WorldWideScience

Sample records for automated fall detection

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

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  2. Fall Detection Using Smartphone Audio Features.

    Science.gov (United States)

    Cheffena, Michael

    2016-07-01

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

  3. Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

    Science.gov (United States)

    Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

    2017-01-01

    Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694

  4. A simple strategy for fall events detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-20

    The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.

  5. Automatic Fall Detection using Smartphone Acceleration Sensor

    Directory of Open Access Journals (Sweden)

    Tran Tri Dang

    2016-12-01

    Full Text Available In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

  6. Radar fall detection using principal component analysis

    Science.gov (United States)

    Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

  7. 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.

  8. Fall Down Detection Under Smart Home System.

    Science.gov (United States)

    Juang, Li-Hong; Wu, Ming-Ni

    2015-10-01

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

  9. Doppler radar sensor positioning in a fall detection system.

    Science.gov (United States)

    Liu, Liang; Popescu, Mihail; Ho, K C; Skubic, Marjorie; Rantz, Marilyn

    2012-01-01

    Falling is a common health problem for more than a third of the United States population over 65. We are currently developing a Doppler radar based fall detection system that already has showed promising results. In this paper, we study the sensor positioning in the environment with respect to the subject. We investigate three sensor positions, floor, wall and ceiling of the room, in two experimental configurations. Within each system configuration, subjects performed falls towards or across the radar sensors. We collected 90 falls and 341 non falls for the first configuration and 126 falls and 817 non falls for the second one. Radar signature classification was performed using a SVM classifier. Fall detection performance was evaluated using the area under the ROC curves (AUCs) for each sensor deployment. We found that a fall is more likely to be detected if the subject is falling toward or away from the sensor and a ceiling Doppler radar is more reliable for fall detection than a wall mounted one.

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

    Directory of Open Access Journals (Sweden)

    Rafael Luque

    2014-10-01

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

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

    Science.gov (United States)

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

    2014-10-08

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

  12. 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

  13. Doppler radar fall activity detection using the wavelet transform.

    Science.gov (United States)

    Su, Bo Yu; Ho, K C; Rantz, Marilyn J; Skubic, Marjorie

    2015-03-01

    We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

  14. 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.

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

    Science.gov (United States)

    Zhao, Hengyang; Zhao, Yan; Zhong, Haibo

    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 caregivers with the patient's location. PMID:25784933

  16. A FRAMEWORK FOR AUTOMATED CHANGE DETECTION SYSTEM

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    To enhance the ability of remote sensing system to provide accurate,timely,and c omplete geo_spatial information at regional or global scale,an automated change detection system has been and will continue to be one of the important and chall enging problems in remote sensing.In this paper,the authors propose a framework for auto mated change detection system at landscape level using various geo_spatial data sources including multi_sensor remotely sensed imagery and ancillary data layers .In this framework,database is the central part and some associated techniques a re discussed.These techniques includes five subsystems:automated feature_based i mage registration,automated change finding,automated change feature extraction a nd identification,intelligent change recognition,change accuracy assessment and database updating and visualization.

  17. Automated Change Detection for Synthetic Aperture Sonar

    Science.gov (United States)

    2014-01-01

    B. D. Van Veen, “ Canonical coordinates are the right coordi- nates for low-rank Gauss - Gauss detection and estimation,” IEEE Trans. Signal Process. 54...features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation...over time scales ranging from hours through several days. Keywords: automated change detection, canonical correlation analysis, coherent change detection

  18. Optimization and evaluation of the human fall detection system

    Science.gov (United States)

    Alzoubi, Hadeel; Ramzan, Naeem; Shahriar, Hasan; Alzubi, Raid; Gibson, Ryan; Amira, Abbes

    2016-10-01

    Falls are the most critical health problem for elderly people, which are often, cause significant injuries. To tackle a serious risk that made by the fall, we develop an automatic wearable fall detection system utilizing two devices (mobile phone and wireless sensor) based on three axes accelerometer signals. The goal of this study is to find an effective machine learning method that distinguish falls from activities of daily living (ADL) using only a single triaxial accelerometer. In addition, comparing the performance results for wearable sensor and mobile device data .The proposed model detects the fall by using seven different classifiers and the significant performance is demonstrated using accuracy, recall, precision and F-measure. Our model obtained accuracy over 99% on wearable device data and over 97% on mobile phone data.

  19. Automated Detection of Solar Eruptions

    CERN Document Server

    Hurlburt, Neal

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-07-23

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

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

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    2015-07-01

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

  2. 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.

  3. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  4. Automated detection of solar eruptions

    Directory of Open Access Journals (Sweden)

    Hurlburt N.

    2015-01-01

    Full Text Available Observation of the solar atmosphere reveals a wide range of motions, from small scale jets and spicules to global-scale coronal mass ejections (CMEs. Identifying and characterizing these motions are essential to advancing our understanding of the drivers of space weather. Both automated and visual identifications are currently used in identifying Coronal Mass Ejections. To date, eruptions near the solar surface, which may be precursors to CMEs, have been identified primarily by visual inspection. Here we report on Eruption Patrol (EP: a software module that is designed to automatically identify eruptions from data collected by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory (SDO/AIA. We describe the method underlying the module and compare its results to previous identifications found in the Heliophysics Event Knowledgebase. EP identifies eruptions events that are consistent with those found by human annotations, but in a significantly more consistent and quantitative manner. Eruptions are found to be distributed within 15 Mm of the solar surface. They possess peak speeds ranging from 4 to 100 km/s and display a power-law probability distribution over that range. These characteristics are consistent with previous observations of prominences.

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

    Directory of Open Access Journals (Sweden)

    Patimakorn Jantaraprim

    2012-12-01

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

  7. Hardware Design of the Energy Efficient Fall Detection Device

    Science.gov (United States)

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

    2016-04-01

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

  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. Fall detection in walking robots by multi-way principal component analysis

    NARCIS (Netherlands)

    Karssen, J.G.; Wisse, M.

    2008-01-01

    Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in

  10. Automating Vendor Fraud Detection in Enterprise Systems

    Directory of Open Access Journals (Sweden)

    Kishore Singh

    2013-06-01

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

  11. Sensitivity Analysis of Automated Ice Edge Detection

    Science.gov (United States)

    Moen, Mari-Ann N.; Isaksem, Hugo; Debien, Annekatrien

    2016-08-01

    The importance of highly detailed and time sensitive ice charts has increased with the increasing interest in the Arctic for oil and gas, tourism, and shipping. Manual ice charts are prepared by national ice services of several Arctic countries. Methods are also being developed to automate this task. Kongsberg Satellite Services uses a method that detects ice edges within 15 minutes after image acquisition. This paper describes a sensitivity analysis of the ice edge, assessing to which ice concentration class from the manual ice charts it can be compared to. The ice edge is derived using the Ice Tracking from SAR Images (ITSARI) algorithm. RADARSAT-2 images of February 2011 are used, both for the manual ice charts and the automatic ice edges. The results show that the KSAT ice edge lies within ice concentration classes with very low ice concentration or open water.

  12. Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use.

    Science.gov (United States)

    Gövercin, Mehmet; Költzsch, Y; Meis, M; Wegel, S; Gietzelt, M; Spehr, J; Winkelbach, S; Marschollek, M; Steinhagen-Thiessen, E

    2010-01-01

    One of the major problems in the development of information and communication technologies for older adults is user acceptance. Here we describe the results of focus group discussions that were conducted with older adults and their relatives to guide the development of assistive devices for fall detection and fall prevention. The aim was to determine the ergonomic and functional requirements of such devices and to include these requirements in a user-centered development process. A semi-structured interview format based on an interview guide was used to conduct three focus group discussions with 22 participants. The average age was 75 years in the first group, 68 years in the second group and 50 years in the third group (relatives). Overall, participants considered a fall prediction system to be as important as a fall detection system. Although the ambient, unobtrusive character of the optical sensor system was appreciated, wearable inertial sensors were preferred because of their wide range of use, which provides higher levels of security. Security and mobility were two major reasons for people at risk of falling to buy a wearable and/or optical fall prediction and fall detection device. Design specifications should include a wearable, non-stigmatising sensor at the user's wrist, with an emergency option in case of falling.

  13. Accelerometer and Camera-Based Strategy for Improved Human Fall Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-10-29

    In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

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

    Science.gov (United States)

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

    2013-01-01

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

  15. Toward Automated Feature Detection in UAVSAR Images

    Science.gov (United States)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  16. Joint Transform Correlation for face tracking: elderly fall detection application

    Science.gov (United States)

    Katz, Philippe; Aron, Michael; Alfalou, Ayman

    2013-03-01

    In this paper, an iterative tracking algorithm based on a non-linear JTC (Joint Transform Correlator) architecture and enhanced by a digital image processing method is proposed and validated. This algorithm is based on the computation of a correlation plane where the reference image is updated at each frame. For that purpose, we use the JTC technique in real time to track a patient (target image) in a room fitted with a video camera. The correlation plane is used to localize the target image in the current video frame (frame i). Then, the reference image to be exploited in the next frame (frame i+1) is updated according to the previous one (frame i). In an effort to validate our algorithm, our work is divided into two parts: (i) a large study based on different sequences with several situations and different JTC parameters is achieved in order to quantify their effects on the tracking performances (decimation, non-linearity coefficient, size of the correlation plane, size of the region of interest...). (ii) the tracking algorithm is integrated into an application of elderly fall detection. The first reference image is a face detected by means of Haar descriptors, and then localized into the new video image thanks to our tracking method. In order to avoid a bad update of the reference frame, a method based on a comparison of image intensity histograms is proposed and integrated in our algorithm. This step ensures a robust tracking of the reference frame. This article focuses on face tracking step optimisation and evalutation. A supplementary step of fall detection, based on vertical acceleration and position, will be added and studied in further work.

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

    NARCIS (Netherlands)

    Josemans, W.; Englebienne, G.; Kröse, B.; Battiato, S.; Braz, J.

    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

  18. 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.

  19. Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

    Directory of Open Access Journals (Sweden)

    Gregory Koshmak

    2016-01-01

    Full Text Available Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

  20. Effective detection method for falls according to the distance between two tri-axial accelerometers

    Science.gov (United States)

    Kim, Jae-Hyung; Park, Geun-Chul; Kim, Soo-Hong; Kim, Soo-Sung; Lee, Hae-Rim; Jeon, Gye-Rok

    2016-04-01

    Falls and fall-related injuries are a significant problem in the elderly population. A number of different approaches for detecting falls and activities of daily living (ADLs) have been conducted in recent years. However, distinguishing between real falls and certain fall-like ADL is often difficult. The aim of this study is to discriminate falls from fall-like ADLs such as jogging, jumping, and jumping down. The distance between two tri-axial accelerometers attached to the abdomen and the sternum was increased from 10 to 30 cm in 10-cm intervals. Experiments for falls and ADLs were performed to investigate the feasibility of the detection system for falls developed in this study. When the distances between the two tri-axial electrometers were 20 and 30 cm, fall-like ADLs were effectively distinguished from falls. The thresholds for three parameters — SVM, Diff Z, and Sum_diff_Z — were set; falls could be distinguished from ADL action sequences when the SVM value was larger than 4 g (TH1), the Diff_Z parameter was larger than 1.25 g (TH2), and the Sum_diff_Z parameter was larger than 15 m/s (TH3). In particular, when the SVM, Diff_Z, and Sum_diff_Z parameter were sequentially applied to thresholds (TH1, TH2, and TH3), fall-like ADL action sequences were accurately discriminated from falls.

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

    Science.gov (United States)

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

    2015-08-01

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

  2. Statistical control chart and neural network classification for improving human fall detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-05

    This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow\\'s fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

  3. 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

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

    Science.gov (United States)

    Huang, Chih-Ning; Chan, Chia-Tai

    2014-04-16

    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.

  5. 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.

  6. Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Dongha Lim

    2014-01-01

    Full Text Available Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL. The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

  7. Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

    Science.gov (United States)

    Li, Yun; Ho, K C; Popescu, Mihail

    2014-03-01

    Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

  8. UHF wearable battery free sensor module for activity and falling detection.

    Science.gov (United States)

    Nam Trung Dang; Thang Viet Tran; Wan-Young Chung

    2016-08-01

    Falling is one of the most serious medical and social problems in aging population. Therefore taking care of the elderly by detecting activity and falling for preventing and mitigating the injuries caused by falls needs to be concerned. This study proposes a wearable, wireless, battery free ultra-high frequency (UHF) smart sensor tag module for falling and activity detection. The proposed tag is powered by UHF RF wave from reader and read by a standard UHF Electronic Product Code (EPC) Class-1 Generation-2 reader. The battery free sensor module could improve the wearability of the wireless device. The combination of accelerometer signal and received signal strength indication (RSSI) from a reader in the passive smart sensor tag detect the activity and falling of the elderly very successfully. The fabricated smart sensor tag module has an operating range of up to 2.5m and conducting in real-time activity and falling detection.

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

    KAUST Repository

    Zerrouki, Nabil

    2016-07-26

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

  10. Detecting Family Resemblance: Automated Genre Classification

    Directory of Open Access Journals (Sweden)

    Yunhyong Kim

    2007-03-01

    Full Text Available This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features, and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.

  11. 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.

  12. Towards an automated detection of oestrus in dairy cattle.

    Science.gov (United States)

    Saint-Dizier, M; Chastant-Maillard, S

    2012-12-01

    Heat detection is a key factor in the profitability of dairy herds. However, this detection demands a significant part of the breeder's working time and is made difficult by the short duration and the discrete behavioural changes associated with oestrus in modern dairy cows. Progress has been made in monitoring cow with electronics, biosensors and computer. As a result, automated heat detection systems have been developed. Currently available tools are automated detectors of standing heat, activity-metres and automated in-line systems measuring milk progesterone. Camera-software systems and monitoring of body temperature are being developed and may also be used as heat detection tools. The heat detection rate of most systems is above 80% with a specificity of detection generally higher than 90%. The accuracy, however, may vary considerably depending on the tool and model developed. The initial investment of several thousands of euros required for these automated systems becomes a source of profit in large herds, provided the recorded data are properly managed.

  13. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.

    Science.gov (United States)

    Casilari, Eduardo; Oviedo-Jiménez, Miguel A

    2015-01-01

    Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of 'false

  14. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    Full Text Available Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone, this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope. In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication. The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements. The proposed system was compared with the cases where only one device (the smartphone or the smartwatch is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio

  15. 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.

  16. 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

    2015-01-01

    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.

  17. 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.

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

    Science.gov (United States)

    Wright, A. J.

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

  19. 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.

  20. Automated baseline change detection phase I. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-12-01

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

  1. 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

  2. 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

  3. (Automated) software modularization using community detection

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius; Manikas, Konstantinos

    2015-01-01

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

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

    Science.gov (United States)

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

    2010-02-01

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

  5. Automated face detection for occurrence and occupancy estimation in chimpanzees.

    Science.gov (United States)

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

    2017-03-01

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

  6. Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer

    Science.gov (United States)

    Nyan, M. N.; Tay, Francis E. H.; Manimaran, M.; Seah, K. H. W.

    2006-04-01

    This paper studied the detection of falls and activities of daily living (ADL) with two objectives: (1) minimum number of sensors for a broad range of activities and (2) maximize the comfort of the wearer for long term use. We used a garment to provide long term comfort for the wearer, with a 3-axis MEMS accelerometer on the shoulder position, as a wearable platform. ADL were detected in time-frequency domain and summation of absolute peak values of 3-D acceleration signals was used as feature in fall detection. 6 male and female subjects performed approximately five-hour long experiment. Sensitivity of 94.98% and specificity of 98.83% for altogether 1495 activities were achieved. Our garment-based detection system fulfilled the objective of providing the comfort of the wearer in long term monitoring of falls and ADL with high sensitivity. In fall detection, our device can summon medical assistances via SMS (Short Message Service). This detection system can raise fall alarm (fall SMS) automatically to individuals to get a shortened interval of the arrival of assistance.

  7. Method and automated apparatus for detecting coliform organisms

    Science.gov (United States)

    Dill, W. P.; Taylor, R. E.; Jeffers, E. L. (Inventor)

    1980-01-01

    Method and automated apparatus are disclosed for determining the time of detection of metabolically produced hydrogen by coliform bacteria cultured in an electroanalytical cell from the time the cell is inoculated with the bacteria. The detection time data provides bacteria concentration values. The apparatus is sequenced and controlled by a digital computer to discharge a spent sample, clean and sterilize the culture cell, provide a bacteria nutrient into the cell, control the temperature of the nutrient, inoculate the nutrient with a bacteria sample, measures the electrical potential difference produced by the cell, and measures the time of detection from inoculation.

  8. Implementation of Advanced Techniques for Automated Freeway Incident Detection

    OpenAIRE

    Abdulhai, Baher; Ritchie, Stephen G.; Iyer, Mahadevan

    1999-01-01

    A significant body of research on advanced techniques for automated freeway incident detection has been conducted at the University of California, Irvine (UCI). Such advanced pattern recognition techniques as artificial neural networks (ANNs) have been thoroughly investigated and their potential superiority to other techniques has been demonstrated. Of the investigated ANN architectures, two have shown the best potential for real-time implementation: namely, the Probabilistic Neural Network (...

  9. 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.

  10. A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations

    Directory of Open Access Journals (Sweden)

    Isu Shin

    2015-01-01

    Full Text Available The short-time Fourier transform- (STFT- based algorithm was suggested to distinguish falls from various activities of daily living (ADLs. Forty male subjects volunteered in the experiments including three types of falls and four types of ADLs. An inertia sensor unit attached to the middle of two anterior superior iliac spines was used to measure the 3-axis accelerations at 100 Hz. The measured accelerations were transformed to signal vector magnitude values to be analyzed using STFT. The powers of low frequency components were extracted, and the fall detection was defined as whether the normalized power was less than the threshold (50% of the normal power. Most power was observed at the frequency band lower than 5 Hz in all activities, but the dramatic changes in the power were found only in falls. The specificity of 1–3 Hz frequency components was the best (100%, but the sensitivity was much smaller compared with 4 Hz component. The 4 Hz component showed the best fall detection with 96.9% sensitivity and 97.1% specificity. We believe that the suggested algorithm based on STFT would be useful in the fall detection and the classification from ADLs as well.

  11. 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.

  12. Automated Detection of Oscillating Regions in the Solar Atmosphere

    CERN Document Server

    Ireland, Jack; Kucera, Therese A; Young, Christopher A; 10.1007/s11207-010-9592-6

    2010-01-01

    Recently observed oscillations in the solar atmosphere have been interpreted and modeled as magnetohydrodynamic wave modes. This has allowed the estimation of parameters that are otherwise hard to derive, such as the coronal magnetic-field strength. This work crucially relies on the initial detection of the oscillations, which is commonly done manually. The volume of Solar Dynamics Observatory (SDO) data will make manual detection inefficient for detecting all of the oscillating regions. An algorithm is presented which automates the detection of areas of the solar atmosphere that support spatially extended oscillations. The algorithm identifies areas in the solar atmosphere whose oscillation content is described by a single, dominant oscillation within a user-defined frequency range. The method is based on Bayesian spectral analysis of time-series and image filtering. A Bayesian approach sidesteps the need for an a-priori noise estimate to calculate rejection criteria for the observed signal, and it also prov...

  13. Automated J wave detection from digital 12-lead electrocardiogram.

    Science.gov (United States)

    Wang, Yi Grace; Wu, Hau-Tieng; Daubechies, Ingrid; Li, Yabing; Estes, E Harvey; Soliman, Elsayed Z

    2015-01-01

    In this report we provide a method for automated detection of J wave, defined as a notch or slur in the descending slope of the terminal positive wave of the QRS complex, using signal processing and functional data analysis techniques. Two different sets of ECG tracings were selected from the EPICARE ECG core laboratory, Wake Forest School of Medicine, Winston Salem, NC. The first set was a training set comprised of 100 ECGs of which 50 ECGs had J-wave and the other 50 did not. The second set was a test set (n=116 ECGs) in which the J-wave status (present/absent) was only known by the ECG Center staff. All ECGs were recorded using GE MAC 1200 (GE Marquette, Milwaukee, Wisconsin) at 10mm/mV calibration, speed of 25mm/s and 500HZ sampling rate. All ECGs were initially inspected visually for technical errors and inadequate quality, and then automatically processed with the GE Marquette 12-SL program 2001 version (GE Marquette, Milwaukee, WI). We excluded ECG tracings with major abnormalities or rhythm disorder. Confirmation of the presence or absence of a J wave was done visually by the ECG Center staff and verified once again by three of the coauthors. There was no disagreement in the identification of the J wave state. The signal processing and functional data analysis techniques applied to the ECGs were conducted at Duke University and the University of Toronto. In the training set, the automated detection had sensitivity of 100% and specificity of 94%. For the test set, sensitivity was 89% and specificity was 86%. In conclusion, test results of the automated method we developed show a good J wave detection accuracy, suggesting possible utility of this approach for defining and detection of other complex ECG waveforms.

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

    Science.gov (United States)

    Leone, Alessandro; Diraco, Giovanni; Siciliano, Pietro

    2011-07-01

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

  15. Fully Automated Lipid Pool Detection Using Near Infrared Spectroscopy

    Directory of Open Access Journals (Sweden)

    Elżbieta Pociask

    2016-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    Grossman, Stanley I.

    Much of the current efforts in remote sensing tackle macro-level problems such as determining the extent of wheat in a field, the general health of vegetation or the extent of mineral deposits in an area. However, for many of the remaining remote sensing challenges being studied currently, such as border protection, drug smuggling, treaty verification, and the war on terror, most targets are very small in nature - a vehicle or even a person. While in typical macro-level problems the objective vegetation is in the scene, for small target detection problems it is not usually known if the desired small target even exists in the scene, never mind finding it in abundance. The ability to find specific small targets, such as vehicles, typifies this problem. Complicating the analyst's life, the growing number of available sensors is generating mountains of imagery outstripping the analysts' ability to visually peruse them. This work presents the important factors influencing spectral exploitation using multispectral data and suggests a different approach to small target detection. The methodology of directed search is presented, including the use of scene-modeled spectral libraries, various search algorithms, and traditional statistical and ROC curve analysis. The work suggests a new metric to calibrate analysis labeled the analytic sweet spot as well as an estimation method for identifying the sweet spot threshold for an image. It also suggests a new visualization aid for highlighting the target in its entirety called nearest neighbor inflation (NNI). It brings these all together to propose that these additions to the target detection arena allow for the construction of a fully automated target detection scheme. This dissertation next details experiments to support the hypothesis that the optimum detection threshold is the analytic sweet spot and that the estimation method adequately predicts it. Experimental results and analysis are presented for the proposed directed

  17. 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.)

  18. 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.

  19. How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

    Directory of Open Access Journals (Sweden)

    Martin Gjoreski

    2016-06-01

    Full Text Available Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data.

  20. How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

    Science.gov (United States)

    Gjoreski, Martin; Gjoreski, Hristijan; Luštrek, Mitja; Gams, Matjaž

    2016-06-01

    Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject's daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data).

  1. Automated Detection and Tracking of Solar Magnetic Bright Points

    CERN Document Server

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

    2009-01-01

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

  2. Automated sleep scoring and sleep apnea detection in children

    Science.gov (United States)

    Baraglia, David P.; Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Martin, A. James; Abbott, Derek

    2005-12-01

    This paper investigates the automated detection of a patient's breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women's and Children's Hospital. The sleep scoring and breathing event detection tasks used neural networks to achieve signal classification. The Fourier transform and the Higuchi fractal dimension were used to extract features for input to the neural network. The filtered skin conductivity appeared visually to bear a similarity to the breathing and heart rate signal, but a more detailed evaluation showed the relation was not consistent. Sleep stage classification was achieved with and accuracy of around 65% with some stages being accurately scored and others poorly scored. The two breathing events hypopnea and apnea were scored with varying degrees of accuracy with the highest scores being around 75% and 30%.

  3. Automated Detection of Trinucleotide Repeats in Fragile X Syndrome.

    Science.gov (United States)

    Hamdan; Tynan; Fenwick; Leon

    1997-12-01

    Background: The conventional method for diagnosis of fragile X syndrome has been amplification of the trinucleotide repeat region of the FMR-1 gene by polymerase chain reaction (PCR) and Southern blot analysis to detect full expansion and hypermethylation. "Stuttering" resulting from incomplete amplification is still observed in the PCR products despite the use of reagents that reduce the secondary structure of the GC-rich template. In addition, PCR products can be detected by autoradiography only after 1 to 2 days of exposure. By combination of a recently reported amplification protocol with fluorescence detection of PCR products in an automated DNA sequencer, the PCR protocol for amplification of trinucleotide repeats was simplified. This modified protocol is highly reproducible, more accurate, and less costly than the conventional protocol because of the elimination of radioisotopes from the PCR. Methods and Results: PCRs were conducted with betaine and Pfu DNA polymerase. This improved PCR protocol allowed immediate detection of PCR products in agarose gels containing ethidium bromide. Stuttering was completely eliminated and fragments of up to 1kb ( approximately 250 repeats) were visible in agarose gels. PCR products were automatically detected by laser fluorescence in an automated DNA sequencer by inclusion of a fluorescently-labeled primer in the PCR reaction. A short electrophoresis run of 100 minutes in denaturing acrylamide gels was sufficient to give high resolution of fragments with higher accuracy and sensitivity than conventional detection by autoradiography. Conclusions: A simple, nonradioactive protocol that is more rapid and less expensive than the conventional PCR protocol for the detection of trinucleotide repeats has been developed. By use of this detection protocol, fragment sizes containing up to 100 repeats could be detected, alleles differing by one trinucleotide repeat were clearly resolved, and heterogeneous repeat patterns such as those

  4. Enhancing time-series detection algorithms for automated biosurveillance.

    Science.gov (United States)

    Tokars, Jerome I; Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A

    2009-04-01

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

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

    Science.gov (United States)

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

    2011-03-01

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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly.

    Science.gov (United States)

    Hwang, J Y; Kang, J M; Jang, Y W; Kim, H

    2004-01-01

    Novel algorithm and real-time ambulatory monitoring system for fall detection in elderly people is described. Our system is comprised of accelerometer, tilt sensor and gyroscope. For real-time monitoring, we used Bluetooth. Accelerometer measures kinetic force, tilt sensor and gyroscope estimates body posture. Also, we suggested algorithm using signals which obtained from the system attached to the chest for fall detection. To evaluate our system and algorithm, we experimented on three people aged over 26 years. The experiment of four cases such as forward fall, backward fall, side fall and sit-stand was repeated ten times and the experiment in daily life activity was performed one time to each subject. These experiments showed that our system and algorithm could distinguish between falling and daily life activity. Moreover, the accuracy of fall detection is 96.7%. Our system is especially adapted for long-time and real-time ambulatory monitoring of elderly people in emergency situation.

  11. 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.

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

    DEFF Research Database (Denmark)

    Carl, Jesper; Nielsen, Henning; Nielsen, Jane

    2006-01-01

    of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection...

  13. Automated detection of actinic keratoses in clinical photographs.

    Directory of Open Access Journals (Sweden)

    Samuel C Hames

    Full Text Available BACKGROUND: Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. OBJECTIVE: The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. METHODS: Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist's assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. RESULTS: The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist's intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001 were detected in the high actinic keratosis group compared to the no actinic keratosis group. CONCLUSIONS: The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve

  14. 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.

  15. 3D depth image analysis for indoor fall detection of elderly people

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2016-02-01

    Full Text Available This paper presents a new fall detection method of elderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre-processed by a median filter both for background and target. The silhouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor plane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual. The centroids of the human body are calculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are lower than some thresholds, fall incident will be detected. Experiments with different falling direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.

  16. Detecting Falls at Home: User-Centered Design of a Pervasive Technology

    Directory of Open Access Journals (Sweden)

    Marc-Eric Bobillier Chaumon

    2016-11-01

    Full Text Available Falling is the main cause of domestic accidents and fatal injuries to seniors at home. In this paper, we describe the design process for a new pervasive technology (CIRDO. The aim of this technology is to detect falls (via audio and video sensors and to alert the elderly's family or caregivers. Two complementary studies were performed. Firstly, the actual risk situations of older adults were analyzed. Secondly, social acceptance was investigated for the different homecare field stakeholders. Our results highlight the tensions among social actors towards the tool and their impacts on technology acceptance by the elderly. Also, we show a significant change in the fall process due to the device. In actuality, the social functions associated with CIRDO implementation and the necessity of iterative design processes suggest that the CIRDO system should be more flexible and versatile to better fit the risk behaviors of seniors that evolve using this device.

  17. 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...

  18. Automated detection of retinal whitening in malarial retinopathy

    Science.gov (United States)

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

    2016-03-01

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

  19. The Automated Planet Finder telescope's automation and first three years of planet detections

    Science.gov (United States)

    Burt, Jennifer

    2016-08-01

    The Automated Planet Finder (APF) is a 2.4m, f/15 telescope located at the UCO's Lick Observatory, atop Mt. Hamilton. The telescope has been specifically optimized to detect and characterize extrasolar planets via high precision, radial velocity (RV) observations using the high-resolution Levy echelle spectrograph. The telescope has demonstrated world-class internal precision levels of 1 m/s when observing bright, RV standard stars. Observing time on the telescope is divided such that ˜80% is spent on exoplanet related research and the remaining ˜20% is made available to the University of California consortium for other science goals. The telescope achieved first light in 2013, and this work describes the APF's early science achievements and its transition from a traditional observing approach to a fully autonomous facility. First we provide a characteristic look at the APF telescope and the Levy spectrograph, focusing on the stability of the instrument and its performance on RV standard stars. Second, we describe the design and implementation of the dynamic scheduling software which has been running our team's nightly observations on the APF for the past year. Third, we discuss the detection of a Neptune-mass planet orbiting the nearby, low-mass star GL687 by the APF in collaboration with the HIRES instrument on Keck I. Fourth, we summarize the APF's detection of two multi-planet systems: the four planet system orbiting HD 141399 and the 6 planet system orbiting HD 219134. Fifth, we expand our science focus to assess the impact that the APF - with the addition of a new, time-varying prioritization scheme to the telescope's dynamic scheduling software - can have on filling out the exoplanet Mass-Radius diagram when pursuing RV follow-up of transiting planets detected by NASA's TESS satellite. Finally, we outline some likely next science goals for the telescope.

  20. Pre-impact fall detection system using dynamic threshold and 3D bounding box

    Science.gov (United States)

    Otanasap, Nuth; Boonbrahm, Poonpong

    2017-02-01

    Fall prevention and detection system have to subjugate many challenges in order to develop an efficient those system. Some of the difficult problems are obtrusion, occlusion and overlay in vision based system. Other associated issues are privacy, cost, noise, computation complexity and definition of threshold values. Estimating human motion using vision based usually involves with partial overlay, caused either by direction of view point between objects or body parts and camera, and these issues have to be taken into consideration. This paper proposes the use of dynamic threshold based and bounding box posture analysis method with multiple Kinect cameras setting for human posture analysis and fall detection. The proposed work only uses two Kinect cameras for acquiring distributed values and differentiating activities between normal and falls. If the peak value of head velocity is greater than the dynamic threshold value, bounding box posture analysis will be used to confirm fall occurrence. Furthermore, information captured by multiple Kinect placed in right angle will address the skeleton overlay problem due to single Kinect. This work contributes on the fusion of multiple Kinect based skeletons, based on dynamic threshold and bounding box posture analysis which is the only research work reported so far.

  1. 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.

  2. 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.

  3. RFI detection by automated feature extraction and statistical analysis

    Science.gov (United States)

    Winkel, B.; Kerp, J.; Stanko, S.

    2007-01-01

    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 algorithm which performs a two-dimensional baseline fit in the time-frequency domain, searching automatically for RFI signals superposed on the spectral data. We demonstrate, that the software operates successfully on computer-generated RFI data as well as on real DFFT data recorded at the Effelsberg 100-m telescope. At 21-cm wavelength RFI signals can be identified down to the 4σ_rms level. A statistical analysis of all RFI events detected in our observational data revealed that: (1) mean signal strength is comparable to the astronomical line emission of the Milky Way, (2) interferences are polarised, (3) electronic devices in the neighbourhood of the telescope contribute significantly to the RFI radiation. We also show that the radiometer equation is no longer fulfilled in presence of RFI signals.

  4. RFI detection by automated feature extraction and statistical analysis

    CERN Document Server

    Winkel, B; Stanko, S; 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 algorithm which performs a two-dimensional baseline fit in the time-frequency domain, searching automatically for RFI signals superposed on the spectral data. We demonstrate, that the software operates successfully on computer-generated RFI data as well as on real DFFT data recorded at the Effelsberg 100-m telescope. At 21-cm wavelength RFI signals can be identified down to the 4-sigma level. A statistical analysis of all RFI events detected in our observational data revealed that: (1) mean signal strength is comparable to the a...

  5. 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.

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

    Science.gov (United States)

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

    2014-01-01

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

  7. 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.

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

    Science.gov (United States)

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

    2009-01-01

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

  9. Dynamic detection model and its application for perimeter security, intruder detection, and automated target recognition

    Science.gov (United States)

    Koltunov, Joseph; Koltunov, Alexander

    2003-09-01

    Under unsteady weather conditions (gusty wind and partial cloudiness), the pixel intensities measured by infrared or optical imaging sensors may change considerably within even minutes. This makes a principal obstacle to automated target detection and recognition in real, outdoor settings. Currently existing automated recognition algorithms require strong similarity between the weather conditions of training and recognition. Empirical attempts to normalize image intensities do not lead to reliable detection in practice (e.g. for scenes with a complex relief). Also if the weather is relatively stable (weak wind, rare clouds), as short as 15-20 minutes delay between the training survey and the recognition survey may badly affect target recognition or detection, unless the targets are well separable from background. Thermal IR technologies based on invariants such as emissivity and thermal inertia are expensive and ineffective in making the recognition automated. Our approach to overcoming the problem is to take advantage of multitemporal prior surveying. It exploits the fact, that any new infrared or optical image of a scene can be accurately predicted based on sufficiently many scene images acquired previously. This removes the above severe constraints to variability of the weather conditions, whereas neither meteorological measurement nor radiometric calibration of the sensor are required. The present paper further generalizes the approach and addresses several points that are important for putting the ideas in practice. Two experimental examples: intruder detection and recognition of a suspicious target illustrate the potential of our method.

  10. Automated Terrestrial EMI Emitter Detection, Classification, and Localization

    Science.gov (United States)

    Stottler, R.; Bowman, C.; Bhopale, A.

    2016-09-01

    Clear operating spectrum at ground station antenna locations is critically important for communicating with, commanding, controlling, and maintaining the health of satellites. Electro Magnetic Interference (EMI) can interfere with these communications so tracking down the source of EMI is extremely important to prevent it from occurring in the future. The Terrestrial RFI-locating Automation with CasE based Reasoning (TRACER) system is designed to automate terrestrial EMI emitter localization and identification, providing improved space situational awareness, realizing significant manpower savings, dramatically shortening EMI response time, providing capabilities for the system to evolve without programmer involvement, and offering increased support for adversarial scenarios (e.g. jamming). TRACER has been prototyped and tested with real data (amplitudes versus frequency over time) for both satellite communication antennas and sweeping Direction Finding (DF) antennas located near them. TRACER monitors the satellite communication and DF antenna signals to detect and classify EMI using neural network technology trained on past cases of both normal communications and EMI events. Based on details of the signal (its classification, its direction and strength, etc.) one or more cases of EMI investigation methodologies are retrieved, represented as graphical behavior transition networks (BTNs), which very naturally represent the flowchart-like process often followed by experts in time pressured situations, are intuitive to SMEs, and easily edited by them. The appropriate actions, as determined by the BTN are executed and the resulting data processed by Bayesian Networks to update the probabilities of the various possible platforms and source types of the EMI. Bearing sweep of the EMI is used to determine if the EMI's platform is aerial, a ground vehicle or ship, or stationary. If moving, the Friis transmission equation is used to plot the emitter's location and compare it

  11. 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.

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

    Science.gov (United States)

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

    2013-10-01

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

  13. Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

    Science.gov (United States)

    Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

    2015-01-01

    An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

  14. 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).

  15. Detection of Operator Performance Breakdown as an Automation Triggering Mechanism

    Science.gov (United States)

    Yoo, Hyo-Sang; Lee, Paul U.; Landry, Steven J.

    2015-01-01

    Performance breakdown (PB) has been anecdotally described as a state where the human operator "loses control of context" and "cannot maintain required task performance." Preventing such a decline in performance is critical to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there is no validated objective way of detecting such a state or the transition to that state. The purpose of this work is: 1) to empirically demonstrate a PB state, and 2) to develop an objective way of detecting such a state. This paper defines PB and proposes an objective method for its detection. A human-in-the-loop study was conducted: 1) to demonstrate PB by increasing workload until the subject reported being in a state of PB, and 2) to identify possible parameters of a detection method for objectively identifying the subjectively-reported PB point, and 3) to determine if the parameters are idiosyncratic to an individual/context or are more generally applicable. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary) for 18 minutes. The difficulty of the primary task was manipulated over time to induce PB while the difficulty of the secondary tasks remained static. The participants' task performance data was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identifies the threshold parameters that best matches the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters is consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable-only 12 out of 15 participants declared PB. The PB detection method based on

  16. 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.

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Science.gov (United States)

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

    2012-11-01

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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Can fully automated detection of corticospinal tract damage be used in stroke patients?

    OpenAIRE

    Leff, Alexander P.; Seghier, Mohamed L.; Kou, Nancy; Park, Chang-hyun; Ward, Nick S.

    2013-01-01

    We compared manual infarct definition, which is time-consuming and open to bias, with an automated abnormal tissue detection method in measuring corticospinal tract-infarct overlap volumes in chronic stroke patients to help predict motor outcome.

  4. 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.

  5. Video Surveillance System for Elderly Person Living Alone by Person Tracking and Fall Detection

    Science.gov (United States)

    Doi, Motonori; Inoue, Hiroshi; Aoki, Yutaro; Oshiro, Osamu

    The detection of accidents on elderly person living alone and the communication between the elderly person and his/her family are very important. This paper describes a new method for tracking and fall detection of elderly person using omni-directional image sensor, and the Itawari-kan communication system that supports their communications and gives alarms for detected accidents on the elderly person. This system tracks the person's head position in real-time by image processing on images captured by some omni-directional image sensor. Then, the system transmits the information of the detected head position to another site. The computer of recipient site generates the computer graphics animation of the tracked person and displays the animation on a monitor. When the system detects an accident from the head position, the system gives an alarm. This method reduces traffic on network and keeps the privacy for the tracked person. We made a prototype system of the Itawari-kan communication system. Experiments on the system showed good feasibility of the proposed system.

  6. 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.

  7. 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

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

    Science.gov (United States)

    Despins, Laurel A

    2016-09-13

    Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

  9. AUTOMATED DETECTION OF STRUCTURAL ALERTS (CHEMICAL FRAGMENTS IN (ECOTOXICOLOGY

    Directory of Open Access Journals (Sweden)

    Alban Lepailleur

    2013-02-01

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

  10. Automated electronic medical record sepsis detection in the emergency department

    OpenAIRE

    Su Q. Nguyen; Edwin Mwakalindile; Booth, James S.; Vicki Hogan; Jordan Morgan; Prickett, Charles T; Donnelly, John P; Wang, Henry E.

    2014-01-01

    Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and lab...

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

    Science.gov (United States)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  12. Automated detection of proliferative retinopathy in clinical practice

    Directory of Open Access Journals (Sweden)

    Audrey Karperien

    2008-03-01

    Full Text Available Audrey Karperien1, Herbert F Jelinek1,2, Jorge JG Leandro3, João VB Soares3, Roberto M Cesar Jr3, Alan Luckie41School of Community Health, Charles Sturt University, Albury, Australia; 2Centre for Research in Complex Systems, Charles Sturt University, Albury, Australia; 3Creative Vision Research Group, Department of Computer Science, IME – University of São Paulo, Brazil; 4Albury Eye Clinic, Albury, AustraliaAbstract: Timely intervention for diabetic retinopathy (DR lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today’s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR and proliferative (PDR changes in retinal vasculature using a fractal analysis method known as local connected dimension (Dconn analysis. The major finding is that compared to other fractal analysis methods, Dconn analysis better differentiates NPDR from PDR (p = 0.05. In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.Keywords: diabetes, proliferative retinopathy, automated clinical assessment, fractal dimension, complex systems

  13. Cell-Detection Technique for Automated Patch Clamping

    Science.gov (United States)

    McDowell, Mark; Gray, Elizabeth

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2015-09-01

    Full Text Available 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.

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

    Institute of Scientific and Technical Information of China (English)

    DONG Hai-yan; WANG Hui-nan

    2006-01-01

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

  16. AUTOMATED DETECTION OF SKIN DISEASES USING TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    DR.RANJAN PAREKH

    2011-06-01

    Full Text Available This paper proposes an automated system for recognizing disease conditions of human skin in context to health informatics. The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM. GLCM defines the probability of grey level i occurring in the neighborhood of another grey level j at a distance d in direction θ. Directional GLCMs are computed along four directions: horizontal (θ = 0º, vertical (θ = 90º, right diagonal (θ = 45º and left diagonal (θ= 135º, and a set of features computed from each, are averaged to provide an estimation of the texture class.The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP as a classifier.

  17. Automated Detection and Removal of Cloud Shadows on HICO Images

    Science.gov (United States)

    2011-01-01

    Gross, F. Moshary and S. Ahmed, "Impacts of atmospheric corrections on algal bloom detection techniques," 89th AMS Annual Meeting, Phoenix, Arizona... Remote Sens. 36, 880-897, (1998). 4] R. Amin, J. Zhou, A. Gilerson, B. Gross, F. Moshary and S. Ahmed, "Novel optical techniques for detecting and...32157 (1998). 11]J. Cihlar, J. Howarth, " Detection and removal of cloud contamination from AVHRR images," IEEE Trans. Geos. Remote Sens., 32, 583

  18. 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.

  19. Fall 2014 SEI Research Review: Behavior Based Analysis and Detection of Mobile Malware

    Science.gov (United States)

    2014-10-01

    Fall 2014 SEI Research Review Presenter Last Name and Date © 2014 Carnegie Mellon University Analysis Methodology - Approach •Strace Android APK ...logcat, network information, apk and signature data - can run up to 30 minutes, average around 4 minutes to complete all activities 11 Fall

  20. 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...

  1. Automated Parameters for Troubled-Cell Indicators Using Outlier Detection

    NARCIS (Netherlands)

    Vuik, M.J.; Ryan, J.K.

    2016-01-01

    In Vuik and Ryan [J. Comput. Phys., 270 (2014), pp. 138--160] we studied the use of troubled-cell indicators for discontinuity detection in nonlinear hyperbolic partial differential equations and introduced a new multiwavelet technique to detect troubled cells. We found that these methods perform we

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

    Science.gov (United States)

    Ippoliti, Dennis

    2014-01-01

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

  3. 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.

  4. 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...

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

    Science.gov (United States)

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

    2009-01-01

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

  6. Detection of 'rare event' fetal erythroblasts in maternal blood using automated microscopy

    NARCIS (Netherlands)

    Tanke, HJ; Oosterwijk, JC; Mesker, WE; vonVelzen, MCMO; Knepfle, CM; Wiesmeyer, CC; vonOmmen, GJB; Kanhai, HHH; Vrolijk, J

    1996-01-01

    This paper describes the use of automated microscopy to detect fetal erythroblasts in maternal blood. The technology is based on the following approach: (1) the use of centrifugal cytology for the preparation of monolayers; (2) simultaneous staining of fetal hemoglobin (immunoalkaline phosphatase) a

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

    Science.gov (United States)

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

    2013-01-01

    The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

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

    NARCIS (Netherlands)

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

    2014-01-01

    Purpose Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates

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

    Science.gov (United States)

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

    2015-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Mohendra Roy

    2016-05-01

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

  11. An automated computer misuse detection system for UNICOS

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-09-27

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

  12. PCA method for automated detection of mispronounced words

    Science.gov (United States)

    Ge, Zhenhao; Sharma, Sudhendu R.; Smith, Mark J. T.

    2011-06-01

    This paper presents a method for detecting mispronunciations with the aim of improving Computer Assisted Language Learning (CALL) tools used by foreign language learners. The algorithm is based on Principle Component Analysis (PCA). It is hierarchical with each successive step refining the estimate to classify the test word as being either mispronounced or correct. Preprocessing before detection, like normalization and time-scale modification, is implemented to guarantee uniformity of the feature vectors input to the detection system. The performance using various features including spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) are compared and evaluated. Best results were obtained using MFCCs, achieving up to 99% accuracy in word verification and 93% in native/non-native classification. Compared with Hidden Markov Models (HMMs) which are used pervasively in recognition application, this particular approach is computational efficient and effective when training data is limited.

  13. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry......Traditionally, different manual, labour intensive and hence costly methods have been used for change detection. Conducting field inspections, comparing the map contents with the real world "on location" is onemethod. In another method two neighbouring images from a flight campaign are used...... to be of great importance. Also the co-registration of the different data types shows to be a problem in practice. The artefacts resulting from this can be partially dealt with using mathematical morphology, but misregistration still accounts for a general degradation of the accuracy....

  14. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry......Traditionally, different manual, labour intensive and hence costly methods have been used for change detection. Conducting field inspections, comparing the map contents with the real world "on location" is one method. In another method two neighbouring images from a flight campaign are used...... to be of great importance. Also the co-registration of the different data types shows to be a problem in practice. The artefacts resulting from this can be partially dealt with using mathematical morphology, but misregistration still accounts for a general degradation of the accuracy....

  15. Automated Detection of Ocular Alignment with Binocular Retinal Birefringence Scanning

    Science.gov (United States)

    Hunter, David G.; Shah, Ankoor S.; Sau, Soma; Nassif, Deborah; Guyton, David L.

    2003-06-01

    We previously developed a retinal birefringence scanning (RBS) device to detect eye fixation. The purpose of this study was to determine whether a new binocular RBS (BRBS) instrument can detect simultaneous fixation of both eyes. Control (nonmyopic and myopic) and strabismic subjects were studied by use of BRBS at a fixation distance of 45 cm. Binocularity (the percentage of measurements with bilateral fixation) was determined from the BRBS output. All nonstrabismic subjects with good quality signals had binocularity >75%. Binocularity averaged 5% in four subjects with strabismus (range of 0 -20%). BRBS may potentially be used to screen individuals for abnormal eye alignment.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  17. 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.

  18. A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation.

    Science.gov (United States)

    Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-08-01

    Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Surendra S Negi

    2009-01-01

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

  1. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection

    Science.gov (United States)

    2015-04-13

    representation for verbal irony detection. Indeed, sociolinguistic theories of verbal irony imply that a 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND... social media REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO 8. PERFORMING ORGANIZATION...contrast to most text classification problems, word counts and syntactic features alone do not constitute an adequate representation for verbal irony

  2. Automated detection of diabetic retinopathy in retinal images

    Directory of Open Access Journals (Sweden)

    Carmen Valverde

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Phlypo Ronald

    2010-01-01

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

  4. Automated muscle wrapping using finite element contact detection.

    Science.gov (United States)

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

    2010-07-20

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

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

    Science.gov (United States)

    2014-03-27

    data can replicate non-debugging ( Cnd ) and debugging conditions (Cd). Evaluation of Cnd and Cd creates boolean values End and Ed respectively. If...the detection of α and β. First, “questioning” creates the sub-program C that provides for “elaboration” to create instrumented sub-programs Cnd and Cd...are created. Then, “questioning” resumes by evaluating Cnd and Cd to create End and Ed. Finally, “questioning” End and Ed to determine an inequality

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

    DEFF Research Database (Denmark)

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

    of time making it difficult to resolve dynamic processes or unstable structures. Tools that assist to get the maximum of information out of recorded images are therefore greatly appreciated. In order to get the most accurate results out of the structure detection, we have optimized the imaging conditions...... used for the FEI Titan ETEM with a monochromator and an objective-lens Cs-corrector. To reduce the knock-on damage of the carbon atoms in the graphene structure, the microscope was operated at 80kV. As this strongly increases the influence of the chromatic aberration of the lenses, the energy spread...

  7. Cooperative Automated Worm Response and Detection Immune Algorithm

    CERN Document Server

    Kim, Jungwon; Aickelin, Uwe; McLeod, Julie

    2010-01-01

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

  8. 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.

  9. An automated detection of glaucoma using histogram features

    Institute of Scientific and Technical Information of China (English)

    Karthikeyan; Sakthivel; Rengarajan; Narayanan

    2015-01-01

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

  10. Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

    Science.gov (United States)

    Kalitzin, Stiliyan N; Bauer, Prisca R; Lamberts, Robert J; Velis, Demetrios N; Thijs, Roland D; Lopes Da Silva, Fernando H

    2016-12-01

    Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

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

    Science.gov (United States)

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

    2014-03-01

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

  12. 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.

  13. 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.

  14. 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 ($\

  15. Hypothesis Testing for Automated Community Detection in Networks

    CERN Document Server

    Bickel, Peter J

    2013-01-01

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

  16. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

    Science.gov (United States)

    Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

    2017-01-01

    Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management. PMID:28338047

  17. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

    Science.gov (United States)

    Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

    2017-03-01

    Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

  18. 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.

  19. 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.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Benito J Regueiro

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

  2. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  3. An Automated Detection and Classification of Suspicious Lesions in Mammograms

    Directory of Open Access Journals (Sweden)

    Dr. S. Chidambaranathan

    2016-02-01

    Full Text Available Breast cancer is the most common cancer among the Indian women and it ranges from 25 to 31% of all cancers among Indian women. It is better to treat this dreadful disease at the earliest in order to save invaluable lives. For this sake, we developed a system that automatically detects and classifies the suspicious lesions present in the mammograms. The results are accurate because two levels of segmentations namely coarse and fine segmentation are employed. Coarse segmentation is done with the help of histogram based fuzzy c means technique, which is known for its accuracy, since it takes degree of truth and false into account. After obtaining the local sketch of the suspicious region, fine segmentation is applied in order to improve the rough representation of coarse segmentation and this is achieved by window based adaptive thresholding method. Finally, the outcome of fine segmentation is superimposed over the coarse segmentation to arrive at the perfect result. Then, the first order and run length features are extracted and the image is classified as normal, benign or malignant. Also, the type of lesion is identified by the maxvote algorithm and it proves 95% of accuracy.

  4. Novel structural descriptors for automated colon cancer detection and grading.

    Science.gov (United States)

    Rathore, Saima; Hussain, Mutawarra; Aksam Iftikhar, Muhammad; Jalil, Abdul

    2015-09-01

    The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer. However, the process is subjective and leads to significant inter/intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists. Therefore, a reliable computer-aided technique, which can automatically classify normal and malignant colon samples, and determine grades of malignant samples, is required. In this paper, we propose a novel colon cancer diagnostic (CCD) system, which initially classifies colon biopsy images into normal and malignant classes, and then automatically determines the grades of colon cancer for malignant images. To this end, various novel structural descriptors, which mathematically model and quantify the variation among the structure of normal colon tissues and malignant tissues of various cancer grades, have been employed. Radial basis function (RBF) kernel of support vector machines (SVM) has been employed as classifier in order to classify/grade colon samples based on these descriptors. The proposed system has been tested on 92 malignant and 82 normal colon biopsy images. The classification performance has been measured in terms of various performance measures, and quite promising performance has been observed. Compared with previous techniques, the proposed system has demonstrated better cancer detection (classification accuracy=95.40%) and grading (classification accuracy=93.47%) capability. Therefore, the proposed CCD system can provide a reliable second opinion to the histopathologists.

  5. An automated approach to detecting signals in electroantennogram data

    Science.gov (United States)

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

    2007-01-01

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

  6. 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.

  7. 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.

  8. An automated detection for axonal boutons in vivo two-photon imaging of mouse

    Science.gov (United States)

    Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

    2017-02-01

    Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

  9. 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.

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

    Science.gov (United States)

    Mishler, Jonathan; Plenz, Dietmar

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

  11. Effects of Response Bias and Judgment Framing on Operator Use of an Automated Aid in a Target Detection Task

    Science.gov (United States)

    Rice, Stephen; McCarley, Jason S.

    2011-01-01

    Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in…

  12. 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.

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

    Science.gov (United States)

    Palafox, Leon F.; Hamilton, Christopher W.; Scheidt, Stephen P.; Alvarez, Alexander M.

    2017-04-01

    The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

  14. Integrating Electrochemical Detection with Centrifugal Microfluidics for Real-Time and Fully Automated Sample Testing

    DEFF Research Database (Denmark)

    Andreasen, Sune Zoëga; Kwasny, Dorota; Amato, Letizia

    2015-01-01

    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...... electrochemical experiment, including all intermediate sample handling steps, is demonstrated by amperometric detection of on-disc mixing of analytes (PBS and ferricyanide)....

  15. A Fully Automated Sequential-Injection Analyser for Dual Electrogenerated Chemiluminescence/Amperometric Detection

    OpenAIRE

    Economou, Anastasios; Nika, Maria

    2006-01-01

    This work describes the development of a dedicated, fully automated sequential-injection analysis (SIA) apparatus suitable for simultaneous electrogenerated chemiluminescence (ECL) and amperometric detection. The instrument is composed of a peristaltic pump, a multiposition selection valve, a home-made potentiostat, a thin-layer electrochemical/optical flow-through cell, and a light detector. Control of the experimental sequence and simultaneous data acquisition of the light and the current i...

  16. 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.

  17. 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.

  18. Home Automation

    OpenAIRE

    Ahmed, Zeeshan

    2010-01-01

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

  19. Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children

    Science.gov (United States)

    Serras Pereira, Mariana; Cozijn, Reinier; Postma, Eric; Shahid, Suleman; Swerts, Marc

    2016-01-01

    The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm. PMID:28018271

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

    Directory of Open Access Journals (Sweden)

    Jing Wu

    2016-01-01

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

  1. 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

    Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning and neurological disorders. We used an Internet interface...... to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance...... of 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...

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

    CERN Document Server

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

    2016-01-01

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

  3. 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 ...

  4. 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.

  5. Automated detection and location of seismic events on Piton de la Fournaise volcano by waveform migration

    Science.gov (United States)

    Maggi, A.; Langet, N.; Brenguier, F.; Michelini, A.

    2012-12-01

    We present the continued development of WaveLoc, and automated seismic event detection and location algorithm based on waveform migration, that therefore bypasses the phase-picking and association phases common to most automated location algorithms. WaveLoc is a 3-step process : 1) we filter and calculate the kurtosis of the raw waveforms in order to highlight the non-stationary characteristics of seismic events; 2) we migrate and stack the first derivatives of the kurtosis waveforms, which highlight the P-wave arrivals, according to an a-priori P-wave velocity model (1D or 3D); 3) we detect and simultaneously locate seismic events by analyzing the local maxima of the resulting 3-D time-dependent stacks. We have applied the WaveLoc algorithm to the seismic swarms recorded on the Piton de la Fournaise volcano (Reunion Island) between 2009 and 2011, using data from the UnderVolc experiment (Brenguier et al., 2012) and the Prono et al. (2009) 3D velocity model. We compare the locations obtained using WaveLoc to those obtained from manual picking in order to evaluate the robustness of the automated algorithm. Automated location of single events is in general limited by "picking" errors (in our case intrinsic variations in stationarity properties of the seismic signals) and inadequacies in the a-priori velocity models. In order to improve both accuracy and precision of our locations, we have systematically searched for multiplets by cross-correlation, and relocated these multiplets using a simple double-difference algorithm.

  6. 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...

  7. High throughput DNA sequence variant detection by conformation sensitive capillary electrophoresis and automated peak comparison.

    Science.gov (United States)

    Davies, Helen; Dicks, Ed; Stephens, Philip; Cox, Charles; Teague, Jon; Greenman, Chris; Bignell, Graham; O'meara, Sarah; Edkins, Sarah; Parker, Adrian; Stevens, Claire; Menzies, Andrew; Blow, Matt; Bottomley, Bill; Dronsfield, Mark; Futreal, P Andrew; Stratton, Michael R; Wooster, Richard

    2006-03-01

    We report the development of a heteroduplex-based mutation detection method using multicapillary automated sequencers, known as conformation-sensitive capillary electrophoresis (CSCE). Our optimized CSCE protocol detected 93 of 95 known base substitution sequence variants. Since the optimization of the method, we have analyzed 215 Mb of DNA and identified 3397 unique variants. An analysis of this data set indicates that the sensitivity of CSCE is above 95% in the central 56% of the average PCR product. To fully exploit the mutation detection capacity of this method, we have developed software, canplot, which automatically compares normal and test results to prioritize samples that are most likely to contain variants. Using multiple fluorescent dyes, CSCE has the capacity to screen over 2.2 Mb on one ABI3730 each day. Therefore this technique is suitable for projects where a rapid and sensitive DNA mutation detection system is required.

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

    CERN Document Server

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

    2016-01-01

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

  9. Automated Detection of Coronal Mass Ejections in STEREO Heliospheric Imager Data

    Science.gov (United States)

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

    2016-12-01

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

  10. Automated mass detection in contrast-enhanced CT colonography: an approach based on contrast and volume

    Energy Technology Data Exchange (ETDEWEB)

    Luboldt, W. [University Hospital Essen, Clinic and Policlinic of Angiology, Essen (Germany); Multiorgan Screening Foundation (Germany); Tryon, C. [Philips Medical Systems, Best (Netherlands); Kroll, M.; Vogl, T.J. [University Hospital Frankfurt, Department of Radiology, Frankfurt (Germany); Toussaint, T.L. [Multiorgan Screening Foundation (Germany); Holzer, K. [University Hospital Frankfurt, Department of Visceral and Vascular Surgery, Frankfurt (Germany); Hoepffner, N. [University Hospital Frankfurt, Department of Gastroenterology, Frankfurt (Germany)

    2005-02-01

    The purpose of this feasibility study was to design and test an algorithm for automating mass detection in contrast-enhanced CT colonography (CTC). Five patients with known colorectal masses underwent a pre-surgical contrast-enhanced (120 ml volume 1.6 g iodine/s injection rate, 60 s scan delay) CTC in high spatial resolution (16-slice CT: collimation: 16 x 0.75 mm, tablefeed: 24 mm/0.5 s, reconstruction increment: 0.5 mm). A CT-density- and volume-based algorithm searched for masses in the colonic wall, which was extracted before by segmenting and dilating the colonic air lumen and subtracting the inner air. A radiologist analyzed the detections and causes of false positives. All masses were detected, and false positives were easy to identify. Combining CT density with volume as a cut-off is a promising approach for automating mass detection that should be further refined and also tested in contrast-enhanced MR colonography. (orig.)

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

    Science.gov (United States)

    Hutton, Joseph; Morgan, Huw

    2016-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Hogeweg, Laurens; Sanchez, Clara I.; Melendez, Jaime; Maduskar, Pragnya; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, Nijmegen 6525 GA (Netherlands); Story, Alistair; Hayward, Andrew [University College London, Centre for Infectious Disease Epidemiology, London NW3 2PF (United Kingdom)

    2013-07-15

    Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.

  13. 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.

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

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-07-25

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

  15. 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

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

    Directory of Open Access Journals (Sweden)

    Ahmet Turan Özdemir

    2016-07-01

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    Haris Ahmad Khan

    2016-03-01

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

  18. Fully automated contour detection algorithm the preliminary step for scatter and attenuation compensation in SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Younes, R.B.; Mas, J.; Bidet, R.

    1988-12-01

    Contour detection is an important step in information extraction from nuclear medicine images. In order to perform accurate quantitative studies in single photon emission computed tomography (SPECT) a new procedure is described which can rapidly derive the best fit contour of an attenuated medium. Some authors evaluate the influence of the detected contour on the reconstructed images with various attenuation correction techniques. Most of the methods are strongly affected by inaccurately detected contours. This approach uses the Compton window to redetermine the convex contour: It seems to be simpler and more practical in clinical SPECT studies. The main advantages of this procedure are the high speed of computation, the accuracy of the contour found and the programme's automation. Results obtained using computer simulated and real phantoms or clinical studies demonstrate the reliability of the present algorithm.

  19. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    Science.gov (United States)

    Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

    2014-03-01

    Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

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

    Science.gov (United States)

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

    2013-06-01

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

  1. Performance of an automated multiplex immunofluorescence assay for detection of Chlamydia trachomatis immunoglobulin G.

    Science.gov (United States)

    Baud, David; Zufferey, Jade; Hohlfeld, Patrick; Greub, Gilbert

    2014-03-01

    Chlamydia serology is indicated to investigate etiology of miscarriage, infertility, pelvic inflammatory disease, and ectopic pregnancy. Here, we assessed the reliability of a new automated-multiplex immunofluorescence assay (InoDiag test) to detect specific anti-C. trachomatis immunoglobulin G. Considering immunofluorescence assay (IF) as gold standard, InoDiag tests exhibited similar sensitivities (65.5%) but better specificities (95.1%-98%) than enzyme-linked immunosorbent assays (ELISAs). InoDiag tests demonstrated similar or lower cross-reactivity rates when compared to ELISA or IF.

  2. 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.

  3. Automated object detection and tracking with a flash LiDAR system

    Science.gov (United States)

    Hammer, Marcus; Hebel, Marcus; Arens, Michael

    2016-10-01

    The detection of objects, or persons, is a common task in the fields of environment surveillance, object observation or danger defense. There are several approaches for automated detection with conventional imaging sensors as well as with LiDAR sensors, but for the latter the real-time detection is hampered by the scanning character and therefore by the data distortion of most LiDAR systems. The paper presents a solution for real-time data acquisition of a flash LiDAR sensor with synchronous raw data analysis, point cloud calculation, object detection, calculation of the next best view and steering of the pan-tilt head of the sensor. As a result the attention is always focused on the object, independent of the behavior of the object. Even for highly volatile and rapid changes in the direction of motion the object is kept in the field of view. The experimental setup used in this paper is realized with an elementary person detection algorithm in medium distances (20 m to 60 m) to show the efficiency of the system for objects with a high angular speed. It is easy to replace the detection part by any other object detection algorithm and thus it is easy to track nearly any object, for example a car or a boat or an UAV in various distances.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-05-02

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

  5. 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.

  6. Semi-automated, occupationally safe immunofluorescence microtip sensor for rapid detection of Mycobacterium cells in sputum.

    Directory of Open Access Journals (Sweden)

    Shinnosuke Inoue

    Full Text Available An occupationally safe (biosafe sputum liquefaction protocol was developed for use with a semi-automated antibody-based microtip immunofluorescence sensor. The protocol effectively liquefied sputum and inactivated microorganisms including Mycobacterium tuberculosis, while preserving the antibody-binding activity of Mycobacterium cell surface antigens. Sputum was treated with a synergistic chemical-thermal protocol that included moderate concentrations of NaOH and detergent at 60°C for 5 to 10 min. Samples spiked with M. tuberculosis complex cells showed approximately 10(6-fold inactivation of the pathogen after treatment. Antibody binding was retained post-treatment, as determined by analysis with a microtip immunosensor. The sensor correctly distinguished between Mycobacterium species and other cell types naturally present in biosafe-treated sputum, with a detection limit of 100 CFU/mL for M. tuberculosis, in a 30-minute sample-to-result process. The microtip device was also semi-automated and shown to be compatible with low-cost, LED-powered fluorescence microscopy. The device and biosafe sputum liquefaction method opens the door to rapid detection of tuberculosis in settings with limited laboratory infrastructure.

  7. 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.

  8. Automated detection of coronal mass ejections in three-dimensions using multi-viewpoint observations

    Science.gov (United States)

    Hutton, J.; Morgan, H.

    2017-03-01

    A new, automated method of detecting coronal mass ejections (CMEs) in three dimensions for the LASCO C2 and STEREO COR2 coronagraphs is presented. By triangulating isolated CME signal from the three coronagraphs over a sliding window of five hours, the most likely region through which CMEs pass at 5 R⊙ is identified. The centre and size of the region gives the most likely direction of propagation and approximate angular extent. The Automated CME Triangulation (ACT) method is tested extensively using a series of synthetic CME images created using a wireframe flux rope density model, and on a sample of real coronagraph data; including halo CMEs. The accuracy of the angular difference (σ) between the detection and true input of the synthetic CMEs is σ = 7.14°, and remains acceptable for a broad range of CME positions relative to the observer, the relative separation of the three observers and even through the loss of one coronagraph. For real data, the method gives results that compare well with the distribution of low coronal sources and results from another instrument and technique made further from the Sun. The true three dimension (3D)-corrected kinematics and mass/density are discussed. The results of the new method will be incorporated into the CORIMP database in the near future, enabling improved space weather diagnostics and forecasting.

  9. 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.

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

    Science.gov (United States)

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

    2014-11-01

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

  11. Detection and removal of ocular artifacts from EEG signals for an automated REM sleep analysis.

    Science.gov (United States)

    Betta, Monica; Gemignani, Angelo; Landi, Alberto; Laurino, Marco; Piaggi, Paolo; Menicucci, Danilo

    2013-01-01

    Rapid eye movements (REMs) are a prominent feature of REM sleep, and their distribution and time density over the night represent important physiological and clinical parameters. At the same time, REMs produce substantial distortions on the electroencephalographic (EEG) signals, which strongly affect the significance of normal REM sleep quantitative study. In this work a new procedure for a complete and automated analysis of REM sleep is proposed, which includes both a REMs detection algorithm and an ocular artifact removal system. The two steps, based respectively on Wavelet Transform and adaptive filtering, are fully integrated and their performance is evaluated using REM simulated signals. Thanks to the integration with the detection algorithm, the proposed artifact removal system shows an enhanced accuracy in the recovering of the true EEG signal, compared to a system based on the adaptive filtering only. Finally the artifact removal system is applied to physiological data and an estimation of the actual distortion induced by REMs on EEG signals is supplied.

  12. 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

    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...

  13. Correlation between skeletal trauma and energy in falls from great height detected by post-mortem multislice computed tomography (MSCT).

    Science.gov (United States)

    Weilemann, Y; Thali, M J; Kneubuehl, B P; Bolliger, S A

    2008-09-18

    Fatal falls from great height are a frequently encountered setting in forensic pathology. They present--by virtue of a calculable energy transmission to the body--an ideal model for the assessment of the effects of blunt trauma to a human body. As multislice computed tomography (MSCT) has proven not only to be invaluable in clinical examinations, but also to be a viable tool in post-mortem imaging, especially in the field of osseous injuries, we performed a MSCT scan on 20 victims of falls from great height. We hereby detected fractures and their distributions were compared with the impact energy. Our study suggests a marked increase of extensive damage to different body regions at about 20 kJ and more. The thorax was most often affected, regardless of the amount of impacting energy and the primary impact site. Cranial fracture frequency displayed a biphasic distribution with regard to the impacting energy; they were more frequent in energies of less than 10, and more than 20 kJ, but rarer in the intermediate energy group, namely that of 10-20 kJ.

  14. 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.

  15. Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

    Science.gov (United States)

    Wickering, Ellis; Gaspard, Nicolas; Zafar, Sahar; Moura, Valdery J; Biswal, Siddharth; Bechek, Sophia; OʼConnor, Kathryn; Rosenthal, Eric S; Westover, M Brandon

    2016-06-01

    The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.

  16. New Approaches on Automated Wrinkle Detection in Sheet Metal Components by Forming Simulation

    Science.gov (United States)

    Liewald, M.; Wurster, K.; Blaich, C.

    2011-05-01

    In production of passenger cars, geometry complexity of deep drawn body panels increases constantly. For that reason, sheet metal components are analyzed within finite element analysis (FEA) with regard to their feasibility in production and expected quality before production equipment, such as drawing dies, is manufactured. Main criteria for characterizing component quality are cracks and sidewall wrinkles. In particular, cracks occur due to local overload in sheet metal plane caused by inadequate process parameters such as too high friction or forming forces. In contrast, sidewall wrinkles are caused by an inadequate level of compressive stress in component areas without contact between sheet metal component and drawing die. In FEA, failure by cracks can be analyzed evaluating scalar values of thinning or strain distribution in forming limit diagram with regard to forming limit curve. In contrast, detecting sidewall wrinkles often requires a manual and visual inspection of simulation results by the user. Therefore, a procedure to detect sidewall wrinkles in an automated manner is presented in this paper. The presented method determines occurrence of sidewall wrinkles based on strain distribution in forming limit diagram. Utilization of the disclosed calculation strategy allows estimation of cracks and sidewall wrinkles simultaneously after one run of simulation code. The presented approach for automated detection of sidewall wrinkles in combination with multivariate statistics shows a tool for virtual engineering to optimize deep drawing processes. Prior to die manufacturing, optimization with regard to both sides of the process window is possible. Hence, an increase in design efficiency, design space and reduction of development time and costs can be achieved at the same time.

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

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    F. Samadzadegan

    2013-09-01

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

  20. 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.

  1. Comparing a perceptual and an automated vision-based method for lie detection in younger children

    Directory of Open Access Journals (Sweden)

    Mariana Serras Pereira

    2016-12-01

    Full Text Available The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception studies (in which one uses eye-tracking technology and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening on different body regions, judges tend to focus their attention mainly on the face region.

  2. Evaluation of an automated procedure for detecting frequency-following responses in American and Chinese neonates.

    Science.gov (United States)

    Jeng, Fuh-Cherng; Peris, Kevin S; Hu, Jiong; Lin, Chia-Der

    2013-04-01

    To date, observations of the scalp-recorded frequency-following response (FFR) to voice pitch have depended on subjective interpretation of the experimenter. The purpose of this study was to develop and evaluate an automated procedure for detecting the presence of a response. Twenty American (9 boys, 1-3 days) and 20 Chinese (10 boys, 1-3 days) neonates were recruited. A Chinese monosyllable that mimicked the English vowel /i/ with a rising pitch (117-166 Hz) was used as the stimulus. Three objective indices (Frequency Error, Tracking Accuracy, and Pitch Strength) were computed from the recorded brain waves and the test results were compared with human judgments to calculate the sensitivity and specificity values. Results demonstrated that the automated procedure produced sensitivity values between 53-90% and specificity values between 80-100%, and could be used to assess the presence of an FFR for neonates who were born in a tonal or non-tonal language environment.

  3. 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.

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

    Science.gov (United States)

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

    2012-06-01

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

  5. Automated Detection of Dwarf Galaxies and Star Clusters in SMASH through the NOAO Data Lab

    Science.gov (United States)

    Olsen, Knut A.; Nidever, David L.; Fitzpatrick, Michael J.; Mighell, Kenneth J.; SMASH Collaboration; NOAO Data Lab Team

    2017-01-01

    We present an automated method, using the NOAO Data Lab environment, for the detection of dwarf galaxy-scale objects in catalog data from the Survey of the Magellanic Stellar History (SMASH). SMASH has imaged ~480 square degrees of the southern sky, over a partially filled area of 2400 square degrees, to 24th mag in gri (uz~23) using the Dark Energy Camera (DECam). The NOAO Data Lab (http://datalab.noao.edu) is being developed to support community research of the massive data sets now being derived from NOAO’s wide-field telescopes, in particular DECam. A key feature of the Data Lab is the ability to perform efficient automated analysis of catalog and imaging data. Our method, which is an example of this feature, allows for the rapid search of candidate dwarf galaxies and stellar clusters in deep catalog data. Using SMASH as the catalog data source, we easily recover the previously discovered Hydra II dwarf galaxy and SMASH-I LMC globular cluster, as well as a number of other potentially interesting candidate stellar systems.

  6. 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

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

    Science.gov (United States)

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

    2005-01-01

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

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

    Science.gov (United States)

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

    2012-02-01

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

  9. 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.

  10. 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.

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

    Science.gov (United States)

    2014-01-01

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

  12. 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.

  13. 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.

  14. NOTE: Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

    Science.gov (United States)

    Holan, Scott H.; Viator, John A.

    2008-06-01

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

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

    Science.gov (United States)

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

    2014-07-01

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

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

    Science.gov (United States)

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

    2013-03-01

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

  17. 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.

  18. A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

    Science.gov (United States)

    Vickers, H.; Eckerstorfer, M.; Malnes, E.; Larsen, Y.; Hindberg, H.

    2016-11-01

    Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Raihana Syahirah Abdullah

    2016-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Manh Cong Tran

    2016-01-01

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

  2. Automated detection of synapses in serial section transmission electron microscopy image stacks.

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

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

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

    Science.gov (United States)

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

    2016-01-01

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

  4. 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

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

    Directory of Open Access Journals (Sweden)

    Ilia Ostrovski

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

  6. An ex ante analysis on the use of activity meters for automated estrus detection, to invest or not to invest?

    NARCIS (Netherlands)

    Rutten, C.J.; Steeneveld, W.; Inchaisri, C.; Hogeveen, H.

    2014-01-01

    The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a

  7. 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

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Zaky AG

    2016-09-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

  11. Automated Abnormal Mass Detection in the Mammogram Images Using Chebyshev Moments

    Directory of Open Access Journals (Sweden)

    Alireza Talebpour

    2013-01-01

    Full Text Available Breast cancer is the second leading cause of cancer mortality among women after lung cancer. Early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease. In this study a new system for automated mass detection in mammography images is presented as being more accurate and valid. After optimization of the image and extracting a better picture of the breast tissue from the image and applying log-polar transformation, Chebyshev moments can be calculated in all areas of breast tissue. Then after extracting effective features in the diagnosis of mammography images, abnormal masses, which are important for the physician and specialists, can be determined with applying the appropriate threshold. To check the system performance, images in the MIAS (Mammographic Image Analysis Society mammogram database have been used and the results allowed us to draw a FROC (Free Response Receiver Operating Characteristic curve. When compared the FROC curve with similar systems experts, the high ability of our system was confirmed. In this system, images of different thresholds, specifically 445, 450, 455 are processed and then put through a sensitivity analysis. The process garnered good results 100, 92 and 84%, respectively and a false positive rate per image 2.56, 0.86, 0.26, respectively have been calculated. Comparing other automatic mass detection systems, the proposed method has a few advantages over prior systems: Our process allows us to determine the amount of false positives and/or sensitivity parameters within the system. This can be determined by the importance of the detection work being done. The proposed system achieves 100% sensitivity and 2.56 false positive for every image.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    DEFF Research Database (Denmark)

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

    2009-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Moeglein, W. A.; Griswold, R.; Mehdi, B. L.; Browning, N. D.; Teuton, J.

    2017-01-03

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

  16. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Cascio, D. [Dipt. di Fisica e Tecnologie Relative, Univ. di Palermo (Italy); Cheran, S.C. [Dipt. di Fisica, Univ. di Genova (Italy); Ist. Nazionale di Fisica Nucleare, Sezione di Torino (Italy); Chincarini, A. [Ist. Nazionale di Fisica Nucleare, Sezione di Genova (Italy); De Nunzio, G. [Dipt. di Scienza dei Materiali, Univ. di Lecce (Italy); Delogu, P.; Fantacci, M.E. [Dipt. di Fisica, Univ. di Pisa (Italy); Ist. Nazionale di Fisica Nucleare, Sezione di Pisa (Italy); Gargano, G. [Dipt. Interateneo di Fisica M. Merlin, Univ. di Bari (Italy); Ist. Nazionale di Fisica Nucleare, Sezione di Bari (Italy); Gori, I.; Retico, A. [Ist. Nazionale di Fisica Nucleare, Sezione di Pisa (Italy); Masala, G.L. [Struttura Dipartimentale di Matematica e Fisica, Univ. di Sassari (Italy); Ist. Nazionale di Fisica Nucleare, Sezione di Cagliari (Italy); Preite Martinez, A. [Centro Studi e Ricerche Enrico Fermi, Roma (Italy); Santoro, M. [Dipt. di Scienze Fisiche, Univ. di Napoli (Italy); Spinelli, C. [Unita Operativa Radiodiagnostica 2, Azienda Ospedaliera Universitaria Pisana, Pisa (Italy); Tarantino, T. [Divisione di Radiologia Diagnostica e Interventistica del Dipt. di Oncologia, Trapianti e Nuove Tecnologie in Medicina, Univ. di Pisa (Italy)

    2007-06-15

    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 ({proportional_to}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 high (75% range) even at 1-6 FP/scan. (orig.)

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

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

  20. 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.

  1. Falling chains

    Science.gov (United States)

    Wong, Chun Wa; Yasui, Kosuke

    2006-06-01

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

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

    Science.gov (United States)

    Vanessen, J. C.

    1990-09-01

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

  3. 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.

  4. 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

  5. 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.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Indra Kanta Maitra

    2011-06-01

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

  8. 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

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

    Directory of Open Access Journals (Sweden)

    Susan S Huang

    2010-02-01

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

  10. Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection

    Science.gov (United States)

    Maurer, Joshua; Rupper, Summer

    2015-10-01

    Declassified historical imagery from the Hexagon spy satellite database has near-global coverage, yet remains a largely untapped resource for geomorphic change studies. Unavailable satellite ephemeris data make DEM (digital elevation model) extraction difficult in terms of time and accuracy. A new fully-automated pipeline for DEM extraction and image orthorectification is presented which yields accurate results and greatly increases efficiency over traditional photogrammetric methods, making the Hexagon image database much more appealing and accessible. A 1980 Hexagon DEM is extracted and geomorphic change computed for the Thistle Creek Landslide region in the Wasatch Range of North America to demonstrate an application of the new method. Surface elevation changes resulting from the landslide show an average elevation decrease of 14.4 ± 4.3 m in the source area, an increase of 17.6 ± 4.7 m in the deposition area, and a decrease of 30.2 ± 5.1 m resulting from a new roadcut. Two additional applications of the method include volume estimates of material excavated during the Mount St. Helens volcanic eruption and the volume of net ice loss over a 34-year period for glaciers in the Bhutanese Himalayas. These results show the value of Hexagon imagery in detecting and quantifying historical geomorphic change, especially in regions where other data sources are limited.

  11. 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.

  12. AUTOMATED SEGMENTATION OF CORTICAL NECROSIS USING A WAVELET BASED ABNORMALITY DETECTION SYSTEM.

    Science.gov (United States)

    Gaonkar, Bilwaj; Erus, Guray; Pohl, Kilian M; Tanwar, Manoj; Margiewicz, Stefan; Bryan, R Nick; Davatzikos, Christos

    2011-03-01

    We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy controls. The model is based on the Jacobians of warps obtained by registering scans of normal subjects to a common coordinate system. For each patient scan a Jacobian is obtained by warping it to the same coordinate system. Large deviations between the model and subject-specific Jacobians are flagged as `abnormalities'. Abnormalities are segmented as cortical necrosis if they are in the cortex and have the intensity profile of CSF. We evaluate our method by using a set of 72 healthy subjects to model cortical variation.We use this model to successfully detect and segment cortical necrosis in a set of 37 patients with CVD. A comparison of the results with segmentations from two independent human experts shows that the overlap between our approach and either of the human experts is in the range of the overlap between the two human experts themselves.

  13. 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.

  14. Automated Detection of Binucleated Cell and Micronuclei using CellProfiler 2.0 Software

    Directory of Open Access Journals (Sweden)

    Dwi Ramadhani

    2013-12-01

    Full Text Available Micronucleus assay in human peripheral lymphocytes usually used to assess chromosomal damage. Manual scoring of micronuclei can be time consuming and large numbers of binucleated cells have to be analyzed to obtain statistically relevant data. Automation of the micronuclei analysis using image processing analysis software can provide a faster and more reliable analysis of micronucleus assay. Here the used of CellProfiler an open access cell image analysis software for automatic detection of binucleated cells and micronuclei were reported. We aimed to know whether there was a significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. Wilcoxon Rank test was used for statistical analysis to test H0 hypothesis that there was no significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. We analyzed 135 images for both manual and CellProfiler counting. Our results showed that there was no significant difference between manual and CellProfiler counting for binucleated cells (P = 0.851 and for micronuclei (P = 0.917. In conclusion, the binucleated cells and micronuclei counting using CellProfiler were comparable but not better than manual counting.

  15. 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

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    P. Fergus

    2016-01-01

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

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

    Science.gov (United States)

    Larsson, Anders; Hansson, Lars-Olof

    2008-01-01

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

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

    Science.gov (United States)

    Habash, Marc; Johns, Robert

    2009-10-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2013-10-01

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

  2. 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.

  3. Automated detection of unstable glacier flow and a spectrum of speedup behavior in the Alaska Range

    Science.gov (United States)

    Herreid, Sam; Truffer, Martin

    2016-01-01

    Surge-type glaciers are loosely defined as glaciers that experience periodic alterations between slow and fast flow regimes. Glaciers from a variety of mountain ranges around the world have been classified as surge type, yet consensus of what defines a glacier as surge type has not always been met. A common source of dispute is the lack of a succinct and globally applicable delimiter between a surging and nonsurging glacier. The attempt is often a Boolean classification; however, glacier speedup events can vary significantly with respect to event magnitude, duration, and the fraction of the glacier that participates in the speedup. For this study, we first updated the inventory of glaciers that show flow instabilities in the Alaska Range and then quantified the spectrum of speedup behavior. We developed a new method that automatically detects glaciers with flow instabilities. Our automated results show a 91% success rate when compared to direct observations of speedup events and glaciers that are suspected to display unstable flow based on surface features. Through a combination of observations from the Landsat archive and previously published data, our inventory now contains 36 glaciers that encompass at least one branch exhibiting unstable flow and we document 53 speedup events that occurred between 1936 and 2014. We then present a universal method for comparing glacier speedup events based on a normalized event magnitude metric. This method provides a consistent way to include and quantify the full spectrum of speedup events and allows for comparisons with glaciers that exhibit clear surge characteristics yet have no observed surge event to date. Our results show a continuous spectrum of speedup magnitudes, from steady flow to clearly surge type, which suggests that qualitative classifications, such as "surge-type" or "pulse-type" behavior, might be too simplistic and should be accompanied by a standardized magnitude metric.

  4. Falling chains

    CERN Document Server

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

    2006-01-01

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

  5. 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.

  6. Design of a wearable fall detection device%一种可穿戴式跌倒检测装置设计

    Institute of Scientific and Technical Information of China (English)

    石欣; 张涛

    2012-01-01

    The accidents caused by fall, especially those happened upon the elders, have caught people's attention. In this article through researching the behavioral characteristics of fall, a kind of portable device based on pressure sensor is designed to achieve fall detection. This device is placed in the insole and uses a film type pressure sensor to collect human body foot pressure information in movement. Then a method that combines threshold analysis with the algorithm of support vector machine is used to analyze the foot pressure value and perform data processing, and judge whether the human body falls. Through experiment test verification, this device has a high reliability and accuracy in detecting fall.%跌倒造成的人身意外事故,尤其是老年人跌倒造成的意外伤害,引起人们的极大关注.通过对跌倒行为特性的研究,设计了一种基于压力传感器的便携装置,进行跌倒检测.装置采用薄膜式压力传感器,将传感器安置于鞋垫,用于采集人体运动中的脚底压力信息,采用阈值分析与支持向量机算法相结合的方法对脚底压力值进行数据处理,判断人体是否跌倒.本装置通过实验测试验证,判断跌倒具有较高的可靠性和准确性.

  7. Automated detection of fiducial screws from CT/DVT volume data for image-guided ENT surgery.

    Science.gov (United States)

    Zheng, Guoyan; Gerber, Nicolas; Widmer, Daniel; Stieger, Christof; Caversaccio, Marco; Nolte, Lutz-Peter; Weber, Stefan

    2010-01-01

    This paper presents an automated solution for precise detection of fiducial screws from three-dimensional (3D) Computerized Tomography (CT)/Digital Volume Tomography (DVT) data for image-guided ENT surgery. Unlike previously published solutions, we regard the detection of the fiducial screws from the CT/DVT volume data as a pose estimation problem. We thus developed a model-based solution. Starting from a user-supplied initialization, our solution detects the fiducial screws by iteratively matching a computer aided design (CAD) model of the fiducial screw to features extracted from the CT/DVT data. We validated our solution on one conventional CT dataset and on five DVT volume datasets, resulting in a total detection of 24 fiducial screws. Our experimental results indicate that the proposed solution achieves much higher reproducibility and precision than the manual detection. Further comparison shows that the proposed solution produces better results on the DVT dataset than on the conventional CT dataset.

  8. 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

  9. 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

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

    Science.gov (United States)

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

    2017-03-16

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

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

    2012-01-01

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

  12. [Accidental falls].

    Science.gov (United States)

    Inokuchi, Koichi

    2013-06-01

    Falls are common cause of injuries among elderly people, and fractures are the most serious consequence of falls. For seniors, hip fractures are the second major cause of bedridden. The feature and acute care of head injury, spinal cord injury, vertebrae fracture, and hip fracture are described. Just had fracture fixation, the patient can not go back to the original ADL. In order not to become bedridden, both medication and physical examination are important based on the new disease concept of locomotive syndrome. To do so, requires hospital and clinic cooperation. Sufficient cooperation is not currently possible, and spread of liaison service is essential.

  13. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-04-17

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

  15. 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

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

    Directory of Open Access Journals (Sweden)

    Christoph eSchmitz

    2014-05-01

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

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

    Science.gov (United States)

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

    2008-04-01

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

  18. 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.

  19. Simplified automated image analysis for detection and phenotyping of Mycobacterium tuberculosis on porous supports by monitoring growing microcolonies.

    Directory of Open Access Journals (Sweden)

    Alice L den Hertog

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

  20. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

    Science.gov (United States)

    Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

    2012-12-01

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  1. 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.

  2. Fast-FISH Detection and Semi-Automated Image Analysis of Numerical Chromosome Aberrations in Hematological Malignancies

    Directory of Open Access Journals (Sweden)

    Arif Esa

    1998-01-01

    Full Text Available A new fluorescence in situ hybridization (FISH technique called Fast-FISH in combination with semi-automated image analysis was applied to detect numerical aberrations of chromosomes 8 and 12 in interphase nuclei of peripheral blood lymphocytes and bone marrow cells from patients with acute myelogenous leukemia (AML and chronic lymphocytic leukemia (CLL. Commercially available α-satellite DNA probes specific for the centromere regions of chromosome 8 and chromosome 12, respectively, were used. After application of the Fast-FISH protocol, the microscopic images of the fluorescence-labelled cell nuclei were recorded by the true color CCD camera Kappa CF 15 MC and evaluated quantitatively by computer analysis on a PC. These results were compared to results obtained from the same type of specimens using the same analysis system but with a standard FISH protocol. In addition, automated spot counting after both FISH techniques was compared to visual spot counting after standard FISH. A total number of about 3,000 cell nuclei was evaluated. For quantitative brightness parameters, a good correlation between standard FISH labelling and Fast-FISH was found. Automated spot counting after Fast-FISH coincided within a few percent to automated and visual spot counting after standard FISH. The examples shown indicate the reliability and reproducibility of Fast-FISH and its potential for automatized interphase cell diagnostics of numerical chromosome aberrations. Since the Fast-FISH technique requires a hybridization time as low as 1/20 of established standard FISH techniques, omitting most of the time consuming working steps in the protocol, it may contribute considerably to clinical diagnostics. This may especially be interesting in cases where an accurate result is required within a few hours.

  3. Automated Detection of the Arterial Inner Walls of the Common Carotid Artery Based on Dynamic B-Mode Signals

    Directory of Open Access Journals (Sweden)

    Shing-Hong Liu

    2010-11-01

    Full Text Available In this paper we propose a novel scheme able to automatically detect the intima and adventitia of both near and far walls of the common carotid artery in dynamic B-mode RF (radiofrequency image sequences, with and without plaques. Via this automated system the lumen diameter changes along the heart cycle can be detected. Three image sequences have been tested and all results are compared to manual tracings made by two professional experts. The average errors for near and far wall detection are 0.058 mm and 0.067 mm, respectively. This system is able to analyze arterial plaques dynamically which is impossible to do manually due to the tremendous human workload involved.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-04-15

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

  7. OGLE‐2008‐BLG‐510: first automated real‐time detection of a weak microlensing anomaly – brown dwarf or stellar binary?★

    DEFF Research Database (Denmark)

    Bozza, V.; Dominik, M.; Rattenbury, N. J.

    2012-01-01

    The microlensing event OGLE‐2008‐BLG‐510 is characterized by an evident asymmetric shape of the peak, promptly detected by the Automated Robotic Terrestrial Exoplanet Microlensing Search (ARTEMiS) system in real time. The skewness of the light curve appears to be compatible both with binary......‐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...

  8. An Innovative Curvelet-only-Based Approach for Automated Change Detection in Multi-Temporal SAR Imagery

    Directory of Open Access Journals (Sweden)

    Andreas Schmitt

    2014-03-01

    Full Text Available This paper presents a novel approach for automated image comparison and robust change detection from noisy imagery, such as synthetic aperture radar (SAR amplitude images. Instead of comparing pixel values and/or pre-classified features this approach clearly highlights structural changes without any preceding segmentation or classification step. The crucial point is the use of the Curvelet transform in order to express the image as composition of several structures instead of numerous individual pixels. Differentiating these structures and weighting their impact according to the image statistics produces a smooth, but detail-preserved change image. The Curvelet-based approach is validated by the standard technique for SAR change detection, the log-ratio with and without additional gamma maximum-a-posteriori (GMAP speckle filtering, and by the results of human interpreters. The validation proves that the new technique can easily compete with these automated as well as visual interpretation techniques. Finally, a sequence of TerraSAR-X High Resolution Spotlight images of a factory building construction site near Ludwigshafen (Germany is processed in order to identify single construction stages by the time of the (dis-appearance of certain objects. Hence, the complete construction monitoring of the whole building and its surroundings becomes feasible.

  9. The automated system of detection and research of pollution in the atmosphere

    Science.gov (United States)

    Isakova, Anna I.; Smal, Oksana V.; Chistyakova, Liliya K.; Penin, Sergei T.

    2004-02-01

    In the paper, the automated system of data processing (ASDP) for a hardware complex DAN-2, assigned for registration of emission and absorption of optical and the microwave radiation initiated by gas-aerosol pollution in the atmosphere, is presented. The complex DAN-2 has been developed in the Institute of Atsmospheric Optics of the Siberian Branch of the Russian Academy of Science. In the ASDP, a problem of automation of recording processes, storage and processing of the information measured in experiment has been solved. Using in ASDP subsystems of the forecast of optical noise, the forecast of distribution of an impurity in a plume of gas-aerosol emission from industrial plants allows us to carry out the express-analysis of ecological pollution in the inspection zone. Application of a modular principle has created an opportunity to realize all subsystems ASPD independently from each other, thus, they can operate as independently, and in the general complex of programs. As a tool for creation of the system software, the object-oriented instrument of programming Delphi 5.0 has been chosen. It has a number of advantages and distinctive features such as the convenient graphic interface with displaying of calculation results as uniform scrolling tables and graphics, access to the data files, high speed of mathematical calculations, an opportunity of the further expansion and change of the calculation algorithms. Use of the ASPD has allowed us to improve quality of data recording, their processing, and visualization of the processed results. For the first time in the automated system, the complex estimation of ecological situation with use of experimental data in real time has been realized. The ASPD can be used also by other experimental equipment intended for the solution of problems of the atmospheric optics.

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

    Directory of Open Access Journals (Sweden)

    Frank Weichert

    2014-10-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  12. 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.

  13. Rapid and sensitive detection of 17beta-estradiol in environmental water using automated immunoassay system with bacterial magnetic particles.

    Science.gov (United States)

    Tanaka, Tsuyoshi; Takeda, Hajime; Ueki, Fumiko; Obata, Kimimichi; Tajima, Hideji; Takeyama, Haruko; Goda, Yasuhiro; Fujimoto, Shigeru; Matsunaga, Tadashi

    2004-03-04

    A fully automated immunoassay of 17beta-estradiol (E2) was performed using anti-E2 monoclonal antibody immobilized on bacterial magnetic particles (AntiE2-BMPs) and alkaline phosphatase-conjugated E2 (ALP-E2). E2 concentration in environmental water samples was evaluated by decrease in luminescence based on competitive reaction. A linear correlation between the luminescence intensity and E2 concentration was obtained between 0.5 and 5 ppb. The minimum detectable concentration of E2 was 20 ppt. All measurement steps were done within 0.5 h. The analysis of environmental water samples by a commercially available ELISA kit and the BMP-based immunoassay gave good correlation plots with a correlation efficient of 0.992. These results suggest that the fully automated system using the BMP-based immunoassay has some advantages in the high rapidity and sensitivity of the measurement. This system will enable us to determine low E2 concentrations without sample condensation.

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

    Directory of Open Access Journals (Sweden)

    Jacopo Aguzzi

    2011-11-01

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

  15. Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network.

    Science.gov (United States)

    Aguzzi, Jacopo; Costa, Corrado; Robert, Katleen; Matabos, Marjolaine; Antonucci, Francesca; Juniper, S Kim; Menesatti, Paolo

    2011-01-01

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

  16. Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

    Science.gov (United States)

    Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

    2016-03-01

    The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

  17. Semi-automated detection of fractional shortening in zebrafish embryo heart videos

    Directory of Open Access Journals (Sweden)

    Nasrat Sara

    2016-09-01

    Full Text Available Quantifying cardiac functions in model organisms like embryonic zebrafish is of high importance in small molecule screens for new therapeutic compounds. One relevant cardiac parameter is the fractional shortening (FS. A method for semi-automatic quantification of FS in video recordings of zebrafish embryo hearts is presented. The software provides automated visual information about the end-systolic and end-diastolic stages of the heart by displaying corresponding colored lines into a Motion-mode display. After manually marking the ventricle diameters in frames of end-systolic and end-diastolic stages, the FS is calculated. The software was evaluated by comparing the results of the determination of FS with results obtained from another established method. Correlations of 0.96 < r < 0.99 between the two methods were found indicating that the new software provides comparable results for the determination of the FS.

  18. Semi-automated, reverse-hybridization detection of multiple mutations causing hereditary fructose intolerance.

    Science.gov (United States)

    Kriegshäuser, Gernot; Halsall, David; Rauscher, Bettina; Oberkanins, Christian

    2007-06-01

    Hereditary fructose intolerance (HFI) is a potentially fatal nutritional disease that is caused by mutations in the liver isoenzyme of fructoaldolase (aldolase B). Our aim was to evaluate a diagnostic assay capable of simultaneously analyzing three-point mutations and a small deletion in the aldolase B (ALDOB) gene. The test under investigation is based on multiplex DNA amplification and hybridization to membrane strips presenting a parallel array of allele-specific oligonucleotide probes. We used the novel reverse-hybridization (RH) protocol to analyze 54 individuals previously genotyped by direct sequencing. RH genotyping for ALDOB mutations Delta4E4, A149P, A174D, and N334K was in complete concordance with results obtained by DNA sequencing. The procedure is rapid (<6h) and may be automated to a large extent. The RH assay tested in this study represents an accurate and robust screening tool to identify common ALDOB mutations.

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

    Science.gov (United States)

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

    2013-12-01

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

  20. Automated tests of ANA immunofluorescence as throughput autoantibody detection technology: strengths and limitations.

    Science.gov (United States)

    Meroni, Pier Luigi; Bizzaro, Nicola; Cavazzana, Ilaria; Borghi, Maria Orietta; Tincani, Angela

    2014-03-03

    Anti-nuclear antibody (ANA) assay is a screening test used for almost all autoimmune rheumatic diseases, and in a number of these cases, it is a diagnostic/classification parameter. In addition, ANA is also a useful test for additional autoimmune disorders. The indirect immunofluorescence technique on monolayers of cultured epithelial cells is the current recommended method because it has higher sensitivity than solid phase assays. However, the technique is time-consuming and requires skilled operators. Automated ANA reading systems have recently been developed, which offer the advantage of faster and much easier performance as well as better harmonization in the interpretation of the results. Preliminary validation studies of these systems have given promising results in terms of analytical specificity and reproducibility. However, these techniques require further validation in clinical studies and need improvement in their recognition of mixed or less common staining patterns.

  1. Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

    Science.gov (United States)

    Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel

    2014-10-01

    An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

  2. 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.

  3. 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

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

    Science.gov (United States)

    Lehner, Matthew; Wang, Shiang-Yu; Alcock, Charles; Reyes-Ruiz, Mauricio; Castro, Joel; Chen, Wen Ping; Chu, You-Hua; Cook, Kem H.; Geary, John C.; Huang, Chung-Kai; Kim, Dae-Won; Norton, Timothy; Szentgyorgyi, Andrew; Yen, WeiLing; Zhang, Zhi-Wei; Figueroa, Liliana

    2016-10-01

    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 ($Construction of the site began in the fall of 2013, and the survey will begin in the summer of 2017. This poster will provide an update on the status of the survey development and the schedule leading to the beginning of survey operations.

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

    Science.gov (United States)

    Wenzel, A

    2001-01-01

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

  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. X-ray based stem detection in an automated tomato weeding system

    Science.gov (United States)

    A stem detection system was developed for automatic weed control in transplanted tomato fields. A portable x-ray source projected an x-ray beam perpendicular to the crop row and parallel to the soil surface. The plant’s main stem absorbs x-ray energy, decreasing the detected signal and allowing stem...

  8. Object Level HSI-LIDAR Data Fusion for Automated Detection of Difficult Targets

    Science.gov (United States)

    2011-10-10

    1992). 2. D. W. J. Stein, S. C. Beaven, L. E. Hoff, E. W. Winter, A. P. Schaum , and A. D. Stocker, “Anomaly detection from hyperspectral imagery...Trans. Signal Process. 49(1), 1–16 (2001). 11. A. P. Schaum and A. D. Stocker, “Hyperspectral change detection and supervised matched filtering

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Tözeren Aydin

    2007-03-01

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

  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. Multicenter Evaluation of a Novel Automated Rapid Detection System of BRAF Status in Formalin-Fixed, Paraffin-Embedded Tissues.

    Science.gov (United States)

    Schiefer, Ana-Iris; Parlow, Laura; Gabler, Lisa; Mesteri, Ildiko; Koperek, Oskar; von Deimling, Andreas; Streubel, Berthold; Preusser, Matthias; Lehmann, Annika; Kellner, Udo; Pauwels, Patrick; Lambin, Suzan; Dietel, Manfred; Hummel, Michael; Klauschen, Frederick; Birner, Peter; Möbs, Markus

    2016-05-01

    The mutated BRAF oncogene represents a therapeutic target in malignant melanoma. Because BRAF mutations are also involved in the pathogenesis of other human malignancies, the use of specific BRAF inhibitors might also be extended to other diseases in the future. A prerequisite for the clinical application of BRAF inhibitors is the reliable detection of activating BRAF mutations in routine histopathological samples. In a multicenter approach, we evaluated a novel and fully automated PCR-based system (Idylla) capable of detecting BRAF V600 mutations in formalin-fixed, paraffin-embedded tissue within 90 minutes with high sensitivity. We analyzed a total of 436 samples with the Idylla system. Valid results were obtained in 421 cases (96.56%). Its performance was compared with conventional methods (pyrosequencing or Sanger sequencing). Concordant results were obtained in 406 cases (96.90%). Reanalysis of eight discordant samples by next-generation sequencing and/or pyrosequencing with newly extracted DNA and the BRAF RGQ Kit confirmed the Idylla result in seven cases, resulting in an overall agreement of 98.57%. In conclusion, the Idylla system is a highly reliable and sensitive platform for detection of BRAF V600 mutations in formalin-fixed, paraffin-embedded material, providing an efficient alternative to conventional diagnostic methods, particularly for routine diagnostics laboratories with limited experience in molecular pathology.

  13. 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.

  14. An automated blood culture system: the detection of anaerobic bacteria using a Malthus Microbiological Growth Analyser.

    Science.gov (United States)

    McMaster, J P; Barr, J G; Campbell, R R; Bennett, R B; Smyth, E T

    1985-10-01

    The Malthus Microbiological Growth Analyser has proved to be sensitive in detecting conductivity changes due to anaerobic metabolism in a number of widely used blood culture media. Freshly prepared cooked meat media and Thiol medium yielded the greatest gross conductivity changes, and were more sensitive of anaerobic metabolism than other media. Failure of the instrument to detect anaerobic metabolism was a problem particularly associated with growth in the thioglycollate medium. False positive detections of growth were attributed to a number of factors including electrode instability (6.0%) and bacterial contamination (8.75%).

  15. Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees

    Directory of Open Access Journals (Sweden)

    Josef Smolle

    2001-01-01

    Full Text Available Objective: To evaluate the feasibility of the CART (Classification and Regression Tree procedure for the recognition of microscopic structures in tissue counter analysis. Methods: Digital microscopic images of H&E stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. Results: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p < 0.001. Conclusions: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

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

    Science.gov (United States)

    Newton, Michael I.

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Masci, Frank J.; Grillmair, Carl J.; Cutri, Roc M. [Infrared Processing and Analysis Center, Caltech 100-22, Pasadena, CA 91125 (United States); Hoffman, Douglas I., E-mail: fmasci@ipac.caltech.edu [NASA Ames Research Center, Moffett Field, CA 94035 (United States)

    2014-07-01

    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.

  18. An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

    OpenAIRE

    Anitha, M.; P. Tamije Selvy

    2012-01-01

    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 cluste...

  19. Load on osseointegrated fixation of a transfemoral amputee during a fall: Determination of the time and duration of descent.

    Science.gov (United States)

    Frossard, Laurent Alain

    2010-12-01

    Mitigation of fall-related injuries for populations of transfemoral amputees fitted with a socket or an osseointegrated fixation is challenging. Wearing a protective device fitted within the prosthesis might be a possible solution, provided that issues with automated fall detection and time of deployment of the protective mechanism are solved. The first objective of this study was to give some examples of the times and durations of descent during a real forward fall of a transfemoral amputee that occurred inadvertently while attending a gait measurement session to assess the load applied on the residuum. The second objective was to present five semi-automated methods of detection of the time of descent using the load data. The load was measured directly at 200 Hz using a six-channel transducer. The average time and duration of descent were 242 ± 42 ms (145-310 ms) and 619 ± 42 ms (550-715 ms), respectively. This study demonstrated that the transition between walking and falling was characterized by times of descent that occurred sequentially. The sensitivity and specificity of an automated algorithm might be improved by combining several methods of detection based on the deviation of the loads measured from their own trends and from a template previously established.

  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. 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.

  2. SpArcFiRe: Scalable Automated Detection of Spiral Galaxy Arm Segments

    Science.gov (United States)

    Davis, Darren R.; Hayes, Wayne B.

    2014-08-01

    Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy (possibly masking nearby stars and other objects if necessary in order to isolate the galaxy itself) and then automatically extracts structural information about the spiral arms. For each arm segment found, we list the pixels in that segment, allowing image analysis on a per-arm-segment basis. We also perform a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, location, etc. The algorithm takes about one minute per galaxies, and can easily be scaled using parallelism. We have run it on all ~644,000 Sloan objects that are larger than 40 pixels across and classified as "galaxies." We find a very good correlation between our quantitative description of a spiral structure and the qualitative description provided by Galaxy Zoo humans. Our objective, quantitative measures of structure demonstrate the difficulty in defining exactly what constitutes a spiral "arm," leading us to prefer the term "arm segment." We find that pitch angle often varies significantly segment-to-segment in a single spiral galaxy, making it difficult to define the pitch angle for a single galaxy. We demonstrate how our new database of arm segments can be queried to find galaxies satisfying specific quantitative visual criteria. For example, even though our code does not explicitly find rings, a good surrogate is to look for galaxies having one long, low-pitch-angle arm—which is how our code views ring galaxies. SpArcFiRe is available at http://sparcfire.ics.uci.edu.

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

    Science.gov (United States)

    Zhang, Lin

    2016-01-01

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

  4. Catheter detection and classification on chest radiographs: an automated prototype computer-aided detection (CAD) system for radiologists

    Science.gov (United States)

    Ramakrishna, Bharath; Brown, Matthew; Goldin, Jonathan; Cagnon, Chris; Enzmann, Dieter

    2011-03-01

    Chest radiographs are the quickest and safest method to check placement of man-made medical devices placed in the body like catheters, stents and pacemakers etc out of which catheters are the most commonly used devices. The two most often used catheters especially in the ICU are the Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed and administer drugs. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. Incorrect tube placements can cause serious complications and can even be fatal. The task of identifying these tubes on chest radiographs is difficult for radiologists and ICU personnel given the high volume of cases. This motivates the need for an automatic detection system to aid radiologists in processing these critical cases in a timely fashion while maintaining patient safety. To-date there has been very little research in this area. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for detection and classification of catheters on chest radiographs using a combination of template matching, morphological processing and region growing. The preliminary evaluation was carried out on 25 cases. The prototype CAD system was able to detect ET and NG tubes with sensitivities of 73.7% and 76.5% respectively and with specificities of 91.3% and 84.0% respectively. The results from the prototype system show that it is feasible to automatically detect both catheters on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

  5. Semi-automated bacterial spore detection system with micro-fluidic chips for aerosol collection, spore treatment and ICAN DNA detection.

    Science.gov (United States)

    Inami, Hisao; Tsuge, Kouichiro; Matsuzawa, Mitsuhiro; Sasaki, Yasuhiko; Togashi, Shigenori; Komano, Asuka; Seto, Yasuo

    2009-07-15

    A semi-automated bacterial spore detection system (BSDS) was developed to detect biological threat agents (e.g., Bacillus anthracis) on-site. The system comprised an aerosol sampler, micro-fluidic chip-A (for spore germination and cell lysis), micro-fluidic chip-B (for extraction and detection of genomic DNA) and an analyzer. An aerosol with bacterial spores was first collected in the collection chamber of chip-A with a velocity of 300 l/min, and the chip-A was taken off from the aerosol sampler and loaded into the analyzer. Reagents packaged in the chip-A were sequentially applied into the chamber. The genomic DNA extract from spore lyzate was manually transferred from chip-A to chip-B and loaded into the analyzer. Genomic DNA in chip-B was first trapped on a glass bead column, washed with various reagents, and eluted to the detection chamber by sequential auto-dispensing. Isothermal and chimeric primer-initiated amplification of nucleic acids (ICAN) with fluorescent measurement was adopted to amplify and detect target DNA. Bacillus subtilis was the stimulant of biological warfare agent in this experiment. Pretreatment conditions were optimized by examining bacterial target DNA recovery in the respective steps (aerosol collection, spore germination, cell lysis, and DNA extraction), by an off-chip experiment using a real-time polymerase chain reaction quantification method. Without the germination step, B. subtilis spores did not demonstrate amplification of target DNA. The detection of 10(4) spores was achieved within 2h throughout the micro-fluidic process.

  6. 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.

  7. 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.

  8. 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

  9. Integrating Electrochemical Detection with Centrifugal Microfluidics for Real-Time and Fully Automated Sample Testing

    DEFF Research Database (Denmark)

    Andreasen, Sune Zoëga; Kwasny, Dorota; Amato, Letizia;

    2015-01-01

    Here we present a robust, stable and low-noise experimental set-up for performing electrochemical detection on a centrifugal microfluidic platform. By using a low-noise electronic component (electrical slip-ring) it is possible to achieve continuous, on-line monitoring of electrochemical experime......Here we present a robust, stable and low-noise experimental set-up for performing electrochemical detection on a centrifugal microfluidic platform. By using a low-noise electronic component (electrical slip-ring) it is possible to achieve continuous, on-line monitoring of electrochemical...

  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. 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.

  13. 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.

  14. Performance evaluation of an automated detection and control system for volunteer potatoes in sugar beet fields

    NARCIS (Netherlands)

    Nieuwenhuizen, A.T.; Hofstee, J.W.; Henten, van E.J.

    2010-01-01

    Incomplete control of volunteer potato plants causes a high environmental load through increased crop protection chemical usage in potato cropping. A joint effort of industry, policy makers and science initiated a four year scientific project on detection and control of volunteer potato plants. A pr

  15. Automated breast cancer detection and classification using ultrasound images: A survey

    OpenAIRE

    2010-01-01

    Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown,early detection and diagnos is is the key for breast cancer control,and it can increase the success of treatment,save lives and reduce cost.

  16. Automated detection and classification of interstitial lung diseases from low-dose CT images

    Science.gov (United States)

    Zheng, Bin; Leader, Joseph K.; Fuhrman, Carl R.; Sciurba, Frank C.; Gur, David

    2004-05-01

    We developed a computer-aided diagnosis (CAD) scheme to detect and quantitatively assess interstitial lung diseases (ILD) depicted on low-dose and multi-slice helical high-resolution computed tomography (CT) examinations. Eighteen CT cases acquired from patients who underwent routine low-dose whole-lung screening examinations for the detection of lung cancer were used to test the scheme. ILD was identified in all of these cases. The CAD scheme involves multiple steps to segment lung areas, identify suspicious ILD regions depicted on each CT slice, and generate volumetric ILD lesions by grouping and matching ILD regions detected on multiple adjacent slices. The scheme computes five "global" features for each identified ILD region, which include size (or volume), contrast, average local pixel value fluctuation, mean of stochastic fractal dimension, and geometric fractal dimension. Two sets of classification rules are applied to remove false-positive detections. The severity of ILD in each case was rated by one experienced chest radiologist into one of the three categories (mild, moderate, and severe). A distance-weighted k-nearest neighbor algorithm and round-robin validation method was applied to classify each testing case into one of the three categories of severity. In this experiment, the CAD scheme classified 78% (14 out of 18) cases into the same categories as rated by the radiologist.

  17. Using an automated emboli detection device in a porcine cardiopulmonary bypass (CPB) model: feasibility and considerations.

    Science.gov (United States)

    Schnürer, Christian; Gyoeri, Georg; Hager, Martina; Jeller, Anton; Moser, Patrizia L; Velik-Salchner, Corinna; Laufer, Guenther; Lorenz, Ingo H; Kolbitsch, Christian

    2007-12-01

    The significant risk of cerebral embolism during cardiopulmonary bypass (CPB) makes monitoring of embolic events advisable already when developing new operation and coagulation management strategies for example in CPB animal models. The present study therefore evaluated in a porcine CPB model the feasibility of bilateral epicarotid Doppler signal recording and the quality of manual or automatic emboli detection. A total of 42 recordings (e.g. right carotid artery (n = 20), left carotid artery (n = 22)) were evaluated. The frequency of emboli counts was comparable for both carotid arteries. Automatic emboli detection, however, found significantly more embolic events per pig than did post-hoc manual off-line analysis of the recordings (172 +/- 217 vs. 13 +/-10). None of the brains, however, showed any emboli or infarction area either in cross-examination or in histological evaluation. In conclusion, the present study showed the feasibility of using an epicarotid Doppler device for bilateral emboli detection in a porcine CPB model. Automatic on-line emboli detection, however, reported more embolic events than did post hoc, off-line manual analysis. Possible reasons for this discrepancy are discussed.

  18. Automated analysis of multiple sections for the detection of occult cells in lymph nodes

    NARCIS (Netherlands)

    Mesker, WE; Doekhie, FS; Vrolijk, H; Keyzer, R; Sloos, WCR; Morreau, H; O'Kelly, PS; de Bock, GH; Tollenaar, RAEM; Tanke, HJ

    2003-01-01

    Purpose: At present, reverse transcription (RT)-PCR against carcino-embryonic antigen mRNA is one of the few research tools for the detection of occult cells in histopathologically assessed negative lymph nodes from patients with colorectal cancer. The aim of this study was to investigate the suitab

  19. Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis

    Science.gov (United States)

    Ichikawa, Tetsuko; Matsubara, Tomoko; Hara, Takeshi; Fujita, Hiroshi; Endo, Tokiko; Iwase, Takuji

    2004-05-01

    As well as mass and microcalcification, architectural distortion is a very important finding for the early detection of breast cancer via mammograms, and such distortions can be classified into three typical types: spiculation, retraction, and distortion. The purpose of this work is to develop an automatic method for detecting areas of architectural distortion with spiculation. The suspect areas are detected by concentration indexes of line-structures extracted by using mean curvature. After that, discrimination analysis of nine features is employed for the classifications of true and false positives. The employed features are the size, the mean pixel value, the mean concentration index, the mean isotropic index, the contrast, and four other features based on the power spectrum. As a result of this work, the accuracy of the classification was 76% and the sensitivity was 80% with 0.9 false positives per image in our database in regard to spiculation. It was concluded that our method was effective in detectiong the area of architectural distortion; however, some architectural distortions were not detected accurately because of the size, the density, or the different appearance of the distorted areas.

  20. Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children

    NARCIS (Netherlands)

    Vieira Da Fonseca Serras Pereira, Mariana; Cozijn, Rein; Postma, Eric; Shahid, Suleman; Swerts, Marc

    2016-01-01

    The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception stu

  1. 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.

  2. Visual surveying platform for the automated detection of road surface distresses

    Science.gov (United States)

    Naidoo, Thegaran; Joubert, Deon; Chiwewe, Tapiwa; Tyatyantsi, Ayanda; Rancati, Bruno; Mbizeni, Asanda

    2014-06-01

    Road distresses, such as potholes and edge cracks, are not only a source of frustration to drivers but also negatively impact the economy due to damage to motor vehicles and costly ro6ad repairs. Regular and rapid pavement inspection and maintenance is vital to preventing pothole formation and growth. To improve the efficiency of maintenance and reduce the cost thereof, the Visual Surveying Platform (VSP) is being developed that will automatically detect and analyse road distresses. The VSP consists of a vehicle mounted sensor system, consisting of a high speed camera and a Global Positioning System (GPS) receiver, and an analysis and visualization software suite. The system extracts both a visual image and the coordinates of a detected road defect from recorded video and presents it in an interactive interface for use by technical experts and maintenance schedulers. The VSP automatically detects and classifies road distresses using a two-stage artificial neural network framework. Video frames first undergo hue, saturation and value (HSV) colour space conversion as well as a spatial frequency transformation before being used as inputs to the neural networks. A road detector neural network first classifies which section of the image contains the road, after which a distress detector neural network identifies those road regions containing defects. Although the VSP can be adapted to detect any type of road distress it has been trained to specifically detect potholes. An initial prototype of the VSP was designed and constructed. The prototype was also trained and tested on real-world data collected from provincial roads.

  3. 基于三轴加速度传感器的人体跌倒检测系统设计与实现%Design and implementation of fall detection system using tri-axis accelerometer

    Institute of Scientific and Technical Information of China (English)

    王荣; 章韵; 陈建新

    2012-01-01

    为了满足老年人的护理需求,减少老年人因跌倒造成的身心伤害,提出了一种基于三轴加速度传感器的人体跌倒检测系统.该系统主要基于姿态测量特性,利用姿态角作为跌倒判断标准;并且考虑到噪声影响和跌倒检测系统对检测正确率的高要求,利用Kalman滤波算法来提高算法精确度.实验结果表明该系统在人体前后、侧向跌倒和跌倒后迅速站起的情况下可以100%报警,达到人体正常跌倒情况的检测标准.%In order to satisfy the requirements of the elderly care, and lessen the physical and psychological hurt caused by falling, the authors presented a fall detection system using a tri-axis accelerometer. The study idea was based on the attitude estimation using tri-axis accelerometer for the judgment of falling detection. In addition, considering the impact from the noise and the high accuracy of the falling detection system, the Kalman filtering algorithm was used to improve the system's reliability. The experimental results show that the system can alarm 100% when the human body is falling fore-and-aft, laterally and rapidly rising after falling, and can achieve the detection level of normal human falling.

  4. 基于智能手机的实时跌倒检测系统研究%Research on real-time fall detection system based on smartphone

    Institute of Scientific and Technical Information of China (English)

    秦昉; 孙子文; 白勇

    2016-01-01

    为减少跌倒对老年人造成的伤害,并对跌倒进行实时检测,提出了一种基于Android智能手机的人体跌倒检测系统,手机安置于腰上采集手机加速度传感器数据,利用了姿态识别和跌倒检测相结合的算法,区分出跌倒行为和人体日正常常活动.当检测到异常跌倒时,报警信息以及从手机中GPS获取的位置被发送.仿真及实验表明:系统能够有效地识别出跌倒和日常行为,算法具有较高实时性、具有较高灵敏度和特异度.%In order to reduce the harm caused by fall in the elderly and detect the fall in real time, a fall detection system based on Android smartphone is designed and developed. The proposed algorithm combines the fall detection with gesture recognition algorithm for identifying daily activities and fall. The alarming information will be sent with the user's posi-tion obtained from GPS when falling is detected. The results of simulation and experiments show that the system can effec-tively distinguish between falls and daily behaviour, and the algorithm has high instantaneity, sensitivity and specificity.

  5. Automated radioanalytical system incorporating microwave-assisted sample preparation, chemical separation, and online radiometric detection for the monitoring of total 99Tc in nuclear waste processing streams.

    Science.gov (United States)

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

    2012-04-03

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

  6. A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application

    Directory of Open Access Journals (Sweden)

    Shashwat Pathak

    2016-09-01

    Full Text Available This paper proposes and evaluates an algorithm to automatically detect the cataracts from color images in adult human subjects. Currently, methods available for cataract detection are based on the use of either fundus camera or Digital Single-Lens Reflex (DSLR camera; both are very expensive. The main motive behind this work is to develop an inexpensive, robust and convenient algorithm which in conjugation with suitable devices will be able to diagnose the presence of cataract from the true color images of an eye. An algorithm is proposed for cataract screening based on texture features: uniformity, intensity and standard deviation. These features are first computed and mapped with diagnostic opinion by the eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Finally, a tele-ophthamology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.

  7. Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.

    Science.gov (United States)

    Großekathöfer, Ulf; Manyakov, Nikolay V; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S

    2017-01-01

    A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier.

  8. 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.

  9. Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis

    Science.gov (United States)

    Großekathöfer, Ulf; Manyakov, Nikolay V.; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S.

    2017-01-01

    A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier. PMID:28261082

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

    Science.gov (United States)

    2014-03-27

    The features of a sunspot and other local sunspots considered part of a group are assigned a classification, defined by the solar astrophysics ...processing. In the second stage, elementary image processing techniques are used to condition the data. The third stage involves the detection of...active regions and coronal holes on euv images, arXiv preprint arXiv:1208.1483, 2012. Foukal, P. V., Solar astrophysics , Wiley-VCH, 2008. Gonzalez, R

  11. 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.

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

    Science.gov (United States)

    Noll, Matthias; Nadolny, Anne; Wesarg, Stefan

    2016-04-01

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

  13. 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.

  14. Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs.

    Science.gov (United States)

    Taşcı, Erdal; Uğur, Aybars

    2015-05-01

    Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.

  15. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

    Science.gov (United States)

    Aung, Min S H; Thies, Sibylle B; Kenney, Laurence P J; Howard, David; Selles, Ruud W; Findlow, Andrew H; Goulermas, John Y

    2013-11-01

    Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains.

  16. SisFall: A Fall and Movement Dataset

    Science.gov (United States)

    Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

    2017-01-01

    Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark. PMID:28117691

  17. 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.

  18. 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.

  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. AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Maqlin Paramanandam

    2016-11-01

    Full Text Available The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN. The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges

  1. Assessment and validation of a simple automated method for the detection of gait events and intervals.

    Science.gov (United States)

    Ghoussayni, Salim; Stevens, Christopher; Durham, Sally; Ewins, David

    2004-12-01

    A simple and rapid automatic method for detection of gait events at the foot could speed up and possibly increase the repeatability of gait analysis and evaluations of treatments for pathological gaits. The aim of this study was to compare and validate a kinematic-based algorithm used in the detection of four gait events, heel contact, heel rise, toe contact and toe off. Force platform data is often used to obtain start and end of contact phases, but not usually heel rise and toe contact events. For this purpose synchronised kinematic, kinetic and video data were captured from 12 healthy adult subjects walking both barefoot and shod at slow and normal self-selected speeds. The data were used to determine the gait events using three methods: force, visual inspection and algorithm methods. Ninety percent of all timings given by the algorithm were within one frame (16.7 ms) when compared to visual inspection. There were no statistically significant differences between the visual and algorithm timings. For both heel and toe contact the differences between the three methods were within 1.5 frames, whereas for heel rise and toe off the differences between the force on one side and the visual and algorithm on the other were higher and more varied (up to 175 ms). In addition, the algorithm method provided the duration of three intervals, heel contact to toe contact, toe contact to heel rise and heel rise to toe off, which are not readily available from force platform data. The ability to automatically and reliably detect the timings of these four gait events and three intervals using kinematic data alone is an asset to clinical gait analysis.

  2. Automated detection of pulmonary nodules in CT: false positive reduction by combining multiple classifiers

    Science.gov (United States)

    Suárez-Cuenca, Jorge Juan; Guo, Wei; Li, Qiang

    2011-03-01

    The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a CAD system for pulmonary nodule detection in CT. We employed CT cases in the publicly available lung image database consortium (LIDC) dataset, which included 85 CT cases with 110 nodules. We first used six individual classifiers for nodule detection in CT, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and three types of support vector machines (SVM). Five informationfusion methods were then employed to combine the classifiers' outputs for improving detection performance. The five combination methods included two supervised (likelihood ratio method and neural network) and three unsupervised ones (the mean, the product, and the majority-vote of the output scores from the six individual classifiers). Leave-one-caseout was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80 %, the numbers of false positives per case for the six individual classifiers were 6.1 for LDA, 19.9 for QDA, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.35 for SVM-ANOVA; the numbers of false positives per case for the five combination methods were 3.4 for the majority-vote rule, 6.2 for the mean, 5.7 for the product, 9.7 for the neural network, and 28.1 for the likelihood ratio method. The majority-vote rule achieved higher performance levels than other combination methods. It also achieved higher performance than the best individual classifier, which is not the case for other combination methods.

  3. 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.

  4. Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators

    OpenAIRE

    Askari, M.; Eslami, S.; van Rijn, M.; Medlock, S.; Moll van Charante, E. P.; van der Velde, N.; Rooij, S.E. de; Abu-Hanna, A.

    2016-01-01

    Summary We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insufficiency in the quality of fall management. Most indicators showed a positive association between increased risk for functional decline and adherence, four of which with statistical significance. Introduct...

  5. Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects.

    Science.gov (United States)

    Kangas, M; Vikman, I; Nyberg, L; Korpelainen, R; Lindblom, J; Jämsä, T

    2012-03-01

    Falling is a common accident among older people. Automatic fall detectors are one method of improving security. However, in most cases, fall detectors are designed and tested with data from experimental falls in younger people. This study is one of the first to provide fall-related acceleration data obtained from real-life falls. Wireless sensors were used to collect acceleration data during a six-month test period in older people. Data from five events representing forward falls, a sideways fall, a backwards fall, and a fall out of bed were collected and compared with experimental falls performed by middle-aged test subjects. The signals from real-life falls had similar features to those from intentional falls. Real-life forward, sideways and backward falls all showed a pre impact phase and an impact phase that were in keeping with the model that was based on experimental falls. In addition, the fall out of bed had a similar acceleration profile as the experimental falls of the same type. However, there were differences in the parameters that were used for the detection of the fall phases. The beginning of the fall was detected in all of the real-life falls starting from a standing posture, whereas the high pre impact velocity was not. In some real-life falls, multiple impacts suggested protective actions. In conclusion, this study demonstrated similarities between real-life falls of older people and experimental falls of middle-aged subjects. However, some fall characteristics detected from experimental falls were not detectable in acceleration signals from corresponding heterogeneous real-life falls.

  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. 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.

  8. Osteoporosis: Preventing Falls

    Science.gov (United States)

    ... page please turn Javascript on. Feature: Osteoporosis Preventing Falls Past Issues / Winter 2011 Table of Contents Bone ... with osteoporosis need to take care not to fall down. Falls can break bones. Some reasons people ...

  9. Falls and Older Adults

    Science.gov (United States)

    ... version of this page please turn Javascript on. Falls and Older Adults About Falls Risk Increases With Age Many people have a ... problems -- rises with age. Click for more information Falls Lead to Fractures, Trauma Each year, more than ...

  10. 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

  11. 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

  12. Automated sleep spindle detection using IIR filters and a Gaussian Mixture Model.

    Science.gov (United States)

    Patti, Chanakya Reddy; Penzel, Thomas; Cvetkovic, Dean

    2015-08-01

    Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model. Features extracted with IIR filters are clustered using a Gaussian Mixture Model without the use of any subject independent thresholds. The Algorithm was tested on a database consisting of overnight sleep PSG of 5 subjects and an online public spindles database consisting of six 30 minute sleep excerpts. An overall sensitivity of 57% and a specificity of 98.24% was achieved in the overnight database group and a sensitivity of 65.19% at a 16.9% False Positive proportion for the 6 sleep excerpts.

  13. Detecting endotoxin activity in bovine serum using an automated testing system.

    Science.gov (United States)

    Suzuki, Kazuyuki; Shimamori, Toshio; Sato, Ayano; Tsukano, Kenji; Tsuchiya, Masakazu; Lakritz, Jeffrey

    2015-08-01

    The aim of the present study was to compare the ability of the commercially available portable test system (PTS(TM)) to detect endotoxin activity in bovine serum, with that of the traditional LAL-kinetic turbidimetric (KT) and chromogenic (KC) assays. Prior to testing, serum samples, which were obtained from endotoxin-challenged cattle, were diluted 1:20 in endotoxin-free water and heated to 80°C for 10 min. The performance of the PTS(TM) was not significantly different from that of the traditional LAL-based assays. The results using PTS(TM) correlated with those using KT (r(2)=0.963, PPTS(TM) could be applied as a simplified system to assess endotoxin activity in bovine serum.

  14. Automated and interactive lesion detection and segmentation in uterine cervix images.

    Science.gov (United States)

    Alush, Amir; Greenspan, Hayit; Goldberger, Jacob

    2010-02-01

    This paper presents a procedure for automatic extraction and segmentation of a class-specific object (or region) by learning class-specific boundaries. We describe and evaluate the method with a specific focus on the detection of lesion regions in uterine cervix images. The watershed segmentation map of the input image is modeled using a Markov random field (MRF) in which watershed regions correspond to binary random variables indicating whether the region is part of the lesion tissue or not. The local pairwise factors on the arcs of the watershed map indicate whether the arc is part of the object boundary. The factors are based on supervised learning of a visual word distribution. The final lesion region segmentation is obtained using a loopy belief propagation applied to the watershed arc-level MRF. Experimental results on real data show state-of-the-art segmentation results on this very challenging task that, if necessary, can be interactively enhanced.

  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. 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...

  17. Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

    Science.gov (United States)

    Sun, Yankui; Li, Shan; Sun, Zhongyang

    2017-01-01

    We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects-15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing-168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

  18. 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

    2016-04-01

    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.

  19. 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.

  20. An algorithm for automated detection, localization and measurement of local calcium signals from camera-based imaging.

    Science.gov (United States)

    Ellefsen, Kyle L; Settle, Brett; Parker, Ian; Smith, Ian F

    2014-09-01

    Local Ca(2+) transients such as puffs and sparks form the building blocks of cellular Ca(2+) signaling in numerous cell types. They have traditionally been studied by linescan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) cameras now enable rapid (>500 frames s(-1)) imaging of subcellular Ca(2+) signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1 Gb min(-1)) than linescan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca(2+) events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca(2+) release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events.

  1. Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

    Science.gov (United States)

    Ding, Yifu; Tavolara, Thomas; Cheng, Keith

    2016-03-01

    Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

  2. 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.

  3. 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

  4. Detection of Clostridium tyrobutyricum in milk to prevent late blowing in cheese by automated ribosomal intergenic spacer analysis.

    Science.gov (United States)

    Panelli, Simona; Brambati, Eva; Bonacina, Cesare; Feligini, Maria

    2013-10-01

    Clostridium tyrobutyricum has been identified as the main causal agent of the late blowing defect in cheese, with major effects on quality and commercial value. In this work, for the first time, we applied automated ribosomal intergenic spacer analysis (ARISA) approach to diagnose the presence of C. tyrobutyricum in raw milk before cheesemaking. A species-specific primer set was designed and used for this original application of the ARISA. Sensitivity of detection, reproducibility of the fluorescent PCR assay, and repeatability of the capillary electrophoretic analysis of amplicons were evaluated using DNA extracted from milk added with known amounts of C. tyrobutyricum genome copies, ranging from 3 × 10(6) to 3. Results indicated that the sensitivity of the technique permits to detect the bacterium in all the samples. The reproducibility, evaluated by analyzing 3 sets of serial dilutions, resulted satisfactory, with little deviation within PCR reactions amplifying the same starting amount of template (standard deviations ≤ 0.1, coefficients of variation ≤ 3%). The peaks' fluorescence displayed an evident correspondence with the number of genome copies contained in each dilution. The capillary electrophoretic analysis, tested by running a single PCR product per dilution point in 10 repeats, resulted efficient and highly repeatable, with excellent coefficients of variation ≤ 2% and standard deviations ≤ 0.1 in all the sample sets. This application of ARISA gives good estimates of the total C. tyrobutyricum DNA content allowing a specific, fine-scale resolution of this pollutant species in a complex system as milk. A further advantage linked to the automatization of the process.

  5. Using Automated HbA1c Testing to Detect Diabetes Mellitus in Orthopedic Inpatients and Its Effect on Outcomes

    Science.gov (United States)

    Ekinci, Elif I.; Kong, Alvin; Churilov, Leonid; Nanayakkara, Natalie; Chiu, Wei Ling; Sumithran, Priya; Djukiadmodjo, Frida; Premaratne, Erosha; Owen-Jones, Elizabeth; Hart, Graeme Kevin; Robbins, Raymond; Hardidge, Andrew; Johnson, Douglas; Baker, Scott T.; Zajac, Jeffrey D.

    2017-01-01

    Aims The prevalence of diabetes is rising, and people with diabetes have higher rates of musculoskeletal-related comorbidities. HbA1c testing is a superior option for diabetes diagnosis in the inpatient setting. This study aimed to (i) demonstrate the feasibility of routine HbA1c testing to detect the presence of diabetes mellitus, (ii) to determine the prevalence of diabetes in orthopedic inpatients and (iii) to assess the association between diabetes and hospital outcomes and post-operative complications in orthopedic inpatients. Methods All patients aged ≥54 years admitted to Austin Health between July 2013 and January 2014 had routine automated HbA1c measurements using automated clinical information systems (CERNER). Patients with HbA1c ≥6.5% were diagnosed with diabetes. Baseline demographic and clinical data were obtained from hospital records. Results Of the 416 orthopedic inpatients included in this study, 22% (n = 93) were known to have diabetes, 4% (n = 15) had previously unrecognized diabetes and 74% (n = 308) did not have diabetes. Patients with diabetes had significantly higher Charlson comorbidity scores compared to patients without diabetes (median, IQR; 1 [0,2] vs 0 [0,0], p<0.001). After adjusting for age, gender, comorbidity score and estimated glomerular filtration rate, no significant differences in the length of stay (IRR = 0.92; 95%CI: 0.79–1.07; p = 0.280), rates of intensive care unit admission (OR = 1.04; 95%CI: 0.42–2.60, p = 0.934), 6-month mortality (OR = 0.52; 95%CI: 0.17–1.60, p = 0.252), 6-month hospital readmission (OR = 0.93; 95%CI: 0.46–1.87; p = 0.828) or any post-operative complications (OR = 0.98; 95%CI: 0.53–1.80; p = 0.944) were observed between patients with and without diabetes. Conclusions Routine HbA1c measurement using CERNER allows for rapid identification of inpatients admitted with diabetes. More than one in four patients admitted to a tertiary hospital orthopedic ward have diabetes. No statistically

  6. Detection of Anoplophora glabripennis (Coleoptera: Cerambycidae) larvae in different host trees and tissues by automated analyses of sound-impulse frequency and temporal patterns.

    Science.gov (United States)

    Mankin, R W; Smith, M T; Tropp, J M; Atkinson, E B; Jong, D Y

    2008-06-01

    Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae), an invasive pest quarantined in the United States, is difficult to detect because the larvae feed unseen inside trees. Acoustic technology has potential for reducing costs and hazards of tree inspection, but development of practical methods for acoustic detection requires the solution of technical problems involving transmission of resonant frequencies in wood and high background noise levels in the urban environments where most infestations have occurred. A study was conducted to characterize sounds from larvae of different ages in cambium, sapwood, and heartwood of bolts from three host tree species. Larval sounds in all of the tested trees and tissues consisted primarily of trains of brief, 3-10-ms impulses. There were no major differences in the spectral or temporal pattern characteristics of signals produced by larvae of different ages in each tissue, but larval sounds in sapwood often had fewer spectral peaks than sounds in cambium and heartwood. A large fraction, but not all background sounds could be discriminated from larval sounds by automated spectral analyses. In 3-min recordings from infested bolts, trains containing impulses in patterns called bursts occurred frequently, featuring 7-49 impulses separated by small intervals. Bursts were rarely detected in uninfested bolts. The occurrence of bursts was found to predict infestations more accurately than previously used automated spectral analyses alone. Bursts and other features of sounds that are identifiable by automated techniques may ultimately lead to improved pest detection applications and new insight into pest behavior.

  7. 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

  8. Automated bare earth extraction technique for complex topography in light detection and ranging surveys

    Science.gov (United States)

    Stevenson, Terry H.; Magruder, Lori A.; Neuenschwander, Amy L.; Bradford, Brian

    2013-01-01

    Bare earth extraction is an important component to light detection and ranging (LiDAR) data analysis in terms of terrain classification. The challenge in providing accurate digital surface models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. Few existing algorithms can handle substantial terrain diversity without significant editing or user interaction. This effort presents a newly developed methodology that provides a flexible, adaptable tool capable of integrating multiple LiDAR data attributes for an accurate terrain assessment. The terrain extraction and segmentation (TEXAS) approach uses a third-order spatial derivative for each point in the digital surface model to determine the curvature of the terrain rather than rely solely on the slope. The utilization of the curvature has shown to successfully preserve ground points in areas of steep terrain as they typically exhibit low curvature. Within the framework of TEXAS, the contiguous sets of points with low curvatures are grouped into regions using an edge-based segmentation method. The process does not require any user inputs and is completely data driven. This technique was tested on a variety of existing LiDAR surveys, each with varying levels of topographic complexity.

  9. 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.

  10. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  11. Automated detection of filaments in the large scale structure of the universe

    CERN Document Server

    Gonzalez, Roberto E

    2009-01-01

    We present a new method to identify large scale filaments and apply it to a cosmological simulation. Using positions of haloes above a given mass as node tracers, we look for filaments between them using the positions and masses of all the remaining dark-matter haloes. In order to detect a filament, the first step consists in the construction of a backbone linking two nodes, which is given by a skeleton-like path connecting the highest local dark matter (DM) density traced by non-node haloes. We estimate the characteristic DM density between two skeleton-candidate haloes using two approximations, i) the Voronoi tessellation density when the distance between haloes is similar or smaller than the sum of their virial radii, and ii) when the distance is larger, using a proxy of the minimum DM density between the two haloes assuming NFW profiles. The filament quality is defined by a density and gap parameters characterising its skeleton, and filament members are selected by their binding energy in the plane perpen...

  12. miRNA assays in the clinical laboratory: workflow, detection technologies and automation aspects.

    Science.gov (United States)

    Kappel, Andreas; Keller, Andreas

    2017-05-01

    microRNAs (miRNAs) are short non-coding RNA molecules that regulate gene expression in eukaryotes. Their differential abundance is indicative or even causative for a variety of pathological processes including cancer or cardiovascular disorders. Due to their important biological function, miRNAs represent a promising class of novel biomarkers that may be used to diagnose life-threatening diseases, and to monitor disease progression. Further, they may guide treatment selection or dosage of drugs. miRNAs from blood or derived fractions are particularly interesting candidates for routine laboratory applications, as they can be measured in most clinical laboratories already today. This assures a good accessibility of respective tests. Albeit their great potential, miRNA-based diagnostic tests have not made their way yet into the clinical routine, and hence no standardized workflows have been established to measure miRNAs for patients' benefit. In this review we summarize the detection technologies and workflow options that exist to measure miRNAs, and we describe the advantages and disadvantages of each of these options. Moreover, we also provide a perspective on data analysis aspects that are vital for translation of raw data into actionable diagnostic test results.

  13. Automated three-dimensional detection and classification of living organisms using digital holographic microscopy with partial spatial coherent source: application to the monitoring of drinking water resources.

    Science.gov (United States)

    El Mallahi, Ahmed; Minetti, Christophe; Dubois, Frank

    2013-01-01

    In this paper, we investigate the use of a digital holographic microscope working with partially coherent spatial illumination for an automated detection and classification of living organisms. A robust automatic method based on the computation of propagating matrices is proposed to detect the 3D position of organisms. We apply this procedure to the evaluation of drinking water resources by developing a classification process to identify parasitic protozoan Giardia lamblia cysts among two other similar organisms. By selecting textural features from the quantitative optical phase instead of morphological ones, a robust classifier is built to propose a new method for the unambiguous detection of Giardia lamblia cyst that present a critical contamination risk.

  14. Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators

    NARCIS (Netherlands)

    Askari, M; Eslami, S; van Rijn, M; Medlock, S; Moll van Charante, E P; van der Velde, N; de Rooij, S E; Abu-Hanna, A

    2016-01-01

    UNLABELLED: We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insuffici

  15. Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V

    Directory of Open Access Journals (Sweden)

    Florent Hawotte

    2016-08-01

    Full Text Available Changes in the snow cover extent are both a cause and a consequence of climate change. Optical remote sensing with heliosynchronous satellites currently provides snow cover data at high spatial resolution with daily revisiting time. However, high latitude image acquisition is limited because reflective sensors of many satellites are switched off at high solar zenith angles (SZA due to lower signal quality. In this study, the relevance and reliability of high SZA acquisition are objectively quantified in the purpose of high latitude snow cover detection, thanks to the PROBA-V (Project for On-Board Autonomy-Vegetation satellite. A snow cover extent classification based on Normalized Difference Snow Index (NDSI and Normalized Difference Vegetation Index (NDVI has been performed for the northern hemisphere on latitudes between 55°N and 75°N during the 2015–2016 winter season. A stratified probabilistic sampling was used to estimate the classification accuracy. The latter has been evaluated among eight SZA intervals to determine the maximum usable angle. The global overall snow classification accuracy with PROBA-V, 82% ± 4%, was significantly larger than the MODIS (Moderate-resolution Imaging Spectroradiometer snow cover extent product (75% ± 4%. User and producer accuracy of snow are above standards and overall accuracy is stable until 88.5° SZA. These results demonstrate that optical remote sensing data can still be used with large SZA. Considering the relevance of snow cover mapping for ecology and climatology, the data acquisition at high solar zenith angles should be continued by PROBA-V.

  16. 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

    2015-06-25

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

  17. Warehouse automation

    OpenAIRE

    Pogačnik, Jure

    2017-01-01

    An automated high bay warehouse is commonly used for storing large number of material with a high throughput. In an automated warehouse pallet movements are mainly performed by a number of automated devices like conveyors systems, trolleys, and stacker cranes. From the introduction of the material to the automated warehouse system to its dispatch the system requires no operator input or intervention since all material movements are done automatically. This allows the automated warehouse to op...

  18. An improved automated procedure for informal and temporary dwellings detection and enumeration, using mathematical morphology operators on VHR satellite data

    Science.gov (United States)

    Jenerowicz, Małgorzata; Kemper, Thomas

    2016-10-01

    Every year thousands of people are displaced by conflicts or natural disasters and often gather in large camps. Knowing how many people have been gathered is crucial for an efficient relief operation. However, it is often difficult to collect exact information on the total number of the population. This paper presents the improved morphological methodology for the estimation of dwellings structures located in several Internally Displaced Persons (IDPs) Camps, based on Very High Resolution (VHR) multispectral satellite imagery with pixel sizes of 1 meter or less including GeoEye-1, WorldView-2, QuickBird-2, Ikonos-2, Pléiades-A and Pléiades-B. The main topic of this paper is the approach enhancement with selection of feature extraction algorithm, the improvement and automation of pre-processing and results verification. For the informal and temporary dwellings extraction purpose the high quality of data has to be ensured. The pre-processing has been extended by including the input data hierarchy level assignment and data fusion method selection and evaluation. The feature extraction algorithm follows the procedure presented in Jenerowicz, M., Kemper, T., 2011. Optical data are analysed in a cyclic approach comprising image segmentation, geometrical, textural and spectral class modeling aiming at camp area identification. The successive steps of morphological processing have been combined in a one stand-alone application for automatic dwellings detection and enumeration. Actively implemented, these approaches can provide a reliable and consistent results, independent of the imaging satellite type and different study sites location, providing decision support in emergency response for the humanitarian community like United Nations, European Union and Non-Governmental relief organizations.

  19. 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.

  20. 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

  1. 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

  2. Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

    Science.gov (United States)

    Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

    2016-06-01

    Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors.

  3. Accounting Automation

    OpenAIRE

    Laynebaril1

    2017-01-01

    Accounting Automation   Click Link Below To Buy:   http://hwcampus.com/shop/accounting-automation/  Or Visit www.hwcampus.com Accounting Automation” Please respond to the following: Imagine you are a consultant hired to convert a manual accounting system to an automated system. Suggest the key advantages and disadvantages of automating a manual accounting system. Identify the most important step in the conversion process. Provide a rationale for your response. ...

  4. Recovering from Falls

    Science.gov (United States)

    ... News & Events Press Releases NOF in the News Osteoporosis in the News Press/Media Kit NOF Events Blog Advocacy NOF Store Shopping Cart Home › Patients › Fractures/Fall Prevention › Exercise/Safe Movement › Recovering from Falls Recovering from Falls ...

  5. First Aid: Falls

    Science.gov (United States)

    ... Your 1- to 2-Year-Old First Aid: Falls KidsHealth > For Parents > First Aid: Falls Print A A A en español Folleto de instructiones: Caídas (Falls) With all the running, climbing, and exploring kids ...

  6. 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

  7. Toward fully automated genotyping: Allele assignment, pedigree construction, phase determination, and recombination detection in Duchenne muscular dystrophy

    Energy Technology Data Exchange (ETDEWEB)

    Perlin, M.W.; Burks, M.B. [Carnegie Mellon Univ., Pittsburgh, PA (United States); Hoop, R.C.; Hoffman, E.P. [Univ. of Pittsburgh School of Medicine, PA (United States)

    1994-10-01

    Human genetic maps have made quantum leaps in the past few years, because of the characterization of >2,000 CA dinucleotide repeat loci: these PCR-based markers offer extraordinarily high PIC, and within the next year their density is expected to reach intervals of a few centimorgans per marker. These new genetic maps open new avenues for disease gene research, including large-scale genotyping for both simple and complex disease loci. However, the allele patterns of many dinucleotide repeat loci can be complex and difficult to interpret, with genotyping errors a recognized problem. Furthermore, the possibility of genotyping individuals at hundreds or thousands of polymorphic loci requires improvements in data handling and analysis. The automation of genotyping and analysis of computer-derived haplotypes would remove many of the barriers preventing optimal use of dense and informative dinucleotide genetic maps. Toward this end, we have automated the allele identification, genotyping, phase determinations, and inheritance consistency checks generated by four CA repeats within the 2.5-Mbp, 10-cM X-linked dystrophin gene, using fluorescein-labeled multiplexed PCR products analyzed on automated sequencers. The described algorithms can deconvolute and resolve closely spaced alleles, despite interfering stutter noise; set phase in females; propagate the phase through the family; and identify recombination events. We show the implementation of these algorithms for the completely automated interpretation of allele data and risk assessment for five Duchenne/Becker muscular dystrophy families. The described approach can be scaled up to perform genome-based analyses with hundreds or thousands of CA-repeat loci, using multiple fluorophors on automated sequencers. 16 refs., 5 figs., 1 tab.

  8. Selective Detection and Automated Counting of Fluorescently-Labeled Chrysotile Asbestos Using a Dual-Mode High-Throughput Microscopy (DM-HTM Method

    Directory of Open Access Journals (Sweden)

    Jung Kyung Kim

    2013-05-01

    Full Text Available Phase contrast microscopy (PCM is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.

  9. Rapid and Specific Detection of tdh, trh1, and trh2 mRNA of Vibrio parahaemolyticus by Transcription-Reverse Transcription Concerted Reaction with an Automated System

    OpenAIRE

    Nakaguchi, Yoshitsugu; Ishizuka, Tetsuya; Ohnaka, Satoru; Hayashi, Toshinori; Yasukawa, Kiyoshi; Ishiguro, Takahiko; Nishibuchi, Mitsuaki

    2004-01-01

    Vibrio parahaemolyticus strains carrying the thermostable direct hemolysin (TDH) tdh gene, the TDH-related hemolysin (trh) gene, or both genes are considered virulent strains. We previously demonstrated that the transcription-reverse transcription concerted (TRC) method could be used to quantify the amount of mRNA transcribed from the tdh gene by using an automated detection system. In this study, we devised two TRC-based assays to quantify the mRNAs transcribed from the trh1 and trh2 genes, ...

  10. Rapid and specific detection of tdh, trh1, and trh2 mRNA of Vibrio parahaemolyticus by transcription-reverse transcription concerted reaction with an automated system.

    Science.gov (United States)

    Nakaguchi, Yoshitsugu; Ishizuka, Tetsuya; Ohnaka, Satoru; Hayashi, Toshinori; Yasukawa, Kiyoshi; Ishiguro, Takahiko; Nishibuchi, Mitsuaki

    2004-09-01

    Vibrio parahaemolyticus strains carrying the thermostable direct hemolysin (TDH) tdh gene, the TDH-related hemolysin (trh) gene, or both genes are considered virulent strains. We previously demonstrated that the transcription-reverse transcription concerted (TRC) method could be used to quantify the amount of mRNA transcribed from the tdh gene by using an automated detection system. In this study, we devised two TRC-based assays to quantify the mRNAs transcribed from the trh1 and trh2 genes, the two representative trh genes. The TRC-based detection assays for the tdh, trh1, and trh2 transcripts could specifically and quantitatively detect 10(3) to 10(7) copies of the corresponding calibrator RNAs. We examined by the three TRC assays the total RNA preparations extracted from 103 strains of Vibrio parahaemolyticus carrying the tdh, trh1, or trh2 gene in various combinations. The tdh, trh1, and trh2 mRNAs in the total RNA preparations were specifically quantified, and the time needed for detection ranged from 9 to 19 min, from 14 to 18 min, and from 9 to 12 min, respectively. The results showed that this automated TRC assays could detect the tdh, trh1, and trh2 mRNAs specifically, quantitatively, and rapidly. The relative levels of TDH determined by the immunological method and that of tdh mRNA determined by the TRC assays for most tdh-positive strains correlated. Interestingly, the levels of TDH produced from the strains carrying both tdh and trh genes were lower than those carrying only the tdh gene, whereas the levels of mRNA did not significantly differ between the two groups.

  11. On-line capillary isotachophoresis-capillary zone electrophoresis analysis of bromate in drinking waters in an automated analyzer with coupled columns and photometric detection.

    Science.gov (United States)

    Marák, Jozef; Staňová, Andrea; Vaváková, Veronika; Hrenáková, Martina; Kaniansky, Dušan

    2012-12-07

    A new, sensitive, and robust analytical method based on capillary zone electrophoresis with on-line capillary isotachophoresis sample pretreatment (ITP-CZE) using a column-coupling (CC) arrangement of automated capillary electrophoretic analyzer was developed for determination of bromate in different type of drinking water samples. Both columns were provided with contact-less conductivity detectors and in CZE step UV detection at 200 nm wavelength was used. Electroosmotic flow of the buffer solutions was suppressed with the addition of 0.1% or 0.05% (m/v) methylhydroxyethylcellulose into the leading and terminating electrolyte, respectively. Hydrodynamic and electroosmotic flows of the buffer solutions were successfully suppressed and therefore, only the electrophoretic transport of ions was significant. Limit of detection for bromate approaching 0.6 μg/L was achieved. Good repeatabilities of migration time (RSD less than 0.3%) and peak area (RSD less than 4.0%) at concentration level 1 μg/L were obtained. Robustness of proposed ITP-CZE method and validation parameters were evaluated. Developed automated ITP-CZE method was applied to the determination of bromate in drinking water samples with different content of inorganic macroconstituents without the need of further sample preparation.

  12. 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...

  13. Detection of Giardia lamblia, Cryptosporidium spp. and Entamoeba histolytica in clinical stool samples by using multiplex real-time PCR after automated DNA isolation.

    Science.gov (United States)

    Van Lint, P; Rossen, J W; Vermeiren, S; Ver Elst, K; Weekx, S; Van Schaeren, J; Jeurissen, A

    2013-01-01

    Diagnosis of intestinal parasites in stool samples is generally still carried out by microscopy; however, this technique is known to suffer from a low sensitivity and is unable to discriminate between certain protozoa. In order to overcome these limitations, a real-time multiplex PCR was evaluated as an alternative approach for diagnosing Giardia lamblia, Cryptosporidium spp. and Entamoeba histolytica in stool samples.Therefore, a total of 631 faecal samples were analysed both by microscopy as well as by real-time PCR following automated DNA extraction. Results showed that real-time PCR exhibited sensitivity and specificity of both 100%, whereas traditional microscopy exhibited sensitivity and specificity of 37.5% and 99.8% respectively. As real-time PCR provides simple, sensitive and specific detection of these three important pathogenic protozoan parasites, this technique, rather than microscopy, has become our diagnostic method of choice for the detection of enteric protozoan parasites for the majority of patients.

  14. A versatile-deployable bacterial detection system for food and environmental safety based on LabTube-automated DNA purification, LabReader-integrated amplification, readout and analysis.

    Science.gov (United States)

    Hoehl, Melanie M; Bocholt, Eva Schulte; Kloke, Arne; Paust, Nils; von Stetten, Felix; Zengerle, Roland; Steigert, Juergen; Slocum, Alexander H

    2014-06-01

    Contamination of foods is a public health hazard that episodically causes thousands of deaths and sickens millions worldwide. To ensure food safety and quality, rapid, low-cost and easy-to-use detection methods are desirable. Here, the LabSystem is introduced for integrated, automated DNA purification, amplification and detection. It consists of a disposable, centrifugally driven DNA purification platform (LabTube) and a low-cost UV/vis-reader (LabReader). For demonstration of the LabSystem in the context of food safety, purification of Escherichia coli (non-pathogenic E. coli and pathogenic verotoxin-producing E. coli (VTEC)) in water and milk and the product-spoiler Alicyclobacillus acidoterrestris (A. acidoterrestris) in apple juice was integrated and optimized in the LabTube. Inside the LabReader, the purified DNA was amplified, readout and analyzed using both qualitative isothermal loop-mediated DNA amplification (LAMP) and quantitative real-time PCR. For the LAMP-LabSystem, the combined detection limits for purification and amplification of externally lysed VTEC and A. acidoterrestris are 10(2)-10(3) cell-equivalents. In the PCR-LabSystem for E. coli cells, the quantification limit is 10(2) cell-equivalents including LabTube-integrated lysis. The demonstrated LabSystem only requires a laboratory centrifuge (to operate the disposable, fully closed LabTube) and a low-cost LabReader for DNA amplification, readout and analysis. Compared with commercial DNA amplification devices, the LabReader improves sensitivity and specificity by the simultaneous readout of four wavelengths and the continuous readout during temperature cycling. The use of a detachable eluate tube as an interface affords semi-automation of the LabSystem, which does not require specialized training. It reduces the hands-on time from about 50 to 3 min with only two handling steps: sample input and transfer of the detachable detection tube.

  15. 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.

  16. 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

  17. 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.

  18. 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

    2015-11-24

    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.

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

    Directory of Open Access Journals (Sweden)

    Priya Choudhry

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

  20. Design and Elementary Evaluation of a Highly-Automated Fluorescence-Based Instrument System for On-Site Detection of Food-Borne Pathogens

    Directory of Open Access Journals (Sweden)

    Zhan Lu

    2017-02-01

    Full Text Available A simple, highly-automated instrument system used for on-site detection of foodborne pathogens based on fluorescence was designed, fabricated, and preliminarily tested in this paper. A corresponding method has been proved effective in our previous studies. This system utilizes a light-emitting diode (LED to excite fluorescent labels and a spectrometer to record the fluorescence signal from samples. A rotation stage for positioning and switching samples was innovatively designed for high-throughput detection, ten at most in one single run. We also developed software based on LabVIEW for data receiving, processing, and the control of the whole system. In the test of using a pure quantum dot (QD solution as a standard sample, detection results from this home-made system were highly-relevant with that from a well-commercialized product and even slightly better reproducibility was found. And in the test of three typical kinds of food-borne pathogens, fluorescence signals recorded by this system are highly proportional to the variation of the sample concentration, with a satisfied limit of detection (LOD (nearly 102–103 CFU·mL−1 in food samples. Additionally, this instrument system is low-cost and easy-to-use, showing a promising potential for on-site rapid detection of food-borne pathogens.

  1. Design and Elementary Evaluation of a Highly-Automated Fluorescence-Based Instrument System for On-Site Detection of Food-Borne Pathogens.

    Science.gov (United States)

    Lu, Zhan; Zhang, Jianyi; Xu, Lizhou; Li, Yanbin; Chen, Siyu; Ye, Zunzhong; Wang, Jianping

    2017-02-23

    A simple, highly-automated instrument system used for on-site detection of foodborne pathogens based on fluorescence was designed, fabricated, and preliminarily tested in this paper. A corresponding method has been proved effective in our previous studies. This system utilizes a light-emitting diode (LED) to excite fluorescent labels and a spectrometer to record the fluorescence signal from samples. A rotation stage for positioning and switching samples was innovatively designed for high-throughput detection, ten at most in one single run. We also developed software based on LabVIEW for data receiving, processing, and the control of the whole system. In the test of using a pure quantum dot (QD) solution as a standard sample, detection results from this home-made system were highly-relevant with that from a well-commercialized product and even slightly better reproducibility was found. And in the test of three typical kinds of food-borne pathogens, fluorescence signals recorded by this system are highly proportional to the variation of the sample concentration, with a satisfied limit of detection (LOD) (nearly 10²-10³ CFU·mL(-1) in food samples). Additionally, this instrument system is low-cost and easy-to-use, showing a promising potential for on-site rapid detection of food-borne pathogens.

  2. 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.

  3. 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…

  4. Falls and comorbidity

    DEFF Research Database (Denmark)

    Jørgensen, Terese Sara Høj; Hansen, Annette Højmann; Sahlberg, Marie

    2014-01-01

    AIMS: To compare nationwide time trends and mortality in hip and proximal humeral fractures; to explore associations between incidences of falls risk related comorbidities (FRICs) and incidence of fractures. METHODS: The study is a retrospective cohort study using nationwide Danish administrative....... CONCLUSIONS: The results suggest that the overall reduction in fractures can be explained by reduction in falls related comorbidity....

  5. Fall detection system based on three-axis acceleration sensor for the elderly%基于三轴加速度传感器的老人摔倒检测

    Institute of Scientific and Technical Information of China (English)

    崔英辉; 詹林

    2013-01-01

    老人因意外摔倒不能及时救助会造成严重的后果,发生意外时若能及时通知救援人员,可大大降低摔倒后的危险程度.三轴加速度传感器能够采集分析人体摔倒时三个方向的加速度变化特征,以判断老人是否摔倒.主要分析了三轴加速度传感器ADXL345的特点及工作原理,最后提出一种检测老人意外摔倒的方案.%That the old man fall down by accident and receive no help in time leads to serious consequences. If the acci-dent happens when the rescue workers are notified promptly, it can greatly reduce the danger. Three-axis acceleration sensor can collect and analyze the acceleration variation characteristics of the human body in three directions to judge whether the old man fall down. The paper mainly analyzes the characteristics and working principles of three-axis acceleration sensor ADXL345, and finally presents a scheme of fall detection for the elderly.

  6. Fall prevention conceptual framework.

    Science.gov (United States)

    Abraham, Sam

    2011-01-01

    Falls can have lasting psychological and physical consequences, particularly fractures and slow-healing processes, and patients may also lose confidence in walking. Injuries from falls lead to functional decline, institutionalization, higher health care costs, and decreased quality of life. The process related to the problem of patient falls in the hospital, using the nursing model developed by the theorist, Ida Jean Orlando, is explained in this article. The useful tool that provides guidance to marketers in this endeavor is Maslow's hierarchy of needs. During acute illness, individuals are greatly in need of satisfying their physiological needs. If these needs are not met, patients leave the hospital lacking a positive experience. Initial fall risk assessment is critical to plan intervention and individualize care plan. Interventions depend on the severity of fall risk factors.

  7. 基于MEMS三轴加速度计的跌倒检测电路的设计%Design of Fall Detection Circuit Based on MEMS Triaxial Accelerometer

    Institute of Scientific and Technical Information of China (English)

    王剑

    2013-01-01

    随着社会老龄化进程的不断发展,老年人口所占比重也逐年增加,而老年人的生理特点造成了他们这一人群的特殊行为特征易跌倒.为了解决跌倒监测、报警求助和步态加速度数据等问题,本文在分析比较国内外跌倒检测及相关技术的基础上,考虑了系统的实用性等因素,设计了一个跌倒检测的实时监测系统,它采用MEMS三轴加速度传感器ADXL345进行加速度数据采集,中央处理器TMS320F2812进行数据分析,并设计报警器来完成监测系统的搭建.实验结果表明该系统在老人跌倒时能够监测并进行报警,为及时进行救援赢得了宝贵的时间.%With the continuous development of social aging process,the share of older population is also growing and physiological characteristic of the elderly causes special behavior---easy to fall.On the basis of analyzing and comparing fall detection at indoor and outdoor and considering the practicality of the system,a real-time monitoring system for fall detection is designed to solve the problem of fall monitoringi(c)alarm for help and gait acceleration data.Triaxial accelerometer monitoring system is built by MEMS triaxial accelerometer ADXL345 to collect acceleration datai(c)central processor analyzing data and alarm.Experimental results show that system can monitor falls,alarm and rescue in time.

  8. 独居老人云智能跌倒实时检测系统的开发%Development of cloud intelligent real-time fall detection system for the aged population

    Institute of Scientific and Technical Information of China (English)

    石栋; 张克华; 徐彪

    2016-01-01

    A cloud intelligent real-time fall detection system is developed to accurately judge the aged people’s fall and get timely help. This cloud intelligent system effectively integrates the new MEMS senor technology, data communication technology and control technology. Firstly, the system collects the data of aged population’s ADL(Activities of Daily Living) through the detecting device. And then the Support Vector Machine(SVM)algorithm is used to deal with the data. Finally, the characteristic data is output and uploaded to the internet of things cloud platform through GPRS, simultaneously sending the fall SMS to the guardian’s mobile phone. The experimental results show that the accuracy of fall judgment is 100 percent, and by using phone APP or the internet of things cloud platform the guardian can view the aged population’s real-time ADL and get the fall SMS. This device can break through the long-distance limit to effectively care for the aged population.%为了准确判断独居老人跌倒并且及时救助,设计开发了一种云智能实时检测系统。该云智能检测系统有效地集成了新型MEMS传感器、通信以及控制等先进技术,实现准确判断、实时检测和及时救助功能。系统通过检测装置采集独居老人日常活动数据,通过支持向量机算法(SVM)对数据进行处理,输出特征数据并通过GPRS将数据上传至物联网云平台,同时将跌倒信息发送给监护人手机。并对各种跌倒状况进行各50次实验,其结果表明:跌倒判断的正确率为100%;并且通过手机APP或者物联网云平台监护人可以实时查看独居老人日常活动,同时能接收跌倒消息以便及时救助。该装置可以突破距离限制,远程实时有效监护独居老人。

  9. 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.

  10. Infrared fluorescent automated detection of thirteen short tandem repeat polymorphisms and one gender-determining system of the CODIS core system.

    Science.gov (United States)

    Ricci, U; Sani, I; Guarducci, S; Biondi, C; Pelagatti, S; Lazzerini, V; Brusaferri, A; Lapini, M; Andreucci, E; Giunti, L; Giovannucci Uzielli, M L

    2000-11-01

    We used an infrared (IR) automated fluorescence monolaser sequencer for the analysis of 13 autosomal short tandem repeat (STR) systems (TPOX, D3S1358, FGA, CSF1PO, D5S818, D7S820, D8S1179, TH01, vWA, D13S317, D16S359, D18S51, D21S11) and the X-Y homologous gene amelogenin system. These two systems represent the core of the combined DNA index systems (CODIS). Four independent multiplex reactions, based on the polymerase chain reaction (PCR) technique and on the direct labeling of the forward primer of every primer pair, with a new molecule (IRDye800), were set up, permitting the exact characterization of the alleles by comparison with ladders of specific sequenced alleles. This is the first report of the whole analysis of the STRs of the CODIS core using an IR automated DNA sequencer. The protocol was used to solve paternity/maternity tests and for population studies. The electrophoretic system also proved useful for the correct typing of those loci differing in size by only 2 bp. A sensibility study demonstrated that the test can detect an average of 10 pg of undegraded human DNA. We also performed a preliminary study analyzing some forensic samples and mixed stains, which suggested the usefulness of using this analytical system for human identification as well as for forensic purposes.

  11. 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.

  12. Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images.

    Science.gov (United States)

    Tsibidis, George D; Burroughs, Nigel J; Gaze, William; Wellington, Elizabeth M H

    2011-12-01

    Interaction between bacteria and protozoa is an increasing area of interest, however there are a few systems that allow extensive observation of the interactions. A semi-automated approach is proposed to analyse a large amount of experimental data and avoid a time demanding manual object classification. We examined a surface system consisting of non nutrient agar with a uniform bacterial lawn that extended over the agar surface, and a spatially localised central population of amoebae. Location and identification of protozoa and quantification of bacteria population are performed by the employment of image analysis techniques in a series of spatial images. The quantitative tools are based on intensity thresholding, or on probabilistic models. To accelerate organism identification, correct classification errors and attain quantitative details of all objects a custom written Graphical User Interfaces has also been developed.

  13. Automation or De-automation

    Science.gov (United States)

    Gorlach, Igor; Wessel, Oliver

    2008-09-01

    In the global automotive industry, for decades, vehicle manufacturers have continually increased the level of automation of production systems in order to be competitive. However, there is a new trend to decrease the level of automation, especially in final car assembly, for reasons of economy and flexibility. In this research, the final car assembly lines at three production sites of Volkswagen are analysed in order to determine the best level of automation for each, in terms of manufacturing costs, productivity, quality and flexibility. The case study is based on the methodology proposed by the Fraunhofer Institute. The results of the analysis indicate that fully automated assembly systems are not necessarily the best option in terms of cost, productivity and quality combined, which is attributed to high complexity of final car assembly systems; some de-automation is therefore recommended. On the other hand, the analysis shows that low automation can result in poor product quality due to reasons related to plant location, such as inadequate workers' skills, motivation, etc. Hence, the automation strategy should be formulated on the basis of analysis of all relevant aspects of the manufacturing process, such as costs, quality, productivity and flexibility in relation to the local context. A more balanced combination of automated and manual assembly operations provides better utilisation of equipment, reduces production costs and improves throughput.

  14. 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%.

  15. Falls and falls efficacy: the role of sustained attention in older adults

    Directory of Open Access Journals (Sweden)

    O'Halloran Aisling M

    2011-12-01

    Full Text Available 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 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 strategies.

  16. Development of a Real-Time PCR Protocol Requiring Minimal Handling for Detection of Vancomycin-Resistant Enterococci with the Fully Automated BD Max System.

    Science.gov (United States)

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

    2016-09-01

    Vancomycin-resistant enterococci (VRE) are an important cause of health care-associated infections, resulting in significant mortality and a significant economic burden in hospitals. Active surveillance for at-risk populations contributes to the prevention of infections with VRE. The availability of a combination of automation and molecular detection procedures for rapid screening would be beneficial. Here, we report on the development of a laboratory-developed PCR for detection of VRE which runs on the fully automated Becton Dickinson (BD) Max platform, which combines DNA extraction, PCR setup, and real-time PCR amplification. We evaluated two protocols: one using a liquid master mix and the other employing commercially ordered dry-down reagents. The BD Max VRE PCR was evaluated in two rounds with 86 and 61 rectal elution swab (eSwab) samples, and the results were compared to the culture results. The sensitivities of the different PCR formats were 84 to 100% for vanA and 83.7 to 100% for vanB; specificities were 96.8 to 100% for vanA and 81.8 to 97% for vanB The use of dry-down reagents and the ExK DNA-2 kit for extraction showed that the samples were less inhibited (3.3%) than they were by the use of the liquid master mix (14.8%). Adoption of a cutoff threshold cycle of 35 for discrimination of vanB-positive samples allowed an increase of specificity to 87.9%. The performance of the BD Max VRE assay equaled that of the BD GeneOhm VanR assay, which was run in parallel. The use of dry-down reagents simplifies the assay and omits any need to handle liquid PCR reagents.

  17. Automated detection and volumetric segmentation of the spleen in CT scans; Automatische Detektion und volumetrische Segmentierung der Milz in CT-Untersuchungen

    Energy Technology Data Exchange (ETDEWEB)

    Hammon, M.; Dankerl, P.; Janka, R.; Uder, M.; Cavallaro, A. [Universitaetsklinikum Erlangen (Germany). Radiologisches Inst.; Kramer, M.; Seifert, S.; Tsymbal, A.; Costa, M.J. [Siemens AG, Erlangen (Germany). Corporate Technology

    2012-08-15

    To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm{sup 3} + 0.58 (width x length x thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 ({+-} 2.4) seconds. The average splenic volume measured by aV was 268.21 {+-} 114.67 cm{sup 3} compared to 281.58 {+-} 130.21 cm{sup 3} in mV and 268.93 {+-} 104.60 cm{sup 3} in eV. The correlation coefficient was 0.99 (coefficient of determination (R{sup 2}) = 0.98) for aV and mV, 0.91 (R{sup 2} = 0.83) for mV and eV and 0.91 (R{sup 2} = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R{sup 2} = 0.84), mV and eV (0.95; R{sup 2} = 0.91) and aV and eV (0.83; R{sup 2} = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

  18. Application of an Automated System for the Processing of VLF signals to Detect, Analyze and Classify Seismic-Ionospheric Precursor Phenomena

    Science.gov (United States)

    Skeberis, Christos; Xenos, Thomas; Contadakis, Michael; Arabelos, Dimitrios; Biagi, Pier Francesco; Maggipinto, Tommaso

    2013-04-01

    This paper studies the development and application of an automated system based on Predictive Modular Neural Networks (PREMONNs) and Self Organizing Maps (SOMs) along with the necessary backend development of database classification required to provide a fully integrated system for detecting disturbances that can be attributed to seismic-ionospheric precursor phenomena using VLF radio signals. The aforementioned system can analyze all the relevant data and bring forth and adaptively discriminate different characteristics in the received signals, in real time in order to provide data segments of interest that can be correlated to subsequent seismic phenomena and can be classified with respect to pre-recorded samples of previous points of interest (POIs). PREMONNs as it was demonstrated in previous studies can be used for time-series switching detection and can be applied to the detection of POIs , whereas SOMs have been extensively used in unsupervised pattern recognition and classification of datasets. For the purpose of this paper, data acquired in Thessaloniki (40.59N, 22,78E) from the VLF station in Tavolara, Italy (ICV station Lat 40.923, Lon. 9.731) for over two years (December 2010 - December 2012) are used. The receiver was developed by Elettronika Srl, and is part of the International Network for Frontier Research on Earthquake Precursors (INFREP). The received VLF signal is normalized and then processed using the Empirical Mode Decomposition Method (EMD). The resulting data are passed to an Artificial Neural Network (ANN) based on PREMONNs trained specifically for this purpose and the output from that stage is passed onto a classifier based on SOMs to compare and classify points of interest based on a current database of received signals and identifying and storing new ones for future reference. The efficacy of the detection and the results of the aforementioned process is then discussed and results are presented. Therefore, based on the results it may be

  19. 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

  20. <