WorldWideScience

Sample records for automated fall detection

  1. Automated Monitoring System for Fall Detection in the Elderly

    Directory of Open Access Journals (Sweden)

    Shadi Khawandi

    2010-12-01

    Full Text Available Falls are a major problem for the elderly people living independently. According to the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event, and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall.

  2. 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

    detection algorithm as either a fall or a nonfall using inputs from 3D accelerometers. Significant differences for impacts recorded, trunk angle changes (p<0.01), and detection performances (p<0.05) were found between fall and nonfall conditions. The proposed algorithm detected fall events during simulated...... 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......., and reduce complications from falls. The performance of a 2-stage fall detection algorithm using impact magnitudes and changes in trunk angles derived from user-based motion sensors was evaluated under laboratory conditions. Ten healthy participants were instrumented on the front and side of the trunk...

  3. Pre-impact fall detection.

    Science.gov (United States)

    Hu, Xinyao; Qu, Xingda

    2016-01-01

    Pre-impact fall detection has been proposed to be an effective fall prevention strategy. In particular, it can help activate on-demand fall injury prevention systems (e.g. inflatable hip protectors) prior to fall impacts, and thus directly prevent the fall-related physical injuries. This paper gave a systematical review on pre-impact fall detection, and focused on the following aspects of the existing pre-impact fall detection research: fall detection apparatus, fall detection indicators, fall detection algorithms, and types of falls for fall detection evaluation. In addition, the performance of the existing pre-impact fall detection solutions were also reviewed and reported in terms of their sensitivity, specificity, and detection/lead time. This review also summarized the limitations in the existing pre-impact fall detection research, and proposed future research directions in this field. PMID:27251528

  4. 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.

  5. 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.

  6. Detecting falls with wearable sensors using machine learning techniques.

    Science.gov (United States)

    Özdemir, Ahmet Turan; Barshan, Billur

    2014-01-01

    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. PMID:24945676

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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

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

    OpenAIRE

    Falin Wu; Hengyang Zhao; Yan Zhao; Haibo Zhong

    2015-01-01

    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 caregi...

  13. Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones

    OpenAIRE

    Medrano, Carlos; Igual, Raul; Plaza, Inmaculada; Castro, Manuel

    2014-01-01

    Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or...

  14. Challenges, issues and trends in fall detection systems

    OpenAIRE

    Igual, Raul; Medrano, Carlos; Plaza, Inmaculada

    2013-01-01

    Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewi...

  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. Challenges, issues and trends in fall detection systems.

    Science.gov (United States)

    Igual, Raul; Medrano, Carlos; Plaza, Inmaculada

    2013-01-01

    Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls. PMID:23829390

  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 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

  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

    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.

  1. Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

    Directory of Open Access Journals (Sweden)

    Yueng Santiago Delahoz

    2014-10-01

    Full Text Available According to nihseniorhealth.gov (a website for older adults, falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD and fall prevention (FP are research areas that have been active for over a decade, and they both strive for improving people’s lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

  2. Automated Methods for Multiplexed Pathogen Detection

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-09-01

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

  3. Towards Automated Android App Collusion Detection

    OpenAIRE

    Asavoae, Irina Mariuca; Blasco, Jorge; Thomas M. Chen; Kalutarage, Harsha Kumara; Muttik, Igor; Nguyen, Hoang Nga; Roggenbach, Markus; Shaikh, Siraj Ahmed

    2016-01-01

    Android OS supports multiple communication methods between apps. This opens the possibility to carry out threats in a collaborative fashion, c.f. the Soundcomber example from 2011. In this paper we provide a concise definition of collusion and report on a number of automated detection approaches, developed in co-operation with Intel Security.

  4. 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.

  5. Automated Wildfire Detection Through Artificial Neural Networks

    Science.gov (United States)

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

    2005-01-01

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

  6. Automated assistance for detecting malicious code

    Energy Technology Data Exchange (ETDEWEB)

    Crawford, R.; Kerchen, P.; Levitt, K.; Olsson, R.; Archer, M.; Casillas, M. [Univ. of California, Davis, CA (US). Dept. of Computer Science

    1993-06-18

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

  7. 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…

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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...

  13. Automated detection of Karnal bunt teliospores

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-02-01

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

  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. 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.

  16. 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

  17. A smart vision sensor for detecting risk factors of a toddler's fall in a home environment

    OpenAIRE

    Na, H; Qin, SF; Wright, D.

    2007-01-01

    This paper presents a smart vision sensor for detecting risk factors of a toddler's fall in an indoor home environment assisting parents' supervision to prevent fall injuries. We identified the risk factors by analyzing real fall injury stories and referring to a related organization's suggestions to prevent falls. In order to detect the risk factors using computer vision, two major image processing methods, clutter detection and toddler tracking, were studied with using only one commercial w...

  18. 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

  19. 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.

  20. 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...

  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

    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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. Automated RNA Extraction and Purification for Multiplexed Pathogen Detection

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-01-01

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

  10. Automated system for crack detection using infrared thermograph

    International Nuclear Information System (INIS)

    The objective of this study was the development of the automated system for crack detection on square steel bars used in the automotive industry for axle and shaft construction. The automated system for thermographic crack detection uses brief pulsed eddy currents to heat steel components under inspection. Cracks, if present, will disturb the current flow and so generate changes in the temperature profile in the crack area. These changes of temperature are visualized using an infrared camera. The image acquired by the infrared camera is evaluated through an image processing system. The advantages afforded by the system are its inspection time, its excellent flaw detection sensitivity and its ability to detect hidden, subsurface cracks. The automated system consists of four IR cameras (each side of steel bar is evaluated at a time), coil, high frequency generator and control place with computers. The system is a part of the inspection line where the subsurface and surface cracks are searched. If the crack is present, the cracked place is automatically marked. The components without cracks are then deposited apart from defective blocks. The system is fully automated and its ability is to evaluate four meter blocks within 20 seconds. This is the real reason for using this system in real industrial applications. (author)

  11. 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.

  12. 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

  13. 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...

  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

    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...... 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....

  16. 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.

  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. 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

  19. 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.

  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. Automated Fault Detection for DIII-D Tokamak Experiments

    International Nuclear Information System (INIS)

    An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity

  2. 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...

  3. 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 ...

  4. 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...

  5. 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.

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

    Science.gov (United States)

    Logeswaran, Rajasvaran

    2009-12-01

    Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases. PMID:20052894

  7. Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2013-07-01

    Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.

  8. 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.

  9. 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

  10. 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

  11. 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.)

  12. 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.)

  13. 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.

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

    Science.gov (United States)

    Jackson, Gail

    2012-01-01

    Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. In order to collect information on the complete global precipitation cycle and to understand the energy budget in terms of precipitation, uniform global estimates of both liquid and frozen precipitation must be collected. Active observations of falling snow are somewhat easier to estimate since the radar will detect the precipitation particles and one only needs to know surface temperature to determine if it is liquid rain or snow. The challenges of estimating falling snow from passive spaceborne observations still exist though progress is being made. While these challenges are still being addressed, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Important information to assess falling snow retrievals includes knowing thresholds of detection for active and passive sensors, various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (2.5 km) cloud tops having an ice water content (Iwe) at the surface of 0.25 g m-3 and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The analysis relies on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Results are presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz (Skofronick-Jackson, et al. submitted to IEEE TGRS, April 2012). The notable results show: (1) the W-Band radar has detection thresholds more

  15. 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

  16. 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.

  17. [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

  18. Embedded DSP-based telehealth radar system for remote in-door fall detection.

    Science.gov (United States)

    Garripoli, Carmine; Mercuri, Marco; Karsmakers, Peter; Jack Soh, Ping; Crupi, Giovanni; Vandenbosch, Guy A E; Pace, Calogero; Leroux, Paul; Schreurs, Dominique

    2015-01-01

    Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women) that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects' weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar's operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas. PMID:25291803

  19. 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.

  20. 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%. 

  1. 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%.  

  2. 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.

  3. 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

  4. Automated transient detection in the STEREO Heliospheric Imagers.

    Science.gov (United States)

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

    2014-05-01

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

  5. Automated detection of irradiated food with the comet assay

    International Nuclear Information System (INIS)

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

  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 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

  8. 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

  9. 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.

  10. 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.

  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. PMID:26729128

  12. Automated detection of microaneurysms using robust blob descriptors

    Science.gov (United States)

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

    2013-03-01

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

  13. Automated analysis for detecting beams in laser wakefield simulations

    International Nuclear Information System (INIS)

    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

  14. 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...

  15. A smart phone-based pocket fall accident detection, positioning, and rescue system.

    Science.gov (United States)

    Kau, Lih-Jen; Chen, Chih-Sheng

    2015-01-01

    We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm. PMID:25486656

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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).

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. A Depth-Based Fall Detection System Using a Kinect® Sensor

    Directory of Open Access Journals (Sweden)

    Samuele Gasparrini

    2014-02-01

    Full Text Available We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.

  6. 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

  7. 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.

  8. Automated shock detection and analysis algorithm for space weather application

    Science.gov (United States)

    Vorotnikov, Vasiliy S.; Smith, Charles W.; Hu, Qiang; Szabo, Adam; Skoug, Ruth M.; Cohen, Christina M. S.

    2008-03-01

    Space weather applications have grown steadily as real-time data have become increasingly available. Numerous industrial applications have arisen with safeguarding of the power distribution grids being a particular interest. NASA uses short-term and long-term space weather predictions in its launch facilities. Researchers studying ionospheric, auroral, and magnetospheric disturbances use real-time space weather services to determine launch times. Commercial airlines, communication companies, and the military use space weather measurements to manage their resources and activities. As the effects of solar transients upon the Earth's environment and society grow with the increasing complexity of technology, better tools are needed to monitor and evaluate the characteristics of the incoming disturbances. A need is for automated shock detection and analysis methods that are applicable to in situ measurements upstream of the Earth. Such tools can provide advance warning of approaching disturbances that have significant space weather impacts. Knowledge of the shock strength and speed can also provide insight into the nature of the approaching solar transient prior to arrival at the magnetopause. We report on efforts to develop a tool that can find and analyze shocks in interplanetary plasma data without operator intervention. This method will run with sufficient speed to be a practical space weather tool providing useful shock information within 1 min of having the necessary data to ground. The ability to run without human intervention frees space weather operators to perform other vital services. We describe ways of handling upstream data that minimize the frequency of false positive alerts while providing the most complete description of approaching disturbances that is reasonably possible.

  9. 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.

  10. 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.

  11. 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.

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

    OpenAIRE

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

    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 sy...

  13. 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

  14. 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)

  15. 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.

  16. Personalized fall detection and classification through walls and in heavy indoor clutter

    Science.gov (United States)

    Amin, Moeness; Ahmad, Fauzia; Jokanovic, Branka

    2015-05-01

    Recent research and developments for in home radar monitoring have shown real promise of the technology in detecting normal and abnormal gross-motor activities of humans inside their residences and at private homes. Attention is now paid to challenges in system integration, operations, and installations. One important question touches on the required number of radar units for a given residence and whether eventually one radar unit per room would become the nominal approach. Towards addressing this question and assessing the effectiveness of radar unit to sense adjacent rooms and hallways of the same residence, this paper examines through-wall radar monitoring where the radar signal faces both wall attenuation and dispersion. We show that typical interior walls do not significantly alter the radar time-frequency (TF) signature of a fall, and the radar signal return is slightly weakened by wall penetration. Additionally, we show that there is a wide variation of the TF feature values associated with fall motions which confuse a classifier, trained with generic subjects, and cause it to falsely declare a different motion.

  17. 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.

  18. 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

  19. 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.

  20. An automated analysis of wide area motion imagery for moving subject detection

    Science.gov (United States)

    Tahmoush, Dave

    2015-05-01

    Automated analysis of wide area motion imagery (WAMI) can significantly reduce the effort required for converting data into reliable decisions. We register consecutive WAMI frames and use false-color frame comparisons to enhance the visual detection of possible subjects in the imagery. The large number of WAMI detections produces the need for a prioritization of detections for further inspection. We create a priority queue of detections for automated revisit with smaller field-ofview assets based on the locations of the movers as well as the probability of the detection. This automated queue works within an operator's preset prioritizations but also allows the flexibility to dynamically respond to new events as well as incorporating additional information into the surveillance tasking.

  1. 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

  2. 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...

  3. 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...

  4. 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 ...

  5. 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 ...

  6. 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.

  7. Automated generation of nonlinear system characterization for sensor failure detection

    International Nuclear Information System (INIS)

    Independent estimation of critical signals is required for use in consistency checking of instrument outputs, and for isolating common-mode failures. When measurement redundancy is not available, this method can be used for monitoring sensor degradation or sensor drifts by on-line prediction of the sensor output. A methodology is developed to characterize a given critical signal as a function of other related variables in a process by a nonlinear minimum-term model. This steady-state characterization is fully automated and implemented on an IBM PC, and is applied for sensor failure monitoring in a power plant and a process control industrial system. 5 refs., 5 figs

  8. 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

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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.

  14. 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.

  15. Reliability Study of the Hitachi H34C Accelerometer in Wireless Body Area Networks for Fall Detection

    OpenAIRE

    Catteeuw, Wim; Hallez, Hans; Boydens, Jeroen

    2013-01-01

    A WBAN (Wireless Body Area Network) allows connecting several sensor nodes into one sensor network. Each sensor node can be provided with a dedicated sensor. In case of fall detection, the physical movements of the body, which show characteristic patterns typical for a falling body, are used to generate a warning signal. Physical movements of the body can be measured by accelerometers. Today there is a lot of progress in the area of MEMS accelerometers. They are very small and hence can get i...

  16. 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.

  17. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    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......)is presented. Height information is used to determine the location of object which stands above terrain, and the CIR-Imagery is used to exclude vegetation, leading to a potential buildings mask. Comparing the existing objects in the map database with these extracted objects leads to a validation of the map...... database and hence change detection. The success of the method is strongly dependent on the representation of buildings in the DSM and hence the possibility to detect their locations. Therefore the method used for estimation of the digital terrain model from the LIDAR based digital surface model has show...

  18. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    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) is...... presented. Height information is used to determine the location of object which stands above terrain, and the CIR-Imagery is used to exclude vegetation, leading to a potential buildings mask. Comparing the existing objects in the map database with these extracted objects leads to a validation of the map...... database and hence change detection. The success of the method is strongly dependent on the representation of buildings in the DSM and hence the possibility to detect their locations. Therefore the method used for estimation of the digital terrain model from the LIDAR based digital surface model has show...

  19. 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...

  20. 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

  1. 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.

  2. 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…

  3. Automated electrochemical detection of iron ions in erythrocytes from melim minipigs suffering from melanoma

    Czech Academy of Sciences Publication Activity Database

    Kremplová, M.; Krejcová, l.; Hynek, D.; Barath, P.; Majzlík, P.; Horák, Vratislav; Adam, V.; Sochor, J.; Cernei, N.; Hubálek, J.; Vrba, R.; Kižek, R.

    2012-01-01

    Roč. 7, č. 7 (2012), s. 5893-5909. ISSN 1452-3981 Institutional research plan: CEZ:AV0Z50450515 Keywords : Automation * Biological sample * Electrochemical detection Subject RIV: CG - Electrochemistry Impact factor: 3.729, year: 2011

  4. 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

  5. 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.

  6. 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.

  7. 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.

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

    Science.gov (United States)

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

    2015-10-06

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

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

    Science.gov (United States)

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

    2008-01-01

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

  10. Challenges in automated detection of cervical intraepithelial neoplasia

    Science.gov (United States)

    Srinivasan, Yeshwanth; Yang, Shuyu; Nutter, Brian; Mitra, Sunanda; Phillips, Benny; Long, Rodney

    2007-03-01

    Cervical Intraepithelial Neoplasia (CIN) is a precursor to invasive cervical cancer, which annually accounts for about 3700 deaths in the United States and about 274,000 worldwide. Early detection of CIN is important to reduce the fatalities due to cervical cancer. While the Pap smear is the most common screening procedure for CIN, it has been proven to have a low sensitivity, requiring multiple tests to confirm an abnormality and making its implementation impractical in resource-poor regions. Colposcopy and cervicography are two diagnostic procedures available to trained physicians for non-invasive detection of CIN. However, many regions suffer from lack of skilled personnel who can precisely diagnose the bio-markers due to CIN. Automatic detection of CIN deals with the precise, objective and non-invasive identification and isolation of these bio-markers, such as the Acetowhite (AW) region, mosaicism and punctations, due to CIN. In this paper, we study and compare three different approaches, based on Mathematical Morphology (MM), Deterministic Annealing (DA) and Gaussian Mixture Models (GMM), respectively, to segment the AW region of the cervix. The techniques are compared with respect to their complexity and execution times. The paper also presents an adaptive approach to detect and remove Specular Reflections (SR). Finally, algorithms based on MM and matched filtering are presented for the precise segmentation of mosaicism and punctations from AW regions containing the respective abnormalities.

  11. 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

  12. 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...

  13. 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...

  14. 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

  15. 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.

  16. Automated detection of meteors in observed image sequence

    Czech Academy of Sciences Publication Activity Database

    Šimberová, Stanislava; Suk, Tomáš

    Melville: American Institute of Physics, 2015 - (Simos, T.), 190009/1-190009/4. (AIP Conference proceedings. 1702). ISBN 978-0-7354-1349-8. ISSN 0094-243X. [International conference of computational methods in sciences and engineering 2015. Athens (GR), 20150320] R&D Projects: GA ČR GA15-16928S Institutional support: RVO:67985815 ; RVO:67985556 Keywords : meteor detection * statistical moments * Hough transformation Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics; IN - Informatics, Computer Science (UTIA-B)

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

    International Nuclear Information System (INIS)

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

  18. 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.

  19. 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...

  20. 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.

  1. 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.

  2. Fast reversible single-step method for enhanced band contrast of polyacrylamide gels for automated detection.

    Science.gov (United States)

    Ling, Wei-Li; Lua, Wai-Heng; Gan, Samuel Ken-En

    2015-05-01

    Staining SDS-PAGE is commonly used in protein analysis for many downstream characterization processes. Although staining and destaining protocols can be adjusted, they can be laborious, and faint bands often become false negatives. Similarly, these faint bands hinder automated software band detections that are necessary for quantitative analyses. To overcome these problems, we describe a single-step rapid and reversible method to increase (up to 500%) band contrast in stained gels. Through the use of alcohols, we improved band detection and facilitated gel storage by drying the gels into compact white sheets. This method is suitable for all stained SDS-PAGE gels, including gradient gels and is shown to improve automated band detection by enhanced band contrast. PMID:25782090

  3. Automated detection of cardiac phase from intracoronary ultrasound image sequences.

    Science.gov (United States)

    Sun, Zheng; Dong, Yi; Li, Mengchan

    2015-01-01

    Intracoronary ultrasound (ICUS) is a widely used interventional imaging modality in clinical diagnosis and treatment of cardiac vessel diseases. Due to cyclic cardiac motion and pulsatile blood flow within the lumen, there exist changes of coronary arterial dimensions and relative motion between the imaging catheter and the lumen during continuous pullback of the catheter. The action subsequently causes cyclic changes to the image intensity of the acquired image sequence. Information on cardiac phases is implied in a non-gated ICUS image sequence. A 1-D phase signal reflecting cardiac cycles was extracted according to cyclical changes in local gray-levels in ICUS images. The local extrema of the signal were then detected to retrieve cardiac phases and to retrospectively gate the image sequence. Results of clinically acquired in vivo image data showed that the average inter-frame dissimilarity of lower than 0.1 was achievable with our technique. In terms of computational efficiency and complexity, the proposed method was shown to be competitive when compared with the current methods. The average frame processing time was lower than 30 ms. We effectively reduced the effect of image noises, useless textures, and non-vessel region on the phase signal detection by discarding signal components caused by non-cardiac factors. PMID:26406038

  4. 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.

  5. 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.

  6. 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 ($\

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

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm; Hansen, Thomas Willum; Larsen, Rasmus; Wagner, Jakob Birkedal

    structure 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...... tensor B-splines is employed, which is deformed by matching model grid points with the C-hexagon centers. Dependent on the Cs and defocus-settings during microscopy these centers appear either dark or bright. One ends up with a deformed hexagonal tessellation, which can easily be transformed into a...... length scale, and at the same time lattice deformations can be visualized. The method will be refined to facilitate the detection of larger defects like holes and the determination of the edge terminations....

  8. 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.

  9. 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.

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

    Science.gov (United States)

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

    2004-08-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-12-01

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

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

    International Nuclear Information System (INIS)

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

  13. 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)

  14. 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

  15. 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...

  16. 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 ...

  17. 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...

  18. 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.

  19. 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

    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 of...... 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....... event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed...

  20. 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.

  1. 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.

  2. 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...

  3. 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...

  4. 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.

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

    OpenAIRE

    Saha, Satadal; Basu, Subhadip; 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 backgrou...

  6. 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...

  7. A fully automated liquid–liquid extraction system utilizing interface detection

    OpenAIRE

    Jeffrey Pan; Robert Schmitt; Eugene Maslana

    2000-01-01

    The development of the Abbott Liquid-Liquid Extraction Station was a result of the need for an automated system to perform aqueous extraction on large sets of newly synthesized organic compounds used for drug discovery. The system utilizes a cylindrical laboratory robot to shuttle sample vials between two loading racks, two identical extraction stations, and a centrifuge. Extraction is performed by detecting the phase interface (by difference in refractive index) of the moving column of fluid...

  8. 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...

  9. Shape based automated detection of pulmonary nodules with surface feature based false positive reduction

    International Nuclear Information System (INIS)

    We proposed a shape based automated detection of pulmonary nodules with surface feature based false positive (FP) reduction. In the proposed system, the FP existing in internal of vessel bifurcation is removed using extracted surface of vessels and nodules. From the validation with 16 chest CT scans, we find that the proposed CAD system achieves 18.7 FPs/scan at 90% sensitivity, and 7.8 FPs/scan at 80% sensitivity. (orig.)

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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...

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

    International Nuclear Information System (INIS)

    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)

  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. 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.

  1. 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 ...

  2. Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

    CERN Document Server

    Marshall, Philip J; Moustakas, Leonidas A; Fassnacht, Christopher D; Bradac, Marusa; Schrabback, Tim; Blandford, Roger D

    2008-01-01

    Lens modeling is the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known abundance of lenses, we find t...

  3. 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.

  4. 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

  5. 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.

  6. A system for automated outbreak detection of communicable diseases in Germany.

    Science.gov (United States)

    Salmon, Maëlle; Schumacher, Dirk; Burmann, Hendrik; Frank, Christina; Claus, Hermann; Höhle, Michael

    2016-03-31

    We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute. PMID:27063588

  7. 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.

  8. 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...

  9. 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.

  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

    2016-01-01

    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

  11. Analysis of the disagreement between automated bioluminescence-based and culture methods for detecting significant bacteriuria, with proposals for standardizing evaluations of bacteriuria detection methods.

    OpenAIRE

    Nichols, W. W.; Curtis, G D; Johnston, H H

    1982-01-01

    A fully automated method for detecting significant bacteriuria is described which uses firefly luciferin and luciferase to detect bacterial ATP in urine. The automated method was calibrated and evaluated, using 308 urine specimens, against two reference culture methods. We obtained a specificity of 0.79 and sensitivity of 0.75 using a quantitative pour plate reference test and a specificity of 0.79 and a sensitivity of 0.90 using a semiquantitative standard loop reference test. The majority o...

  12. 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.

  13. 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.

  14. Fully automated and colorimetric foodborne pathogen detection on an integrated centrifugal microfluidic device.

    Science.gov (United States)

    Oh, Seung Jun; Park, Byung Hyun; Choi, Goro; Seo, Ji Hyun; Jung, Jae Hwan; Choi, Jong Seob; Kim, Do Hyun; Seo, Tae Seok

    2016-05-21

    This work describes fully automated and colorimetric foodborne pathogen detection on an integrated centrifugal microfluidic device, which is called a lab-on-a-disc. All the processes for molecular diagnostics including DNA extraction and purification, DNA amplification and amplicon detection were integrated on a single disc. Silica microbeads incorporated in the disc enabled extraction and purification of bacterial genomic DNA from bacteria-contaminated milk samples. We targeted four kinds of foodborne pathogens (Escherichia coli O157:H7, Salmonella typhimurium, Vibrio parahaemolyticus and Listeria monocytogenes) and performed loop-mediated isothermal amplification (LAMP) to amplify the specific genes of the targets. Colorimetric detection mediated by a metal indicator confirmed the results of the LAMP reactions with the colour change of the LAMP mixtures from purple to sky blue. The whole process was conducted in an automated manner using the lab-on-a-disc and a miniaturized rotary instrument equipped with three heating blocks. We demonstrated that a milk sample contaminated with foodborne pathogens can be automatically analysed on the centrifugal disc even at the 10 bacterial cell level in 65 min. The simplicity and portability of the proposed microdevice would provide an advanced platform for point-of-care diagnostics of foodborne pathogens, where prompt confirmation of food quality is needed. PMID:27112702

  15. 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...

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. Detection and quantification of circulating immature platelets: agreement between flow cytometric and automated detection.

    Science.gov (United States)

    Ibrahim, Homam; Nadipalli, Srinivas; Usmani, Saba; DeLao, Timothy; Green, LaShawna; Kleiman, Neal S

    2016-07-01

    Immature platelets-also termed reticulated platelets (RP)-are platelets newly released into the circulation, and have been associated with a variety of pathological thrombotic events. They can be assessed by flow cytometry after staining with thiazole orange (TO) or by using a module added to a fully automated analyzer that is currently in wide clinical use and expressed as a fraction of the total platelet count (IPF). We sought to assess the correlation and agreement between these two methods. IPF was measured using Sysmex XE 2100-and at the same time point- we used TO staining and flow cytometry to measure RP levels. Two different gates were used for the flow cytometry method, 1 and 0.5 %. Measurements from the automated analyzer were then compared separately to measurements performed using each gate. Agreement between methods was assessed using Bland-Altman method. Pearson's correlation coefficient was also calculated. 129 subjects were enrolled and stratified into 5 groups: (1) Healthy subjects, (2) End stage renal disease, (3) Chronic stable coronary artery disease, (4) Post Coronary artery bypass surgery, (5) Peripheral thrombocytopenia. Median IPF levels were increased for patients in groups 2, 3, 4 and 5 (4.0, 4.7, 4.3, and 8.3 % respectively) compared to healthy subjects (2.5 %) p = 0.0001. Although the observed correlation between the two methods tended to be good in patients with high IPF values (i.e., group 5), the overall observed correlation was poor (Pearson's correlation coefficient r = 0.27). Furthermore, there was poor agreement between the two methods in all groups. Despite the good correlation that was observed between the two methods at higher IPF values, the lack of agreement was significant. PMID:26831482

  1. 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...

  2. 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.

  3. 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

  4. 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 ...

  5. How Small Can Impact Craters Be Detected at Large Scale by Automated Algorithms?

    Science.gov (United States)

    Bandeira, L.; Machado, M.; Pina, P.; Marques, J. S.

    2013-12-01

    The last decade has seen a widespread publication of crater detection algorithms (CDA) with increasing detection performances. The adaptive nature of some of the algorithms [1] has permitting their use in the construction or update of global catalogues for Mars and the Moon. Nevertheless, the smallest craters detected in these situations by CDA have 10 pixels in diameter (or about 2 km in MOC-WA images) [2] or can go down to 16 pixels or 200 m in HRSC imagery [3]. The availability of Martian images with metric (HRSC and CTX) and centimetric (HiRISE) resolutions is permitting to unveil craters not perceived before, thus automated approaches seem a natural way of detecting the myriad of these structures. In this study we present the efforts, based on our previous algorithms [2-3] and new training strategies, to push the automated detection of craters to a dimensional threshold as close as possible to the detail that can be perceived on the images, something that has not been addressed yet in a systematic way. The approach is based on the selection of candidate regions of the images (portions that contain crescent highlight and shadow shapes indicating a possible presence of a crater) using mathematical morphology operators (connected operators of different sizes) and on the extraction of texture features (Haar-like) and classification by Adaboost, into crater and non-crater. This is a supervised approach, meaning that a training phase, in which manually labelled samples are provided, is necessary so the classifier can learn what crater and non-crater structures are. The algorithm is intensively tested in Martian HiRISE images, from different locations on the planet, in order to cover the largest surface types from the geological point view (different ages and crater densities) and also from the imaging or textural perspective (different degrees of smoothness/roughness). The quality of the detections obtained is clearly dependent on the dimension of the craters

  6. 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.

  7. 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

  8. Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: comparison with invasive catheterisation

    International Nuclear Information System (INIS)

    True automated detection of coronary artery stenoses might be useful whenever expert evaluation is not available, or as a ''second reader'' to enhance diagnostic confidence. We evaluated the accuracy of a PC-based stenosis detection tool alone and combined with expert interpretation. One hundred coronary CT angiography datasets were evaluated with the automated software alone, by manual interpretation (axial images, multiplanar reformations and maximum intensity projections in free double-oblique planes), and by expert interpretation aware of the automated findings. Stenoses ≥ 50 % were noted per-vessel and per-patient, and compared with invasive angiography. Automated post-processing was successful in 90 % of patients (88 % of vessels). When excluding uninterpretable datasets, per-patient sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89 %, 79 %, 74 % and 92 % (per-vessel: 82 %, 85 %, 48 % and 96 %). All 100 datasets were evaluable by expert interpretation. Per-patient sensitivity, specificity, PPV and NPV were 95 %, 95 %, 93 % and 97 % (per-vessel: 89 %,98 %, 88 % and 98 %). Knowing the results of automated interpretation did not improve the performance of expert readers. Automated off-line post-processing of coronary CT angiography shows adequate sensitivity, but relatively low specificity in coronary stenosis detection. It does not increase accuracy of expert interpretation. Failure of post-processing in 10 % of all patients necessitates additional manual image work-up. (orig.)

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

    International Nuclear Information System (INIS)

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

  10. 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.

  11. 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 to that...

  12. Automated Weight-Window Generation for Threat Detection Applications Using ADVANTG

    International Nuclear Information System (INIS)

    Deterministic transport codes have been used for some time to generate weight-window parameters that can improve the efficiency of Monte Carlo simulations. As the use of this hybrid computational technique is becoming more widespread, the scope of applications in which it is being applied is expanding. An active source of new applications is the field of homeland security--particularly the detection of nuclear material threats. For these problems, automated hybrid methods offer an efficient alternative to trial-and-error variance reduction techniques (e.g., geometry splitting or the stochastic weight window generator). The ADVANTG code has been developed to automate the generation of weight-window parameters for MCNP using the Consistent Adjoint Driven Importance Sampling method and employs the TORT or Denovo 3-D discrete ordinates codes to generate importance maps. In this paper, we describe the application of ADVANTG to a set of threat-detection simulations. We present numerical results for an 'active-interrogation' problem in which a standard cargo container is irradiated by a deuterium-tritium fusion neutron generator. We also present results for two passive detection problems in which a cargo container holding a shielded neutron or gamma source is placed near a portal monitor. For the passive detection problems, ADVANTG obtains an O(104) speedup and, for a detailed gamma spectrum tally, an average O(102) speedup relative to implicit-capture-only simulations, including the deterministic calculation time. For the active-interrogation problem, an O(104) speedup is obtained when compared to a simulation with angular source biasing and crude geometry splitting

  13. Facile electrochemical method and corresponding automated instrument for the detection of furfural in insulation oil.

    Science.gov (United States)

    Wang, Ruili; Huang, Xinjian; Wang, Lishi

    2016-02-01

    Determining the concentration of furfural contained in the insulation oil of a transformer has been established as a method to evaluate the health status of the transformer. However, the detection of furfural involves the employment of expensive instruments and/or time-consuming laboratorial operations. In this paper, we proposed a convenient electrochemical method to make the detection. The quantification of furfural was realized by extraction of furfural from oil phase to aqueous phase followed by reductive detection of furfural with differential pulse voltammetry (DPV) at a mercury electrode. This method is very sensitive and the limit of detection, corresponding to furfural contained in oil, is estimated to be 0.03 μg g(-1). Furthermore, excellent linearity can be obtained in the range of 0-10 μg g(-1). These features make the method very suitable for the determination of furfural in real situation. A fully automated instrument that can perform the operations of extraction and detection was developed, and this instrument enables the whole measurement to be finished within eight minutes. The methodology and the instrument were tested with real samples, and very favorable agreement between results obtained with this instrument and HPLC indicates that the proposed method along with instrument can be employed as a facile tool to diagnose the health status of aged transformers. PMID:26653467

  14. 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.

  15. 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.

  16. Automated crown detection algorithm: an analysis of two tropical Amazonian forests

    Science.gov (United States)

    Palace, M.; Keller, M.; Asner, G.; Hagen, S.; Braswell, B.

    2002-12-01

    Spatial analysis of crowns in high-resolution images can improve the estimate of carbon stocks on regional and local scales, aid in demographic studies on the stand level, begin to analyze tree structural properties at the landscape level, and aid in forestry efforts. Radiative inverse transfer models, gap models, and cohort models may be parameterized with the spatial analysis of crowns and subsequently derived forest structural characteristics. We developed an algorithm to automatically detect tree crowns in two tropical Amazonian forests. IKONOS panchromatic images were used from two Amazonian forests in Para, Brazil: the Tapajos National Forest, (3.08° S, 54.94° W) and the Fazenda Cauaxi, (3.75° S, 48.37° W). Analysis was conducted on undisturbed forests from both sites. Our method combines local maximum filtering and local minima value finding methods with analysis of extracted transect data from the local maxima. We use a derivative threshold that ends the transect. Once all pixels of a given brightness value are analyzed, an iterative step examines the next lower brightness value. Pixels where crowns have been delineated are taken out of further analysis. Our method allows for overlap of crowns, gaps between crowns, and complex and noisy canopies to be analyzed. A sensitivity analysis was run on the derivative threshold and the minimum local maximum value to seed the transect analysis. Least-squares goodness of fit is conducted to examine parameterization from the sensitivity analysis. The best fit for the derivative threshold is found set at -8. The sensitivity analysis finds that the minimum local maxima is related to the difference between the maximum brightness value and brightness value with the highest frequency. Mean, minimum and maximum crown widths for field data are (mean 9.0 m +/- 1.6 S.D., min 1.0 m, max 40.7 m) and automated estimation are (mean 11.9 m +/- 5.0 S.D., min 2.0 m, max 34.0 m). The Kolmogorov-Smirov test for difference between

  17. 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.

  18. Detection - NIR, Luminescence and Other Rapid Methods-Pit Falls and Opportunities

    International Nuclear Information System (INIS)

    The proliferation of rapid, on-site biological detectors over the last 15 years has caused confusion within the user community and in some cases a diversion of resources. There remains no panacea; all systems have issues and no system provides the total answer. In 1995, with much enthusiasm, members of a US National Lab presented a mock-up of a hand held Biological Detector. This system, compared to a 'Tricorder' from science fiction, was envisioned to be available within 5 years. It would be able to scan a substance and within minutes provide an answer. Clearly that remains the goal of detector programs, but unfortunately science is the limiting factor. There are technologies, such as fluorescence and luminescence that provide minimally acceptable results when utilizing a defined bio-air sample. Many of these systems are also expensive, limiting their utility. But when these FLAPS, BARTS, BAWS, BioLerts and other are challenged with dirty or non-aerosol samples, they begin to have problems. With the relatively high cost of test kits, the significant number of potential hoax or negative samples; the issue of usefulness versus performance versus cost has further complicated the environment. Consequently, the utilization of cost effective, simple screening systems is needed for on site use. The current trend is to determine cost effective approaches to triage samples prior to in depth analysis. Therefore, a pH test, protein strip and Bioluminescence screen can indicate threat/non-threat prior to in-depth analysis. Experiences from 2001/2002 indicate over 90% of the first responder events are hoax related. Adapting the paradigm, screening out negatives become a priority. Near Infra Red (NIR) has been utilized in chemical agent detection and has been recently utilized to identify powders, salts, sugars and numerous potential hoax samples. The system is a non-destructive screening method that can be integrated with other technologies as a front end triage system

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. Development of an automated detection system for microcalcifications lesion in mammography (in Japanese)

    International Nuclear Information System (INIS)

    An automated detection system for clustered microcalcifications in digital mammograms was developed for computer-aided diagnosis (CAD) systems. We developed new detection filters of microcalcifications based on the gradient-vector image analysis. The 'triple-ring filter' extracts the region in a pattern similar to that of a microcalcification shadow. Then the 'variable-ring filter' recalculates two vector-feature values (vector-direction and vector-magnitude feature values) and determines the gray-level threshold values adaptively. These filters significantly improved the diagnostic sensitivity for the detection of microcalcifications in comparison with our previously developed scheme based on image contrast analysis. It was proved by an F (free-response) ROC examination: the true positive fraction of our new system was always 10% or more higher than our previous system while the number of false positives per image remained constant. We also developed a ''contrast-correction technique'' to correct the effects of the background content (mammary gland tissue) around the microcalcification and film contrast characteristic on its image contrast. This scheme improved the diagnostic performance for detecting the microcalcifications, and its technology was applied to the image data of two facilities where the imaging characteristic in terms of contrast was different. The diagnostic sensitivity for the detection of clustered microcalcifications in our database of 165 mammograms was 94.3% with 0.63 false positives per image, which demonstrates the effectiveness of our method. In addition, we also propose to use an artificial neural network to detect the microcalcifications in the first step instead of the triple-ring filter method: this may make it possible to develop a CAD system with an even higher detection performance level

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. First Science Results from Solar Data Mining Using Automated Feature Detection

    Science.gov (United States)

    Martens, P. C.

    2014-12-01

    The SDO Feature Finding Team (FFT) has produced 16 automated feature tracking modules for data from SDO, LASCO, and ground-based H-alpha observatories. The metadata produced by those modules and others are available from the Heliophysics Events Knowledgebase (HEK) and the Virtual Solar Observatory (VSO). Having metadata available for large amounts of events and phenomena, obtained with consistent detection criteria unlike catalogs produced by human observers, allows researchers to effectively search solar data for patterns. I will show a number of science results obtained recently. Not surprisingly several of the patterns are well known (e.g. flares occur mostly in active regions), but some really surprising new trends have been discovered as well, in at least one case upending scientific consensus. These results show the power and promise that systematic feature recognition and data mining holds for solar physics.

  12. 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.

  13. 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.

  14. Evaluation of automated nucleic acid extraction methods for virus detection in a multicenter comparative trial

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Bruun; Uttenthal, Åse; Hakhverdyan, M.; Belak, S.; Wakeley, P. R.; Reid, S. M.; Ebert, K.; King, D. P.

    2009-01-01

    Five European veterinary laboratories participated in an exercise to compare the performance of nucleic acid extraction robots. Identical sets of coded samples were prepared using serial dilutions of bovine viral diarrhoea virus (BVDV) from serum and cell culture propagated material. Each...... laboratory extracted nucleic acid from this panel using available robotic equipment (12 separate instruments, comprising 8 different models), after which the processed samples were frozen and sent to a single laboratory for subsequent testing by real-time RT-PCR. In general, there was good concordance...... between the results obtained for the different automated extraction platforms. In particular, the limit of detection was identical for 9/12 and 8/12 best performing robots (using dilutions of BVDV infected-serum and cell culture material, respectively), which was similar to a manual extraction method used...

  15. 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

  16. The Challenge of Automated Change Detection: Developing a Method for the Updating of Land Parcels

    Science.gov (United States)

    Matikainen, L.; Karila, K.; Litkey, P.; Ahokas, E.; Munck, A.; Karjalainen, M.; Hyyppä, J.

    2012-07-01

    Development of change detection methods that are functional and reliable enough for operational work is still a demanding task. This article discusses automated change detection from the viewpoint of one case study: the Finnish Land Parcel Identification System (FLPIS). The objective of the study is to develop a change detection method that could be used as an aid in the updating of the FLPIS. The method is based on object-based interpretation, and it uses existing parcel boundaries and new aerial ortho images as input data. Rules for classifying field and non-field objects are defined automatically by using the classification tree method and training data. Additional, manually created rules are used to improve the results. Classification tests carried out during the development work suggest that real changes can be detected relatively well. According to a recent visual evaluation, 96% of changes larger than 100 m2 were detected, at least partly. The overall accuracy of the change detection results was 93% when compared with reference data pixel-by-pixel. On the other hand, there are also missing changes and numerous false alarms. The main challenges encountered in the method development include the wide diversity of agricultural fields and other land cover objects locally, across the country, and at different times of the spring and summer, variability in the digital numbers (DNs) of the aerial images, the different nature of visual and automatic interpretation, and the small percentage of the total field area that has really changed. These challenges and possible solutions are discussed in the article.

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

    Science.gov (United States)

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

    2012-03-01

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

  18. 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 ...

  19. 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

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

    International Nuclear Information System (INIS)

    To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm3 + 0.58 (width 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 cm3 compared to 281.58 ± 130.21 cm3 in mV and 268.93 ± 104.60 cm3 in eV. The correlation coefficient was 0.99 (coefficient of determination (R2) = 0.98) for aV and mV, 0.91 (R2 = 0.83) for mV and eV and 0.91 (R2 = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R2 = 0.84), mV and eV (0.95; R2 = 0.91) and aV and eV (0.83; R2 = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

  1. 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.

  2. 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

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

    International Nuclear Information System (INIS)

    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.)

  4. Computer-aided detection of cancer in automated 3-D breast ultrasound.

    Science.gov (United States)

    Tan, Tao; Platel, Bram; Mus, Roel; Tabar, László; Mann, Ritse M; Karssemeijer, Nico

    2013-09-01

    Automated 3-D breast ultrasound (ABUS) has gained a lot of interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and prevent errors. In the multi-stage system we propose, segmentations of the breast, the nipple and the chestwall are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and depth are extracted. Using an ensemble of neural-network classifiers, a likelihood map indicating potential abnormality is computed. Local maxima in the likelihood map are determined and form a set of candidates in each image. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. On region level, classification experiments were performed using different classifiers including an ensemble of neural networks, a support vector machine, a k-nearest neighbors, a linear discriminant, and a gentle boost classifier. Performance was determined using a dataset of 238 patients with 348 images (views), including 169 malignant and 154 benign lesions. Using free response receiver operating characteristic (FROC) analysis, the system obtains a view-based sensitivity of 64% at 1 false positives per image using an ensemble of neural-network classifiers. PMID:23693128

  5. Load-differential features for automated detection of fatigue cracks using guided waves

    Science.gov (United States)

    Chen, Xin; Lee, Sang Jun; Michaels, Jennifer E.; Michaels, Thomas E.

    2012-05-01

    Guided wave structural health monitoring (SHM) is being considered to assess the integrity of plate-like structures for many applications. Prior research has investigated how guided wave propagation is affected by applied loads, which induce anisotropic changes in both dimensions and phase velocity. In addition, it is well-known that applied tensile loads open fatigue cracks and thus enhance their detectability using ultrasonic methods. Here we describe load-differential methods in which signals recorded from different loads at the same damage state are compared without using previously obtained damage-free data. Changes in delay-and-sum images are considered as a function of differential loads and damage state. Load-differential features are extracted from these images that capture the effects of loading as fatigue cracks are opened. Damage detection thresholds are adaptively set based upon the load-differential behavior of the various features, which enables implementation of an automated fatigue crack detection process. The efficacy of the proposed approach is examined using data from a fatigue test performed on an aluminum plate specimen that is instrumented with a sparse array of surface-mounted ultrasonic guided wave transducers.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata

    International Nuclear Information System (INIS)

    Purpose. Automated collection of image data from DICOM headers enables monitoring of patient dose and image quality parameters. Manual monitoring is time consuming, owing to the large number of exposure scenarios, thus automated methods for monitoring needs to be investigated. The aim of the present work was to develop and optimise such a method. Material and methods. Exposure index values from digital systems in projection radiography were collected over a period of five years, representing data from 1.2 million projection images. The exposure index values were converted to detector dose and an automated method for detection of sustained level shifts in the resulting detector dose time series was applied using the statistical analysis tool R. The method combined handling of outliers, filtering and estimation of variation in combination with two different statistical rank tests for level shift detection. A set of 304 time series representing central body parts was selected and the level shift detection method was optimised using level shifts identified by ocular evaluation as the gold standard. Results. Two hundred and eighty-one level changes were identified that were deemed in need of further investigation. The majority of these changes were abrupt. The sensitivity and specificity of the optimised and automated detection method concerning the ocular evaluation were 0.870 and 0.997, respectively, for detected abrupt changes. Conclusions. An automated analysis of exposure index values, with the purpose of detecting changes in exposure, can be performed using the R software in combination with a DICOM header metadata repository containing the exposure index values from the images. The routine described has good sensitivity and acceptable specificity for a wide range of central body part projections and can be optimised for more specialised purposes

  11. 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

  12. 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

  13. 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.

  14. Automated Detection of Galaxy-Scale Gravitational Lenses in High-Resolution Imaging Data

    Science.gov (United States)

    Marshall, Philip J.; Hogg, David W.; Moustakas, Leonidas A.; Fassnacht, Christopher D.; Bradač, Maruša; Schrabback, Tim; Blandford, Roger D.

    2009-04-01

    We expect direct lens modeling to be the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Having optimized a simple model for "typical" galaxy-scale gravitational lenses, we generate four assessments of model quality that are then used in an automated classification. The robot infers from these four data the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set of candidates, including realistic simulated lenses and known false positives drawn from the Hubble Space Telescope (HST) Extended Groth Strip (EGS) survey. We compute the expected purity, completeness, and rejection rate, and find that these statistics can be optimized for a particular application by changing the prior probability distribution for H; this is equivalent to defining the robot's "character." Adopting a realistic prior based on expectations for the abundance of lenses, we find that a lens sample may be generated that is ~100% pure, but only ~20% complete. This shortfall is due primarily to the oversimplicity of the model of both the lens light and mass. With a more optimistic robot, ~90% completeness can be achieved while rejecting ~90% of the candidate objects. The remaining candidates must be classified by human inspectors. Displaying the images used and produced by the robot on a custom "one-click" web interface, we are able to inspect and classify lens candidates at a rate of a few seconds per system, suggesting that a future 1000 deg2 imaging survey containing 107 BRGs, and some 104 lenses, could be successfully, and reproducibly, searched in a modest amount of time. We have verified our projected survey statistics, albeit at low significance, using the HST EGS data, discovering

  15. 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.

  16. 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

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

    OpenAIRE

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

    1996-01-01

    We evaluated the COBAS AMPLICOR (CA) PCR system (Roche Diagnostic Systems) designed for automated PCR amplification and detection of nucleic acids from infectious agents in clinical samples. The Roche AMPLICOR microwell plate (MWP) PCR was the reference method. CA amplifies target nucleic acid, captures the biotinylated amplification products by using magnetic particles coated with specific oligonucleotide probes, and detects the bound products colorimetrically. For Mycobacterium tuberculosis...

  18. 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.

  19. 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...

  20. 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.

  1. 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

  2. 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

  3. 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.

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

    Science.gov (United States)

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

    2012-10-01

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

  5. 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.

  6. 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.

  7. 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

  8. Automated detection of rare-event pathogens through time-gated luminescence scanning microscopy.

    Science.gov (United States)

    Lu, Yiqing; Jin, Dayong; Leif, Robert C; Deng, Wei; Piper, James A; Yuan, Jingli; Duan, Yusheng; Huo, Yujing

    2011-05-01

    Many microorganisms have a very low threshold (time-gated luminescent scanning for accurate counting of rare-event cells, which exploits the large difference in luminescence lifetimes between the lanthanide biolabels, >100 μs, and the autofluorescence backgrounds, analysis, the background-free feature allows a single-element photomultiplier to locate rare-event cells, so that requirements for data storage and analysis are minimized to the level of image confirmation only at the final step. We have evaluated this concept in a prototype instrument using a 2D scanning stage and applied it to rare-event Giardia detection labeled by a europium complex. For a slide area of 225 mm(2) , the time-gated scanning method easily reduced the original 40,000 adjacent elements (0.075 mm × 0.075 mm) down to a few "elements of interest" containing the Giardia cysts. We achieved an averaged signal-to-background ratio of 41.2 (minimum ratio of 12.1). Such high contrasts ensured the accurate mapping of all the potential Giardia cysts free of false positives or negatives. This was confirmed by the automatic retrieving and time-gated luminescence bioimaging of these Giardia cysts. Such automated microscopy based on time-gated scanning can provide novel solutions for quantitative diagnostics in advanced biological, environmental, and medical sciences. PMID:21462305

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

    Science.gov (United States)

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

    2015-08-01

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

  10. Fully Automated Detection of Corticospinal Tract Damage in Chronic Stroke Patients

    Directory of Open Access Journals (Sweden)

    Ming Yang

    2014-01-01

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

  11. 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

  12. 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

  13. Automated Visual Event Detection, Tracking, and Data Management System for Cabled- Observatory Video

    Science.gov (United States)

    Edgington, D. R.; Cline, D. E.; Schlining, B.; Raymond, E.

    2008-12-01

    Ocean observatories and underwater video surveys have the potential to unlock important discoveries with new and existing camera systems. Yet the burden of video management and analysis often requires reducing the amount of video recorded through time-lapse video or similar methods. It's unknown how many digitized video data sets exist in the oceanographic community, but we suspect that many remain under analyzed due to lack of good tools or human resources to analyze the video. To help address this problem, the Automated Visual Event Detection (AVED) software and The Video Annotation and Reference System (VARS) have been under development at MBARI. For detecting interesting events in the video, the AVED software has been developed over the last 5 years. AVED is based on a neuromorphic-selective attention algorithm, modeled on the human vision system. Frames are decomposed into specific feature maps that are combined into a unique saliency map. This saliency map is then scanned to determine the most salient locations. The candidate salient locations are then segmented from the scene using algorithms suitable for the low, non-uniform light and marine snow typical of deep underwater video. For managing the AVED descriptions of the video, the VARS system provides an interface and database for describing, viewing, and cataloging the video. VARS was developed by the MBARI for annotating deep-sea video data and is currently being used to describe over 3000 dives by our remotely operated vehicles (ROV), making it well suited to this deepwater observatory application with only a few modifications. To meet the compute and data intensive job of video processing, a distributed heterogeneous network of computers is managed using the Condor workload management system. This system manages data storage, video transcoding, and AVED processing. Looking to the future, we see high-speed networks and Grid technology as an important element in addressing the problem of processing and

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

    International Nuclear Information System (INIS)

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

  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. 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.

  17. 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

  18. 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.

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

    International Nuclear Information System (INIS)

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

  20. 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...

  1. 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

  2. Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

    Science.gov (United States)

    Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

    2013-10-01

    We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

  3. 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.

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

    Science.gov (United States)

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

    2014-01-01

    Ciguatoxins are potent neurotoxins with a significant public health impact. Cytotoxicity assays have allowed the most sensitive means of detection of ciguatoxin-like activity without reliance on mouse bioassays and have been invaluable in studying outbreaks. An improvement of these cell-based assays is presented here in which rapid flow cytometric detection of ciguatoxins and saxitoxins is demonstrated using fluorescent voltage sensitive dyes. A depolarization response can be detected directly due to ciguatoxin alone; however, an approximate 1000-fold increase in sensitivity is observed in the presence of veratridine. These results demonstrate that flow cytometric assessment of ciguatoxins is possible at levels approaching the trace detection limits of our earlier cytotoxicity assays, however, with a significant reduction in analysis time. Preliminary results are also presented for detection of brevetoxins and for automation and throughput improvements to a previously described method for detecting saxitoxins in shellfish extracts. PMID:24830140

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

    International Nuclear Information System (INIS)

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

  6. 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......, an established transgenic model of HD. The results indicate that the IntelliCage is a reliable system for recording exploratory activity, drinking behavior, circadian rhythm, spatial preference, and cognition in mice during prolonged periods of assessment. The system detected early dysfunctional...

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

    International Nuclear Information System (INIS)

    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

  8. 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

  9. 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.

  10. 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

  11. Development of an automated method for the detection of chronic lacunar infarct regions in brain MR images

    International Nuclear Information System (INIS)

    The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: ''isolated lacunar infarct regions'' and ''lacunar infarct regions adjacent to hyperintensive structures.'' The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective. (author)

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

    Science.gov (United States)

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

    2014-04-01

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

  13. 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

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

    Science.gov (United States)

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

    2010-05-01

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

  15. 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; Brøgger, Anna Line; Bosco, Filippo; Andersen, Karsten Brandt; Svendsen, Winnie Edith; Boisen, Anja

    2015-01-01

    experiments, even when the microfluidic disc is spinning at high velocities. Automated sample handling is achieved by designing a microfluidic system to release analyte sequentially, utilizing on-disc passive valving. In addition, the microfluidic system is designed to trap and keep the liquid sample...

  16. 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.

  17. 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

  18. 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.

  19. LOW-COST BACTERIAL DETECTION SYSTEM FOR FOOD SAFETY BASED ON AUTOMATED DNA EXTRACTION, AMPLIFICATION AND READOUT

    OpenAIRE

    Hoehl, Melanie Margarete; Bocholt, Eva Schulte; Karippai, Nobu; Zengerle, Roland; Steigert, Juergen; Slocum, Alexander H.

    2013-01-01

    To ensure food, medical and environmental safety and quality, rapid, low-cost and easy-to-use detection methods are desirable. Here, the LabSystem is introduced for integrated, automated DNA purification and amplification. It consists of a disposable, centrifugally-driven DNA purification platform (LabTube) and the subsequent amplification in a low-cost UV/vis-reader (LabReader). In this paper, food safety was chosen as the first sample application with pathogenic verotoxin-producing (VTEC) E...

  20. Automated analysis technique developed for detection of ODSCC on the tubes of OPR1000 steam generator

    International Nuclear Information System (INIS)

    A steam generator (SG) tube is an important component of a nuclear power plant (NPP). It works as a pressure boundary between the primary and secondary systems. The integrity of a SG tube can be assessed by an eddy current test every outage. The eddy current technique(adopting a bobbin probe) is currently the main technique used to assess the integrity of the tubing of a steam generator. An eddy current signal analyst for steam generator tubes continuously analyzes data over a given period of time. However, there are possibilities that the analyst conducting the test may get tired and cause mistakes, such as: missing indications or not being able to separate a true defect signal from one that is more complicated. This error could lead to confusion and an improper interpretation of the signal analysis. In order to avoid these possibilities, many countries of opted for automated analyses. Axial ODSCC (outside diameter stress corrosion cracking) defects on the tubes of OPR1000 steam generators have been found on the tube that are in contract with tube support plates. In this study, automated analysis software called CDS (computer data screening) made by Zetec was used. This paper will discuss the results of introducing an automated analysis system for an axial ODSCC on the tubes of an OPR1000 steam generator.

  1. A comparison between automated detection methods of high-frequency oscillations (80–500 Hz) during seizures

    Science.gov (United States)

    Salami, Pariya; Lévesque, Maxime; Gotman, Jean; Avoli, Massimo

    2016-01-01

    High-frequency oscillations (HFOs, ripples: 80–200 Hz, fast ripples: 250–500 Hz) recorded from the epileptic brain are thought to reflect abnormal network-driven activity. They are also better markers of seizure onset zones compared to interictal spikes. There is thus an increasing number of studies analysing HFOs in vitro, in vivo and in the EEG of human patients with refractory epilepsy. However, most of these studies have focused on HFOs during interictal events or at seizure onset, and few have analysed HFOs during seizures. In this study, we are comparing three different automated methods of HFO detection to two methods of visual analysis, during the pre-ictal, ictal and post-ictal periods on multiple channels using the rat pilocarpine model of temporal lobe epilepsy. The first method (method 1) detected HFOs using the average of the normalised period, the second (method 2) detected HFOs using the average of the normalised period in 1 s windows and the third (method 3) detected HFOs using the average of a reference period before seizure onset. Overall, methods 2 and 3 showed higher sensitivity compared to method 1. When dividing the analysed traces in pre-, ictal and post-ictal periods, method 3 showed the highest sensitivity during the ictal period compared to method 1, while method 2 was not significantly different from method 1. These findings suggest that method 3 could be used for automated and reliable detection of HFOs on large data sets containing multiple channels during the ictal period. PMID:22983173

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

    Science.gov (United States)

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

    2016-01-01

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

  3. 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

  4. Capillary electrophoresis with contactless conductivity detection coupled to a sequential injection analysis manifold for extended automated monitoring applications

    International Nuclear Information System (INIS)

    A capillary electrophoresis (CE) instrument with capacitively coupled contactless conductivity detection (C4D) based on a sequential injection analysis (SIA) manifold was refined. Hydrodynamic injection was implemented to avoid a sampling bias by using a split-injection device based on a needle valve for precise adjustment. For safety and reliability, the integrity of the high voltage compartment at the detection end was fully maintained by implementing flushing of the high voltage interface through the capillary. With this set-up, extended fully automated monitoring applications are possible. The system was successfully tested in the field for the determination of the concentration levels of major inorganic cations and anions in a creek over a period of 5 days.

  5. 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.

  6. Automated Extraction and Amplification for Direct Detection of Mycobacterium tuberculosis Complex in Various Clinical Samples▿

    OpenAIRE

    Simonnet, Christine; Lacoste, Vincent; Drogoul, Anne Sophie; Rastogi, Nalin

    2011-01-01

    With the incidence of culture-positive tuberculosis (TB) cases at 25.3 per 100,000 and a 25% rate of TB/HIV coinfection, the TB incidence in French Guiana is the highest of all French regions. In this context, there is an urgent need for simple, automated systems for molecular diagnosis of TB that can be adapted to small laboratories. Introduction of a nuclear amplification test in a routine clinical laboratory is an additional expense, and its cost-effectiveness and clinical utility need to b...

  7. 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...

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

    Science.gov (United States)

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

    2009-03-01

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

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

    OpenAIRE

    Omar A. Batarfi; Aisha M. Alshiky; Alaa A. Almarzuki; Nora A. Farraj

    2014-01-01

    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 a...

  10. 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

  11. The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness.

    Science.gov (United States)

    Jackson, Melinda L; Kennedy, Gerard A; Clarke, Catherine; Gullo, Melissa; Swann, Philip; Downey, Luke A; Hayley, Amie C; Pierce, Rob J; Howard, Mark E

    2016-02-01

    Slowed eyelid closure coupled with increased duration and frequency of closure is associated with drowsiness. This study assessed the utility of two devices for automated measurement of slow eyelid closure in a standard poor performance condition (alcohol) and following 12-h sleep deprivation. Twenty-two healthy participants (mean age=20.8 (SD 1.9) years) with no history of sleep disorders participated in the study. Participants underwent one baseline and one counterbalanced session each over two weeks; one 24-hour period of sleep deprivation, and one daytime session during which alcohol was consumed after a normal night of sleep. Participants completed a test battery consisting of a 30-min simulated driving task, a 10-min Psychomotor Vigilance Task (PVT) and the Karolinska Sleepiness Scale (KSS) each in two baseline sessions, and in two randomised, counterbalanced experimental sessions; following sleep deprivation and following alcohol consumption. Eyelid closure was measured during both tasks using two automated devices (Copilot and Optalert™). There was an increase in the proportion of time with eyelids closed and the Johns Drowsiness Score (incorporating relative velocity of eyelid movements) following sleep deprivation using Optalert (palgorithms inclusive of ocular parameters may be a better indicator of performance impairment following sleep loss. PMID:26687538

  12. 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

  13. 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

  14. 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.

  15. 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

  16. 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)

  17. Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

    International Nuclear Information System (INIS)

    The aim of this study was to evaluate the performance of a proposed computer-aided detection (CAD) system in automated breast ultrasonography (ABUS). Eighty-nine two-dimensional images (20 cysts, 42 benign lesions, and 27 malignant lesions) were obtained from 47 patients who underwent ABUS (ACUSON S2000). After boundary detection and removal, we detected mass candidates by using the proposed adjusted Otsu's threshold; the threshold was adaptive to the variations of pixel intensities in an image. Then, the detected candidates were segmented. Features of the segmented objects were extracted and used for training/testing in the classification. In our study, a support vector machine classifier was adopted. Eighteen features were used to determine whether the candidates were true lesions or not. A five-fold cross validation was repeated 20 times for the performance evaluation. The sensitivity and the false positive rate per image were calculated, and the classification accuracy was evaluated for each feature. In the classification step, the sensitivity of the proposed CAD system was 82.67% (SD, 0.02%). The false positive rate was 0.26 per image. In the detection/segmentation step, the sensitivities for benign and malignant mass detection were 90.47% (38/42) and 92.59% (25/27), respectively. In the five-fold cross-validation, the standard deviation of pixel intensities for the mass candidates was the most frequently selected feature, followed by the vertical position of the centroids. In the univariate analysis, each feature had 50% or higher accuracy. The proposed CAD system can be used for lesion detection in ABUS and may be useful in improving the screening efficiency.

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

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

    International Nuclear Information System (INIS)

    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. 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. 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. 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 (graded) by different pathologists. The region of interest

  4. 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.

  5. 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.; Andersen, S.

    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 was...... the definitive standard. The primary isolation both for the culture and the EIA methods was carried out by overnight selective enrichment in Preston broth. The results showed good sensitivities for both EIA methods in cattle (95% and 84%) and swine (88% and 69%) samples. However, when testing cattle...... samples, EIA-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....

  6. 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.

  7. 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.

  8. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor.

    Science.gov (United States)

    Choi, Wook-Jin; Choi, Tae-Sun

    2014-01-01

    Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan. PMID:24148147

  9. 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

  10. Automated flow-through amperometric immunosensor for highly sensitive and on-line detection of okadaic acid in mussel sample.

    Science.gov (United States)

    Dominguez, Rocio B; Hayat, Akhtar; Sassolas, Audrey; Alonso, Gustavo A; Munoz, Roberto; Marty, Jean-Louis

    2012-09-15

    An electrochemical immunosensor for okadaic acid (OA) detection has been developed, and used in an indirect competitive immunoassay format under automated flow conditions. The biosensor was fabricated by injecting OA modified magnetic beads onto screen printed carbon electrode (SPCE) in the flow system. The OA present in the sample competed with the immobilized OA to bind with anti-okadaic acid monoclonal antibody (anti-OA-MAb). The secondary alkaline phosphatase labeled antibody was used to perform electrochemical detection. The current response obtained from the labeled alkaline phosphatase to 1-naphthyl phosphate decreased proportionally to the concentration of free OA in the sample. The calculated limit of detection (LOD) was 0.15 μg/L with a linear range of 0.19-25 μg/L. The good recoveries percentages validated the immunosensor application for real mussel samples. The developed system automatically controlled the incubation, washing and current measurement steps, showing its potential use for OA determination in field analysis. PMID:22967546

  11. 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.)

  12. 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.

  13. Automated classification of periodic variable stars detected by the wide-field infrared survey explorer

    International Nuclear Information System (INIS)

    We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately ±2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.

  14. 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

  15. Automated Extraction Improves Multiplex Molecular Detection of Infection in Septic Patients

    OpenAIRE

    Regueiro, Benito J.; Varela-Ledo, Eduardo; Martinez-Lamas, Lucia; Rodriguez-Calviño, Javier; Aguilera, Antonio; Santos, Antonio; Gomez-Tato, Antonio; Alvarez-Escudero, Julian

    2010-01-01

    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 Mgrade (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 ...

  16. 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.

  17. 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

  18. 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.

  19. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

    International Nuclear Information System (INIS)

    Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

  20. 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.

  1. Antinuclear antibody detection by automated multiplex immunoassay in untreated patients at the time of diagnosis.

    Science.gov (United States)

    Op De Beéck, Katrijn; Vermeersch, Pieter; Verschueren, Patrick; Westhovens, René; Mariën, Godelieve; Blockmans, Daniel; Bossuyt, Xavier

    2012-12-01

    Fully automated multiplex immunoassays are increasingly used as first line screening for antinuclear antibodies. The diagnostic performance of such multiplex assays in untreated patients at the time of diagnosis has not been reported. Antinuclear antibodies were measured by indirect immunofluorescence (IIF) (dilution 1:160) and by BioPlex 2200 ANA screen (antibodies to dsDNA, chromatin, ribosomal protein, SSA-52, SSA-60, SSB, Sm, SmRNP, RNP-A, RNP-68, Scl-70, Jo-1, and centromere B) in 236 patients with a systemic rheumatic disease at the time of diagnosis, 149 blood donors, 139 patients with chronic fatigue syndrome (CFS), and 134 diseased controls. BioPlex ANA screen and IIF were positive in, respectively, 79% and 90% of patients with systemic lupus erythematosus (SLE), 60% and 60% with cutaneous lupus, 72% and 93% with systemic sclerosis (SSc), 100% and 100% with mixed connective tissue disease (MCTD), 89% and 56% with primary Sjögren's (SS) syndrome, 36% and 36% with polymyositis/dermatomyositis, 5.4% and 6% of blood donors, 7.2% and 3.6% of patients with CFS, and 11% and 18% of diseased controls. BioPlex test result interval specific likelihood ratios increased with increasing antibody concentration. The simultaneous presence of at least three antibodies by BioPlex was found in 35% of patients with SLE, 4% with SSc, 100% with MCTD, 64% with SS, 7% with inflammatory myopathy, 0.7% of CFS and diseased controls, and none of the blood donors. In conclusion, test result specific likelihood ratios and the presence of multiple autoantibodies help with the interpretation of data generated by multiplex immunoassays. PMID:22387973

  2. 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

  3. 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

    This paper describes the development of a 5' nuclease assay for detection of virulence factor genes responsible for colonization factors and toxins in Escherichia coil isolated from pigs. Colonization factors were F4, F5, F6, F18, F41 and the outer membrane protein intimin. Toxins were heat stable...... 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....... 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....

  4. 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

  5. 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.

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

    Science.gov (United States)

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

    2011-03-01

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

  7. 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

  8. 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

  9. A new multivariate time series data analysis technique: Automated detection of flux transfer events using Cluster data

    Science.gov (United States)

    Karimabadi, H.; Sipes, T. B.; Wang, Y.; Lavraud, B.; Roberts, A.

    2009-06-01

    A new data mining technique called MineTool-TS is introduced which captures the time-lapse information in multivariate time series data through extraction of global features and metafeatures. This technique is developed into a JAVA-based data mining software which automates all the steps in the model building to make it more accessible to nonexperts. As its first application in space sciences, MineTool-TS is used to develop a model for automated detection of flux transfer events (FTEs) at Earth's magnetopause in the Cluster spacecraft time series data. The model classifies a given time series into one of three categories of non-FTE, magnetosheath FTE, or magnetospheric FTE. One important feature of MineTool-TS is the ability to explore the importance of each variable or combination of variables as indicators of FTEs. FTEs have traditionally been identified on the basis of their magnetic field signatures, but here we find that some plasma variables can also be effective indicators of FTEs. For example, the perpendicular ion temperature yields a model accuracy of ˜93%, while a model based solely on the normal magnetic field BN yields an accuracy of ˜95%. This opens up the possibility of searching for more unusual FTEs that may, for example, have no clear BN signature and create a more comprehensive and less biased list of FTEs for statistical studies. We also find that models using GSM coordinates yield comparable accuracy to those using boundary normal coordinates. This is useful since there are regions where magnetopause models are not accurate. Another surprising result is the finding that the algorithm can largely detect FTEs, and even distinguish between magnetosheath and magnetospheric FTEs, solely on the basis of models built from single parameters, something that experts may not do so straightforwardly on the basis of short time series intervals. The most accurate models use a combination of plasma and magnetic field variables and achieve a very high

  10. 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

  11. 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.

  12. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories

    Directory of Open Access Journals (Sweden)

    Stefan Lang

    2012-05-01

    Full Text Available Geoinformation derived from Earth observation (EO plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM, including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI and an interpretation of PSI (Persistent Scatterer Interferometry measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI and 50.4% (PAI, respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

    Directory of Open Access Journals (Sweden)

    Wook-Jin Choi

    2013-01-01

    Full Text Available A computer-aided detection (CAD system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.

  18. DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

    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.

    2016-02-01

    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.

  19. 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.

  20. 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.

  1. 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)

  2. 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.

  3. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  4. 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...

  5. A new automated cycle slip detection and repair method for a single dual-frequency GPS receiver

    Science.gov (United States)

    Liu, Zhizhao

    2011-03-01

    This paper develops a new automated cycle slip detection and repair method that is based on only one single dual-frequency GPS receiver. This method jointly uses the ionospheric total electron contents (TEC) rate (TECR) and Melbourne-Wübbena wide lane (MWWL) linear combination to uniquely determine the cycle slip on both L1 and L2 frequencies. The cycle slips are inferred from the information of ionospheric physical TECR and MWWL ambiguity at the current epoch and that at the previous epoch. The principle of this method is that when there are cycle slips, the MWWL ambiguity will change and the ionospheric TECR will usually be significantly amplified, the part of artificial TECR (caused by cycle slips) being significantly larger than the normal physical TECR. The TECR is calculated based on the dual-frequency carrier phase measurements, and it is highly accurate. We calculate the ionospheric change information (including TECR and TEC acceleration) using the previous epochs (30 epochs in this study) and use the previous data to predict the TECR for the epoch needing cycle slip detection. If the discrepancy is larger than our defined threshold 0.15 TECU/s, cycle slips are regarded to exist at that epoch. The key rational of method is that during a short period (1.0 s in this study) the TECR of physical ionospheric phenomenon will not exceed the threshold. This new algorithm is tested with eight different datasets (including one spaceborne GPS dataset), and the results show that the method can detect and correctly repair almost any cycle slips even under very high level of ionospheric activities (with an average Kp index 7.6 on 31 March 2001). The only exception of a few detected but incorrectly repaired cycle slip is due to a sudden increased pseudorange error on a single satellite (PRN7) under very active ionosphere on 31 March 2001. This method requires dual-frequency carrier phase and pseudorange data from only one single GPS receiver. The other requirement is

  6. 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.

  7. 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...... 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....... 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....

  8. 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.

  9. 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 ...

  10. 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...

  11. 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...

  12. 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

  13. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information.

    Science.gov (United States)

    Tajbakhsh, Nima; Gurudu, Suryakanth R; Liang, Jianming

    2016-02-01

    This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds. PMID:26462083

  14. 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.

  15. 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.

  16. 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...

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. MIDAS: software for automated detection and analysis of Moon impact flashes

    Science.gov (United States)

    Madiedo, J. M.; Ortiz, J. L.; Morales, N.

    2011-10-01

    One of the techniques suitable for the estimation of the flux of interplanetary matter impacting the Earth is based on the monitoring of the night side of the Moon visible from the Earth to detect flashes produced by the impact of large meteoroids on the lunar surface. Our team is performing a continuous monitoring of our natural satellite and a software package has been developed in order to automatically identify these impact flashes. The main features of this computer program and some preliminary results are presented here.

  3. 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

    ‐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 demonstrate that: (1) automated real‐time detection of weak microlensing anomalies with immediate feedback is feasible......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......, 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...

  4. 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%.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

    Science.gov (United States)

    Melendez, Jaime; Sánchez, Clara I.; Philipsen, Rick H. H. M.; Maduskar, Pragnya; Dawson, Rodney; Theron, Grant; Dheda, Keertan; van Ginneken, Bram

    2016-01-01

    Lack of human resources and radiological interpretation expertise impair tuberculosis (TB) screening programmes in TB-endemic countries. Computer-aided detection (CAD) constitutes a viable alternative for chest radiograph (CXR) reading. However, no automated techniques that exploit the additional clinical information typically available during screening exist. To address this issue and optimally exploit this information, a machine learning-based combination framework is introduced. We have evaluated this framework on a database containing 392 patient records from suspected TB subjects prospectively recruited in Cape Town, South Africa. Each record comprised a CAD score, automatically computed from a CXR, and 12 clinical features. Comparisons with strategies relying on either CAD scores or clinical information alone were performed. Our results indicate that the combination framework outperforms the individual strategies in terms of the area under the receiving operating characteristic curve (0.84 versus 0.78 and 0.72), specificity at 95% sensitivity (49% versus 24% and 31%) and negative predictive value (98% versus 95% and 96%). Thus, it is believed that combining CAD and clinical information to estimate the risk of active disease is a promising tool for TB screening. PMID:27126741

  12. 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

  13. 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...

  14. Automated 3D detection and classification of Giardia lamblia cysts using digital holographic microscopy with partially coherent source

    Science.gov (United States)

    El Mallahi, A.; Detavernier, A.; Yourassowsky, C.; Dubois, F.

    2012-06-01

    Over the past century, monitoring of Giardia lamblia became a matter of concern for all drinking water suppliers worldwide. Indeed, this parasitic flagellated protozoan is responsible for giardiasis, a widespread diarrhoeal disease (200 million symptomatic individuals) that can lead immunocompromised individuals to death. The major difficulty raised by Giardia lamblia's cyst, its vegetative transmission form, is its ability to survive for long periods in harsh environments, including the chlorine concentrations and treatment duration used traditionally in water disinfection. Currently, there is a need for a reliable, inexpensive, and easy-to-use sensor for the identification and quantification of cysts in the incoming water. For this purpose, we investigated the use of a digital holographic microscope working with partially coherent spatial illumination that reduces the coherent noise. Digital holography allows one to numerically investigate a volume by refocusing the different plane of depth of a hologram. In this paper, we perform an automated 3D analysis that computes the complex amplitude of each hologram, detects all the particles present in the whole volume given by one hologram and refocuses them if there are out of focus using a refocusing criterion based on the integrated complex amplitude modulus and we obtain the (x,y,z) coordinates of each particle. Then the segmentation of the particles is processed and a set of morphological and textures features characteristic to Giardia lamblia cysts is computed in order to classify each particles in the right classes.

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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

  20. 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

  1. SoFAST: Automated Flare Detection with the PROBA2/SWAP EUV Imager

    Science.gov (United States)

    Bonte, K.; Berghmans, D.; De Groof, A.; Steed, K.; Poedts, S.

    2013-08-01

    The Sun Watcher with Active Pixels and Image Processing (SWAP) EUV imager onboard PROBA2 provides a non-stop stream of coronal extreme-ultraviolet (EUV) images at a cadence of typically 130 seconds. These images show the solar drivers of space-weather, such as flares and erupting filaments. We have developed a software tool that automatically processes the images and localises and identifies flares. On one hand, the output of this software tool is intended as a service to the Space Weather Segment of ESA's Space Situational Awareness (SSA) program. On the other hand, we consider the PROBA2/SWAP images as a model for the data from the Extreme Ultraviolet Imager (EUI) instrument prepared for the future Solar Orbiter mission, where onboard intelligence is required for prioritising data within the challenging telemetry quota. In this article we present the concept of the software, the first statistics on its effectiveness and the online display in real time of its results. Our results indicate that it is not only possible to detect EUV flares automatically in an acquired dataset, but that quantifying a range of EUV dynamics is also possible. The method is based on thresholding of macropixelled image sequences. The robustness and simplicity of the algorithm is a clear advantage for future onboard use.

  2. 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.

  3. 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.

  4. Development and Evaluation of a Real-Time PCR Assay for Detection of Pneumocystis jirovecii on the Fully Automated BD MAX Platform

    OpenAIRE

    Dalpke, Alexander H.; Hofko, Marjeta; Zimmermann, Stefan

    2013-01-01

    Pneumocystis jirovecii is an opportunistic pathogen in immunocompromised and AIDS patients. Detection by quantitative PCR is faster and more sensitive than microscopic diagnosis yet requires specific infrastructure. We adapted a real-time PCR amplifying the major surface glycoprotein (MSG) target from Pneumocystis jirovecii for use on the new BD MAX platform. The assay allowed fully automated DNA extraction and multiplex real-time PCR. The BD MAX assay was evaluated against manual DNA extract...

  5. 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...

  6. 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

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

    Science.gov (United States)

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

    2016-03-01

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

  8. 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 ...

  9. 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...

  10. 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...

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Home Improvements Prevent Falls

    Science.gov (United States)

    ... turn JavaScript on. Feature: Falls and Older Adults Home Improvements Prevent Falls Past Issues / Winter 2014 Table ... and ensure your safety. "Safe-ty-fy" Your Home Some Questions for Your Provider Will my medicines ...

  18. Cell detection in phase-contrast images used for alpha-particle track-etch dosimetry: a semi-automated approach

    Science.gov (United States)

    Altman, Michael B.; Wang, Steven J.; Whitlock, Jenny L.; Roeske, John C.

    2005-01-01

    A novel alpha-particle irradiator has recently been developed that provides the ability to characterize cell response. The irradiator is comprised of a collimated, planar alpha-particle source which, from below, irradiates cells cultured on a track-etch material. Cells are imaged using phase-contrast microscopy before and following irradiation to obtain geometric information and survival rates; these can be used with data from alpha-particle track images to assess cell response. A key step in this process is determining cell location within the pre-irradiation images. Although this can be done completely by a human observer, the number of images requiring analysis makes the process time-consuming and tedious. To reduce the potential human error and decrease user interaction time, a semi-automated, computer-aided method of cell detection has been developed. The method employs a two-level adaptive thresholding technique to obtain size and position information about potential cell cytoplasms and nuclei. Proximity and geometry-based thresholds are then used to mark structures as cells. False-positive detections from the automated algorithm are due mostly to imperfections in the track-etch background, camera effects and cellular residue. To correct for these, a human observer reviews all detected structures, discarding false positives. When analysing two randomly selected cell dish image databases, the semi-automated method detected 92-94% of all cells and 94-97% of cells with a well-defined cytoplasm and nucleus while reducing human workload by 32-83%.

  19. 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

  20. FilmArray, an automated nested multiplex PCR system for multi-pathogen detection: development and application to respiratory tract infection.

    Directory of Open Access Journals (Sweden)

    Mark A Poritz

    Full Text Available The ideal clinical diagnostic system should deliver rapid, sensitive, specific and reproducible results while minimizing the requirements for specialized laboratory facilities and skilled technicians. We describe an integrated diagnostic platform, the "FilmArray", which fully automates the detection and identification of multiple organisms from a single sample in about one hour. An unprocessed biologic/clinical sample is subjected to nucleic acid purification, reverse transcription, a high-order nested multiplex polymerase chain reaction and amplicon melt curve analysis. Biochemical reactions are enclosed in a disposable pouch, minimizing the PCR contamination risk. FilmArray has the potential to detect greater than 100 different nucleic acid targets at one time. These features make the system well-suited for molecular detection of infectious agents. Validation of the FilmArray technology was achieved through development of a panel of assays capable of identifying 21 common viral and bacterial respiratory pathogens. Initial testing of the system using both cultured organisms and clinical nasal aspirates obtained from children demonstrated an analytical and clinical sensitivity and specificity comparable to existing diagnostic platforms. We demonstrate that automated identification of pathogens from their corresponding target amplicon(s can be accomplished by analysis of the DNA melting curve of the amplicon.

  1. Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004

    OpenAIRE

    Lessmann, B.; Wang, Yongbo; Bergmann, Melanie; Kämpfe, T.; Nattkemper, T. W.

    2007-01-01

    Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004High latitudes are amongst the most sensitive ecosystems with respect to climate change, which prompted the launch of the first and only deep-sea long-term observatory beyond the polar circle, HAUSGARTEN (eastern Fram Strait), in 1999. An understanding of the abundance and spatial distribution of organisms is vital to assess the effects of global change. To map...

  2. 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

  3. 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 %...

  4. 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...

  5. 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...

  6. The use of risk concept for the monitoring of high risk vessels and an automated detection of high risk crossings

    OpenAIRE

    DEGRE,T

    2006-01-01

    The European Parliament and the Council have decided, in Directive 2002/59/CE issued on 27 june 2002 relating to the establishement of a community vessel traffic monitoring and information system to monitor hazardous ships. The research and development on this subject, which falls within a framework on which public opinion is very sensitive - the preservation of the environment - has started in the framework of the european projects EMBARC of the 5th FP and is going on within project MARNIS (...

  7. National Metal Casting Research Institute final report. Development of an automated ultrasonic inspection cell for detecting subsurface discontinuities in cast gray iron. Volume 3

    Energy Technology Data Exchange (ETDEWEB)

    Burningham, J.S. [University of Northern Iowa, Cedar Falls, IA (United States). Dept. of Industrial Technology

    1995-08-01

    This inspection cell consisted of an ultrasonic flaw detector, transducer, robot, immersion tank, computer, and software. Normal beam pulse-echo ultrasonic nondestructive testing, using the developed automated cell, was performed on 17 bosses on each rough casting. Ultrasonic transducer selection, initial inspection criteria, and ultrasonic flow detector (UFD) setup parameters were developed for the gray iron castings used in this study. The software were developed for control of the robot and UFD in real time. The software performed two main tasks: emulating the manual operation of the UFD, and evaluating the ultrasonic signatures for detecting subsurface discontinuities. A random lot of 105 castings were tested; the 100 castings that passed were returned to the manufacturer for machining into finished parts and then inspection. The other 5 castings had one boss each with ultrasonic signatures consistent with subsurface discontinuities. The cell was successful in quantifying the ultrasonic echo signatures for the existence of signature characteristics consistent with Go/NoGo criteria developed from simulated defects. Manual inspection showed that no defects in the areas inspected by the automated cell avoided detection in the 100 castings machined into finished parts. Of the 5 bosses found to have subsurface discontinuities, two were verified by manual inspection. The cell correctly classified 1782 of the 1785 bosses (99.832%) inspected.

  8. Status of the Transneptunian Automated Occultation Survey (TAOS II)

    Science.gov (United States)

    Lehner, Matthew J.; Wang, Shiang Yu; Reyes-Ruiz, Mauricio; Chu, You Hua; Lee, William; Zhang, Zhi Wei; Cook, Kem H.; Norton, Timothy; Szentgyorgyi, Andrew; Alcock, Charles

    2015-11-01

    The Transneptunian Automated Occultation Survey (TAOS II) will aim to detect occultations of stars by small (~1 km diameter) objects in the Kuiper Belt and beyond. Such events are very rare (field of view and a high speed camera comprising CMOS imagers, the survey will monitor 10,000 stars simultaneously with all three telescopes at a nominal readout cadence of 20 Hz. Construction of the site began in the fall of 2013. We present here an update on the status of the TAOS II survey, including the site development, camera fabrication, and project schedule.

  9. 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...

  10. 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. PMID:26071001

  11. 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

  12. Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software.

    Science.gov (United States)

    Faron, Matthew L; Buchan, Blake W; Vismara, Chiara; Lacchini, Carla; Bielli, Alessandra; Gesu, Giovanni; Liebregts, Theo; van Bree, Anita; Jansz, Arjan; Soucy, Genevieve; Korver, John; Ledeboer, Nathan A

    2016-03-01

    Recently, systems have been developed to create total laboratory automation for clinical microbiology. These systems allow for the automation of specimen processing, specimen incubation, and imaging of bacterial growth. In this study, we used the WASPLab to validate software that discriminates and segregates positive and negative chromogenic methicillin-resistant Staphylococcus aureus (MRSA) plates by recognition of pigmented colonies. A total of 57,690 swabs submitted for MRSA screening were enrolled in the study. Four sites enrolled specimens following their standard of care. Chromogenic agar used at these sites included MRSASelect (Bio-Rad Laboratories, Redmond, WA), chromID MRSA (bioMérieux, Marcy l'Etoile, France), and CHROMagar MRSA (BD Diagnostics, Sparks, MD). Specimens were plated and incubated using the WASPLab. The digital camera took images at 0 and 16 to 24 h and the WASPLab software determined the presence of positive colonies based on a hue, saturation, and value (HSV) score. If the HSV score fell within a defined threshold, the plate was called positive. The performance of the digital analysis was compared to manual reading. Overall, the digital software had a sensitivity of 100% and a specificity of 90.7% with the specificity ranging between 90.0 and 96.0 across all sites. The results were similar using the three different agars with a sensitivity of 100% and specificity ranging between 90.7 and 92.4%. These data demonstrate that automated digital analysis can be used to accurately sort positive from negative chromogenic agar cultures regardless of the pigmentation produced. PMID:26719443

  13. xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

    Directory of Open Access Journals (Sweden)

    Uppal Karan

    2013-01-01

    Full Text Available Abstract Background Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. Results xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1 improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2 evaluate sample quality and feature consistency, 3 detect feature overlap between datasets, and 4 characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. Conclusions xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

  14. The Automated Breast Volume Scanner (ABVS: initial experiences in lesion detection compared with conventional handheld B-mode ultrasound: a pilot study of 50 cases

    Directory of Open Access Journals (Sweden)

    Wojcinski S

    2011-10-01

    Full Text Available Sebastian Wojcinski1, Andre Farrokh1, Ursula Hille2, Jakub Wiskirchen3, Samuel Gyapong1, Amr A Soliman1,4, Friedrich Degenhardt1, Peter Hillemanns21Department of OB/GYN, Franziskus Hospital, Bielefeld, Germany; 2Department of OB/GYN, Hannover Medical School, Hannover, Germany; 3Department of Radiology, Franziskus Hospital, Bielefeld, Germany; 4Department of OB/GYN, Faculty of Medicine, University of Alexandria, Alexandria, EgyptAbstract: The idea of an automated whole breast ultrasound was developed three decades ago. We present our initial experiences with the latest technical advance in this technique, the automated breast volume scanner (ABVS ACUSON S2000TM. Volume data sets were collected from 50 patients and a database containing 23 women with no detectable lesions in conventional ultrasound (BI-RADS®-US 1, 13 women with clearly benign lesions (BI-RADS®-US 2, and 14 women with known breast cancer (BI-RADS®-US 5 was created. An independent examiner evaluated the ABVS data on a separate workstation without any prior knowledge of the patients’ histories. The diagnostic accuracy for the experimental ABVS was 66.0% (95% confidence interval [CI]: 52.9–79.1. The independent examiner detected all breast cancers in the volume data resulting in a calculated sensitivity of 100% in the described setting (95% CI: 73.2%–100%. After the ABVS examination, there were a high number of requests for second-look ultrasounds in 47% (95% CI: 30.9–63.5 of the healthy women (with either a clearly benign lesion or no breast lesions at all in conventional handheld ultrasound. Therefore, the specificity remained at 52.8% (95% CI: 35.7–69.2. When comparing the concordance of the ABVS with the gold standard (conventional handheld ultrasound, Cohen’s Kappa value as an estimation of the inter-rater reliability was κ = 0.37, indicating fair agreement. In conclusion, the ABVS must still be regarded as an experimental technique for breast ultrasound, which

  15. 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

  16. 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…

  17. 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...

  18. 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 ...

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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 ...

  4. Automated outlier detection framework for identifying damage states in multi-girder steel bridges using long-term wireless monitoring data

    Science.gov (United States)

    O'Connor, Sean M.; Zhang, Yilan; Lynch, Jerome P.

    2015-04-01

    Advances in wireless sensor technology have enabled low cost and extremely scalable sensing platforms prompting high density sensor installations. High density long-term monitoring generates a wealth of sensor data demanding an efficient means of data storage and data processing for information extraction that is pertinent to the decision making of bridge owners. This paper reports on decision making inferences drawn from automated data processing of long-term highway bridge data. The Telegraph Road Bridge (TRB) demonstration testbed for sensor technology innovation and data processing tool development has been instrumented with a long-term wireless structural monitoring system that has been in operation since September 2011. The monitoring system has been designed to specifically address stated concerns by the Michigan Department of Transportation regarding pin and hanger steel girder bridges. The sensing strategy consists of strain, acceleration and temperature sensors deployed in a manner to track specific damage modalities common to multigirder steel concrete composite bridges using link plate assemblies. To efficiently store and process long-term sensor data, the TRB monitoring system operates around the SenStore database system. SenStore combines sensor data with bridge information (e.g., material properties, geometry, boundary conditions) and exposes an application programming interface to enable automated data extraction by processing tools. Large long-term data sets are modeled for environmental and operational influence by regression methods. Response processes are defined by statistical parameters extracted from long-term data and used to automate decision support in an outlier detection, or statistical process control, framework.

  5. FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis.

    Directory of Open Access Journals (Sweden)

    Alex D Herbert

    Full Text Available Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.

  6. 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.

  7. 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.

  8. Detection of κ and λ Light Chain Monoclonal Proteins in Human Serum: Automated Immunoassay versus Immunofixation Electrophoresis

    OpenAIRE

    Jaskowski, Troy D; Litwin, Christine M.; Hill, Harry R.

    2006-01-01

    Recently, turbidimetric immunoassays for detecting and quantifying κ and λ free light chains (FLC) have become available and are promoted as being more sensitive than immunofixation electrophoresis (IFE) in detecting FLC monoclonal proteins. In this study, we assessed the ability of these turbidimetric assays to detect serum monoclonal proteins involving both free and heavy-chain-bound κ and λ light chains compared to standard immunofixation electrophoresis. Sera demonstrating a restricted ba...

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

    Science.gov (United States)

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

    1996-01-01

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

  11. 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

  12. 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

  13. 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

  14. Implementation and Evaluation of a Fully Automated Multiplex Real-Time PCR Assay on the BD Max Platform to Detect and Differentiate Herpesviridae from Cerebrospinal Fluids

    Science.gov (United States)

    Köller, Thomas; Kurze, Daniel; Lange, Mirjam; Scherdin, Martin; Podbielski, Andreas; Warnke, Philipp

    2016-01-01

    A fully automated multiplex real-time PCR assay—including a sample process control and a plasmid based positive control—for the detection and differentiation of herpes simplex virus 1 (HSV1), herpes simplex virus 2 (HSV2) and varicella-zoster virus (VZV) from cerebrospinal fluids (CSF) was developed on the BD Max platform. Performance was compared to an established accredited multiplex real time PCR protocol utilizing the easyMAG and the LightCycler 480/II, both very common devices in viral molecular diagnostics. For clinical validation, 123 CSF specimens and 40 reference samples from national interlaboratory comparisons were examined with both methods, resulting in 97.6% and 100% concordance for CSF and reference samples, respectively. Utilizing the BD Max platform revealed sensitivities of 173 (CI 95%, 88–258) copies/ml for HSV1, 171 (CI 95%, 148–194) copies/ml for HSV2 and 84 (CI 95%, 5–163) copies/ml for VZV. Cross reactivity could be excluded by checking 25 common viral, bacterial and fungal human pathogens. Workflow analyses displayed shorter test duration as well as remarkable fewer and easier preparation steps with the potential to reduce error rates occurring when manually assessing patient samples. This protocol allows for a fully automated PCR assay on the BD Max platform for the simultaneously detection of herpesviridae from CSF specimens. Singular or multiple infections due to HSV1, HSV2 and VZV can reliably be differentiated with good sensitivities. Control parameters are included within the assay, thereby rendering its suitability for current quality management requirements. PMID:27092772

  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. 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

  17. 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; Emnéus, Jenny; Ruzgas, Tautgirdas; Taboryski, Rafael Jozef

    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...... cells immobilized on the electrode. Quantal characteristics, such as the number of released molecules per recorded event, are equivalent to data obtained using conventional carbon fiber microelectrodes. The detection sensitivity of the device allows for the detection of less than 10 000 dopamine...

  18. 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

  19. 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

  20. 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.

  1. 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.

  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. History of falls, gait, balance, and fall risks in older cancer survivors living in the community

    Directory of Open Access Journals (Sweden)

    Huang MH

    2015-09-01

    identifying a faller using the positive history of falls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls may help detect individuals at risk of falling. Keywords: balance, falls, risk factor, aging, cancer survivor

  4. Climate Prediction Center (CPC) U.S. Daily Snow Fall Observations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Observational reports of daily snow fall (1200 UTC to 1200 UTC) are made by members of the NWS Automated Surface Observing Systems (ASOS) network and NWS...

  5. 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

  6. Detection of kappa and lambda light chain monoclonal proteins in human serum: automated immunoassay versus immunofixation electrophoresis.

    Science.gov (United States)

    Jaskowski, Troy D; Litwin, Christine M; Hill, Harry R

    2006-02-01

    Recently, turbidimetric immunoassays for detecting and quantifying kappa and lambda free light chains (FLC) have become available and are promoted as being more sensitive than immunofixation electrophoresis (IFE) in detecting FLC monoclonal proteins. In this study, we assessed the ability of these turbidimetric assays to detect serum monoclonal proteins involving both free and heavy-chain-bound kappa and lambda light chains compared to standard immunofixation electrophoresis. Sera demonstrating a restricted band of protein migration (other than a definite M spike) by serum protein electrophoresis (SPE), which may represent early monoclonal proteins, were also examined. When compared to IFE, percent agreement, sensitivity, and specificity for the kappa-FLC and lambda-FLC were 94.6, 72.9, and 99.5% and 98.5, 91.4, and 99.7%, respectively, in detecting monoclonal proteins involving free and heavy-chain-bound light chains. The majority of sera (73.7%) demonstrating a restricted band of protein migration on SPE demonstrated abnormal IFE patterns suggestive of multiple myeloma or monoclonal gammopathy of unknown significance, but gave normal kappa/lambda FLC ratios using the turbidimetric immunoassays. In conclusion, the kappa and lambda FLC assays are significantly less sensitive (72.9 to 91.4%) than IFE, but specific in detecting serum monoclonal proteins. Moreover, the kappa/lambda ratio has little value in routine screening since the majority of sera with abnormal IFE patterns had normal kappa/lambda FLC ratios. PMID:16467338

  7. RVM Based Human Fall Analysis for Video Surveillance Applications

    Directory of Open Access Journals (Sweden)

    B.Yogameena

    2012-12-01

    Full Text Available For the safety of the elderly people, developed countries need to establish new healthcare systems to ensure their safety at home. Computer vision and video surveillance provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. The main fall detection problem is to recognize a fall among all the daily life activities, especially sitting down and crouching down activities which have similar characteristics to falls (especially a large vertical velocity. In this study, a method is proposed to detect falls by analyzing human shape deformation during a video sequence. In this study, Relevance Vector Machine (RVM is used to detect the fall of an individual based on the results obtained from torso angle through skeletonization. Experimental results on benchmark datasets demonstrate that the proposed algorithm is efficient. Further it is computationally inexpensive.

  8. On-line low and high frequency acoustic leak detection and location for an automated steam generator protection system

    International Nuclear Information System (INIS)

    Two on-line acoustic leak detection systems were operated and installed on a 76 MW hockey stick steam generator in the Sodium Components Test Installation (SCTI) at the Energy Technology Engineering Center (ETEC) in Southern California. The low frequency system demonstrated the capability to detect and locate leaks, both intentional and unintentional. No false alarms were issued during the two year test program even with adjacent blasting activities, pneumatic drilling, shuttle rocket engine testing nearby, scrams of the SCTI facility, thermal/hydraulic transient testing, and pump/control valve operations. For the high frequency system the capability to detect water into sodium reactions was established utilizing frequencies as high as 300 kHz. The high frequency system appeared to be sensitive to noise generated by maintenance work and system valve operations. Subsequent development work which is incomplete as of this date showed much more promise for the high frequency system. (author). 13 figs

  9. 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.

  10. Detection of breast carcinoma: comparison of automated water-path whole-breast sonography, mammography, and physical examination

    International Nuclear Information System (INIS)

    A comparative study of independently conducted physical examination, x-ray mammography, and sonography of the breast was carried out on 786 women having 77 excisional biopsies with 31 proven breast carcinomas. On breast sonography, 68% of the carcinomas were demonstrated with three false-positive diagnoses, compared with 65% cancer detection rate on physical examination with 37 false postives, and 77% detection rate on mammography with 15 false positives. Sonography was considered complementary to the other methods and of distinct usefulness after mammography 1) to examine the dense breast; 2) to study dense, poorly demonstrated areas; 3) to differentiate cystic from solid masses; and 4) to study breasts with augmentation mammoplasties

  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. Miniaturized capillary electrophoresis system with ultraviolet photometric detection combined with flow injection sample introduction using a modified falling-drop interface

    International Nuclear Information System (INIS)

    A miniaturized capillary electrophoresis (CE) system with UV-Vis detection was coupled to a flow injection (FI) system for achieving high throughput continuous sample introduction. The cassette of a commercial CE instrument was modified to hold a 6.5 cm long silica capillary and a flow-through waste reservoir. The cassette was inserted into the flow-cell chamber of a commercial UV detector, with the light beam focused on the capillary and collected by two ball lenses on the cassette. The capillary inlet, left outside the cassette and detector, was positioned on the top of a vertical 3.5 mm diameter glass rod, in close contact with an electrode. Samples injected through the FI system dropped freely on top of the pillar, covering the capillary inlet and electrode. Continuous sample introduction was achieved for CE separations under non-interrupted separation voltage, which was isolated from the FI system through the discontinuity of droplets. The newly developed interface and UV detection system was used for fast separation of sulphamethoxazole (SMZ) and trimethoprim (TMP) in sulphatrim tablets, achieving a high throughput of over 48 h-1, and a low carryover of 2%. Separation efficiencies of 8 μm plate height and detection limits of 1.0 mg l-1 for SMZ and 0.5 mg l-1 (3σ) for TMP were obtained

  13. Long-term detection of fluorescently labeled human mesenchymal stem cell in vitro and in vivo by semi-automated microscopy.

    Science.gov (United States)

    Polzer, Hans; Volkmer, Elias; Saller, Maximilian M; Prall, Wolf C; Haasters, Florian; Drosse, Inga; Anz, David; Mutschler, Wolf; Schieker, Matthias

    2012-02-01

    The use of seeded scaffolds in regenerative medicine is limited by the low survival of transplanted mesenchymal stem cells (MSC). Current approaches aim at improving cell viability but require an adequate long-term detection of the transplanted cells. Unfortunately, commonly performed labeling techniques have not been validated for this purpose, and studies often reveal inconclusive results. Consequently, we intended to identify the most suitable method for long-term detection of human MSC (hMSC) in vitro and in vivo. hMSC were labeled using the vital stainings PKH26 and carboxyfluorescein diacetate succinimidyl ester (CFDA-SE) as well as enhanced green fluorescent protein (eGFP) transduction. Metabolic activity and relative fluorescence intensity (RFI) were quantified in vitro over 21 days at 8 time points using standardized semi-automated microscopy and flow cytometry. In vivo, cell seeded scaffolds were subcutaneously implanted in nude mice, and RFI was analyzed over 42 days at 5 time points. In vitro, PKH26 and CFDA-SE significantly reduced metabolic activity. RFI of both stainings significantly decreased after 1 day and further faded to RFI over the total period of 21 days. In vivo, RFI of eGFP labeled cells reached a plateau phase after 21 days and displayed a 3.8-fold higher RFI compared with PKH26 and CFDA-SE on day 42 evaluated in 280 field of views per scaffold using three scaffolds for each labeling technique and time point. We conclude that PKH26 and CFDA-SE are unsuitable for long-term detection of hMSC. eGFP transduction, in turn, allows long-term detection of hMSC in vitro and in vivo. Our results suggest that eGFP is currently the best option among the fluorescent labeling techniques to follow the fate of transplanted hMSC. PMID:21951128

  14. 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

  15. Real-time and sensitive detection of Salmonella Typhimurium using an automated quartz crystal microbalance (QCM) instrument with nanoparticles amplification.

    Science.gov (United States)

    Salam, Faridah; Uludag, Yildiz; Tothill, Ibtisam E

    2013-10-15

    The accidental contamination of Salmonella in raw and processed foods is a major problem for the food industry worldwide. At present many of the currently used methods for Salmonella detection are time and labour intensive. Therefore, rapid detection is a key to the prevention and identification of problems related to health and safety. This paper describes the application of a new quartz crystal microbalance (QCM) instrument with a microfluidic system for the rapid and real time detection of Salmonella Typhimurim. The QCMA-1 bare gold sensor chip which contain two sensing array was modified by covalently immobilising the monoclonal capture antibody on the active spot and a mouse IgG antibody on the control spot using a conventional amine coupling chemistry (EDC-NHS). The binding of the Salmonella cells onto the immobilised anti-Salmonella antibody alters the sensor frequency which was correlated to cells concentration in the buffer samples. Salmonella cells were detected using direct, sandwich, and sandwich assay with antibody conjugated gold-nanoparticles. The performance of the QCM immunosensor developed with gold-nanoparticles gave the highest sensitivity with a limit of detection (LOD) ~10-20 colony forming unit (CFU) ml(-1) compared to direct and sandwich assay (1.83 × 10(2) CFU ml(-1) and 1.01 × 10(2) CFU ml(-1), respectively). The sensor showed good sensitivity and selectivity for Salmonella in the presence of other bacteria in real food samples and helped in reducing the pre-enrichment step, hence, demonstrating the potential of this technology for the rapid and sensitive microbial analysis. PMID:24054660

  16. Spectrally Enhanced Cloud Objects—A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 1. Multispectral analysis

    Science.gov (United States)

    Pavolonis, Michael J.; Sieglaff, Justin; Cintineo, John

    2015-08-01

    While satellites are a proven resource for detecting and tracking volcanic ash and dust clouds, existing algorithms for automatically detecting volcanic ash and dust either exhibit poor overall skill or can only be applied to a limited number of sensors and/or geographic regions. As such, existing techniques are not optimized for use in real-time applications like volcanic eruption alerting and data assimilation. In an effort to significantly improve upon existing capabilities, the Spectrally Enhanced Cloud Objects (SECO) algorithm was developed. The SECO algorithm utilizes a combination of radiative transfer theory, a statistical model, and image processing techniques to identify volcanic ash and dust clouds in satellite imagery with a very low false alarm rate. This fully automated technique is globally applicable (day and night) and can be adapted to a wide range of low earth orbit and geostationary satellite sensors or even combinations of satellite sensors. The SECO algorithm consists of four primary components: conversion of satellite measurements into robust spectral metrics, application of a Bayesian method to estimate the probability that a given satellite pixel contains volcanic ash and/or dust, construction of cloud objects, and the selection of cloud objects deemed to have the physical attributes consistent with volcanic ash and/or dust clouds. The first two components of the SECO algorithm are described in this paper, while the final two components are described in a companion paper.

  17. Automated detection of residual cells after sex-mismatched stem-cell transplantation – evidence for presence of disease-marker negative residual cells

    Directory of Open Access Journals (Sweden)

    Johannes Tilman

    2009-05-01

    Full Text Available Abstract Background A new chimerism analysis based on automated interphase fluorescence in situ hybridization (FISH evaluation was established to detect residual cells after allogene sex-mismatched bone marrow or blood stem-cell transplantation. Cells of 58 patients were characterized as disease-associated due to presence of a bcr/abl-gene-fusion or a trisomy 8 and/or a simultaneous hybridization of gonosome-specific centromeric probes. The automatic slide scanning platform Metafer with its module MetaCyte was used to analyse 3,000 cells per sample. Results Overall 454 assays of 58 patients were analyzed. 13 of 58 patients showed residual recipient cells at one stage of more than 4% and 12 of 58 showed residual recipient cells less than 4%, respectively. As to be expected, patients of the latter group were associated with a higher survival rate (48 vs. 34 month. In only two of seven patients with disease-marker positive residual cells between 0.1–1.3% a relapse was observed. Besides, disease-marker negative residual cells were found in two patients without relapse at a rate of 2.8% and 3.3%, respectively. Conclusion The definite origin and meaning of disease-marker negative residual cells is still unclear. Overall, with the presented automatic chimerism analysis of interphase FISH slides, a sensitive method for detection of disease-marker positive residual cells is on hand.

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

    Science.gov (United States)

    Kim, Kee-Tae

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

  19. Design and development of an automated D.C. ground fault detection and location system for Cirus

    International Nuclear Information System (INIS)

    Full text: The original design of Cirus safety system provided for automatic detection of ground fault in class I D.C. power supply system and its annunciation followed by delayed reactor trip. Identification of a faulty section was required to be done manually by switching off various sections one at a time thus requiring a lot of shutdown time to identify the faulty section. Since class I power supply is provided for safety control system, quick detection and location of ground faults in this supply is necessary as these faults have potential to bypass safety interlocks and hence the need for a new system for automatic location of a faulty section. Since such systems are not readily available in the market, in-house efforts were made to design and develop a plant-specific system, which has been installed and commissioned

  20. Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

    Science.gov (United States)

    Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H.; Medeiros, Felipe A.; Zangwill, Linda M.; Weinreb, Robert N.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Bowd, Christopher

    2016-01-01

    Purpose To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. Methods GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Results Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. Conclusions GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Translational Relevance Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning. PMID:27152250