase with fluorescence detection.

    Science.gov (United States)

    Wang, Chien Chun; Sombra, Lorena; Fernández, Liliana

    2012-08-30

    In this work, Amberlite XAD-1180 resin is used for on-line surfactant-mediated pre-concentration of sildenafil as a prior step for its fluorescent detection. In order to activate the column for sildenafil pre-concentration, the cationic surfactant (hexadecyltrimethylammoniunm bromide, HTAB) is adsorbed onto the resin. In these conditions, sildenafil is retained by HTAB-resin and then it is eluted with ethanol and analyzed by spectrofluorimetry. Drug-surfactant association produces a considerable fluorescence enhancement, increasing considerably the sensitivity of detection. Therefore, sildenafil can be pre-concentrated and quantitatively determined, with a detection limit of 0.2 ng mL(-1). The proposed method was successfully applied to the analysis of bulk drug, human urine, tablets, and local herbal medicine. Validation processes were performed by recovering studies and statistical analysis with satisfactory results.

  12. Viral RNA testing and automation on the bead-based CBNE detection microsystem.

    Energy Technology Data Exchange (ETDEWEB)

    Galambos, Paul C.; Bourdon, Christopher Jay; Farrell, Cara M.; Rossito, Paul (University of California at Davis); McClain, Jaime L.; Derzon, Mark Steven; Cullor, James Sterling (University of California at Davis); Rahimian, Kamayar

    2008-09-01

    We developed prototype chemistry for nucleic acid hybridization on our bead-based diagnostics platform and we established an automatable bead handling protocol capable of 50 part-per-billion (ppb) sensitivity. We are working towards a platform capable of parallel, rapid (10 minute), raw sample testing for orthogonal (in this case nucleic acid and immunoassays) identification of biological (and other) threats in a single sensor microsystem. In this LDRD we developed the nucleic acid chemistry required for nucleic acid hybridization. Our goal is to place a non-cell associated RNA virus (Bovine Viral Diarrhea, BVD) on the beads for raw sample testing. This key pre-requisite to showing orthogonality (nucleic acid measurements can be performed in parallel with immunoassay measurements). Orthogonal detection dramatically reduces false positives. We chose BVD because our collaborators (UC-Davis) can supply samples from persistently infected animals; and because proof-of-concept field testing can be performed with modification of the current technology platform at the UC Davis research station. Since BVD is a cattle-prone disease this research dovetails with earlier immunoassay work on Botulinum toxin simulant testing in raw milk samples. Demonstration of BVD RNA detection expands the repertoire of biological macromolecules that can be adapted to our bead-based detection. The resources of this late start LDRD were adequate to partially demonstrate the conjugation of the beads to the nucleic acids. It was never expected to be adequate for a full live virus test but to motivate that additional investment. In addition, we were able to reduce the LOD (Limit of Detection) for the botulinum toxin stimulant to 50 ppb from the earlier LOD of 1 ppm. A low LOD combined with orthogonal detection provides both low false negatives and low false positives. The logical follow-on steps to this LDRD research are to perform live virus identification as well as concurrent nucleic acid and

  13. Integrating Electrochemical Detection with Centrifugal Microfluidics for Real-Time and Fully Automated Sample Testing

    DEFF Research Database (Denmark)

    Andreasen, Sune Zoëga; Kwasny, Dorota; Amato, Letizia;

    2015-01-01

    Here we present a robust, stable and low-noise experimental set-up for performing electrochemical detection on a centrifugal microfluidic platform. By using a low-noise electronic component (electrical slip-ring) it is possible to achieve continuous, on-line monitoring of electrochemical experime......Here we present a robust, stable and low-noise experimental set-up for performing electrochemical detection on a centrifugal microfluidic platform. By using a low-noise electronic component (electrical slip-ring) it is possible to achieve continuous, on-line monitoring of electrochemical...

  14. An Automated System for Detecting Sigmoids in Solar X-ray Images

    Science.gov (United States)

    LaBonte, B. J.; Rust, D. M.; Bernasconi, P. N.

    2003-05-01

    The probability of a coronal mass ejection (CME) occurring is linked to the appearance of structures, called sigmoids, in satellite X-ray images of the sun. By examination of near real time images, we can detect sigmoids visually and estimate the probability of a CME and the probability that it will cause a major geomagnetic storm. We have devised a pattern recognition system to detect the sigmoids in Yohkoh SXT and GOES SXI X-ray images automatically. When implemented in a near real time environment, this system should allow long term, 3 - 7 day, forecasts of CMEs and their potential for causing major geomagnetic storms.

  15. Low-Cost 3D Printers Enable High-Quality and Automated Sample Preparation and Molecular Detection.

    Directory of Open Access Journals (Sweden)

    Kamfai Chan

    Full Text Available Most molecular diagnostic assays require upfront sample preparation steps to isolate the target's nucleic acids, followed by its amplification and detection using various nucleic acid amplification techniques. Because molecular diagnostic methods are generally rather difficult to perform manually without highly trained users, automated and integrated systems are highly desirable but too costly for use at point-of-care or low-resource settings. Here, we showcase the development of a low-cost and rapid nucleic acid isolation and amplification platform by modifying entry-level 3D printers that cost between $400 and $750. Our modifications consisted of replacing the extruder with a tip-comb attachment that houses magnets to conduct magnetic particle-based nucleic acid extraction. We then programmed the 3D printer to conduct motions that can perform high-quality extraction protocols. Up to 12 samples can be processed simultaneously in under 13 minutes and the efficiency of nucleic acid isolation matches well against gold-standard spin-column-based extraction technology. Additionally, we used the 3D printer's heated bed to supply heat to perform water bath-based polymerase chain reactions (PCRs. Using another attachment to hold PCR tubes, the 3D printer was programmed to automate the process of shuttling PCR tubes between water baths. By eliminating the temperature ramping needed in most commercial thermal cyclers, the run time of a 35-cycle PCR protocol was shortened by 33%. This article demonstrates that for applications in resource-limited settings, expensive nucleic acid extraction devices and thermal cyclers that are used in many central laboratories can be potentially replaced by a device modified from inexpensive entry-level 3D printers.

  16. Low-Cost 3D Printers Enable High-Quality and Automated Sample Preparation and Molecular Detection.

    Science.gov (United States)

    Chan, Kamfai; Coen, Mauricio; Hardick, Justin; Gaydos, Charlotte A; Wong, Kah-Yat; Smith, Clayton; Wilson, Scott A; Vayugundla, Siva Praneeth; Wong, Season

    2016-01-01

    Most molecular diagnostic assays require upfront sample preparation steps to isolate the target's nucleic acids, followed by its amplification and detection using various nucleic acid amplification techniques. Because molecular diagnostic methods are generally rather difficult to perform manually without highly trained users, automated and integrated systems are highly desirable but too costly for use at point-of-care or low-resource settings. Here, we showcase the development of a low-cost and rapid nucleic acid isolation and amplification platform by modifying entry-level 3D printers that cost between $400 and $750. Our modifications consisted of replacing the extruder with a tip-comb attachment that houses magnets to conduct magnetic particle-based nucleic acid extraction. We then programmed the 3D printer to conduct motions that can perform high-quality extraction protocols. Up to 12 samples can be processed simultaneously in under 13 minutes and the efficiency of nucleic acid isolation matches well against gold-standard spin-column-based extraction technology. Additionally, we used the 3D printer's heated bed to supply heat to perform water bath-based polymerase chain reactions (PCRs). Using another attachment to hold PCR tubes, the 3D printer was programmed to automate the process of shuttling PCR tubes between water baths. By eliminating the temperature ramping needed in most commercial thermal cyclers, the run time of a 35-cycle PCR protocol was shortened by 33%. This article demonstrates that for applications in resource-limited settings, expensive nucleic acid extraction devices and thermal cyclers that are used in many central laboratories can be potentially replaced by a device modified from inexpensive entry-level 3D printers. PMID:27362424

  17. Low-Cost 3D Printers Enable High-Quality and Automated Sample Preparation and Molecular Detection

    Science.gov (United States)

    Chan, Kamfai; Coen, Mauricio; Hardick, Justin; Gaydos, Charlotte A.; Wong, Kah-Yat; Smith, Clayton; Wilson, Scott A.; Vayugundla, Siva Praneeth; Wong, Season

    2016-01-01

    Most molecular diagnostic assays require upfront sample preparation steps to isolate the target’s nucleic acids, followed by its amplification and detection using various nucleic acid amplification techniques. Because molecular diagnostic methods are generally rather difficult to perform manually without highly trained users, automated and integrated systems are highly desirable but too costly for use at point-of-care or low-resource settings. Here, we showcase the development of a low-cost and rapid nucleic acid isolation and amplification platform by modifying entry-level 3D printers that cost between $400 and $750. Our modifications consisted of replacing the extruder with a tip-comb attachment that houses magnets to conduct magnetic particle-based nucleic acid extraction. We then programmed the 3D printer to conduct motions that can perform high-quality extraction protocols. Up to 12 samples can be processed simultaneously in under 13 minutes and the efficiency of nucleic acid isolation matches well against gold-standard spin-column-based extraction technology. Additionally, we used the 3D printer’s heated bed to supply heat to perform water bath-based polymerase chain reactions (PCRs). Using another attachment to hold PCR tubes, the 3D printer was programmed to automate the process of shuttling PCR tubes between water baths. By eliminating the temperature ramping needed in most commercial thermal cyclers, the run time of a 35-cycle PCR protocol was shortened by 33%. This article demonstrates that for applications in resource-limited settings, expensive nucleic acid extraction devices and thermal cyclers that are used in many central laboratories can be potentially replaced by a device modified from inexpensive entry-level 3D printers. PMID:27362424

  18. Automated detection of external ventricular and lumbar drain-related meningitis using laboratory and microbiology results and medication data.

    Directory of Open Access Journals (Sweden)

    Maaike S M van Mourik

    Full Text Available OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM, a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD or lumbar drain (ELD (2004 to 2009; n = 742 had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk. The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97. The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9% and specificity of 87.9% (84.6% to 90.8%. Positive and negative predictive values were 56.9% (50.8% to 67.9% and 99.9% (98.6% to 99.9%, respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately

  19. Radar fall detectors: a comparison

    Science.gov (United States)

    Erol, Baris; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of accidents in elderly people. Even simple falls can lead to severe injuries, and sometimes result in death. Doppler fall detection has drawn much attention in recent years. Micro-Doppler signatures play an important role for the Doppler-based radar systems. Numerous studies have demonstrated the offerings of micro-Doppler characteristics for fall detection. In this respect, a plethora of micro-Doppler signature features have been proposed, including those stemming from speech recognition and wavelet decomposition. In this work, we consider four different sets of features for fall detection. These can be categorized as spectrogram based features, wavelet based features, mel-frequency cepstrum coefficients, and power burst curve features. Support vector machine is employed as the classifier. Performance of the respective fall detectors is investigated using real data obtained with the same radar operating resources and under identical sensing conditions. For the considered data, the spectrogram based feature set is shown to provide superior fall detection performance.

  20. Automated detection and classification of interstitial lung diseases from low-dose CT images

    Science.gov (United States)

    Zheng, Bin; Leader, Joseph K.; Fuhrman, Carl R.; Sciurba, Frank C.; Gur, David

    2004-05-01

    We developed a computer-aided diagnosis (CAD) scheme to detect and quantitatively assess interstitial lung diseases (ILD) depicted on low-dose and multi-slice helical high-resolution computed tomography (CT) examinations. Eighteen CT cases acquired from patients who underwent routine low-dose whole-lung screening examinations for the detection of lung cancer were used to test the scheme. ILD was identified in all of these cases. The CAD scheme involves multiple steps to segment lung areas, identify suspicious ILD regions depicted on each CT slice, and generate volumetric ILD lesions by grouping and matching ILD regions detected on multiple adjacent slices. The scheme computes five "global" features for each identified ILD region, which include size (or volume), contrast, average local pixel value fluctuation, mean of stochastic fractal dimension, and geometric fractal dimension. Two sets of classification rules are applied to remove false-positive detections. The severity of ILD in each case was rated by one experienced chest radiologist into one of the three categories (mild, moderate, and severe). A distance-weighted k-nearest neighbor algorithm and round-robin validation method was applied to classify each testing case into one of the three categories of severity. In this experiment, the CAD scheme classified 78% (14 out of 18) cases into the same categories as rated by the radiologist.

  1. Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

    Energy Technology Data Exchange (ETDEWEB)

    Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.; Turner, David D.; Comstock, Jennifer M.

    2015-11-01

    A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.

  2. Automated detection of kinks from blood vessels for optic cup segmentation in retinal images

    Science.gov (United States)

    Wong, D. W. K.; Liu, J.; Lim, J. H.; Li, H.; Wong, T. Y.

    2009-02-01

    The accurate localization of the optic cup in retinal images is important to assess the cup to disc ratio (CDR) for glaucoma screening and management. Glaucoma is physiologically assessed by the increased excavation of the optic cup within the optic nerve head, also known as the optic disc. The CDR is thus an important indicator of risk and severity of glaucoma. In this paper, we propose a method of determining the cup boundary using non-stereographic retinal images by the automatic detection of a morphological feature within the optic disc known as kinks. Kinks are defined as the bendings of small vessels as they traverse from the disc to the cup, providing physiological validation for the cup boundary. To detect kinks, localized patches are first generated from a preliminary cup boundary obtained via level set. Features obtained using edge detection and wavelet transform are combined using a statistical approach rule to identify likely vessel edges. The kinks are then obtained automatically by analyzing the detected vessel edges for angular changes, and these kinks are subsequently used to obtain the cup boundary. A set of retinal images from the Singapore Eye Research Institute was obtained to assess the performance of the method, with each image being clinically graded for the CDR. From experiments, when kinks were used, the error on the CDR was reduced to less than 0.1 CDR units relative to the clinical CDR, which is within the intra-observer variability of 0.2 CDR units.

  3. DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Feature

    Science.gov (United States)

    Kaddi, Chanchala D.; Bennett, Rachel V.; Paine, Martin R. L.; Banks, Mitchel D.; Weber, Arthur L.; Fernández, Facundo M.; Wang, May D.

    2016-01-01

    Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis. PMID:26508443

  4. An automated cloud detection method based on green channel of total sky visible images

    Directory of Open Access Journals (Sweden)

    J. Yang

    2015-05-01

    Full Text Available Getting an accurate cloud cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total sky images. By analyzing the imaging principle of cameras, green channel has been selected to replace the 2-D red-to-blue band for total sky cloud detection. The brightness distribution in a total sky image is usually non-uniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT, which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, adaptive threshold, and binarization. Several experimental cases show that the GBSAT algorithm is robust for all types of test total sky images and has more complete and accurate retrievals of visual effects than those found through traditional methods.

  5. Performance of an external transtelephonic loop recorder for automated detection of paroxysmal atrial fibrillation

    NARCIS (Netherlands)

    Oude velthuis, Bob; Bos, Jorieke; Kraaier, Karin; Stevenhagen, Jeroen; Opstal, van Jurren M.; Palen, van der Job; Scholten, Marcoen

    2013-01-01

    Background Although atrial fibrillation (AF) is the most commonly encountered arrhythmia, some of the properties make its detection challenging. In daily practice, underdiagnosis can lead to less effective treatment in prevention of stroke. Based on data from studies on treatment of AF, more intensi

  6. Automated breast cancer detection and classification using ultrasound images: A survey

    OpenAIRE

    Cheng, H. D.; JuanShan; WenJu; YanhuiGuo; Ling Zhang

    2010-01-01

    Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown,early detection and diagnos is is the key for breast cancer control,and it can increase the success of treatment,save lives and reduce cost.

  7. Comparative evaluation of commercially available manual and automated nucleic acid extraction methods for rotavirus RNA detection in stools.

    Science.gov (United States)

    Esona, Mathew D; McDonald, Sharla; Kamili, Shifaq; Kerin, Tara; Gautam, Rashi; Bowen, Michael D

    2013-12-01

    Rotaviruses are a major cause of viral gastroenteritis in children. For accurate and sensitive detection of rotavirus RNA from stool samples by reverse transcription-polymerase chain reaction (RT-PCR), the extraction process must be robust. However, some extraction methods may not remove the strong RT-PCR inhibitors known to be present in stool samples. The objective of this study was to evaluate and compare the performance of six extraction methods used commonly for extraction of rotavirus RNA from stool, which have never been formally evaluated: the MagNA Pure Compact, KingFisher Flex and NucliSENS easyMAG instruments, the NucliSENS miniMAG semi-automated system, and two manual purification kits, the QIAamp Viral RNA kit and a modified RNaid kit. Using each method, total nucleic acid or RNA was extracted from eight rotavirus-positive stool samples with enzyme immunoassay optical density (EIA OD) values ranging from 0.176 to 3.098. Extracts prepared using the MagNA Pure Compact instrument yielded the most consistent results by qRT-PCR and conventional RT-PCR. When extracts prepared from a dilution series were extracted by the 6 methods and tested, rotavirus RNA was detected in all samples by qRT-PCR but by conventional RT-PCR testing, only the MagNA Pure Compact and KingFisher Flex extracts were positive in all cases. RT-PCR inhibitors were detected in extracts produced with the QIAamp Viral RNA Mini kit. The findings of this study should prove useful for selection of extraction methods to be incorporated into future rotavirus detection and genotyping protocols. PMID:24036075

  8. An automated cross-correlation based event detection technique and its application to surface passive data set

    Science.gov (United States)

    Forghani-Arani, Farnoush; Behura, Jyoti; Haines, Seth S.; Batzle, Mike

    2013-01-01

    In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

  9. Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine

    Science.gov (United States)

    Štern, Darko; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2010-01-01

    We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and MR images and were evaluated on 29 CT and 13 MR images of lumbar spine. The overall mean distance between the obtained and the ground truth spinal centrelines and centres of vertebral bodies and intervertebral discs were 1.8 ± 1.1 mm and 2.8 ± 1.9 mm, respectively, and no considerable differences were detected among the results for CT, T1-weighted MR and T2-weighted MR images. The knowledge of the location of the spinal centreline and the centres of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. The proposed method may therefore be used to initialize the techniques for labelling and segmentation of vertebrae.

  10. Automated chromatographic system with polarimetric detection laser applied in the control of fermentation processes and seaweed extracts characterization

    International Nuclear Information System (INIS)

    There are presented applications and innovations of chromatographic and polarimetric systems in which develop methodologies for measuring the input molasses and the resulting product of a fermentation process of alcohol from a rich honey and evaluation of the fermentation process honey servery in obtaining a drink native to the Yucatan region. Composition was assessed optically active substances in seaweed, of interest to the pharmaceutical industry. The findings provide measurements alternative raw materials and products of the sugar industry, beekeeping and pharmaceutical liquid chromatography with automated polarimetric detection reduces measurement times up to 15 min, making it comparable to the times of high chromatography resolution, significantly reducing operating costs. By chromatography system with polarimetric detection (SCDP) is new columns have included standard size designed by the authors, which allow process samples with volumes up to 1 ml and reduce measurement time to 15 min, decreasing to 5 times the volume sample and halving the time of measurement. Was evaluated determining the concentration of substances using the peaks of the chromatograms obtained for the different columns and calculate the uncertainty of measurements. The results relating to the improvement of a data acquisition program (ADQUIPOL v.2.0) and new programs for the preparation of chromatograms (CROMAPOL CROMAPOL V.1.0 and V.1.2) provide important benefits, which allow a considerable saving of time the processing of the results and can be applied in other chromatography systems with the appropriate adjustments. (Author)

  11. Evaluation of bacterial contamination rate of the anterior chamber during phacoemulsification surgery using an automated microbial detection system

    Institute of Scientific and Technical Information of China (English)

    Ibrahim; Kocak; Funda; Kocak; Bahri; Teker; Ali; Aydin; Faruk; Kaya; Hakan; Baybora

    2014-01-01

    ·AIM: To assess the incidence of anterior chamber bacterial contamination during phacoemulsification surgery using an automated microbial detection system(BacT/Alert).·METHODS: Sixty-nine eyes of 60 patients who had uneventful phacoemulsification surgery, enrolled in this prospective study. No prophylactic topical or systemic antibiotics were used before surgery. After antisepsis with povidone-iodine, two intraoperative anterior chamber aqueous samples were obtained, the first whilst entering anterior chamber, and the second at the end of surgery. BacT/Alert culture system was used to detect bacterial contamination in the aqueous samples.·RESULTS: Neither aqueous samples obtained at the beginning nor conclusion of the surgery was positive for microorganisms on BacT/Alert culture system. The rate of bacterial contamination during surgery was 0%. None of the eyes developed acute-onset endophthalmitis after surgery.· CONCLUSION: In this study, no bacterial contamination of anterior chamber was observed during cataract surgery. This result shows that meticulous surgical preparation and technique can prevent anterior chamber contamination during phacoemulsification cataract surgery.

  12. Validation of an automated ELISA system for detection of antibodies to Aleutian mink disease virus using blood samples collected in filter paper strips

    OpenAIRE

    Knuuttila, Anna; Aronen, Pirjo; Eerola, Majvor; Gardner, Ian A; Virtala, Anna-Maija K; Vapalahti, Olli

    2014-01-01

    Background Aleutian mink disease virus (AMDV) is the cause of a chronic immune complex disease, Aleutian disease (AD), which is common in mink-producing countries. In 2005, implementation of an AMDV eradication programme in Finland created a need for an automated high-throughput assay. The aim of this study was to validate an AMDV-VP2 -recombinant antigen ELISA, which we developed earlier, in an automated assay format for the detection of anti-AMDV antibodies in mink blood and to determine th...

  13. Automated 5 ' nuclease assay for detection of virulence factors in porcine Escherichia coli

    DEFF Research Database (Denmark)

    Frydendahl, K.; Imberechts, H.; Lehmann, S.

    2001-01-01

    (STa, STb, EAST1) and heat labile LT) enterotoxins and the verocytotoxin variant 2e (VT2e). To correctly identify false negative results, an endogenous internal control targeting the E. coil 16S rRNA gene was incorporated in each test tube. The assay was evaluated using a collection of E. coil....... When testing field strains there was generally excellent agreement with results obtained by laboratories in Belgium and Germany. In conclusion, the 5' nuclease assay developed is a fast and specific tool for detection of E. coli virulence genes in the veterinary diagnostic laboratory....... reference strains which have previously been examined with phenotypical assays or DNA hybridization. Furthermore, the assay was evaluated by testing porcine E. coil field strains, previously characterized. The 5' nuclease assay correctly detected the presence of virulence genes in all reference strains...

  14. Results from Automated Cloud and Dust Devil Detection Onboard the MER

    Science.gov (United States)

    Chien, Steve; Castano, Rebecca; Bornstein, Benjamin; Fukunaga, Alex; Castano, Andres; Biesiadecki, Jeffrey; Greeley, Ron; Whelley, Patrick; Lemmon, Mark

    2008-01-01

    We describe a new capability to automatically detect dust devils and clouds in imagery onboard rovers, enabling downlink of just the images with the targets or only portions of the images containing the targets. Previously, the MER rovers conducted campaigns to image dust devils and clouds by commanding a set of images be collected at fixed times and downloading the entire image set. By increasing the efficiency of the campaigns, more campaigns can be executed. Software for these new capabilities was developed, tested, integrated, uploaded, and operationally checked out on both rovers as part of the R9.2 software upgrade. In April 2007 on Sol 1147 a dust devil was automatically detected onboard the Spirit rover for the first time. We discuss the operational usage of the capability and present initial dust devil results showing how this preliminary application has demonstrated the feasibility and potential benefits of the approach.

  15. A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application

    Directory of Open Access Journals (Sweden)

    Shashwat Pathak

    2016-09-01

    Full Text Available This paper proposes and evaluates an algorithm to automatically detect the cataracts from color images in adult human subjects. Currently, methods available for cataract detection are based on the use of either fundus camera or Digital Single-Lens Reflex (DSLR camera; both are very expensive. The main motive behind this work is to develop an inexpensive, robust and convenient algorithm which in conjugation with suitable devices will be able to diagnose the presence of cataract from the true color images of an eye. An algorithm is proposed for cataract screening based on texture features: uniformity, intensity and standard deviation. These features are first computed and mapped with diagnostic opinion by the eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Finally, a tele-ophthamology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.

  16. Automated microaneurysm detection method based on double ring filter in retinal fundus images

    Science.gov (United States)

    Mizutani, Atsushi; Muramatsu, Chisako; Hatanaka, Yuji; Suemori, Shinsuke; Hara, Takeshi; Fujita, Hiroshi

    2009-02-01

    The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy, which is one of the leading causes of vision loss. We have been investigating a computerized method for the detection of microaneurysms on retinal fundus images, which were obtained from the Retinopathy Online Challenge (ROC) database. The ROC provides 50 training cases, in which "gold standard" locations of microaneurysms are provided, and 50 test cases without the gold standard locations. In this study, the computerized scheme was developed by using the training cases. Although the results for the test cases are also included, this paper mainly discusses the results for the training cases because the "gold standard" for the test cases is not known. After image preprocessing, candidate regions for microaneurysms were detected using a double-ring filter. Any potential false positives located in the regions corresponding to blood vessels were removed by automatic extraction of blood vessels from the images. Twelve image features were determined, and the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial neural network. The true positive fraction of the proposed method was 0.45 at 27 false positives per image. Forty-two percent of microaneurysms in the 50 training cases were considered invisible by the consensus of two co-investigators. When the method was evaluated for visible microaneurysms, the sensitivity for detecting microaneurysms was 65% at 27 false positives per image. Our computerized detection scheme could be improved for helping ophthalmologists in the early diagnosis of diabetic retinopathy.

  17. An end-to-end hybrid algorithm for automated medication discrepancy detection

    OpenAIRE

    Li, Qi; Spooner, Stephen Andrew; Kaiser, Megan; Lingren, Nataline; Robbins, Jessica; Lingren, Todd; Tang, Huaxiu; Solti, Imre; Ni, Yizhao

    2015-01-01

    Background In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients’ discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm ...

  18. Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach

    OpenAIRE

    Humayun Irshad; Sepehr Jalali; Ludovic Roux; Daniel Racoceanu; Lim Joo Hwee; Gilles Le Naour; Frédérique Capron

    2013-01-01

    Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach...

  19. Automated Broad-Range Molecular Detection of Bacteria in Clinical Samples.

    Science.gov (United States)

    Budding, Andries E; Hoogewerf, Martine; Vandenbroucke-Grauls, Christina M J E; Savelkoul, Paul H M

    2016-04-01

    Molecular detection methods, such as quantitative PCR (qPCR), have found their way into clinical microbiology laboratories for the detection of an array of pathogens. Most routinely used methods, however, are directed at specific species. Thus, anything that is not explicitly searched for will be missed. This greatly limits the flexibility and universal application of these techniques. We investigated the application of a rapid universal bacterial molecular identification method, IS-pro, to routine patient samples received in a clinical microbiology laboratory. IS-pro is a eubacterial technique based on the detection and categorization of 16S-23S rRNA gene interspace regions with lengths that are specific for each microbial species. As this is an open technique, clinicians do not need to decide in advance what to look for. We compared routine culture to IS-pro using 66 samples sent in for routine bacterial diagnostic testing. The samples were obtained from patients with infections in normally sterile sites (without a resident microbiota). The results were identical in 20 (30%) samples, IS-pro detected more bacterial species than culture in 31 (47%) samples, and five of the 10 culture-negative samples were positive with IS-pro. The case histories of the five patients from whom these culture-negative/IS-pro-positive samples were obtained suggest that the IS-pro findings are highly clinically relevant. Our findings indicate that an open molecular approach, such as IS-pro, may have a high added value for clinical practice. PMID:26763956

  20. Real Time Automated Counterfeit Integrated Circuit Detection using X-ray Microscopy

    OpenAIRE

    Mahmood, Kaleel; Latorre Carmona, Pedro; Shahbazmohamadi, Sina; Pla Bañón, Filiberto; Javidi, Bahram

    2015-01-01

    Determining the authenticity of integrated circuits is paramount to preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. Existing techniques to distinguish between authentic and counterfeit integrated circuits (ICs) often include destructive testing requiring subject matter experts. We present a nondestructive technique to detect ICs using x-ray microscopy and advanced imaging analysis with different pa...

  1. Automated radioanalytical system incorporating microwave-assisted sample preparation, chemical separation, and online radiometric detection for the monitoring of total 99Tc in nuclear waste processing streams.

    Science.gov (United States)

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

    2012-04-01

    An automated fluidic instrument is described that rapidly determines the total (99)Tc content of aged nuclear waste samples, where the matrix is chemically and radiologically complex and the existing speciation of the (99)Tc 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 (99)Tc. We developed a 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 (99)Tc 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.

  2. An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

    Directory of Open Access Journals (Sweden)

    M. Anitha

    2012-04-01

    Full Text Available Settings White Matter lesions (WMLs are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM and Grey Matter (GM. The aim of this paper is to
    automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

  3. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

    Science.gov (United States)

    Aung, Min S H; Thies, Sibylle B; Kenney, Laurence P J; Howard, David; Selles, Ruud W; Findlow, Andrew H; Goulermas, John Y

    2013-11-01

    Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains. PMID:23322764

  4. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

    Science.gov (United States)

    Aung, Min S H; Thies, Sibylle B; Kenney, Laurence P J; Howard, David; Selles, Ruud W; Findlow, Andrew H; Goulermas, John Y

    2013-11-01

    Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains.

  5. Detection and characterization of verocytotoxin-producing Escherichia coli by automated 5 ' nuclease PCR assay

    DEFF Research Database (Denmark)

    Nielsen, Eva Møller; Andersen, Marianne Thorup

    2003-01-01

    included assays for the detection of verocytotoxin genes (vtx1, vtx2), pO157-associated genes (ehxA, katP, espP, and etpD), a recently identified adhesin (saa), intimin (eae, all variants), seven subtypes of eae, four subtypes of tir, and three subtypes of espD. A number of reference strains (VTEC and......In recent years increased attention has been focused on infections caused by isolates of verocytotoxin-producing Escherichia coli (VTEC) serotypes other than O157. These non-O157 VTEC isolates are commonly present in food and food production animals. Easy detection, isolation, and characterization...... of non-O157 VTEC isolates are essential for improving our knowledge of these organisms. In the present study, we detected VTEC isolates in bovine fecal samples by a duplex 5' nuclease PCR assay (real-time PCR) that targets vtx1 and vtx2. VTEC isolates were obtained by colony replication by use of...

  6. Automated contralateral subtraction of dental panoramic radiographs for detecting abnormalities in paranasal sinus

    Science.gov (United States)

    Hara, Takeshi; Mori, Shintaro; Kaneda, Takashi; Hayashi, Tatsuro; Katsumata, Akitoshi; Fujita, Hiroshi

    2011-03-01

    Inflammation in the paranasal sinus is often observed in seasonal allergic rhinitis or with colds, but is also an indication for odontogenic tumors, carcinoma of the maxillary sinus or a maxillary cyst. The detection of those findings in dental panoramic radiographs is not difficult for radiologists, but general dentists may miss the findings since they focus on treatments of teeth. The purpose of this work is to develop a contralateral subtraction method for detecting the odontogenic sinusitis region on dental panoramic radiographs. We developed a contralateral subtraction technique in paranasal sinus region, consisting of 1) image filtering of the smoothing and sobel operation for noise reduction and edge extraction, 2) image registration of mirrored image by using mutual information, and 3) image display method of subtracted pixel data. We employed 56 cases (24 normal and 32 abnormal). The abnormal regions and the normal cases were verified by a board-certified radiologist using CT scans. Observer studies with and without subtraction images were performed for 9 readers. The true-positive rate at a 50% confidence level in 7 out of 9 readers was improved, but there was no statistical significance in the difference of area-under-curve (AUC) in each radiologist. In conclusion, the contralateral subtraction images of dental panoramic radiographs may improve the detection rate of abnormal regions in paranasal sinus.

  7. Selection of an optimal neural network architecture for computer-aided detection of microcalcifications - Comparison of automated optimization techniques

    International Nuclear Information System (INIS)

    Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are 'optimized' by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area Az under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost surface

  8. Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

    Science.gov (United States)

    Zhang, Yuhan; Lu, Dr. Thomas

    2010-01-01

    The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

  9. Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins

    Science.gov (United States)

    Muramatsu, Chisako; Hatanaka, Yuji; Iwase, Tatsuhiko; Hara, Takeshi; Fujita, Hiroshi

    2010-03-01

    Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels, large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of optic discs. The target segments in the training cases were classified into arteries and veins by using the linear discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%) of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of A/V ratio.

  10. Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks.

    Directory of Open Access Journals (Sweden)

    Alexander Mellmann

    2006-03-01

    Full Text Available BACKGROUND: The detection of methicillin-resistant Staphylococcus aureus (MRSA usually requires the implementation of often rigorous infection-control measures. Prompt identification of an MRSA epidemic is crucial for the control of an outbreak. In this study we evaluated various early warning algorithms for the detection of an MRSA cluster. METHODS AND FINDINGS: Between 1998 and 2003, 557 non-replicate MRSA strains were collected from staff and patients admitted to a German tertiary-care university hospital. The repeat region of the S. aureus protein A (spa gene in each of these strains was sequenced. Using epidemiological and typing information for the period 1998-2002 as reference data, clusters in 2003 were determined by temporal-scan test statistics. Various early warning algorithms (frequency, clonal, and infection control professionals [ICP] alerts were tested in a prospective analysis for the year 2003. In addition, a newly implemented automated clonal alert system of the Ridom StaphType software was evaluated. A total of 549 of 557 MRSA were typeable using spa sequencing. When analyzed using scan test statistics, 42 out of 175 MRSA in 2003 formed 13 significant clusters (p < 0.05. These clusters were used as the "gold standard" to evaluate the various algorithms. Clonal alerts (spa typing and epidemiological data were 100% sensitive and 95.2% specific. Frequency (epidemiological data only and ICP alerts were 100% and 62.1% sensitive and 47.2% and 97.3% specific, respectively. The difference in specificity between clonal and ICP alerts was not significant. Both methods exhibited a positive predictive value above 80%. CONCLUSIONS: Rapid MRSA outbreak detection, based on epidemiological and spa typing data, is a suitable alternative for classical approaches and can assist in the identification of potential sources of infection.

  11. Automated detection of lunar craters based on object-oriented approach

    Institute of Scientific and Technical Information of China (English)

    YUE ZongYu; LIU JianZhong; WU GanGuo

    2008-01-01

    The object-oriented approach is a powerful method in making classification. With the segmentation of images to objects, many features can be calculated based on the objects so that the targets can be distinguished. However, this method has not been applied to lunar study. In this paper we attempt to apply this method to detecting lunar craters with promising results. Craters are the most obvious features on the moon and they are important for lunar geologic study. One of the important questions in lunar research is to estimate lunar surface ages by examination of crater density per unit area. Hence,proper detection of lunar craters is necessary. Manual crater identification is inefficient, and a more efficient and effective method is needed. This paper describes an object-oriented method to detect lunar craters using lunar reflectance images. In the method, many objects were first segmented from the image based on size, shape, color, and the weights to every layer. Then the feature of "contrast to neighbor objects" was selected to identify craters from the lunar image. In the next step, by merging the adjacent objects belonging to the same class, almost every crater can be taken as an independent object except several very big craters in the study area. To remove the crater rays diagnosed as craters,the feature of "length/width" was further used with suitable parameters to finish recognizing craters.Finally, the result was exported to ArcGIS for manual modification to those big craters and the number of craters was acquired.

  12. Assessment and validation of a simple automated method for the detection of gait events and intervals.

    Science.gov (United States)

    Ghoussayni, Salim; Stevens, Christopher; Durham, Sally; Ewins, David

    2004-12-01

    A simple and rapid automatic method for detection of gait events at the foot could speed up and possibly increase the repeatability of gait analysis and evaluations of treatments for pathological gaits. The aim of this study was to compare and validate a kinematic-based algorithm used in the detection of four gait events, heel contact, heel rise, toe contact and toe off. Force platform data is often used to obtain start and end of contact phases, but not usually heel rise and toe contact events. For this purpose synchronised kinematic, kinetic and video data were captured from 12 healthy adult subjects walking both barefoot and shod at slow and normal self-selected speeds. The data were used to determine the gait events using three methods: force, visual inspection and algorithm methods. Ninety percent of all timings given by the algorithm were within one frame (16.7 ms) when compared to visual inspection. There were no statistically significant differences between the visual and algorithm timings. For both heel and toe contact the differences between the three methods were within 1.5 frames, whereas for heel rise and toe off the differences between the force on one side and the visual and algorithm on the other were higher and more varied (up to 175 ms). In addition, the algorithm method provided the duration of three intervals, heel contact to toe contact, toe contact to heel rise and heel rise to toe off, which are not readily available from force platform data. The ability to automatically and reliably detect the timings of these four gait events and three intervals using kinematic data alone is an asset to clinical gait analysis.

  13. Fall Protection Introduction, #33462

    Energy Technology Data Exchange (ETDEWEB)

    Chochoms, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-06-23

    The proper use of fall prevention and fall protection controls can reduce the risk of deaths and injuries caused by falls. This course, Fall Protection Introduction (#33462), is designed as an introduction to various types of recognized fall prevention and fall protection systems at Los Alamos National Laboratory (LANL), including guardrail systems, safety net systems, fall restraint systems, and fall arrest systems. Special emphasis is given to the components, inspection, care, and storage of personal fall arrest systems (PFASs). This course also presents controls for falling object hazards and emergency planning considerations for persons who have fallen.

  14. Fully automated screening of immunocytochemically stained specimens for early cancer detection

    Science.gov (United States)

    Bell, André A.; Schneider, Timna E.; Müller-Frank, Dirk A. C.; Meyer-Ebrecht, Dietrich; Böcking, Alfred; Aach, Til

    2007-03-01

    Cytopathological cancer diagnoses can be obtained less invasive than histopathological investigations. Cells containing specimens can be obtained without pain or discomfort, bloody biopsies are avoided, and the diagnosis can, in some cases, even be made earlier. Since no tissue biopsies are necessary these methods can also be used in screening applications, e.g., for cervical cancer. Among the cytopathological methods a diagnosis based on the analysis of the amount of DNA in individual cells achieves high sensitivity and specificity. Yet this analysis is time consuming, which is prohibitive for a screening application. Hence, it will be advantageous to retain, by a preceding selection step, only a subset of suspicious specimens. This can be achieved using highly sensitive immunocytochemical markers like p16 ink4a for preselection of suspicious cells and specimens. We present a method to fully automatically acquire images at distinct positions at cytological specimens using a conventional computer controlled microscope and an autofocus algorithm. Based on the thus obtained images we automatically detect p16 ink4a-positive objects. This detection in turn is based on an analysis of the color distribution of the p16 ink4a marker in the Lab-colorspace. A Gaussian-mixture-model is used to describe this distribution and the method described in this paper so far achieves a sensitivity of up to 90%.

  15. Automated classification of mandibular cortical bone on dental panoramic radiographs for early detection of osteoporosis

    Science.gov (United States)

    Horiba, Kazuki; Muramatsu, Chisako; Hayashi, Tatsuro; Fukui, Tatsumasa; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

    2015-03-01

    Findings on dental panoramic radiographs (DPRs) have shown that mandibular cortical index (MCI) based on the morphology of mandibular inferior cortex was significantly correlated with osteoporosis. MCI on DPRs can be categorized into one of three groups and has the high potential for identifying patients with osteoporosis. However, most DPRs are used only for diagnosing dental conditions by dentists in their routine clinical work. Moreover, MCI is not generally quantified but assessed subjectively. In this study, we investigated a computer-aided diagnosis (CAD) system that automatically classifies mandibular cortical bone for detection of osteoporotic patients at early stage. First, an inferior border of mandibular bone was detected by use of an active contour method. Second, regions of interest including the cortical bone are extracted and analyzed for its thickness and roughness. Finally, support vector machine (SVM) differentiate cases into three MCI categories by features including the thickness and roughness. Ninety eight DPRs were used to evaluate our proposed scheme. The number of cases classified to Class I, II, and III by a dental radiologist are 56, 25 and 17 cases, respectively. Experimental result based on the leave-one-out cross-validation evaluation showed that the sensitivities for the classes I, II, and III were 94.6%, 57.7% and 94.1%, respectively. Distribution of the groups in the feature space indicates a possibility of MCI quantification by the proposed method. Therefore, our scheme has a potential in identifying osteoporotic patients at an early stage.

  16. Low-Cost Impact Detection and Location for Automated Inspections of 3D Metallic Based Structures

    Directory of Open Access Journals (Sweden)

    Carlos Morón

    2015-05-01

    Full Text Available This paper describes a new low-cost means to detect and locate mechanical impacts (collisions on a 3D metal-based structure. We employ the simple and reasonably hypothesis that the use of a homogeneous material will allow certain details of the impact to be automatically determined by measuring the time delays of acoustic wave propagation throughout the 3D structure. The location of strategic piezoelectric sensors on the structure and an electronic-computerized system has allowed us to determine the instant and position at which the impact is produced. The proposed automatic system allows us to fully integrate impact point detection and the task of inspecting the point or zone at which this impact occurs. What is more, the proposed method can be easily integrated into a robot-based inspection system capable of moving over 3D metallic structures, thus avoiding (or minimizing the need for direct human intervention. Experimental results are provided to show the effectiveness of the proposed approach.

  17. Pedestrian detection in thermal images: An automated scale based region extraction with curvelet space validation

    Science.gov (United States)

    Lakshmi, A.; Faheema, A. G. J.; Deodhare, Dipti

    2016-05-01

    Pedestrian detection is a key problem in night vision processing with a dozen of applications that will positively impact the performance of autonomous systems. Despite significant progress, our study shows that performance of state-of-the-art thermal image pedestrian detectors still has much room for improvement. The purpose of this paper is to overcome the challenge faced by the thermal image pedestrian detectors, which employ intensity based Region Of Interest (ROI) extraction followed by feature based validation. The most striking disadvantage faced by the first module, ROI extraction, is the failed detection of cloth insulted parts. To overcome this setback, this paper employs an algorithm and a principle of region growing pursuit tuned to the scale of the pedestrian. The statistics subtended by the pedestrian drastically vary with the scale and deviation from normality approach facilitates scale detection. Further, the paper offers an adaptive mathematical threshold to resolve the problem of subtracting the background while extracting cloth insulated parts as well. The inherent false positives of the ROI extraction module are limited by the choice of good features in pedestrian validation step. One such feature is curvelet feature, which has found its use extensively in optical images, but has as yet no reported results in thermal images. This has been used to arrive at a pedestrian detector with a reduced false positive rate. This work is the first venture made to scrutinize the utility of curvelet for characterizing pedestrians in thermal images. Attempt has also been made to improve the speed of curvelet transform computation. The classification task is realized through the use of the well known methodology of Support Vector Machines (SVMs). The proposed method is substantiated with qualified evaluation methodologies that permits us to carry out probing and informative comparisons across state-of-the-art features, including deep learning methods, with six

  18. A scalable system for microcalcification cluster automated detection in a distributed mammographic database

    CERN Document Server

    Delogu, P; Pérez-Martínez, A; Retico, A; Stefanini, A; Tata, A

    2007-01-01

    A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different datasets of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis.

  19. Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters

    International Nuclear Information System (INIS)

    Sleep apnoea is a very common sleep disorder which can cause symptoms such as daytime sleepiness, irritability and poor concentration. To monitor patients with this sleeping disorder we measured the electrical activity of the heart. The resulting electrocardiography (ECG) signals are both non-stationary and nonlinear. Therefore, we used nonlinear parameters such as approximate entropy, fractal dimension, correlation dimension, largest Lyapunov exponent and Hurst exponent to extract physiological information. This information was used to train an artificial neural network (ANN) classifier to categorize ECG signal segments into one of the following groups: apnoea, hypopnoea and normal breathing. ANN classification tests produced an average classification accuracy of 90%; specificity and sensitivity were 100% and 95%, respectively. We have also proposed unique recurrence plots for the normal, hypopnea and apnea classes. Detecting sleep apnea with this level of accuracy can potentially reduce the need of polysomnography (PSG). This brings advantages to patients, because the proposed system is less cumbersome when compared to PSG

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1997-03-01

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

  1. The Development of Automated Detection Techniques for Passive Acoustic Monitoring as a Tool for Studying Beaked Whale Distribution and Habitat Preferences in the California Current Ecosystem

    Science.gov (United States)

    Yack, Tina M.

    The objectives of this research were to test available automated detection methods for passive acoustic monitoring and integrate the best available method into standard marine mammal monitoring protocols for ship based surveys. The goal of the first chapter was to evaluate the performance and utility of PAMGUARD 1.0 Core software for use in automated detection of marine mammal acoustic signals during towed array surveys. Three different detector configurations of PAMGUARD were compared. These automated detection algorithms were evaluated by comparing them to the results of manual detections made by an experienced bio-acoustician (author TMY). This study provides the first detailed comparisons of PAMGUARD automated detection algorithms to manual detection methods. The results of these comparisons clearly illustrate the utility of automated detection methods for odontocete species. Results of this work showed that the majority of whistles and click events can be reliably detected using PAMGUARD software. The second chapter moves beyond automated detection to examine and test automated classification algorithms for beaked whale species. Beaked whales are notoriously elusive and difficult to study, especially using visual survey methods. The purpose of the second chapter was to test, validate, and compare algorithms for detection of beaked whales in acoustic line-transect survey data. Using data collected at sea from the PAMGUARD classifier developed in Chapter 2 it was possible to measure the clicks from visually verified Baird's beaked whale encounters and use this data to develop classifiers that could discriminate Baird's beaked whales from other beaked whale species in future work. Echolocation clicks from Baird's beaked whales, Berardius bairdii, were recorded during combined visual and acoustic shipboard surveys of cetacean populations in the California Current Ecosystem (CCE) and with autonomous, long-term recorders at four different sites in the Southern

  2. Improving the automated detection of refugee/IDP dwellings using the multispectral bands of the WorldView-2 satellite

    Science.gov (United States)

    Kemper, Thomas; Gueguen, Lionel; Soille, Pierre

    2012-06-01

    The enumeration of the population remains a critical task in the management of refugee/IDP camps. Analysis of very high spatial resolution satellite data proofed to be an efficient and secure approach for the estimation of dwellings and the monitoring of the camp over time. In this paper we propose a new methodology for the automated extraction of features based on differential morphological decomposition segmentation for feature extraction and interactive training sample selection from the max-tree and min-tree structures. This feature extraction methodology is tested on a WorldView-2 scene of an IDP camp in Darfur Sudan. Special emphasis is given to the additional available bands of the WorldView-2 sensor. The results obtained show that the interactive image information tool is performing very well by tuning the feature extraction to the local conditions. The analysis of different spectral subsets shows that it is possible to obtain good results already with an RGB combination, but by increasing the number of spectral bands the detection of dwellings becomes more accurate. Best results were obtained using all eight bands of WorldView-2 satellite.

  3. Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

    Science.gov (United States)

    Ding, Yifu; Tavolara, Thomas; Cheng, Keith

    2016-03-01

    Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

  4. Web-Based Data Processing System for Automated Detection of Oscillations with Applications to the Solar Atmosphere

    CERN Document Server

    Sych, R A; Anfinogentov, S; Ofman, L

    2010-01-01

    A web-based, interactive system for the remote processing of imaging data sets (i.e., EUV, X-ray and microwave) and the automated interactive detection of wave and oscillatory phenomena in the solar atmosphere is presented.The system targets localised, but spatially resolved, phenomena, such as kink, sausage, and longitudinal propagating and standing waves. The system implements the methods of Periodmapping for pre-analysis, and Pixelised Wavelet Filtering for detailed analysis of the imaging data cubes. The system is implemented on the dedicated data processing server http://pwf.iszf.irk.ru, which is situated at the Institute of Solar-Terrestrial Physics, Irkutsk, Russia. The input data in the .sav, .fits or .txt formats can be submitted via the local and/or global network (the Internet). The output data can be in the png, jpeg and binary formats, on the user's request. The output data are periodmaps; narrowband amplitude, power, phase and correlation maps of the wave's sources at significant harmonics and i...

  5. Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

    Science.gov (United States)

    Hassan, Bilal; Raja, Gulistan; Hassan, Taimur; Usman Akram, M

    2016-04-01

    Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively. PMID:27140751

  6. Detection of DNA Aneuploidy in Exfoliated Airway Epithelia Cells of Sputum Specimens by the Automated Image Cytometry and Its Clinical Value in the Identification of Lung Cancer

    Institute of Scientific and Technical Information of China (English)

    杨健; 周宜开

    2004-01-01

    To evaluate the value of detecton of DNA aneuploidy in exfoliated airway epithelia cells of sputum specimens by the automated image cytometry for the identification of lung cancer, 100patients were divided into patient group (50 patients with lung cancer)and control group (30 patients with tuberculosis and 20 healthy people). Sputum was obtained for the quantitative analysis of DNA content of exfoliated airway epithelial cells with the automated image cytometry, together with the examinations of brush cytology and conventional sputum cytology. Our results showed that DNA aneuploidy (DI>2.5 or 5c) was found in 20 out of 50 sputum samples of lung cancer, 1 out of 30 sputum samples from tuberculosis patients, and none of 20 sputum samples from healthy people. The positive rates of conventional sputum cytology and brush cytology were 16 % and 32 %,which was lower than that of DNA aneuploidy detection by the automated image cytometry (P<0.01 ,P>0.05). Our study showed that automated image cytometry, which uses DNA aneuploidy as a marker for tumor, can detect the malignant cells in sputum samples of lung cancer and it is a sensitive and specific method serving as a complement for the diagnosis of lung cancer.

  7. Individually adapted, interactive multiplanar reformations vs. semi-automated coronary segmentation and curved planar reformations for stenosis detection in coronary computed tomography angiography

    International Nuclear Information System (INIS)

    Objective: To evaluate, whether semi-automated vessel extraction and curved planar reformations ('automated vessel extraction') increases diagnostic accuracy in the detection of relevant coronary artery lesions compared to manual, interactive multiplanar interpretation ('manual approach'). Materials and methods: 50 coronary CT angiography datasets were evaluated by four independent readers (two experienced, two novice) for the presence of stenoses exceeding 50% diameter reduction. One experienced and one novice reader each used the 'manual approach' for cases 1-25 and 'automated vessel extraction' for cases 26-50, while the other two readers used the complementary method. Results were compared to those of invasive coronary angiography. Results: Using the 'manual approach', 37 of 42 stenoses were correctly detected by experienced as well as novice readers. 14 vs. 17 lesions were false positive (sensitivity 88%, specificity 91% vs. 89%, PPV 73% vs. 69%, NPV 97%, n.s.). Using 'automated vessel extraction', experienced readers detected 35/42 stenoses compared to 31/42 for novice readers. 7 vs. 11 lesions were missed and 17 vs. 15 false-positive lesions reported (sensitivity 83% vs. 74%, specificity 89% vs. 90%, PPV 67%, NPV 95% vs. 93%, n.s.).In patient-based analysis, for novice readers sensitivity was higher using the 'manual approach' (97%, 29/30 pts. vs. 80%, 24/30 pts., p = 0.069). Conclusions: Semi-automated vessel extraction and curved multiplanar reconstructions do not improve the diagnostic accuracy of coronary CT angiography compared to the use of interactive multiplanar reformations. Especially for less experienced readers, the use of automatically rendered curved multiplanar reconstructions alone cannot be recommended.

  8. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    Science.gov (United States)

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    Mobility characteristics associated with activity of daily living such as sitting down, lying down, rising up, and walking are considered to be important in maintaining functional independence and healthy life style especially for the growing elderly population. Characteristics of postural transitions such as sit-to-stand are widely used by clinicians as a physical indicator of health, and walking is used as an important mobility assessment tool. Many tools have been developed to assist in the assessment of functional levels and to detect a person’s activities during daily life. These include questionnaires, observation, diaries, kinetic and kinematic systems, and validated functional tests. These measures are costly and time consuming, rely on subjective patient recall and may not accurately reflect functional ability in the patient’s home. In order to provide a low-cost, objective assessment of functional ability, inertial measurement unit (IMU) using MEMS technology has been employed to ascertain ADLs. These measures facilitate long-term monitoring of activity of daily living using wearable sensors. IMU system are desirable in monitoring human postures since they respond to both frequency and the intensity of movements and measure both dc (gravitational acceleration vector) and ac (acceleration due to body movement) components at a low cost. This has enabled the development of a small, lightweight, portable system that can be worn by a free-living subject without motion impediment – TEMPO (Technology Enabled Medical Precision Observation). Using this IMU system, we acquired indirect measures of biomechanical variables that can be used as an assessment of individual mobility characteristics with accuracy and recognition rates that are comparable to the modern motion capture systems. In this study, five subjects performed various ADLs and mobility measures such as posture transitions and gait characteristics were obtained. We developed postural event detection

  9. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  10. An Automated Malware Detection System for Android Using Behavior-Based Analysis AMDA

    Directory of Open Access Journals (Sweden)

    Kevin Joshua Abela

    2015-05-01

    Full Text Available The Android platform is the fastest growing market in smartphone operating systems to date. As such, it has become the most viable target of security threats. The reliance of the Android Market Security Model on its reactive anti-malware system presents an opportunity for malware to be present in the Official Android Market and does not encompass applications outside the official market. This allows applications to masquerade as harmless applications which lead to the loss of credentials if precautions are not taken. Most anti-malware applications in the Market use static analysis for detection because it is fast and relatively simple. However, static analysis requires regular updates of threat databases and it may be circumvented by obfuscation techniques. As a solution to these problems, the study utilizes behavior analysis of applications as basis for malware. As a first step, features of known-benign and known-malicious applications are extracted for machine learning to provide baseline behavior datasets. Test applications are then passed through the behavior based module for identification of its being malware or benign. A classification scheme is provided for applications identified as malware by the system.

  11. AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THRESHOLDING AND SVM

    Directory of Open Access Journals (Sweden)

    Weiwei Gao

    2016-02-01

    Full Text Available One common cause of visual impairment among people of working age in the industrialized countries is Diabetic Retinopathy (DR. Automatic recognition of hard exudates (EXs which is one of DR lesions in fundus images can contribute to the diagnosis and screening of DR.The aim of this paper was to automatically detect those lesions from fundus images. At first,green channel of each original fundus image was segmented by improved Otsu thresholding based on minimum inner-cluster variance, and candidate regions of EXs were obtained. Then, we extracted features of candidate regions and selected a subset which best discriminates EXs from the retinal background by means of logistic regression (LR. The selected features were subsequently used as inputs to a SVM to get a final segmentation result of EXs in the image. Our database was composed of 120 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 50 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 95.05% and a mean positive predictive value of 95.37%. With an image-based criterion, our approach reached a 100% mean sensitivity, 90.9% mean specificity and 96.0% mean accuracy. Furthermore, the average time cost in processing an image is 8.31 seconds. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

  12. Automated detection of sleep apnea in infants: A multi-modal approach.

    Science.gov (United States)

    Cohen, Gregory; de Chazal, Philip

    2015-08-01

    This study explores the use and applicability of two minimally invasive sensors, electrocardiogram (ECG) and pulse oximetry, in addressing the high costs and difficulty associated with the early detection of sleep apnea hypopnea syndrome in infants. An existing dataset of 396 scored overnight polysomnography recordings were used to train and test a linear discriminants classifier. The dataset contained data from healthy infants, infants diagnosed with sleep apnea, infants with siblings who had died from sudden infant death syndrome (SIDS) and pre-term infants. Features were extracted from the ECG and pulse-oximetry data and used to train the classifier. The performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 66.7% was achieved, with a specificity of 67.0% and a sensitivity of 58.1%. Although the performance of the system is not yet at the level required for clinical use, this work forms an important step in demonstrating the validity and potential for such low-cost and minimally invasive diagnostic systems. PMID:26073098

  13. Automated detection of filaments in the large scale structure of the universe

    CERN Document Server

    Gonzalez, Roberto E

    2009-01-01

    We present a new method to identify large scale filaments and apply it to a cosmological simulation. Using positions of haloes above a given mass as node tracers, we look for filaments between them using the positions and masses of all the remaining dark-matter haloes. In order to detect a filament, the first step consists in the construction of a backbone linking two nodes, which is given by a skeleton-like path connecting the highest local dark matter (DM) density traced by non-node haloes. We estimate the characteristic DM density between two skeleton-candidate haloes using two approximations, i) the Voronoi tessellation density when the distance between haloes is similar or smaller than the sum of their virial radii, and ii) when the distance is larger, using a proxy of the minimum DM density between the two haloes assuming NFW profiles. The filament quality is defined by a density and gap parameters characterising its skeleton, and filament members are selected by their binding energy in the plane perpen...

  14. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  15. Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

    International Nuclear Information System (INIS)

    Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets

  16. Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Hsin-Chen; Tan, Jun; Dolly, Steven; Kavanaugh, James; Harold Li, H.; Altman, Michael; Gay, Hiram; Thorstad, Wade L.; Mutic, Sasa; Li, Hua, E-mail: huli@radonc.wustl.edu [Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110 (United States); Anastasio, Mark A. [Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63110 (United States); Low, Daniel A. [Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095 (United States)

    2015-02-15

    Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets

  17. Automated three-dimensional detection and classification of living organisms using digital holographic microscopy with partial spatial coherent source: application to the monitoring of drinking water resources.

    Science.gov (United States)

    El Mallahi, Ahmed; Minetti, Christophe; Dubois, Frank

    2013-01-01

    In this paper, we investigate the use of a digital holographic microscope working with partially coherent spatial illumination for an automated detection and classification of living organisms. A robust automatic method based on the computation of propagating matrices is proposed to detect the 3D position of organisms. We apply this procedure to the evaluation of drinking water resources by developing a classification process to identify parasitic protozoan Giardia lamblia cysts among two other similar organisms. By selecting textural features from the quantitative optical phase instead of morphological ones, a robust classifier is built to propose a new method for the unambiguous detection of Giardia lamblia cyst that present a critical contamination risk.

  18. Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators

    OpenAIRE

    Askari, M.; Eslami, S.; van Rijn, M.; Medlock, S.; Moll van Charante, E. P.; van der Velde, N.; Rooij, S.E. de; Abu-Hanna, A.

    2016-01-01

    Summary We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insufficiency in the quality of fall management. Most indicators showed a positive association between increased risk for functional decline and adherence, four of which with statistical significance. Introduct...

  19. Detection of rubella-specific immunoglobulin G: comparison of the enzyme-linked immunosorbent assay and an automated microparticle enzyme immunoassay (IMx).

    OpenAIRE

    Skurrie, I J; Head, J L; Garland, S M

    1991-01-01

    An automated microparticle enzyme immunoassay (IMx Rubella IgG Antibody Assay; Abbott Laboratories, North Chicago, Ill.) was compared with a conventional enzyme-linked immunosorbent assay (ELISA) for detection of rubella-specific immunoglobulin G (IgG) in 400 consecutive antenatal patients. There was complete agreement between the two tests in this population, which had a positivity rate of 99% for rubella-specific IgG antibodies. The performance of the IMx was also evaluated at the cutoff zo...

  20. On-site detection of foot-and-mouth disease virus using a portable, automated sample preparation and PCR system

    International Nuclear Information System (INIS)

    Full text: Foot-and-mouth disease (FMD) is a highly contagious and economically devastating disease of farm livestock. The etiological agent, FMD virus (FMDV), is a single-stranded, positive-sense RNA virus belonging to the genus Aphthovirus within the family Picornaviridae. Rapid and accurate confirmation of the presence of FMDV is needed for effective control and eradication of the disease. An on-site detection test would be highly advantageous as the time taken to transport suspect clinical material to a central laboratory can often be lengthy, thus delaying a definitive diagnosis in the event of an outbreak. This study describes the development of a molecular assay for the detection of all seven serotypes of FMDV using novel technology, namely: Linear-After-The- Exponential (LATE)-PCR, for transfer onto a portable, easy-to-use, fully automated sample preparation and RT-PCR instrument. Primers and a mismatch tolerant probe were designed from consensus sequences in the FMDV 3D (RNA polymerase) gene to detect the target and its variants at low temperature. An internal control (IC) was included to validate negative results. After demonstrating that the LATE RT-PCR signal at end-point was proportional to number of target molecules over the range 10 to 1 million copies, the assay was compared with a one-step real-time RT-PCR (rRT-PCR) assay (also targeting the 3D) used routinely by reference laboratories. The LATE RT-PCR assay amplified RNA extracted from multiple strains of all FMDV serotypes. Of the 121 FMDV-positive samples tested, 119 were positive by both rRT-PCR and LATE RT-PCR tests while 118 had tested positive by virus isolation at the time of receipt. Twenty-eight FMDVnegative samples failed to react in all 3 tests. There were no false positive signals with RNA from other vesicular disease-causing viruses. Each FMDV-negative sample generated a signal from the IC, ruling out amplification failures. A dilution series of an FMDV reference strain demonstrated

  1. Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators

    NARCIS (Netherlands)

    Askari, M; Eslami, S; van Rijn, M; Medlock, S; Moll van Charante, E P; van der Velde, N; de Rooij, S E; Abu-Hanna, A

    2016-01-01

    UNLABELLED: We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insuffici

  2. Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection

    OpenAIRE

    Adrian Carrio; Carlos Sampedro; Jose Luis Sanchez-Lopez; Miguel Pimienta; Pascual Campoy

    2015-01-01

    Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral fl...

  3. Automated and Accurate Detection of Soma Location and Surface Morphology in Large-Scale 3D Neuron Images

    OpenAIRE

    Cheng Yan; Anan Li; Bin Zhang,; Wenxiang Ding; Qingming Luo; Hui Gong

    2013-01-01

    Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three step...

  4. Development of an automated updated selvester QRS scoring system using SWT-based QRS fractionation detection and classification

    OpenAIRE

    Bono, Valentina; Mazomenos, Evangelos B.; Chen, Taihai; Rosengarten, James; Acharyya, Amit; Maharatna, Koushik; Morgan, John M.; Curzen, Nick

    2014-01-01

    The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from lowcost ECG recordings. Automation of such a system is deemed to help implementing low-cost high-volume screening mechanisms of scar in the primary care. This article describes, for the first time to the best of our knowledge, an automated implementation of the updated Selvester scoring system for that purpose, where fractionated QRS morphologies and patterns are identified and classified ...

  5. Statistical Analysis of Filament Features Based on the Hα Solar Images from 1988 to 2013 by Computer Automated Detection Method

    Science.gov (United States)

    Hao, Q.; Fang, C.; Cao, W.; Chen, P. F.

    2015-12-01

    We improve our filament automated detection method which was proposed in our previous works. It is then applied to process the full disk Hα data mainly obtained by the Big Bear Solar Observatory from 1988 to 2013, spanning nearly three solar cycles. The butterfly diagrams of the filaments, showing the information of the filament area, spine length, tilt angle, and the barb number, are obtained. The variations of these features with the calendar year and the latitude band are analyzed. The drift velocities of the filaments in different latitude bands are calculated and studied. We also investigate the north-south (N-S) asymmetries of the filament numbers in total and in each subclass classified according to the filament area, spine length, and tilt angle. The latitudinal distribution of the filament number is found to be bimodal. About 80% of all the filaments have tilt angles within [0°, 60°]. For the filaments within latitudes lower (higher) than 50°, the northeast (northwest) direction is dominant in the northern hemisphere and the southeast (southwest) direction is dominant in the southern hemisphere. The latitudinal migrations of the filaments experience three stages with declining drift velocities in each of solar cycles 22 and 23, and it seems that the drift velocity is faster in shorter solar cycles. Most filaments in latitudes lower (higher) than 50° migrate toward the equator (polar region). The N-S asymmetry indices indicate that the southern hemisphere is the dominant hemisphere in solar cycle 22 and the northern hemisphere is the dominant one in solar cycle 23.

  6. Automated detection and measurement of isolated retinal arterioles by a combination of edge enhancement and cost analysis.

    Directory of Open Access Journals (Sweden)

    José A Fernández

    Full Text Available Pressure myography studies have played a crucial role in our understanding of vascular physiology and pathophysiology. Such studies depend upon the reliable measurement of changes in the diameter of isolated vessel segments over time. Although several software packages are available to carry out such measurements on small arteries and veins, no such software exists to study smaller vessels (<50 µm in diameter. We provide here a new, freely available open-source algorithm, MyoTracker, to measure and track changes in the diameter of small isolated retinal arterioles. The program has been developed as an ImageJ plug-in and uses a combination of cost analysis and edge enhancement to detect the vessel walls. In tests performed on a dataset of 102 images, automatic measurements were found to be comparable to those of manual ones. The program was also able to track both fast and slow constrictions and dilations during intraluminal pressure changes and following application of several drugs. Variability in automated measurements during analysis of videos and processing times were also investigated and are reported. MyoTracker is a new software to assist during pressure myography experiments on small isolated retinal arterioles. It provides fast and accurate measurements with low levels of noise and works with both individual images and videos. Although the program was developed to work with small arterioles, it is also capable of tracking the walls of other types of microvessels, including venules and capillaries. It also works well with larger arteries, and therefore may provide an alternative to other packages developed for larger vessels when its features are considered advantageous.

  7. The HTS barcode checker pipeline, a tool for automated detection of illegally traded species from high-throughput sequencing data

    Science.gov (United States)

    2014-01-01

    Background Mixtures of internationally traded organic substances can contain parts of species protected by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). These mixtures often raise the suspicion of border control and customs offices, which can lead to confiscation, for example in the case of Traditional Chinese medicines (TCMs). High-throughput sequencing of DNA barcoding markers obtained from such samples provides insight into species constituents of mixtures, but manual cross-referencing of results against the CITES appendices is labor intensive. Matching DNA barcodes against NCBI GenBank using BLAST may yield misleading results both as false positives, due to incorrectly annotated sequences, and false negatives, due to spurious taxonomic re-assignment. Incongruence between the taxonomies of CITES and NCBI GenBank can result in erroneous estimates of illegal trade. Results The HTS barcode checker pipeline is an application for automated processing of sets of 'next generation’ barcode sequences to determine whether these contain DNA barcodes obtained from species listed on the CITES appendices. This analytical pipeline builds upon and extends existing open-source applications for BLAST matching against the NCBI GenBank reference database and for taxonomic name reconciliation. In a single operation, reads are converted into taxonomic identifications matched with names on the CITES appendices. By inclusion of a blacklist and additional names databases, the HTS barcode checker pipeline prevents false positives and resolves taxonomic heterogeneity. Conclusions The HTS barcode checker pipeline can detect and correctly identify DNA barcodes of CITES-protected species from reads obtained from TCM samples in just a few minutes. The pipeline facilitates and improves molecular monitoring of trade in endangered species, and can aid in safeguarding these species from extinction in the wild. The HTS barcode checker pipeline is

  8. Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

    Science.gov (United States)

    Leckie, Donald G.; Cloney, Ed; Joyce, Steve P.

    2005-05-01

    Jack pine budworm ( Choristoneura pinus pinus (Free.)) is a native insect defoliator of mainly jack pine ( Pinus banksiana Lamb.) in North America east of the Rocky Mountains. Periodic outbreaks of this insect, which generally last two to three years, can cause growth loss and mortality and have an important impact ecologically and economically in terms of timber production and harvest. The jack pine budworm prefers to feed on current year needles. Their characteristic feeding habits cause discolouration or reddening of the canopy. This red colouration is used to map the distribution and intensity of defoliation that has taken place that year (current defoliation). An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable. Automated classification of multispectral imagery, such as is available from airborne and new high resolution satellite systems, was explored as a viable tool for objectively classifying current discolouration. Airborne multispectral imagery was acquired at a 2.5 m resolution with the Multispectral Electro-optical Imaging Sensor (MEIS). It recorded imagery in six nadir looking spectral bands specifically designed to detect discolouration caused by budworm and a near-infrared band viewing forward at 35° was also used. A 2200 nm middle infrared image was acquired with a Daedalus scanner. Training and test areas of different levels of discolouration were created based on field observations and a maximum likelihood supervized classification was used to estimate four classes of discolouration (nil-trace, light, moderate and severe). Good discrimination was achieved with an overall accuracy of 84% for the four discolouration levels. The moderate discolouration class was the poorest at 73%, because of confusion with both the severe and light classes. Accuracy on a stand basis was also good, and regional and within stand

  9. Spatial and temporal patterns in Arctic river ice breakup revealed by automated ice detection from MODIS imagery

    Science.gov (United States)

    Cooley, Sarah; Pavelsky, Tamlin

    2016-04-01

    The annual spring breakup of river ice has important consequences for northern ecosystems and significant economic implications for Arctic industry and transportation. River ice breakup research is restricted by the sparse distribution of hydrological stations in the Arctic, where limited available data suggests a trend towards earlier ice breakup. The specific climatic mechanisms driving this trend, however, are complex and can vary both regionally and within river systems. Consequently, understanding the response of river ice processes to a warming Arctic requires simultaneous examination of spatial and temporal patterns in breakup timing. Here we present an automated algorithm for river ice breakup detection using MODIS satellite imagery that enables identification of spatial and temporal breakup patterns at large scales. We examine breakup timing on the Mackenzie, Lena, Ob' and Yenisey rivers for the period 2000-2014. First, we split each river into 10 km segments. Next, for each day of the breakup season, we classify each river pixel as snow/ice, mixed ice/water or open water based on MODIS reflectance values and remove all cloud-covered segments using the MODIS cloud product. We then define the breakup date as the first day where the segment is 75% open water. Using this method, we are able to determine breakup dates with a mean uncertainty of +/-1.3 days. We find our remotely sensed breakup dates to be highly correlated to ground breakup dates and the timing of peak discharge. All statistically significant temporal trends in breakup timing are negative, indicating an overall shift towards earlier breakup. Considerable variability in the statistical significance and magnitude of trends along each river suggests that different climatic and physiographic drivers are impacting spatial patterns in breakup. Trends detected on the lower Mackenzie corroborate recent studies indicating weakening ice resistance and earlier breakup timing near the Mackenzie Delta. In

  10. A falling droplet as it falls apart

    CERN Document Server

    Jalaal, M; Mehravaran, K

    2011-01-01

    Using direct numerical simulations, the fragmentation of falling liquid droplets in a quiescent media is studied. Three simulations with different Eotvos numbers were performed. An adaptive volume of fluid(VOF) method based on octree meshing is used, providing a notable reduction of computational cost. The current video includes 4 main parts describing the fragmentation of the falling droplet.

  11. Toward fully automated genotyping: Allele assignment, pedigree construction, phase determination, and recombination detection in Duchenne muscular dystrophy

    Energy Technology Data Exchange (ETDEWEB)

    Perlin, M.W.; Burks, M.B. [Carnegie Mellon Univ., Pittsburgh, PA (United States); Hoop, R.C.; Hoffman, E.P. [Univ. of Pittsburgh School of Medicine, PA (United States)

    1994-10-01

    Human genetic maps have made quantum leaps in the past few years, because of the characterization of >2,000 CA dinucleotide repeat loci: these PCR-based markers offer extraordinarily high PIC, and within the next year their density is expected to reach intervals of a few centimorgans per marker. These new genetic maps open new avenues for disease gene research, including large-scale genotyping for both simple and complex disease loci. However, the allele patterns of many dinucleotide repeat loci can be complex and difficult to interpret, with genotyping errors a recognized problem. Furthermore, the possibility of genotyping individuals at hundreds or thousands of polymorphic loci requires improvements in data handling and analysis. The automation of genotyping and analysis of computer-derived haplotypes would remove many of the barriers preventing optimal use of dense and informative dinucleotide genetic maps. Toward this end, we have automated the allele identification, genotyping, phase determinations, and inheritance consistency checks generated by four CA repeats within the 2.5-Mbp, 10-cM X-linked dystrophin gene, using fluorescein-labeled multiplexed PCR products analyzed on automated sequencers. The described algorithms can deconvolute and resolve closely spaced alleles, despite interfering stutter noise; set phase in females; propagate the phase through the family; and identify recombination events. We show the implementation of these algorithms for the completely automated interpretation of allele data and risk assessment for five Duchenne/Becker muscular dystrophy families. The described approach can be scaled up to perform genome-based analyses with hundreds or thousands of CA-repeat loci, using multiple fluorophors on automated sequencers. 16 refs., 5 figs., 1 tab.

  12. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

    Directory of Open Access Journals (Sweden)

    Yong He

    Full Text Available Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1 concave points clustering to determine the seed points of touching cells; and 2 random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.

  13. Automated detection of hepatotoxic compounds in human hepatocytes using HepaRG cells and image-based analysis of mitochondrial dysfunction with JC-1 dye

    International Nuclear Information System (INIS)

    In this study, our goal was to develop an efficient in situ test adapted to screen hepatotoxicity of various chemicals, a process which remains challenging during the early phase of drug development. The test was based on functional human hepatocytes using the HepaRG cell line, and automation of quantitative fluorescence microscopy coupled with automated imaging analysis. Differentiated HepaRG cells express most of the specific liver functions at levels close to those found in primary human hepatocytes, including detoxifying enzymes and drug transporters. A triparametric analysis was first used to evaluate hepatocyte purity and differentiation status, mainly detoxication capacity of cells before toxicity testing. We demonstrated that culturing HepaRG cells at high density maintained high hepatocyte purity and differentiation level. Moreover, evidence was found that isolating hepatocytes from 2-week-old confluent cultures limited variations associated with an ageing process occurring over time in confluent cells. Then, we designed a toxicity test based on detection of early mitochondrial depolarisation associated with permeability transition (MPT) pore opening, using JC-1 as a metachromatic fluorescent dye. Maximal dye dimerization that would have been strongly hampered by efficient efflux due to the active, multidrug-resistant (MDR) pump was overcome by coupling JC-1 with the MDR inhibitor verapamil. Specificity of this test was demonstrated and its usefulness appeared directly dependent on conditions supporting hepatic cell competence. This new hepatotoxicity test adapted to automated, image-based detection should be useful to evaluate the early MPT event common to cell apoptosis and necrosis and simultaneously to detect involvement of the multidrug resistant pump with target drugs in a human hepatocyte environment. - Highlights: → We define conditions to preserve differentiation of selective pure HepaRG hepatocyte cultures. → In these conditions, CYPs

  14. Automated serological technique with special emphasis on a solid phase test for red cell antibody detection in routine blood banking

    OpenAIRE

    Sallander, Suzanne

    1999-01-01

    Automated serological techniques for erythrocyte antigen typing and antibody screening are presented and evaluated in a larger number of samples and throughout routine processing. Both techniques are microplate-adapted with computerised sample identification, sample and reagent dispensing, and interpretation of results. The method described for typing of the RBC antigens K, Fya, and C, c, E, e compared well to the manual haernagglutination test. The concurrence was >= 99.4 %...

  15. Detection of Perinatal Cytomegalovirus Infection and Sensorineural Hearing Loss in Belgian Infants by Measurement of Automated Auditory Brainstem Response▿

    OpenAIRE

    Verbeeck, Jannick; Van Kerschaver, Erwin; Wollants, Elke; Beuselinck, Kurt; Stappaerts, Luc; Van Ranst, Marc

    2008-01-01

    Since auditory disability causes serious problems in the development of speech and in the total development of a child, it is crucial to diagnose possible hearing impairment as soon as possible after birth. This study evaluates the neonatal hearing screening program in Flanders, Belgium. The auditory ability of 118,438 babies was tested using the automated auditory brainstem response. We selected 194 babies with indicative hearing impairment and 332 matched controls to investigate the associa...

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

    OpenAIRE

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

  17. Falls in Parkinson's disease.

    NARCIS (Netherlands)

    Grimbergen, Y.A.M.; Munneke, M.; Bloem, B.R.

    2004-01-01

    PURPOSE OF REVIEW: To summarize the latest insights into the clinical significance, assessment, pathophysiology and treatment of falls in Parkinson's disease. RECENT FINDINGS: Recent studies have shown that falls are common in Parkinson's disease, even when compared with other fall-prone populations

  18. Use of expert system and data analysis technologies in automation of error detection, diagnosis and recovery for ATLAS Trigger-DAQ Controls framework

    CERN Document Server

    Kazarov, A; The ATLAS collaboration; Magnoni, L; Lehmann Miotto, G

    2012-01-01

    Trigger and DAQ (Data AQuisition) System of the ATLAS experiment on LHC at CERN is a very complex distributed computing system, composed of O(10000) applications running on a farm of commodity CPUs. The system is being designed and developed by dozens of software engineers and physicists since end of 1990's and it will be maintained in operational mode during the lifetime of the experiment. The TDAQ system is controlled by the Controls framework, which includes a set of software components and tools used for system configuration, distributed processes handling, synchronization of Run Control state transitions etc. The huge flow of operational monitoring data produced is constantly monitored by operators and experts in order to detect problems or misbehaviour. Given the scale of the system and the rates of data to be analyzed, the automation of the Controls framework functionality in the areas of operational monitoring, system verification, error detection and recovery is a strong requirement. The paper descri...

  19. A versatile-deployable bacterial detection system for food and environmental safety based on LabTube-automated DNA purification, LabReader-integrated amplification, readout and analysis.

    Science.gov (United States)

    Hoehl, Melanie M; Bocholt, Eva Schulte; Kloke, Arne; Paust, Nils; von Stetten, Felix; Zengerle, Roland; Steigert, Juergen; Slocum, Alexander H

    2014-06-01

    Contamination of foods is a public health hazard that episodically causes thousands of deaths and sickens millions worldwide. To ensure food safety and quality, rapid, low-cost and easy-to-use detection methods are desirable. Here, the LabSystem is introduced for integrated, automated DNA purification, amplification and detection. It consists of a disposable, centrifugally driven DNA purification platform (LabTube) and a low-cost UV/vis-reader (LabReader). For demonstration of the LabSystem in the context of food safety, purification of Escherichia coli (non-pathogenic E. coli and pathogenic verotoxin-producing E. coli (VTEC)) in water and milk and the product-spoiler Alicyclobacillus acidoterrestris (A. acidoterrestris) in apple juice was integrated and optimized in the LabTube. Inside the LabReader, the purified DNA was amplified, readout and analyzed using both qualitative isothermal loop-mediated DNA amplification (LAMP) and quantitative real-time PCR. For the LAMP-LabSystem, the combined detection limits for purification and amplification of externally lysed VTEC and A. acidoterrestris are 10(2)-10(3) cell-equivalents. In the PCR-LabSystem for E. coli cells, the quantification limit is 10(2) cell-equivalents including LabTube-integrated lysis. The demonstrated LabSystem only requires a laboratory centrifuge (to operate the disposable, fully closed LabTube) and a low-cost LabReader for DNA amplification, readout and analysis. Compared with commercial DNA amplification devices, the LabReader improves sensitivity and specificity by the simultaneous readout of four wavelengths and the continuous readout during temperature cycling. The use of a detachable eluate tube as an interface affords semi-automation of the LabSystem, which does not require specialized training. It reduces the hands-on time from about 50 to 3 min with only two handling steps: sample input and transfer of the detachable detection tube.

  20. A fully automated system for analysis of pesticides in water: on-line extraction followed by liquid chromatography-tandem photodiode array/postcolumn derivatization/fluorescence detection.

    Science.gov (United States)

    Patsias, J; Papadopoulou-Mourkidou, E

    1999-01-01

    A fully automated system for on-line solid phase extraction (SPE) followed by high-performance liquid chromatography (HPLC) with tandem detection with a photodiode array detector and a fluorescence detector (after postcolumn derivatization) was developed for analysis of many chemical classes of pesticides and their major conversion products in aquatic systems. An automated on-line-SPE system (Prospekt) operated with reversed-phase cartridges (PRP-1) extracts analytes from 100 mL acidified (pH = 3) filtered water sample. On-line HPLC analysis is performed with a 15 cm C18 analytical column eluted with a mobile phase of phosphate (pH = 3)-acetonitrile in 25 min linear gradient mode. Solutes are detected by tandem diode array/derivatization/fluorescence detection. The system is controlled and monitored by a single computer operated with Millenium software. Recoveries of most analytes in samples fortified at 1 microgram/L are > 90%, with relative standard deviation values of < 5%. For a few very polar analytes, mostly N-methylcarbamoyloximes (i.e., aldicarb sulfone, methomyl, and oxamyl), recoveries are < 20%. However, for these compounds, as well as for the rest of the N-methylcarbamates except for aldicarb sulfoxide and butoxycarboxim, the limits of detection (LODs) are 0.005-0.05 microgram/L. LODs for aldicarb sulfoxide and butoxycarboxim are 0.2 and 0.1 microgram, respectively. LODs for the rest of the analytes except 4-nitrophenol, bentazone, captan, decamethrin, and MCPA are 0.05-0.1 microgram/L. LODs for the latter compounds are 0.2-1.0 microgram/L. The system can be operated unattended. PMID:10444834

  1. Performance of the new automated Abbott RealTime MTB assay for rapid detection of Mycobacterium tuberculosis complex in respiratory specimens.

    Science.gov (United States)

    Chen, J H K; She, K K K; Kwong, T-C; Wong, O-Y; Siu, G K H; Leung, C-C; Chang, K-C; Tam, C-M; Ho, P-L; Cheng, V C C; Yuen, K-Y; Yam, W-C

    2015-09-01

    The automated high-throughput Abbott RealTime MTB real-time PCR assay has been recently launched for Mycobacterium tuberculosis complex (MTBC) clinical diagnosis. This study would like to evaluate its performance. We first compared its diagnostic performance with the Roche Cobas TaqMan MTB assay on 214 clinical respiratory specimens. Prospective analysis of a total 520 specimens was then performed to further evaluate the Abbott assay. The Abbott assay showed a lower limit of detection at 22.5 AFB/ml, which was more sensitive than the Cobas assay (167.5 AFB/ml). The two assays demonstrated a significant difference in diagnostic performance (McNemar's test; P = 0.0034), in which the Abbott assay presented significantly higher area under curve (AUC) than the Cobas assay (1.000 vs 0.880; P = 0.0002). The Abbott assay demonstrated extremely low PCR inhibition on clinical respiratory specimens. The automated Abbott assay required only very short manual handling time (0.5 h), which could help to improve the laboratory management. In the prospective analysis, the overall estimates for sensitivity and specificity of the Abbott assay were both 100 % among smear-positive specimens, whereas the smear-negative specimens were 96.7 and 96.1 %, respectively. No cross-reactivity with non-tuberculosis mycobacterial species was observed. The superiority in sensitivity of the Abbott assay for detecting MTBC in smear-negative specimens could further minimize the risk in MTBC false-negative detection. The new Abbott RealTime MTB assay has good diagnostic performance which can be a useful diagnostic tool for rapid MTBC detection in clinical laboratories.

  2. Automated gas chromatography

    Science.gov (United States)

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

    1999-01-01

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

  3. Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection.

    Science.gov (United States)

    Carrio, Adrian; Sampedro, Carlos; Sanchez-Lopez, Jose Luis; Pimienta, Miguel; Campoy, Pascual

    2015-01-01

    Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results. PMID:26610513

  4. Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection

    Directory of Open Access Journals (Sweden)

    Adrian Carrio

    2015-11-01

    Full Text Available Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results.

  5. Library Automation

    OpenAIRE

    Dhakne, B. N.; Giri, V. V; Waghmode, S. S.

    2010-01-01

    New technologies library provides several new materials, media and mode of storing and communicating the information. Library Automation reduces the drudgery of repeated manual efforts in library routine. By use of library automation collection, Storage, Administration, Processing, Preservation and communication etc.

  6. Infrared fluorescent automated detection of thirteen short tandem repeat polymorphisms and one gender-determining system of the CODIS core system.

    Science.gov (United States)

    Ricci, U; Sani, I; Guarducci, S; Biondi, C; Pelagatti, S; Lazzerini, V; Brusaferri, A; Lapini, M; Andreucci, E; Giunti, L; Giovannucci Uzielli, M L

    2000-11-01

    We used an infrared (IR) automated fluorescence monolaser sequencer for the analysis of 13 autosomal short tandem repeat (STR) systems (TPOX, D3S1358, FGA, CSF1PO, D5S818, D7S820, D8S1179, TH01, vWA, D13S317, D16S359, D18S51, D21S11) and the X-Y homologous gene amelogenin system. These two systems represent the core of the combined DNA index systems (CODIS). Four independent multiplex reactions, based on the polymerase chain reaction (PCR) technique and on the direct labeling of the forward primer of every primer pair, with a new molecule (IRDye800), were set up, permitting the exact characterization of the alleles by comparison with ladders of specific sequenced alleles. This is the first report of the whole analysis of the STRs of the CODIS core using an IR automated DNA sequencer. The protocol was used to solve paternity/maternity tests and for population studies. The electrophoretic system also proved useful for the correct typing of those loci differing in size by only 2 bp. A sensibility study demonstrated that the test can detect an average of 10 pg of undegraded human DNA. We also performed a preliminary study analyzing some forensic samples and mixed stains, which suggested the usefulness of using this analytical system for human identification as well as for forensic purposes.

  7. Process automation

    International Nuclear Information System (INIS)

    Process automation technology has been pursued in the chemical processing industries and to a very limited extent in nuclear fuel reprocessing. Its effective use has been restricted in the past by the lack of diverse and reliable process instrumentation and the unavailability of sophisticated software designed for process control. The Integrated Equipment Test (IET) facility was developed by the Consolidated Fuel Reprocessing Program (CFRP) in part to demonstrate new concepts for control of advanced nuclear fuel reprocessing plants. A demonstration of fuel reprocessing equipment automation using advanced instrumentation and a modern, microprocessor-based control system is nearing completion in the facility. This facility provides for the synergistic testing of all chemical process features of a prototypical fuel reprocessing plant that can be attained with unirradiated uranium-bearing feed materials. The unique equipment and mission of the IET facility make it an ideal test bed for automation studies. This effort will provide for the demonstration of the plant automation concept and for the development of techniques for similar applications in a full-scale plant. A set of preliminary recommendations for implementing process automation has been compiled. Some of these concepts are not generally recognized or accepted. The automation work now under way in the IET facility should be useful to others in helping avoid costly mistakes because of the underutilization or misapplication of process automation. 6 figs

  8. Automated flaw detection scheme for cast austenitic stainless steel weld specimens using Hilbert-Huang transform of ultrasonic phased array data

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Tariq; Majumdar, Shantanu; Udpa, Lalita [Dept. of Electrical and Comupter Engineering, Michian State University, East Lansing, MI 48824 (United States); Ramuhalli, Pradeep; Crawford, Susan; Diaz, Aaron; Anderson, Michael T. [Pacific Northwest National Laboratory, Richland, WA 99354 (United States)

    2012-05-17

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

  9. Low cost, robust and real time system for detecting and tracking moving objects to automate cargo handling in port terminals

    OpenAIRE

    Vaquero, Victor; Repiso, Ely; Sanfeliu, Alberto; Vissers, John; Kwakkernaat, Maurice

    2015-01-01

    The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm is presented to be used in autonomous guided vehicles and autonomous trucks for efficient transportation of cargo in ports. The method first detects moving objects and then tracks them, taking int...

  10. Falls and falls efficacy: the role of sustained attention in older adults

    LENUS (Irish Health Repository)

    O'Halloran, Aisling M

    2011-12-19

    Abstract Background Previous evidence indicates that older people allocate more of their attentional resources toward their gait and that the attention-related changes that occur during aging increase the risk of falls. The aim of this study was to investigate whether performance and variability in sustained attention is associated with falls and falls efficacy in older adults. Methods 458 community-dwelling adults aged ≥ 60 years underwent a comprehensive geriatric assessment. Mean and variability of reaction time (RT), commission errors and omission errors were recorded during a fixed version of the Sustained Attention to Response Task (SART). RT variability was decomposed using the Fast Fourier Transform (FFT) procedure, to help characterise variability associated with the arousal and vigilance aspects of sustained attention. The number of self-reported falls in the previous twelve months, and falls efficacy (Modified Falls Efficacy Scale) were also recorded. Results Significant increases in the mean and variability of reaction time on the SART were significantly associated with both falls (p < 0.01) and reduced falls efficacy (p < 0.05) in older adults. An increase in omission errors was also associated with falls (p < 0.01) and reduced falls efficacy (p < 0.05). Upon controlling for age and gender affects, logistic regression modelling revealed that increasing variability associated with the vigilance (top-down) aspect of sustained attention was a retrospective predictor of falling (p < 0.01, OR = 1.14, 95% CI: 1.03 - 1.26) in the previous year and was weakly correlated with reduced falls efficacy in non-fallers (p = 0.07). Conclusions Greater variability in sustained attention is strongly correlated with retrospective falls and to a lesser degree with reduced falls efficacy. This cognitive measure may provide a novel and valuable biomarker for falls in older adults, potentially allowing for early detection and the implementation of preventative intervention

  11. Experiments in Free Fall

    Science.gov (United States)

    Art, Albert

    2006-01-01

    A model lift containing a figure of Albert Einstein is released from the side of a tall building and its free fall is arrested by elastic ropes. This arrangement allows four simple experiments to be conducted in the lift to demonstrate the effects of free fall and show how they can lead to the concept of the equivalence of inertial and…

  12. Fall armyworm migration patterns.

    Science.gov (United States)

    Fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), infestations in most of North America arise from annual migrations of populations that overwinter in southern Texas and Florida. Cytochrome Oxidase I haplotype profiles within the fall armyworm corn-strain, the subgroup tha...

  13. First Aid: Falls

    Science.gov (United States)

    ... Story" 5 Things to Know About Zika & Pregnancy First Aid: Falls KidsHealth > For Parents > First Aid: Falls Print A A A Text Size en ... Floors, Doors & Windows, Furniture, Stairways: Household Safety Checklist First Aid: Broken Bones Head Injuries Preventing Children's Sports Injuries ...

  14. Fall Leaf Portraits

    Science.gov (United States)

    O'Hara, Cristina

    2012-01-01

    In this article, the author describes how students can create a stunning as well as economical mosaic utilizing fall's brilliantly colored leaves, preserved at their peak in color. Start by choosing a beautiful fall day to take students on a nature walk to collect a variety of leaves in different shapes, sizes, and colors. Focus on collecting a…

  15. Low cost, robust and real time system for detecting and tracking moving objects to automate cargo handling in port terminals

    NARCIS (Netherlands)

    Vaquero, V.; Repiso, E.; Sanfeliu, A.; Vissers, J.; Kwakkernaat, M.

    2016-01-01

    The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm

  16. Fall prevention conceptual framework.

    Science.gov (United States)

    Abraham, Sam

    2011-01-01

    Falls can have lasting psychological and physical consequences, particularly fractures and slow-healing processes, and patients may also lose confidence in walking. Injuries from falls lead to functional decline, institutionalization, higher health care costs, and decreased quality of life. The process related to the problem of patient falls in the hospital, using the nursing model developed by the theorist, Ida Jean Orlando, is explained in this article. The useful tool that provides guidance to marketers in this endeavor is Maslow's hierarchy of needs. During acute illness, individuals are greatly in need of satisfying their physiological needs. If these needs are not met, patients leave the hospital lacking a positive experience. Initial fall risk assessment is critical to plan intervention and individualize care plan. Interventions depend on the severity of fall risk factors.

  17. Application of an Automated System for the Processing of VLF signals to Detect, Analyze and Classify Seismic-Ionospheric Precursor Phenomena

    Science.gov (United States)

    Skeberis, Christos; Xenos, Thomas; Contadakis, Michael; Arabelos, Dimitrios; Biagi, Pier Francesco; Maggipinto, Tommaso

    2013-04-01

    This paper studies the development and application of an automated system based on Predictive Modular Neural Networks (PREMONNs) and Self Organizing Maps (SOMs) along with the necessary backend development of database classification required to provide a fully integrated system for detecting disturbances that can be attributed to seismic-ionospheric precursor phenomena using VLF radio signals. The aforementioned system can analyze all the relevant data and bring forth and adaptively discriminate different characteristics in the received signals, in real time in order to provide data segments of interest that can be correlated to subsequent seismic phenomena and can be classified with respect to pre-recorded samples of previous points of interest (POIs). PREMONNs as it was demonstrated in previous studies can be used for time-series switching detection and can be applied to the detection of POIs , whereas SOMs have been extensively used in unsupervised pattern recognition and classification of datasets. For the purpose of this paper, data acquired in Thessaloniki (40.59N, 22,78E) from the VLF station in Tavolara, Italy (ICV station Lat 40.923, Lon. 9.731) for over two years (December 2010 - December 2012) are used. The receiver was developed by Elettronika Srl, and is part of the International Network for Frontier Research on Earthquake Precursors (INFREP). The received VLF signal is normalized and then processed using the Empirical Mode Decomposition Method (EMD). The resulting data are passed to an Artificial Neural Network (ANN) based on PREMONNs trained specifically for this purpose and the output from that stage is passed onto a classifier based on SOMs to compare and classify points of interest based on a current database of received signals and identifying and storing new ones for future reference. The efficacy of the detection and the results of the aforementioned process is then discussed and results are presented. Therefore, based on the results it may be

  18. Automated detection and volumetric segmentation of the spleen in CT scans; Automatische Detektion und volumetrische Segmentierung der Milz in CT-Untersuchungen

    Energy Technology Data Exchange (ETDEWEB)

    Hammon, M.; Dankerl, P.; Janka, R.; Uder, M.; Cavallaro, A. [Universitaetsklinikum Erlangen (Germany). Radiologisches Inst.; Kramer, M.; Seifert, S.; Tsymbal, A.; Costa, M.J. [Siemens AG, Erlangen (Germany). Corporate Technology

    2012-08-15

    To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm{sup 3} + 0.58 (width x length x thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 ({+-} 2.4) seconds. The average splenic volume measured by aV was 268.21 {+-} 114.67 cm{sup 3} compared to 281.58 {+-} 130.21 cm{sup 3} in mV and 268.93 {+-} 104.60 cm{sup 3} in eV. The correlation coefficient was 0.99 (coefficient of determination (R{sup 2}) = 0.98) for aV and mV, 0.91 (R{sup 2} = 0.83) for mV and eV and 0.91 (R{sup 2} = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R{sup 2} = 0.84), mV and eV (0.95; R{sup 2} = 0.91) and aV and eV (0.83; R{sup 2} = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

  19. Design and Implementation of Wearable Fall Detection and Location System%可穿戴式人体跌倒监测与定位系统设计与实现

    Institute of Scientific and Technical Information of China (English)

    晏勇

    2016-01-01

    系统以FPGA作为嵌入式控制器,三轴加速度传感器采集人体跌倒姿态参数,卡尔曼滤波算法抑制噪声输出,三级阀值比较跌倒特征向量; GPS模块检测跌倒发生位置, GSM模块发送远程报警短信;手机APP实时接收报警信号,查询人体跌倒位置及时救援。经测试,系统检测精度高、稳定可靠、报警延时小、待机时间长,可广泛用于老年人摔倒检测。%In order to track the fallen person and carry out an emergency rescue, the author presents a system with a FPGA embedded controller, using three-axis acceleration sensor to collect human fall attitude parameters, Kalman filter algorithm to suppress noise out-put and three threshold to compare falls feature vector. In addition, the current information of fall positions is captured through GPS module and remote alarm messages are sent through GSM network to smart phones, in which an APP is installed for receiving real-time alarm signals. The test results show that the system has advantages of high precision, reliability, less alarm delay, longer standby time and it can be widely used in fall detection for the elderly.

  20. Targeted virus detection in next-generation sequencing data using an automated e-probe based approach.

    Science.gov (United States)

    Visser, Marike; Burger, Johan T; Maree, Hans J

    2016-08-01

    The use of next-generation sequencing for plant virus detection is rapidly expanding, necessitating the development of bioinformatic pipelines to support analysis of these large datasets. Pipelines need to be easy implementable to mitigate potential insufficient computational infrastructure and/or skills. In this study user-friendly software was developed for the targeted detection of plant viruses based on e-probes. It can be used for both custom e-probe design, as well as screening preloaded probes against raw NGS data for virus detection. The pipeline was compared to de novo assembly-based virus detection in grapevine and produced comparable results, requiring less time and computational resources. The software, named Truffle, is available for the design and screening of e-probes tailored for user-specific virus species and data, along with preloaded probe-sets for grapevine virus detection. PMID:27209446

  1. Seismic and Acoustic Investigations of Rock Fall Initiation, Processes, and Mechanics

    OpenAIRE

    Zimmer, Valerie Louise

    2011-01-01

    Rock falls were monitored in Yosemite Valley using seismic and infrasound sensors in order to gain insights into the feasibility of rock fall detection and rock fall processes. The research objectives were to characterize the rock fall seismic signal and use that data to study the initiation, triggering, and dynamics of rock falls, correlate the data with physical and environmental conditions, and to search for potential rock fall precursors. Yosemite Valley has approximately one rock fall ...

  2. INVESTIGATION OF NEURAL NETWORK ALGORITHM FOR DETECTION OF NETWORK HOST ANOMALIES IN THE AUTOMATED SEARCH FOR XSS VULNERABILITIES AND SQL INJECTIONS

    Directory of Open Access Journals (Sweden)

    Y. D. Shabalin

    2016-03-01

    Full Text Available A problem of aberrant behavior detection for network communicating computer is discussed. A novel approach based on dynamic response of computer is introduced. The computer is suggested as a multiple-input multiple-output (MIMO plant. To characterize dynamic response of the computer on incoming requests a correlation between input data rate and observed output response (outgoing data rate and performance metrics is used. To distinguish normal and aberrant behavior of the computer one-class neural network classifieris used. General idea of the algorithm is shortly described. Configuration of network testbed for experiments with real attacks and their detection is presented (the automated search for XSS and SQL injections. Real found-XSS and SQL injection attack software was used to model the intrusion scenario. It would be expectable that aberrant behavior of the server will reveal itself by some instantaneous correlation response which will be significantly different from any of normal ones. It is evident that correlation picture of attacks from different malware running, the site homepage overriding on the server (so called defacing, hardware and software failures will differ from correlation picture of normal functioning. Intrusion detection algorithm is investigated to estimate false positive and false negative rates in relation to algorithm parameters. The importance of correlation width value and threshold value selection was emphasized. False positive rate was estimated along the time series of experimental data. Some ideas about enhancement of the algorithm quality and robustness were mentioned.

  3. Evaluating the Strengths and Weaknesses of Mining Audit Data for Automated Models for Intrusion Detection in Tcpdump and Basic Security Module Data

    Directory of Open Access Journals (Sweden)

    A. Arul Lawrence Selvakumar

    2012-01-01

    Full Text Available Problem statement: Intrusion Detection System (IDS have become an important component of infrastructure protection mechanism to secure the current and emerging networks, its services and applications by detecting, alerting and taking necessary actions against the malicious activities. The network size, technology diversities and security policies make networks more challenging and hence there is a requirement for IDS which should be very accurate, adaptive, extensible and more reliable. Although there exists the novel framework for this requirement namely Mining Audit Data for Automated Models for Intrusion Detection (MADAM ID, it is having some performance shortfalls in processing the audit data. Approach: Few experiments were conducted on tcpdump data of DARPA and BCM audit files by applying the algorithms and tools of MADAM ID in the processing of audit data, mine patterns, construct features and build RIPPER classifiers. By putting it all together, four main categories of attacks namely DOS, R2L, U2R and PROBING attacks were simulated. Results: This study outlines the experimentation results of MADAM ID in testing the DARPA and BSM data on a simulated network environment. Conclusion: The strengths and weakness of MADAM ID has been identified thru the experiments conducted on tcpdump data and also on Pascal based audit files of Basic Security Module (BSM. This study also gives some additional directions about the future applications of MADAM ID.

  4. Fully automated determination of the sterol composition and total content in edible oils and fats by online liquid chromatography-gas chromatography-flame ionization detection.

    Science.gov (United States)

    Nestola, Marco; Schmidt, Torsten C

    2016-09-01

    Sterol analysis of edible oils and fats is important in authenticity control. The gas chromatographic determination of the sterol distribution and total content is described by ISO norm 12228. Extraction, purification, and detection of the sterols are time-consuming and error-prone. Collaborative trials prove this regularly. Purification by thin-layer chromatography (TLC) and robust GC determination of all mentioned sterols is not straightforward. Therefore, a fully automated LC-GC-FID method was developed to facilitate the determination of sterols. The only manual step left was to weigh the sample into an autosampler vial. Saponification and extraction were performed by an autosampler while purification, separation, and detection were accomplished by online coupled normal-phase LC-GC-FID. Interlacing of sample preparation and analysis allowed an average sample throughput of one sample per hour. The obtained quantitative results were fully comparable with the ISO method with one apparent exception. In the case of sunflower oils, an additional unknown sterol was detected generally missed by ISO 12228. The reason was found in the omission of sterol silylation before subjection to GC-FID. The derivatization reaction changed the retention time and hid this compound behind a major sterol. The compound could be identified as 14-methyl fecosterol. Its structure was elucidated by GC-MS and ensured by HPLC and GC retention times. Finally, validation of the designed method confirmed its suitability for routine environments. PMID:27522150

  5. The Detection and Exclusion of the Prostate Neuro-Vascular Bundle (NVB) in Automated HIFU Treatment Planning Using a Pulsed-Wave Doppler Ultrasound System

    Science.gov (United States)

    Chen, Wohsing; Carlson, Roy F.; Fedewa, Russell; Seip, Ralf; Sanghvi, Narendra T.; Dines, Kris A.; Pfile, Richard; Penna, Michael A.; Gardner, Thomas A.

    2005-03-01

    Men with prostate cancer are likely to develop impotence after prostate cancer therapy if the treatment damages the neuro-vascular bundles (NVB). The NVB are generally located at the periphery of the prostate gland. To preserve the NVB, a Doppler system is used to detect and localize the associated blood vessels. This information is used during the therapy planning procedure to avoid treatment surrounding the blood vessel areas. The Sonablate®500 (Focus Surgery, Inc.) image-guided HIFU device is enhanced with a pulse-wave multi-gate Doppler system that uses the current imaging transducer and mechanical scanner to acquire Doppler data. Doppler detection is executed after the regular B-mode images are acquired from the base to the apex of the prostate using parallel sector scans. The results are stored and rendered in 3-D display, registered with additional models generated for the capsule, urethra, and rectal wall, and the B-mode data and treatment plan itself. The display of the blood flow can be in 2-D color overlaid on the B-mode image or in 3-D color structure. Based on this 3-D model, the HIFU treatment planning can be executed in automated or manual mode by the physician to remove originally defined treatment zones that overlap with the NVB (for preservation of NVB). The results of the NVB detection in animal experiments, and the 3-D modeling and data registration of the prostate will be presented.

  6. Automated detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images: multiscale hierachical expectation-maximization segmentation of vessels and PEs

    Science.gov (United States)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Patel, Smita; Cascade, Philip N.; Sahiner, Berkman; Wei, Jun; Ge, Jun; Kazerooni, Ella A.

    2007-03-01

    CT pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of pulmonary embolism (PE). We are developing a computer-aided detection (CAD) system to assist radiologist in PE detection in CTPA images. 3D multiscale filters in combination with a newly designed response function derived from the eigenvalues of Hessian matrices is used to enhance vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. A hierarchical EM estimation is then used to segment the vessels by extracting the high response voxels at each scale. The segmented vessels are pre-screened for suspicious PE areas using a second adaptive multiscale EM estimation. A rule-based false positive (FP) reduction method was designed to identify the true PEs based on the features of PE and vessels. 43 CTPA scans were used as an independent test set to evaluate the performance of PE detection. Experienced chest radiologists identified the PE locations which were used as "gold standard". 435 PEs were identified in the artery branches, of which 172 and 263 were subsegmental and proximal to the subsegmental, respectively. The computer-detected volume was considered true positive (TP) when it overlapped with 10% or more of the gold standard PE volume. Our preliminary test results show that, at an average of 33 and 24 FPs/case, the sensitivities of our PE detection method were 81% and 78%, respectively, for proximal PEs, and 79% and 73%, respectively, for subsegmental PEs. The study demonstrates the feasibility that the automated method can identify PE accurately on CTPA images. Further study is underway to improve the sensitivity and reduce the FPs.

  7. Radiologists' performance in the detection of benign and malignant masses with 3D automated breast ultrasound (ABUS)

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Jung Min [Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of); Moon, Woo Kyung, E-mail: moonwk@radcom.snu.ac.kr [Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of); Cho, Nariya [Department of Radiology and Clinical Research Institute, Seoul National University Hospital and the Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of); Park, Jeong Seon [Department of Radiology, Hanyang University College of Medicine, Hanyang University Hospital (Korea, Republic of); Kim, Seung Ja [Department of Radiology, Seoul National Universtiy Boramea Hospital (Korea, Republic of)

    2011-04-15

    Objectives: To retrospectively evaluate the detection performance of benign and malignant breast masses using 3D volume data obtained by ABUS and to determine lesion variables which affect detectability. Methods: Between November and December of 2007, bilateral whole breast US images were obtained using ABUS in 67 consecutive women who were scheduled to undergo US-guided needle biopsy due to suspicious breast masses. Twenty-four invasive ductal cancers in 23 breasts, 46 benign breast lesions in 44 breasts and 38 normal breasts were included. Three breast radiologists (experience range, 8-16 years) who did not perform the examinations and were blinded to the histology independently reviewed the ABUS data of the 105 breasts to detect suspicious solid masses with pathology as the standard of reference. Sensitivity and specificity in detecting benign and malignant masses were calculated, and lesion characteristics affecting detectability were analyzed. Results: Sensitivities for benign and malignant mass detections were 65.2% (30/46), 95.8% (23/24) for reader 1 (p = 0.007), 66.7% (31/46), 87.5% (21/24) for reader 2 (p = 0.087), and 56.3% (24/46), 91.7% (22/24), for reader 3 (p = 0.001), respectively. Logistic analysis showed that mass size (odds ratio, 95% CI; 1.12, 1.02-1.24), surrounding tissue changes (odds ratio, 95% CI; 0.11, 0.02-0.47), and shape of the mass (odds ratio, 95% CI; 3.12, 1.02-9.55) were the variables associated with detectability at ABUS. Conclusion: In reader studies using ABUS data, significantly higher sensitivity was noted for malignant breast masses than for benign masses.

  8. metAlignID: A high-throughout sofware tool set for automated detection of trace level contaminants in comprehensive LECO two-dimensional gas chromatography time-of-flight mass spectrometry data

    NARCIS (Netherlands)

    Lommen, A.; Kamp, van der H.J.; Kools, H.J.; Lee, van der M.K.; Weg, van der G.

    2012-01-01

    A new alternative data processing tool set, metAlignID, is developed for automated pre-processing and library-based identification and concentration estimation of target compounds after analysis by comprehensive two-dimensional gas chromatography with mass spectrometric detection. The tool set has b

  9. Design of Fall Detection Device for Elderly People based on Wearable Microelectron Mechanical System Sensor%基于 MEMS 传感器的可穿戴式老年人跌倒监测系统的设计

    Institute of Scientific and Technical Information of China (English)

    张云浦; 李玉榕; 陈建国

    2014-01-01

    To design a falling detection device, aiming to reduce the delay of helping the tumbling old men and to enhance their safety.This device combined MEMS (micro-electro-mechanical systems) sensor and digital signal processing with wireless trans-mission technology so as to be used on smart phones.To distinguish falling accidents from daily behaviors, it could be judged from body′s triaxial accelerometer and angular speed and proper threshold value selected from lots of experiments.The data could be sent to their phones, through Bluetooth and given out alarm through cellular voice, and besides, their location located by GPS and their condi-tions would be texted to the hospital and their guardians.Then after falling,the old people could be helped in the shortest time.Lots of experiments showed that the accuracy rate of the judgment from the combination of triaxial accelerometer and angular speed was 100%, compared to that of traditional judgment from triaxial accelerometer which was 84.29%.The result shows that the wearable falling de-tection device based on MEMS sensor has the characteristics of convenience, accuracy and low power dissipation and has the ability of detecting falling accurately and giving out alarm.It meets the requirements of falling monitoring.%为了缩短老年人跌倒后的救助时间和提高安全保障,我们设计了一种基于加速度和角速度传感器的跌倒监测装置。该装置结合机械微电子系统( microelectro mechanical system,MEMS)传感器、数字信号处理及无线传输技术应用于智能手机上。为区分人体跌倒事件和日常行为,结合人体三轴加速度和角速度联合对跌倒事件进行判断,通过大量实验选取合适阈值。针对老年人的生活特点,数据通过蓝牙装置发送至手机上进行处理,通过手机语音报警、手机GPS定位系统和短信通知医院和用户监护人,使得老年人跌倒后能够在第一时间获

  10. Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States

    Science.gov (United States)

    Jin, Suming; Homer, Collin G.; Yang, Limin; Xian, George; Fry, Joyce; Danielson, Patrick; Townsend, Philip A.

    2013-01-01

    A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

  11. Automated detection and quantitative measurement of small rounded opacities in X-ray CT images of pneumoconiosis

    International Nuclear Information System (INIS)

    This paper presents a new method for quantitative diagnosis of pneumoconiosis by using X-ray CT images. The method consists of extraction of lung regions, detection of small rounded opacities, and measurement of profusion and size of the opacities. A kind of directional difference operator is proposed for detection of the opacities, which enhances opacities as well as suppresses the shadows of blood vessels. Furthermore, we develop a method to measure the profusion and the size of the opacities to classify pneumoconiosis X-ray CT images. (author)

  12. How does interference fall?

    CERN Document Server

    Orlando, Patrick J; Modi, Kavan

    2016-01-01

    We study how single- and double-slit interference patterns fall in the presence of gravity. First, we demonstrate that universality of free fall still holds in this case, i.e., interference patterns fall just like classical objects. Next, we explore lowest order relativistic effects in the Newtonian regime by employing a recent quantum formalism which treats mass as an operator. This leads to interactions between non-degenerate internal degrees of freedom (like spin in an external magnetic field) and external degrees of freedom (like position). Based on these effects, we present an unusual phenomenon, in which a falling double slit interference pattern periodically decoheres and recoheres. The oscillations in the visibility of this interference occur due to correlations built up between spin and position. Finally, we connect the interference visibility revivals with non-Markovian quantum dynamics.

  13. Seneca Falls. Classroom Focus.

    Science.gov (United States)

    Balantic, Jeannette; Libresco, Andrea S.

    1995-01-01

    Presents a secondary school lesson based on the Seneca Falls Declaration of Sentiments. Provides lesson objectives and step-by-step instructional procedures. Includes quoted sections of the Declaration of Sentiments. (CFR)

  14. Fall Bottom Trawl Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The standardized NEFSC Fall Bottom Trawl Survey was initiated in 1963 and covered an area from Hudson Canyon, NY to Nova Scotia, Canada. Throughout the years,...

  15. Survival of falling robots

    Science.gov (United States)

    Cameron, Jonathan M.; Arkin, Ronald C.

    1992-01-01

    As mobile robots are used in more uncertain and dangerous environments, it will become important to design them so that they can survive falls. In this paper, we examine a number of mechanisms and strategies that animals use to withstand these potentially catastrophic events and extend them to the design of robots. A brief survey of several aspects of how common cats survive falls provides an understanding of the issues involved in preventing traumatic injury during a falling event. After outlining situations in which robots might fall, a number of factors affecting their survival are described. From this background, several robot design guidelines are derived. These include recommendations for the physical structure of the robot as well as requirements for the robot control architecture. A control architecture is proposed based on reactive control techniques and action-oriented perception that is geared to support this form of survival behavior.

  16. Falls prevention in primary care

    OpenAIRE

    Nazarko, Linda

    2009-01-01

    Each year 1.57 million older people fall more than three times and 70 000 fracture their hips. Falls can lead to disability and even death. The NSF for Older People identified falls prevention as a major health priority. This paper explains how primary care practitioners can contribute to falls prevention, reduce falls risk and improve quality of life for the older person.

  17. Approach to Fall in Elderly Population

    Directory of Open Access Journals (Sweden)

    Mehmet Ilkin Naharci

    2009-10-01

    Full Text Available Falls are one of the geriatric syndromes which occur commonly and significantly increase morbidity and mortality rates in elderly. The incidence of falls increases with age. Falls usually occur when impairments in cognitive, behavioral, and executive function begin. The incidence of fall is between 30 and 40 percent of community-dwelling people and approximately 50 percent of individuals in the long-term care setting over the age of 65 years. Fracture (hip, arm, wrist, pelvis, head trauma or major lacerations, as defined serious wounding, occur 10-25% of elderly cases. Fall is overlooked in clinical examination due to various reasons; the patient never mentions the event to a doctor; there is no injury at the time of the fall; the doctor fails to ask the patient about a history of falls; or either doctor or patient erroneously believes that falls are an inevitable part of the aging process. Elderly give not usually any self-information about fall, for this reason, all older patients should be asked at least once per year about falls and should be assessed in terms of balance and gait disorders. There are many distinct causes for falls in old people. Falls in older individuals occur when a threat to the normal homeostatic mechanisms that maintain postural stability is superimposed on underlying age-related declines in balance, ambulation, and cardiovascular function. This factor may be an acute illness (eg, fever, water loss, arrhythmia, a new medication, an environmental stress (eg, unfamiliar surrounding, or an unsafe walking surface. The elderly person can not cope with happened additional stress. To prevent and decrease the frequency of falls, effective approaches are medical interventions, environmental modifications, education-exercise programs, and assisted device. Detection and amelioration of risk factors can significantly reduce the rate of future falls. The assessment of fall, causing mobility restriction, use of nursing home, and

  18. Fully automated microchip system for the detection of quantal exocytosis from single and small ensembles of cells

    DEFF Research Database (Denmark)

    Spégel, Christer; Heiskanen, Arto; Pedersen, Simon;

    2008-01-01

    A lab-on-a-chip device that enables positioning of single or small ensembles of cells on an aperture in close proximity to a mercaptopropionic acid (MPA) modified sensing electrode has been developed and characterized. The microchip was used for the detection of Ca2+-dependent quantal catecholamine...

  19. ESBL Detection: Comparison of a Commercially Available Chromogenic Test for Third Generation Cephalosporine Resistance and Automated Susceptibility Testing in Enterobactericeae

    Science.gov (United States)

    El-Jade, Mohamed Ramadan; Parcina, Marijo; Schmithausen, Ricarda Maria; Stein, Christoph; Meilaender, Alina; Hoerauf, Achim; Molitor, Ernst

    2016-01-01

    Rapid detection and reporting of third generation cephalosporine resistance (3GC-R) and of extended spectrum betalactamases in Enterobacteriaceae (ESBL-E) is a diagnostic and therapeutic priority to avoid inefficacy of the initial antibiotic regimen. In this study we evaluated a commercially available chromogenic screen for 3GC-R as a predictive and/or confirmatory test for ESBL and AmpC activity in clinical and veterinary Enterobacteriaceae isolates. The test was highly reliable in the prediction of cefotaxime and cefpodoxime resistance, but there was no correlation with ceftazidime and piperacillin/tazobactam minimal inhibitory concentrations. All human and porcine ESBL-E tested were detected with exception of one genetically positive but phenotypically negative isolate. By contrast, AmpC detection rates lay below 30%. Notably, exclusion of piperacillin/tazobactam resistant, 3GC susceptible K1+ Klebsiella isolates increased the sensitivity and specificity of the test for ESBL detection. Our data further imply that in regions with low prevalence of AmpC and K1 positive E. coli strains chromogenic testing for 3GC-R can substitute for more time consuming ESBL confirmative testing in E. coli isolates tested positive by Phoenix or VITEK2 ESBL screen. We, therefore, suggest a diagnostic algorithm that distinguishes 3GC-R screening from primary culture and species-dependent confirmatory ESBL testing by βLACTATM and discuss the implications of MIC distribution results on the choice of antibiotic regimen. PMID:27494134

  20. Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks

    NARCIS (Netherlands)

    Mellmann, Alexander; Friedrich, Alexander W; Rosenkötter, Nicole; Rothgänger, Jörg; Karch, Helge; Reintjes, Ralf; Harmsen, Dag

    2006-01-01

    BACKGROUND: The detection of methicillin-resistant Staphylococcus aureus (MRSA) usually requires the implementation of often rigorous infection-control measures. Prompt identification of an MRSA epidemic is crucial for the control of an outbreak. In this study we evaluated various early warning algo