WorldWideScience

Sample records for automatic incident detection

  1. Sensitivity Analysis Based SVM Application on Automatic Incident Detection of Rural Road in China

    Directory of Open Access Journals (Sweden)

    Xingliang Liu

    2018-01-01

    Full Text Available Traditional automatic incident detection methods such as artificial neural networks, backpropagation neural network, and Markov chains are not suitable for addressing the incident detection problem of rural roads in China which have a relatively high accident rate and a low reaction speed caused by the character of small traffic volume. This study applies the support vector machine (SVM and parameter sensitivity analysis methods to build an accident detection algorithm in a rural road condition, based on real-time data collected in a field experiment. The sensitivity of four parameters (speed, front distance, vehicle group time interval, and free driving ratio is analyzed, and the data sets of two parameters with a significant sensitivity are chosen to form the traffic state feature vector. The SVM and k-fold cross validation (K-CV methods are used to build the accident detection algorithm, which shows an excellent performance in detection accuracy (98.15% of the training data set and 87.5% of the testing data set. Therefore, the problem of low incident reaction speed of rural roads in China could be solved to some extent.

  2. Automatic EEG spike detection.

    Science.gov (United States)

    Harner, Richard

    2009-10-01

    Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

  3. Automatic detection of laughter

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2005-01-01

    In the context of detecting ‘paralinguistic events’ with the aim to make classification of the speaker’s emotional state possible, a detector was developed for one of the most obvious ‘paralinguistic events’, namely laughter. Gaussian Mixture Models were trained with Perceptual Linear Prediction

  4. Automatic Conflict Detection on Contracts

    Science.gov (United States)

    Fenech, Stephen; Pace, Gordon J.; Schneider, Gerardo

    Many software applications are based on collaborating, yet competing, agents or virtual organisations exchanging services. Contracts, expressing obligations, permissions and prohibitions of the different actors, can be used to protect the interests of the organisations engaged in such service exchange. However, the potentially dynamic composition of services with different contracts, and the combination of service contracts with local contracts can give rise to unexpected conflicts, exposing the need for automatic techniques for contract analysis. In this paper we look at automatic analysis techniques for contracts written in the contract language mathcal{CL}. We present a trace semantics of mathcal{CL} suitable for conflict analysis, and a decision procedure for detecting conflicts (together with its proof of soundness, completeness and termination). We also discuss its implementation and look into the applications of the contract analysis approach we present. These techniques are applied to a small case study of an airline check-in desk.

  5. Automatic Detection of Terminology Evolution

    Science.gov (United States)

    Tahmasebi, Nina

    As archives contain documents that span over a long period of time, the language used to create these documents and the language used for querying the archive can differ. This difference is due to evolution in both terminology and semantics and will cause a significant number of relevant documents being omitted. A static solution is to use query expansion based on explicit knowledge banks such as thesauri or ontologies. However as we are able to archive resources with more varied terminology, it will be infeasible to use only explicit knowledge for this purpose. There exist only few or no thesauri covering very domain specific terminologies or slang as used in blogs etc. In this Ph.D. thesis we focus on automatically detecting terminology evolution in a completely unsupervised manner as described in this technical paper.

  6. Real time freeway incident detection.

    Science.gov (United States)

    2014-04-01

    The US Department of Transportation (US-DOT) estimates that over half of all congestion : events are caused by highway incidents rather than by rush-hour traffic in big cities. Real-time : incident detection on freeways is an important part of any mo...

  7. Criticality incident detection assessment methodology

    International Nuclear Information System (INIS)

    Haley, Richard M.; Warburton, Simon J.; Bowden, Russell L.

    2003-01-01

    In the United Kingdom, all nuclear facilities that handle, treat or store fissile material require a Criticality Incident Detection and Alarm System (CIDAS) to be installed, unless a case is made for the omission of such a system. Where it is concluded that a CIDAS is required, the primary objective is the reliable detection of criticality and the initiation of prompt evacuation of plant workers from the vicinity of the incident. This paper will examine and compare various methods that can be used to demonstrate that a CIDAS will satisfy the detection criterion. The paper will focus on fit-for-purpose and cost-effective methods for the assessment of gamma-based systems. In the experience of the authors this is particularly useful in demonstrating the efficacy of existing systems in operational plant. (author)

  8. Automatic Detection of Fake News

    OpenAIRE

    Pérez-Rosas, Verónica; Kleinberg, Bennett; Lefevre, Alexandra; Mihalcea, Rada

    2017-01-01

    The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news...

  9. Detection of a regulating valve closure failure during review of recorded data after an automatic reactor shut down. Incident at the NPP Beznau-1, 27 April 1995

    International Nuclear Information System (INIS)

    Deutschmann, H.

    1996-01-01

    After recognizing a leak in the oil system of the running main feedwater pump 1 during rated power operation of the plant the operator changed feedwater supply manually to the stand-by pump 2. A short time later pump 2 was automatically tripped by the signal ''low oil pressure''. Immediate reduction of the reactor power by the operator was not successful because the scram signal ''low steam generator level and mismatch of steam/feedwater flow'' occurred and scram was actuated. In this plant a special operating feature, actuated by the scram signal, is implemented to reduce steam release to atmosphere in case of scram. The signal ''scram and average primary Temperature >287 deg. C opens the feedwater regulating valves, and later, if the average primary temperature decreases to <287 deg. C, they reclose by a redundant signal. In the experienced event, after the scram actuation, in the steam generator A a feedwater overfill occurred. The overfill protection tripped the operating feedwater pumps (main feedwater pump 3 and two auxiliary feedwater pumps). The large injection of water produced an overcooling of the primary with isolation of the volume control system outlet of the primary. The operator repaired the defective oil coolers of the feedwater pumps and restarted the plant. At that time, he had not recognized, that the plant response, which caused the steam generator overfill, was wrong. One day later, as all the recorded data were reviewed in more detail, it was found that the closure time of the feedwater regulating valve to steam generator A was much longer than designed (19 s instead 7 s). The operator requested an LCO for continued operation in spite of the fact, that the closure time was not fixed in the Technical specification. 3 figs

  10. Oocytes Polar Body Detection for Automatic Enucleation

    Directory of Open Access Journals (Sweden)

    Di Chen

    2016-02-01

    Full Text Available Enucleation is a crucial step in cloning. In order to achieve automatic blind enucleation, we should detect the polar body of the oocyte automatically. The conventional polar body detection approaches have low success rate or low efficiency. We propose a polar body detection method based on machine learning in this paper. On one hand, the improved Histogram of Oriented Gradient (HOG algorithm is employed to extract features of polar body images, which will increase success rate. On the other hand, a position prediction method is put forward to narrow the search range of polar body, which will improve efficiency. Experiment results show that the success rate is 96% for various types of polar bodies. Furthermore, the method is applied to an enucleation experiment and improves the degree of automatic enucleation.

  11. Automatic Smoker Detection from Telephone Speech Signals

    DEFF Research Database (Denmark)

    Poorjam, Amir Hossein; Hesaraki, Soheila; Safavi, Saeid

    2017-01-01

    This paper proposes an automatic smoking habit detection from spontaneous telephone speech signals. In this method, each utterance is modeled using i-vector and non-negative factor analysis (NFA) frameworks, which yield low-dimensional representation of utterances by applying factor analysis...... method is evaluated on telephone speech signals of speakers whose smoking habits are known drawn from the National Institute of Standards and Technology (NIST) 2008 and 2010 Speaker Recognition Evaluation databases. Experimental results over 1194 utterances show the effectiveness of the proposed approach...... for the automatic smoking habit detection task....

  12. Detecting Terrorism Incidence Type from News Summary

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah

    2012-01-01

    The paper presents the experiments to detect terrorism incidence type from news summary data. We have applied classification techniques on news summary data to analyze the incidence and detect the type of incidence. A number of experiments are conducted using various classification algorithms...... and results show that a simple decision tree classifier can learn incidence type with satisfactory results from news data....

  13. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    NARCIS (Netherlands)

    Bouma, H.; Baan, J.; Burghouts, G.J.; Eendebak, P.T.; Huis, J.R. van; Dijk, J.; Rest, J.H.C. van

    2014-01-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature

  14. Automatic target detection using binary template matching

    Science.gov (United States)

    Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

    2005-03-01

    This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

  15. Automatic blood detection in capsule endoscopy video

    Czech Academy of Sciences Publication Activity Database

    Novozámský, Adam; Flusser, Jan; Tachecí, I.; Sulík, L.; Bureš, J.; Krejcar, O.

    2016-01-01

    Roč. 21, č. 12 (2016), s. 1-8, č. článku 126007. ISSN 1083-3668 R&D Projects: GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Automatic blood detection * capsule endoscopy video Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.530, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0466936.pdf

  16. Automatic Detection of Electric Power Troubles (ADEPT)

    Science.gov (United States)

    Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

    1988-11-01

    Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

  17. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

    2012-01-01

    Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...... of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure detection...... sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded...

  18. Automatic detection, tracking and sensor integration

    Science.gov (United States)

    Trunk, G. V.

    1988-06-01

    This report surveys the state of the art of automatic detection, tracking, and sensor integration. In the area of detection, various noncoherent integrators such as the moving window integrator, feedback integrator, two-pole filter, binary integrator, and batch processor are discussed. Next, the three techniques for controlling false alarms, adapting thresholds, nonparametric detectors, and clutter maps are presented. In the area of tracking, a general outline is given of a track-while-scan system, and then a discussion is presented of the file system, contact-entry logic, coordinate systems, tracking filters, maneuver-following logic, tracking initiating, track-drop logic, and correlation procedures. Finally, in the area of multisensor integration the problems of colocated-radar integration, multisite-radar integration, radar-IFF integration, and radar-DF bearing strobe integration are treated.

  19. Automatic system for detecting pornographic images

    Science.gov (United States)

    Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

    2002-09-01

    Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

  20. Improvement and development of automatic detection techniques

    International Nuclear Information System (INIS)

    Yamada, Kiyomi; Takai, Setsuo; Togashi, Chikako; Itami, Jun

    2000-01-01

    For detection of radiation-induced mutation, establishment of a new sample preparation method and its procedures suitable for its automation is thought to be the key step to improve the detection efficacy and save labor. In this study, an investigation was made on the sensitivity to radiation exposure in respect of the occurrence of chromosomal breakage by high precision chromosome coloring method utilizing FISH. The number of chromosome breakage per cell was determined in chromosome 1, 4, 5, 9, 11 and 13 prepared from an identical sample exposed to three different grays. The breakage number was found to increase linearly as an increase in the amount of chromosomal DNA and hotspots of the radiation-induced breakages tended to concentrate in R band and the position of R band was almost coincident with the sites of chromosomal translocation breakages specific to leukemia, showing a correlation of radiation exposure to leukemia. Chromosome 13, 14 and 15, which were different in band pattern but similar in its length taken from cells exposed to X-ray at 5 Gy were investigated in detail and it was found that the sensitivity of chromosome to radiation was depending on the quantity and the quality of R band in each chromosome. The benefits of this chromosome coloring method for the analysis of chromosome breakage were as follows: when compared with the conventional dicentric method, the kinds of chromosomal abnormalities to be detectable were much more and its detection rate as well as accuracy was higher. In addition, the time required for determination was loess than one tenth of the conventional one. A breakage site was detectable with differences in color tone and thus, any special technique was not necessary. Therefore, the chromosome coloring method by FISH was demonstrated to be much suitable for automatic image analysis by computer. (M.N.)

  1. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    OpenAIRE

    Bouma, H.; Baan, J.; Burghouts, G.J.; Eendebak, P.T.; Huis, J.R. van; Dijk, J.; Rest, J.H.C. van

    2014-01-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snat...

  2. Automatic Earthquake Detection by Active Learning

    Science.gov (United States)

    Bergen, K.; Beroza, G. C.

    2017-12-01

    In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

  3. Automatic Detect and Trace of Solar Filaments

    Science.gov (United States)

    Fang, Cheng; Chen, P. F.; Tang, Yu-hua; Hao, Qi; Guo, Yang

    We developed a series of methods to automatically detect and trace solar filaments in solar Hα images. The programs are able to not only recognize filaments and determine their properties, such as the position, the area and other relevant parameters, but also to trace the daily evolution of the filaments. For solar full disk Hα images, the method consists of three parts: first, preprocessing is applied to correct the original images; second, the Canny edge-detection method is used to detect the filaments; third, filament properties are recognized through the morphological operators. For each Hα filament and its barb features, we introduced the unweighted undirected graph concept and adopted Dijkstra shortest-path algorithm to recognize the filament spine; then, using polarity inversion line shift method for measuring the polarities in both sides of the filament to determine the filament axis chirality; finally, employing connected components labeling method to identify the barbs and calculating the angle between each barb and spine to indicate the barb chirality. Our algorithms are applied to the observations from varied observatories, including the Optical & Near Infrared Solar Eruption Tracer (ONSET) in Nanjing University, Mauna Loa Solar Observatory (MLSO) and Big Bear Solar Observatory (BBSO). The programs are demonstrated to be effective and efficient. We used our method to automatically process and analyze 3470 images obtained by MLSO from January 1998 to December 2009, and a butterfly diagram of filaments is obtained. It shows that the latitudinal migration of solar filaments has three trends in the Solar Cycle 23: The drift velocity was fast from 1998 to the solar maximum; after the solar maximum, it became relatively slow and after 2006, the migration became divergent, signifying the solar minimum. About 60% filaments with the latitudes larger than 50 degree migrate towards the Polar Regions with relatively high velocities, and the latitudinal migrating

  4. Brain correlates of automatic visual change detection.

    Science.gov (United States)

    Cléry, H; Andersson, F; Fonlupt, P; Gomot, M

    2013-07-15

    A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. Automatic Encoding and Language Detection in the GSDL

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2014-10-01

    Full Text Available Automatic detection of encoding and language of the text is part of the Greenstone Digital Library Software (GSDL for building and distributing digital collections. It is developed by the University of Waikato (New Zealand in cooperation with UNESCO. The automatic encoding and language detection in Slavic languages is difficult and it sometimes fails. The aim is to detect cases of failure. The automatic detection in the GSDL is based on n-grams method. The most frequent n-grams for Czech are presented. The whole process of automatic detection in the GSDL is described. The input documents to test collections are plain texts encoded in ISO-8859-1, ISO-8859-2 and Windows-1250. We manually evaluated the quality of automatic detection. To the causes of errors belong the improper language model predominance and the incorrect switch to Windows-1250. We carried out further tests on documents that were more complex.

  6. Automatic polyp detection in colonoscopy videos

    Science.gov (United States)

    Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

    2017-02-01

    Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

  7. Detecting accuracy of flaws by manual and automatic ultrasonic inspections

    International Nuclear Information System (INIS)

    Iida, K.

    1988-01-01

    As the final stage work in the nine year project on proving tests of the ultrasonic inspection technique applied to the ISI of LWR plants, automatic ultrasonic inspection tests were carried out on EDM notches, surface fatigue cracks, weld defects and stress corrosion cracks, which were deliberately introduced in full size structural components simulating a 1,100 MWe BWR. Investigated items are the performance of a newly assembled automatic inspection apparatus, detection limit of flaws, detection resolution of adjacent collinear or parallel EDM notches, detection reproducibility and detection accuracy. The manual ultrasonic inspection of the same flaws as inspected by the automatic ultrasonic inspection was also carried out in order to have comparative data. This paper reports how it was confirmed that the automatic ultrasonic inspection is much superior to the manual inspection in the flaw detection rate and in the detection reproducibility

  8. Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a

  9. Automatic video surveillance of outdoor scenes using track before detect

    DEFF Research Database (Denmark)

    Hansen, Morten; Sørensen, Helge Bjarup Dissing; Birkemark, Christian M.

    2005-01-01

    This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due...

  10. Automatic invariant detection in dynamic web applications

    NARCIS (Netherlands)

    Groeneveld, F.; Mesbah, A.; Van Deursen, A.

    2010-01-01

    The complexity of modern web applications increases as client-side JavaScript and dynamic DOM programming are used to offer a more interactive web experience. In this paper, we focus on improving the dependability of such applications by automatically inferring invariants from the client-side and

  11. Using Polarization features of visible light for automatic landmine detection

    NARCIS (Netherlands)

    Jong, W. de; Schavemaker, J.G.M.

    2007-01-01

    This chapter describes the usage of polarization features of visible light for automatic landmine detection. The first section gives an introduction to land-mine detection and the usage of camera systems. In section 2 detection concepts and methods that use polarization features are described.

  12. IMPLEMENTATION OF INCIDENT DETECTION ALGORITHM BASED ON FUZZY LOGIC IN PTV VISSIM

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-05-01

    Full Text Available Traffic incident management is a major challenge in the management of movement, requiring constant attention and significant investment, as well as fast and accurate solutions in order to re-establish normal traffic conditions. Automatic control methods are becoming an important factor for the reduction of traffic congestion caused by an arising incident. In this paper, the algorithm of automatic detection incident based on fuzzy logic is implemented in the software PTV VISSIM. 9 different types of tests were conducted on the two lane road section segment with changing traffic conditions: the location of the road accident, loading of traffic. The main conclusion of the research is that the proposed algorithm for the incidents detection demonstrates good performance in the time of detection and false alarms

  13. Automatic change detection to facial expressions in adolescents

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Jiannong, Shi

    2016-01-01

    Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group) were...... in facial expressions between the two age groups. The current findings demonstrated that the adolescent group featured more negative vMMN amplitudes than the adult group in the fronto-central region during the 120–200 ms interval. During the time window of 370–450 ms, only the adult group showed better...... automatic processing on fearful faces than happy faces. The present study indicated that adolescent’s posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information....

  14. Real-time incident detection using social media data.

    Science.gov (United States)

    2016-05-09

    The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems : is labor-intensive. This research project mines tweet texts to extract incident information on bot...

  15. Automatic Detection of Wild-type Mouse Cranial Sutures

    DEFF Research Database (Denmark)

    Ólafsdóttir, Hildur; Darvann, Tron Andre; Hermann, Nuno V.

    , automatic detection of the cranial sutures becomes important. We have previously built a craniofacial, wild-type mouse atlas from a set of 10 Micro CT scans using a B-spline-based nonrigid registration method by Rueckert et al. Subsequently, all volumes were registered nonrigidly to the atlas. Using......, the observer traced the sutures on each of the mouse volumes as well. The observer outperforms the automatic approach by approximately 0.1 mm. All mice have similar errors while the suture error plots reveal that suture 1 and 2 are cumbersome, both for the observer and the automatic approach. These sutures can...

  16. Development of automatic flaw detection systems for magnetic particle examination

    International Nuclear Information System (INIS)

    Shirai, T.; Kimura, J.; Amako, T.

    1988-01-01

    Utilizing a video camera and an image processor, development was carried out on automatic flaw detection and discrimination techniques for the purpose of achieving automated magnetic particle examination. Following this, fluorescent wet magnetic particle examination systems for blade roots and rotor grooves of turbine rotors and the non-fluorescent dry magnetic particle examination system for butt welds, were developed. This paper describes these automatic magnetic particle examination (MT) systems and the functional test results

  17. Automatic patient respiration failure detection system with wireless transmission

    Science.gov (United States)

    Dimeff, J.; Pope, J. M.

    1968-01-01

    Automatic respiration failure detection system detects respiration failure in patients with a surgically implanted tracheostomy tube, and actuates an audible and/or visual alarm. The system incorporates a miniature radio transmitter so that the patient is unencumbered by wires yet can be monitored from a remote location.

  18. Automatic detection of microaneurysms using microstructure and ...

    Indian Academy of Sciences (India)

    with DIARETDB0 and DIARETDB1 datasets using a classifier based on multi- ... developed, the accurate detection of microaneurysm in colour retinal images .... Two dictionary elements of vessel and non-vessel were used in the sparse rep- ..... Images are analyzed from the point of view of its specific microstructures: ridges,.

  19. Automatic Emergence Detection in Complex Systems

    Directory of Open Access Journals (Sweden)

    Eugene Santos

    2017-01-01

    Full Text Available Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs, learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

  20. Automatic Student Plagiarism Detection: Future Perspectives

    Science.gov (United States)

    Mozgovoy, Maxim; Kakkonen, Tuomo; Cosma, Georgina

    2010-01-01

    The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to…

  1. Automatic hair detection in the wild

    DEFF Research Database (Denmark)

    Julian, Pauline; Dehais, Christophe; Lauze, Francois Bernard

    2010-01-01

    This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour....... The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images....

  2. Automatic detection of anomalies in screening mammograms

    Science.gov (United States)

    2013-01-01

    Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature

  3. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

    are considered weeds. We have used a Sugar beet field as a case for evaluating the proposed crop detection method. The suggested image processing consists of: 1) locating vegetation regions in the image by thresholding the excess green image derived from the orig- inal image, 2) calculate the Hough transform......Images from Unmanned Aerial Vehicles can provide information about the weed distribution in fields. A direct way is to quantify the amount of vegetation present in different areas of the field. The limitation of this approach is that it includes both crops and weeds in the reported num- bers. To get...... of the segmented image 3) determine the dominating crop row direction by analysing output from the Hough transform and 4) use the found crop row direction to locate crop rows....

  4. Automatic food detection in egocentric images using artificial intelligence technology

    Science.gov (United States)

    Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

  5. Data mining to detect clinical mastitis with automatic milking

    NARCIS (Netherlands)

    Kamphuis, C.; Mollenhorst, H.; Heesterbeek, J.A.P.; Hogeveen, H.

    2010-01-01

    Our objective was to use data mining to develop and validate a detection model for clinical mastitis (CM) using sensor data collected at nine Dutch dairy herds milking automatically. Sensor data was available for almost 3.5 million quarter milkings (QM) from 1,109 cows; 348 QM with CM were observed

  6. Comparing different approaches for automatic pronunciation error detection

    NARCIS (Netherlands)

    Strik, Helmer; Truong, Khiet Phuong; de Wet, Febe; Cucchiarini, Catia

    2009-01-01

    One of the biggest challenges in designing computer assisted language learning (CALL) applications that provide automatic feedback on pronunciation errors consists in reliably detecting the pronunciation errors at such a detailed level that the information provided can be useful to learners. In our

  7. Automatic detection of omissions in medication lists.

    Science.gov (United States)

    Hasan, Sharique; Duncan, George T; Neill, Daniel B; Padman, Rema

    2011-01-01

    Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. Results show that collaborative filtering identifies the missing drug in the top-10 list about 40-50% of the time and the therapeutic class of the missing drug 50%-65% of the time at the three clinics in this study. Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).

  8. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

    Sorensen, Thomas Lynggaard; Olsen, Ulrich L.; Conradsen, Isa

    2010-01-01

    . The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial...... electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures....

  9. Child vocalization composition as discriminant information for automatic autism detection.

    Science.gov (United States)

    Xu, Dongxin; Gilkerson, Jill; Richards, Jeffrey; Yapanel, Umit; Gray, Sharmi

    2009-01-01

    Early identification is crucial for young children with autism to access early intervention. The existing screens require either a parent-report questionnaire and/or direct observation by a trained practitioner. Although an automatic tool would benefit parents, clinicians and children, there is no automatic screening tool in clinical use. This study reports a fully automatic mechanism for autism detection/screening for young children. This is a direct extension of the LENA (Language ENvironment Analysis) system, which utilizes speech signal processing technology to analyze and monitor a child's natural language environment and the vocalizations/speech of the child. It is discovered that child vocalization composition contains rich discriminant information for autism detection. By applying pattern recognition and machine learning approaches to child vocalization composition data, accuracy rates of 85% to 90% in cross-validation tests for autism detection have been achieved at the equal-error-rate (EER) point on a data set with 34 children with autism, 30 language delayed children and 76 typically developing children. Due to its easy and automatic procedure, it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

  10. System for automatic detection of lung nodules exhibiting growth

    Science.gov (United States)

    Novak, Carol L.; Shen, Hong; Odry, Benjamin L.; Ko, Jane P.; Naidich, David P.

    2004-05-01

    Lung nodules that exhibit growth over time are considered highly suspicious for malignancy. We present a completely automated system for detection of growing lung nodules, using initial and follow-up multi-slice CT studies. The system begins with automatic detection of lung nodules in the later CT study, generating a preliminary list of candidate nodules. Next an automatic system for registering locations in two studies matches each candidate in the later study to its corresponding position in the earlier study. Then a method for automatic segmentation of lung nodules is applied to each candidate and its matching location, and the computed volumes are compared. The output of the system is a list of nodule candidates that are new or have exhibited volumetric growth since the previous scan. In a preliminary test of 10 patients examined by two radiologists, the automatic system identified 18 candidates as growing nodules. 7 (39%) of these corresponded to validated nodules or other focal abnormalities that exhibited growth. 4 of the 7 true detections had not been identified by either of the radiologists during their initial examinations of the studies. This technique represents a powerful method of surveillance that may reduce the probability of missing subtle or early malignant disease.

  11. Automatic detection of AutoPEEP during controlled mechanical ventilation

    Directory of Open Access Journals (Sweden)

    Nguyen Quang-Thang

    2012-06-01

    Full Text Available Abstract Background Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow does not return to zero before the next inspiration, AutoPEEP is present. This simple detection however requires the eye of an expert clinician at the patient’s bedside. An automatic detection of AutoPEEP should be helpful to optimize care. Methods In this paper, a platform for automatic detection of AutoPEEP based on the flow signal available on most of recent mechanical ventilators is introduced. The detection algorithms are developed on the basis of robust non-parametric hypothesis testings that require no prior information on the signal distribution. In particular, two detectors are proposed: one is based on SNT (Signal Norm Testing and the other is an extension of SNT in the sequential framework. The performance assessment was carried out on a respiratory system analog and ex-vivo on various retrospectively acquired patient curves. Results The experiment results have shown that the proposed algorithm provides relevant AutoPEEP detection on both simulated and real data. The analysis of clinical data has shown that the proposed detectors can be used to automatically detect AutoPEEP with an accuracy of 93% and a recall (sensitivity of 90%. Conclusions The proposed platform provides an automatic early detection of AutoPEEP. Such functionality can be integrated in the currently used mechanical ventilator for continuous monitoring of the patient-ventilator interface and, therefore, alleviate the clinician task.

  12. Automatic Emotional State Detection using Facial Expression Dynamic in Videos

    Directory of Open Access Journals (Sweden)

    Hongying Meng

    2014-11-01

    Full Text Available In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems.

  13. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    Science.gov (United States)

    Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; van Huis, Jasper R.; Dijk, Judith; van Rest, Jeroen H. C.

    2014-10-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

  14. Automatic detection and visualisation of MEG ripple oscillations in epilepsy

    Directory of Open Access Journals (Sweden)

    Nicole van Klink

    2017-01-01

    Full Text Available High frequency oscillations (HFOs, 80–500 Hz in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

  15. 46 CFR 161.002-10 - Automatic fire detecting system control unit.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting system control unit. 161.002-10...-10 Automatic fire detecting system control unit. (a) General. The fire detecting system control unit... and the battery to be charged. (h) Automatic fire detecting system, battery charging and control—(1...

  16. Comparing Automatic CME Detections in Multiple LASCO and SECCHI Catalogs

    Energy Technology Data Exchange (ETDEWEB)

    Hess, Phillip [NRC Research Associate, U.S. Naval Research Laboratory, Washington, DC (United States); Colaninno, Robin C., E-mail: phillip.hess.ctr@nrl.navy.mil, E-mail: robin.colaninno@nrl.navy.mil [U.S. Naval Research Laboratory, Washington, DC (United States)

    2017-02-10

    With the creation of numerous automatic detection algorithms, a number of different catalogs of coronal mass ejections (CMEs) spanning the entirety of the Solar and Heliospheric Observatory ( SOHO ) Large Angle Spectrometric Coronagraph (LASCO) mission have been created. Some of these catalogs have been further expanded for use on data from the Solar Terrestrial Earth Observatory ( STEREO ) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) as well. We compare the results from different automatic detection catalogs (Solar Eruption Event Detection System (SEEDS), Computer Aided CME Tracking (CACTus), and Coronal Image Processing (CORIMP)) to ensure the consistency of detections in each. Over the entire span of the LASCO catalogs, the automatic catalogs are well correlated with one another, to a level greater than 0.88. Focusing on just periods of higher activity, these correlations remain above 0.7. We establish the difficulty in comparing detections over the course of LASCO observations due to the change in the instrument image cadence in 2010. Without adjusting catalogs for the cadence, CME detection rates show a large spike in cycle 24, despite a notable drop in other indices of solar activity. The output from SEEDS, using a consistent image cadence, shows that the CME rate has not significantly changed relative to sunspot number in cycle 24. These data, and mass calculations from CORIMP, lead us to conclude that any apparent increase in CME rate is a result of the change in cadence. We study detection characteristics of CMEs, discussing potential physical changes in events between cycles 23 and 24. We establish that, for detected CMEs, physical parameters can also be sensitive to the cadence.

  17. Automatic Detection of Vehicles Using Intensity Laser and Anaglyph Image

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

    Full Text Available In this work is presented a methodology to automatic car detection motion presents in digital aerial image on urban area using intensity, anaglyph and subtracting images. The anaglyph image is used to identify the motion cars on the expose take, because the cars provide red color due the not homology between objects. An implicit model was developed to provide a digital pixel value that has the specific propriety presented early, using the ratio between the RGB color of car object in the anaglyph image. The intensity image is used to decrease the false positive and to do the processing to work into roads and streets. The subtracting image is applied to decrease the false positives obtained due the markings road. The goal of this paper is automatically detect motion cars presents in digital aerial image in urban areas. The algorithm implemented applies normalization on the left and right images and later form the anaglyph with using the translation. The results show the applicability of proposed method and it potentiality on the automatic car detection and presented the performance of proposed methodology.

  18. A Novel Cascade Classifier for Automatic Microcalcification Detection.

    Directory of Open Access Journals (Sweden)

    Seung Yeon Shin

    Full Text Available In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC. Our framework comprises three classification stages: i a random forest (RF classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii a more complex discriminative restricted Boltzmann machine (DRBM classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC curve for detection of clustered μCs.

  19. Automatic Emboli Detection System for the Artificial Heart

    Science.gov (United States)

    Steifer, T.; Lewandowski, M.; Karwat, P.; Gawlikowski, M.

    In spite of the progress in material engineering and ventricular assist devices construction, thromboembolism remains the most crucial problem in mechanical heart supporting systems. Therefore, the ability to monitor the patient's blood for clot formation should be considered an important factor in development of heart supporting systems. The well-known methods for automatic embolus detection are based on the monitoring of the ultrasound Doppler signal. A working system utilizing ultrasound Doppler is being developed for the purpose of flow estimation and emboli detection in the clinical artificial heart ReligaHeart EXT. Thesystem will be based on the existing dual channel multi-gate Doppler device with RF digital processing. A specially developed clamp-on cannula probe, equipped with 2 - 4 MHz piezoceramic transducers, enables easy system setup. We present the issuesrelated to the development of automatic emboli detection via Doppler measurements. We consider several algorithms for the flow estimation and emboli detection. We discuss their efficiency and confront them with the requirements of our experimental setup. Theoretical considerations are then met with preliminary experimental findings from a) flow studies with blood mimicking fluid and b) in-vitro flow studies with animal blood. Finally, we discuss some more methodological issues - we consider several possible approaches to the problem of verification of the accuracy of the detection system.

  20. Automatic, non-intrusive, flame detection in pipelines

    Energy Technology Data Exchange (ETDEWEB)

    Morgan, M.D.; Mehta, S.A.; Moore, R.G. [Calgary Univ., AB (Canada). Dept. of Chemical and Petroleum Engineering; Al-Himyary, T.J. [Al-Himyary Consulting Inc., Calgary, AB (Canada)

    2004-07-01

    Flames have been known to occur within small diameter pipes operating under conditions of high turbulent flow. Although there are several methods of flame detection, few offer remote, non-line-of-site detection. In particular, combustion cannot be detected in cases where flammable mixtures are carried in flare lines, storage tank vents, air drilling or improperly designed purging operations. Combustion noise is being examined as a means to address this problem. A study was conducted in which flames within a small diameter tube were automatically detected using high speed pressure measurements and a newly developed algorithm. Commercially available, high-pressure, dynamic-pressure transducers were used for the measurements. The results of an experimental study showed that combustion noise can be distinguished from other sources of noise by its inverse power law relationship with frequency. This paper presented a newly developed algorithm which provides early detection of flames when combined with high-speed pressure measurements. The algorithm can also separate combustion noise automatically from other sources of noise when combined with other filters. In this study, the noise generated by a fluttering check valve was attenuated using a stop band filter. This detection method was found to be very reliable under the conditions tests, as long as there was no flow restriction between the sensor and the flame. A flow restriction would have resulted in the detection of only the strongest flame noise. It was shown that acoustic flame detection can be applied successfully in flare stacks, industrial burners and turbine combustors. It can be 15 times more sensitive than optical or electrical methods in diagnosing combustion problems with lean burning combustors. It may also be the only method available in applications that require remote, non-line-of-sight detection. 11 refs., 3 tabs., 15 figs.

  1. Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2017-01-01

    Full Text Available Recently, microaneurysm (MA detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL. The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

  2. Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration

    Directory of Open Access Journals (Sweden)

    Jose María Armingol

    2010-03-01

    Full Text Available There are increasing applications that require precise calibration of cameras to perform accurate measurements on objects located within images, and an automatic algorithm would reduce this time consuming calibration procedure. The method proposed in this article uses a pattern similar to that of a chess board, which is found automatically in each image, when no information regarding the number of rows or columns is supplied to aid its detection. This is carried out by means of a combined analysis of two Hough transforms, image corners and invariant properties of the perspective transformation. Comparative analysis with more commonly used algorithms demonstrate the viability of the algorithm proposed, as a valuable tool for camera calibration.

  3. Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sorensen, Helge B D; Nikolic, Miki

    2014-01-01

    SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs...... during REM sleep. PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification...... the number of outliers during REM sleep was used as a quantitative measure of muscle activity. RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. CONCLUSION: The proposed work is considered...

  4. Expert knowledge for automatic detection of bullies in social networks

    NARCIS (Netherlands)

    Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of

  5. AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

    Directory of Open Access Journals (Sweden)

    P. Duncan

    2012-08-01

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

  6. Automatic detection of artifacts in converted S3D video

    Science.gov (United States)

    Bokov, Alexander; Vatolin, Dmitriy; Zachesov, Anton; Belous, Alexander; Erofeev, Mikhail

    2014-03-01

    In this paper we present algorithms for automatically detecting issues specific to converted S3D content. When a depth-image-based rendering approach produces a stereoscopic image, the quality of the result depends on both the depth maps and the warping algorithms. The most common problem with converted S3D video is edge-sharpness mismatch. This artifact may appear owing to depth-map blurriness at semitransparent edges: after warping, the object boundary becomes sharper in one view and blurrier in the other, yielding binocular rivalry. To detect this problem we estimate the disparity map, extract boundaries with noticeable differences, and analyze edge-sharpness correspondence between views. We pay additional attention to cases involving a complex background and large occlusions. Another problem is detection of scenes that lack depth volume: we present algorithms for detecting at scenes and scenes with at foreground objects. To identify these problems we analyze the features of the RGB image as well as uniform areas in the depth map. Testing of our algorithms involved examining 10 Blu-ray 3D releases with converted S3D content, including Clash of the Titans, The Avengers, and The Chronicles of Narnia: The Voyage of the Dawn Treader. The algorithms we present enable improved automatic quality assessment during the production stage.

  7. Automatic detection of animals in mowing operations using thermal cameras.

    Science.gov (United States)

    Steen, Kim Arild; Villa-Henriksen, Andrés; Therkildsen, Ole Roland; Green, Ole

    2012-01-01

    During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix) or pheasant (Phasianus colchicus) are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus) and fawns of roe deer (Capreolus capreolus) to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus) and a rabbit (Oryctolagus cuniculus) in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

  8. Fully automatic AI-based leak detection system

    Energy Technology Data Exchange (ETDEWEB)

    Tylman, Wojciech; Kolczynski, Jakub [Dept. of Microelectronics and Computer Science, Technical University of Lodz in Poland, ul. Wolczanska 221/223, Lodz (Poland); Anders, George J. [Kinectrics Inc., 800 Kipling Ave., Toronto, Ontario M8Z 6C4 (Canada)

    2010-09-15

    This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network - the decision-making component of the system - using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in and form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed. (author)

  9. Automatic Constraint Detection for 2D Layout Regularization.

    Science.gov (United States)

    Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2016-08-01

    In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

  10. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong

    2015-09-18

    In this paper, we address the problem of constraint detection for layout regularization. As layout we consider a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important for digitizing plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate the layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm to automatically detect constraints. In our results, we evaluate the proposed framework on a variety of input layouts from different applications, which demonstrates our method has superior performance to the state of the art.

  11. Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo; Medeiros, Fatima; Ushizima, Daniela

    2008-08-29

    Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).

  12. Automatic sentence extraction for the detection of scientific paper relations

    Science.gov (United States)

    Sibaroni, Y.; Prasetiyowati, S. S.; Miftachudin, M.

    2018-03-01

    The relations between scientific papers are very useful for researchers to see the interconnection between scientific papers quickly. By observing the inter-article relationships, researchers can identify, among others, the weaknesses of existing research, performance improvements achieved to date, and tools or data typically used in research in specific fields. So far, methods that have been developed to detect paper relations include machine learning and rule-based methods. However, a problem still arises in the process of sentence extraction from scientific paper documents, which is still done manually. This manual process causes the detection of scientific paper relations longer and inefficient. To overcome this problem, this study performs an automatic sentences extraction while the paper relations are identified based on the citation sentence. The performance of the built system is then compared with that of the manual extraction system. The analysis results suggested that the automatic sentence extraction indicates a very high level of performance in the detection of paper relations, which is close to that of manual sentence extraction.

  13. Automatic Shadow Detection and Removal from a Single Image.

    Science.gov (United States)

    Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

    2016-03-01

    We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

  14. Automatic detection of REM sleep in subjects without atonia

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Nikolic, Miki

    2012-01-01

    Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored...... hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity...

  15. Development of an automatic human duress detection system

    International Nuclear Information System (INIS)

    Greene, E.R.; Davis, J.G.; Tuttle, W.C.

    1979-01-01

    A method for automatically detecting duress in security personnel utilizes real-time assessment of physiological data (heart rate) to evaluate psychological stress. Using body-worn tape recorders, field data have been collected on 22 Albuquerque police officers (20 male, 2 female) to determine actual heart rate responses in both routine and life-threatening situations. Off-line computer analysis has been applied to the data to determine the speed and reliability with which an alarm could be triggered. Alarm algorithms relating field responses to laboratory collected baseline responses have been developed

  16. ARCOCT: Automatic detection of lumen border in intravascular OCT images.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

    2017-11-01

    Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

  17. Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

    Science.gov (United States)

    Sentana, I. W. B.; Jawas, N.; Asri, S. A.

    2018-01-01

    Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

  18. Traffic incidents in motorways : An empirical proposal for incident detection using data from mobile phone operators

    NARCIS (Netherlands)

    Steenbruggen, John; Tranos, Emmanouil; Rietveld, P.

    2016-01-01

    This paper proves that mobile phone usage data is an easy to use, cheap and most importantly, reliable predictor of motorway incidents. Using econometric modelling, this paper provides a proof of concept of how mobile phone usage data can be utilised to detect motorway incidents. Greater Amsterdam

  19. Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations.

    Science.gov (United States)

    Van De Gucht, Tim; Saeys, Wouter; Van Meensel, Jef; Van Nuffel, Annelies; Vangeyte, Jurgen; Lauwers, Ludwig

    2018-01-01

    Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  20. AUTOMATIC ROAD GAP DETECTION USING FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    S. Hashemi

    2012-09-01

    Full Text Available Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1 Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2 Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3 Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4 Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  1. Automatic Road Gap Detection Using Fuzzy Inference System

    Science.gov (United States)

    Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

    2011-09-01

    Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  2. Automatic correspondence detection in mammogram and breast tomosynthesis images

    Science.gov (United States)

    Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

    2012-02-01

    Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for

  3. Automatic Detection of Animals in Mowing Operations Using Thermal Cameras

    Directory of Open Access Journals (Sweden)

    Ole Green

    2012-06-01

    Full Text Available During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix or pheasant (Phasianus colchicus are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus and fawns of roe deer (Capreolus capreolus to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus and a rabbit (Oryctolagus cuniculus in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

  4. Automatic detection and classification of human epicardial atrial unipolar electrograms

    International Nuclear Information System (INIS)

    Dubé, B; Vinet, A; Xiong, F; Yin, Y; LeBlanc, A-R; Pagé, P

    2009-01-01

    This paper describes an unsupervised signal processing method applied to three-channel unipolar electrograms recorded from human atria. These were obtained by epicardial wires sutured on the right and left atria after coronary artery bypass surgery. Atrial (A) and ventricular (V) activations had to be detected and identified on each channel, and gathered across the channels when belonging to the same global event. The algorithm was developed and optimized on a training set of 19 recordings of 5 min. It was assessed on twenty-seven 2 h recordings taken just before the onset of a prolonged atrial fibrillation for a total of 1593697 activations that were validated and classified as normal atrial or ventricular activations (A, V) and premature atrial or ventricular activations (PAA, PVA). 99.93% of the activations were detected, and amongst these, 99.89% of the A and 99.75% of the V activations were correctly labelled. In the subset of the 39705 PAA, 99.83% were detected and 99.3% were correctly classified as A. The false positive rate was 0.37%. In conclusion, a reliable fully automatic detection and classification algorithm was developed that can detect and discriminate A and V activations from atrial recordings. It can provide the time series needed to develop a monitoring system aiming to identify dynamic predictors of forthcoming cardiac events such as postoperative atrial fibrillation

  5. Automatic Detection of Cortical Bones Haversian Osteonal Boundaries

    Directory of Open Access Journals (Sweden)

    Ilige Hage

    2015-10-01

    Full Text Available This work aims to automatically detect cement lines in decalcified cortical bone sections stained with H&E. Employed is a methodology developed previously by the authors and proven to successfully count and disambiguate the micro-architectural features (namely Haversian canals, canaliculi, and osteocyte lacunae present in the secondary osteons/Haversian system (osteon of cortical bone. This methodology combines methods typically considered separately, namely pulse coupled neural networks (PCNN, particle swarm optimization (PSO, and adaptive threshold (AT. In lieu of human bone, slides (at 20× magnification from bovid cortical bone are used in this study as proxy of human bone. Having been characterized, features with same orientation are used to detect the cement line viewed as the next coaxial layer adjacent to the outermost lamella of the osteon. Employed for this purpose are three attributes for each and every micro-sized feature identified in the osteon lamellar system: (1 orientation, (2 size (ellipse perimeter and (3 Euler number (a topological measure. From a training image, automated parameters for the PCNN network are obtained by forming fitness functions extracted from these attributes. It is found that a 3-way combination of these features attributes yields good representations of the overall osteon boundary (cement line. Near-unity values of classical metrics of quality (precision, sensitivity, specificity, accuracy, and dice suggest that the segments obtained automatically by the optimized artificial intelligent methodology are of high fidelity as compared with manual tracing. For bench marking, cement lines segmented by k-means did not fare as well. An analysis based on the modified Hausdorff distance (MHD of the segmented cement lines also testified to the quality of the detected cement lines vis-a-vis the k-means method.

  6. Automatic food detection in egocentric images using artificial intelligence technology.

    Science.gov (United States)

    Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

    2018-03-26

    To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

  7. An Automatic Cloud Detection Method for ZY-3 Satellite

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

    Full Text Available Automatic cloud detection for optical satellite remote sensing images is a significant step in the production system of satellite products. For the browse images cataloged by ZY-3 satellite, the tree discriminate structure is adopted to carry out cloud detection. The image was divided into sub-images and their features were extracted to perform classification between clouds and grounds. However, due to the high complexity of clouds and surfaces and the low resolution of browse images, the traditional classification algorithms based on image features are of great limitations. In view of the problem, a prior enhancement processing to original sub-images before classification was put forward in this paper to widen the texture difference between clouds and surfaces. Afterwards, with the secondary moment and first difference of the images, the feature vectors were extended in multi-scale space, and then the cloud proportion in the image was estimated through comprehensive analysis. The presented cloud detection algorithm has already been applied to the ZY-3 application system project, and the practical experiment results indicate that this algorithm is capable of promoting the accuracy of cloud detection significantly.

  8. Statistical text classifier to detect specific type of medical incidents.

    Science.gov (United States)

    Wong, Zoie Shui-Yee; Akiyama, Masanori

    2013-01-01

    WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

  9. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

    Science.gov (United States)

    Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

    2017-10-15

    Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

  10. Automatic age-related macular degeneration detection and staging

    Science.gov (United States)

    van Grinsven, Mark J. J. P.; Lechanteur, Yara T. E.; van de Ven, Johannes P. H.; van Ginneken, Bram; Theelen, Thomas; Sánchez, Clara I.

    2013-03-01

    Age-related macular degeneration (AMD) is a degenerative disorder of the central part of the retina, which mainly affects older people and leads to permanent loss of vision in advanced stages of the disease. AMD grading of non-advanced AMD patients allows risk assessment for the development of advanced AMD and enables timely treatment of patients, to prevent vision loss. AMD grading is currently performed manually on color fundus images, which is time consuming and expensive. In this paper, we propose a supervised classification method to distinguish patients at high risk to develop advanced AMD from low risk patients and provide an exact AMD stage determination. The method is based on the analysis of the number and size of drusen on color fundus images, as drusen are the early characteristics of AMD. An automatic drusen detection algorithm is used to detect all drusen. A weighted histogram of the detected drusen is constructed to summarize the drusen extension and size and fed into a random forest classifier in order to separate low risk from high risk patients and to allow exact AMD stage determination. Experiments showed that the proposed method achieved similar performance as human observers in distinguishing low risk from high risk AMD patients, obtaining areas under the Receiver Operating Characteristic curve of 0.929 and 0.934. A weighted kappa agreement of 0.641 and 0.622 versus two observers were obtained for AMD stage evaluation. Our method allows for quick and reliable AMD staging at low costs.

  11. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

  12. Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy.

    Science.gov (United States)

    Zhi, Ying Xuan; Lukasik, Michelle; Li, Michael H; Dolatabadi, Elham; Wang, Rosalie H; Taati, Babak

    2018-01-01

    Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

  13. Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

    Directory of Open Access Journals (Sweden)

    Annalisa Milella

    2012-09-01

    Full Text Available Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

  14. Vortex flows in the solar chromosphere. I. Automatic detection method

    Science.gov (United States)

    Kato, Y.; Wedemeyer, S.

    2017-05-01

    Solar "magnetic tornadoes" are produced by rotating magnetic field structures that extend from the upper convection zone and the photosphere to the corona of the Sun. Recent studies show that these kinds of rotating features are an integral part of atmospheric dynamics and occur on a large range of spatial scales. A systematic statistical study of magnetic tornadoes is a necessary next step towards understanding their formation and their role in mass and energy transport in the solar atmosphere. For this purpose, we develop a new automatic detection method for chromospheric swirls, meaning the observable signature of solar tornadoes or, more generally, chromospheric vortex flows and rotating motions. Unlike existing studies that rely on visual inspections, our new method combines a line integral convolution (LIC) imaging technique and a scalar quantity that represents a vortex flow on a two-dimensional plane. We have tested two detection algorithms, based on the enhanced vorticity and vorticity strength quantities, by applying them to three-dimensional numerical simulations of the solar atmosphere with CO5BOLD. We conclude that the vorticity strength method is superior compared to the enhanced vorticity method in all aspects. Applying the method to a numerical simulation of the solar atmosphere reveals very abundant small-scale, short-lived chromospheric vortex flows that have not been found previously by visual inspection.

  15. The application of automatic chemiluminescence machine in rapid immune detection

    International Nuclear Information System (INIS)

    Lin Aizhen; Li Xuanwei; Chen Binhong; Li Zhenqian; Chen Zhaoxuan

    2004-01-01

    Objective: To provide high-quality, rapid and dependable result for clinical practice, and give satisfactory service to patients of different economical status by supplementation with other labeling immune examination. With an innovative attitude, we carried out efficient technical reform on ACS180 automatic chemiluminescence machine, making it possible for patients to complete the whole process including examination, check-out, diagnosis and getting drugs. The reported will be issued within an hour, thus a rapid immune detection service was established in out-patients department. Methods: 1. ACS-180 automatic chemiluminescence machine is used based on the principle of chemiluminescence immune methods. 2. The reagents are provided by Ciba-Comig Company of USA, composed of anti acridinium ester antibody of liquid phase and particulate antigen of solid phase wrapped in magnetic powder. 3. Calibration and quality control: high and low concentration are set for each calibration fluid with attached standard curve. Product for quality controlling includes three concentration of low, moderate and high. Results: 1. rapid machine detection for sample: serum is replaced with plasma coagulated by heparin, and comparison among series of methods using serum or plasma suggest no significant difference exists. 2. The problem about fasting detection: chemiluminescence machine measure optical density directly, with the results hardly being influenced by turbidity. But attention should be paid to the treatment of lipid turbid samples. 3. Other innovations: (1) direct placement of sample tube on machine: a cushion is placed on sample plate to transfer sample to machine directly after centrifugation, saving time and reducing the accident in sample transference. (2) for HCG quantification in blood and urine, 'gold criteria' is used firstly in screening to determine approximately the dilution range, with an advantage of saving time and reagent as well as accuracy. (3) we design a

  16. Automatic delamination defect detection in radiographic sequence of rocket boosters

    International Nuclear Information System (INIS)

    Rebuffel, V.; Pires, S.; Caplier, A.; Lamarque, P.

    2003-01-01

    Solid rocket motors are routinely examined in real-time X-ray radioscopic mode. The large and cylindrical boosters are rotating between a high energy source and a two dimensional detector. The purpose of this control is to detect possible defects all through the sample. In the tangential configuration, the part of the object that intersects the X-rays beam is the peripheral one, allowing to detect the delamination defect between the propellant and the external metal envelope. But the defect detectability is very poor due to the strong attenuation of the X-rays through the motors. During the rotation of the booster, the system acquires a sequence of radiographs where the defects are visible over several successive instants. We have previously developed a real-time tomo-synthesis system, processing the radiographs on line, and based on a tomo-synthesis reconstruction algorithm in order to improve the signal-to-noise ratio. This system is installed at the industrial site of Kourou, and is currently used by the operators in charge of the visual inspection of the boosters. In this paper, we present a method that processes the digital images obtained by the system in the purpose of automatically extracting the delamination defects. Due to the size and the poor contrast of the defects, a single image is not sufficient to perform this detection. A spatio-temporal aspect is required for the algorithm to be robust and efficient. In a first step, the proposed method computes the apparent local displacement between the current radiograph and a reference one. This reference image is acquired at the beginning of the rotation, with few noise, and is supposed to be defect free. The apparent displacement is due to the non-perfect rotation positioning. It may be uniform or not, depending on the deformation of the insulation liner of the metallic wall. The images are then registered and compared. On the resulting difference image we apply a smoothed threshold to obtain an

  17. Automatic detection of blurred images in UAV image sets

    Science.gov (United States)

    Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

    2016-12-01

    Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of

  18. Towards the automatic detection and analysis of sunspot rotation

    Science.gov (United States)

    Brown, Daniel S.; Walker, Andrew P.

    2016-10-01

    Torsional rotation of sunspots have been noted by many authors over the past century. Sunspots have been observed to rotate up to the order of 200 degrees over 8-10 days, and these have often been linked with eruptive behaviour such as solar flares and coronal mass ejections. However, most studies in the literature are case studies or small-number studies which suffer from selection bias. In order to better understand sunspot rotation and its impact on the corona, unbiased large-sample statistical studies are required (including both rotating and non-rotating sunspots). While this can be done manually, a better approach is to automate the detection and analysis of rotating sunspots using robust methods with well characterised uncertainties. The SDO/HMI instrument provide long-duration, high-resolution and high-cadence continuum observations suitable for extracting a large number of examples of rotating sunspots. This presentation will outline the analysis of SDI/HMI data to determine the rotation (and non-rotation) profiles of sunspots for the complete duration of their transit across the solar disk, along with how this can be extended to automatically identify sunspots and initiate their analysis.

  19. Automatic detection of atrial fibrillation in cardiac vibration signals.

    Science.gov (United States)

    Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

    2013-01-01

    We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

  20. Development of a system for automatic detection of pellet failures

    International Nuclear Information System (INIS)

    Lavagnino, C.E.

    1996-01-01

    Nowadays, the failure controls in UO 2 pellets for Atucha and Embalse reactors are performed visually. In this work it is presented the first stage of the development of a system that allows an automatic approach to the task. For this purpose, the problem has been subdivided in three jobs: choosing the illumination environment, finding the algorithm that detects failures with user-defined tolerance and engineering the mechanic system that supports the desired manipulations of the pellets. In this paper, the former two are developed. a) Finding the illumination conditions that allow subtracting the failure from the normal element surface, knowing, in first place, the cylindrical characteristics of it and, as a consequence, the differences in the light reflection direction and, in second place, the texture differences in relation to the rectification type of the pellet. b) Writing a fast and simple algorithm that allows the identification of the failure following the production specifications. Examples of the developed algorithm are shown. (author). 4 refs

  1. Automatic Detection of Inactive Solar Cell Cracks in Electroluminescence Images

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

    We propose an algorithm for automatic determination of the electroluminescence (EL) signal threshold level corresponding to inactive solar cell cracks, resulting from their disconnection from the electrical circuit of the cell. The method enables automatic quantification of the cell crack size an...

  2. Automatic Detection of Inactive Solar Cell Cracks in Electroluminescence Images

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

    We propose an algorithm for automatic determination of the electroluminescence (EL) signal threshold level corresponding to inactive solar cell cracks, resulting from their disconnection from the electrical circuit of the cell. The method enables automatic quantification of the cell crack size...

  3. Tailoring automatic exposure control toward constant detectability in digital mammography.

    Science.gov (United States)

    Salvagnini, Elena; Bosmans, Hilde; Struelens, Lara; Marshall, Nicholas W

    2015-07-01

    The automatic exposure control (AEC) modes of most full field digital mammography (FFDM) systems are set up to hold pixel value (PV) constant as breast thickness changes. This paper proposes an alternative AEC mode, set up to maintain some minimum detectability level, with the ultimate goal of improving object detectability at larger breast thicknesses. The default "opdose" AEC mode of a Siemens MAMMOMAT Inspiration FFDM system was assessed using poly(methyl methacrylate) (PMMA) of thickness 20, 30, 40, 50, 60, and 70 mm to find the tube voltage and anode/filter combination programmed for each thickness; these beam quality settings were used for the modified AEC mode. Detectability index (d'), in terms of a non-prewhitened model observer with eye filter, was then calculated as a function of tube current-time product (mAs) for each thickness. A modified AEC could then be designed in which detectability never fell below some minimum setting for any thickness in the operating range. In this study, the value was chosen such that the system met the achievable threshold gold thickness (Tt) in the European guidelines for the 0.1 mm diameter disc (i.e., Tt ≤ 1.10 μm gold). The default and modified AEC modes were compared in terms of contrast-detail performance (Tt), calculated detectability (d'), signal-difference-to-noise ratio (SDNR), and mean glandular dose (MGD). The influence of a structured background on object detectability for both AEC modes was examined using a CIRS BR3D phantom. Computer-based CDMAM reading was used for the homogeneous case, while the images with the BR3D background were scored by human observers. The default opdose AEC mode maintained PV constant as PMMA thickness increased, leading to a reduction in SDNR for the homogeneous background 39% and d' 37% in going from 20 to 70 mm; introduction of the structured BR3D plate changed these figures to 22% (SDNR) and 6% (d'), respectively. Threshold gold thickness (0.1 mm diameter disc) for the default

  4. Tailoring automatic exposure control toward constant detectability in digital mammography

    Energy Technology Data Exchange (ETDEWEB)

    Salvagnini, Elena, E-mail: elena.salvagnini@uzleuven.be [Department of Imaging and Pathology, Medical Physics and Quality Assessment, KUL, Herestraat 49, Leuven B-3000, Belgium and SCK-CEN, Boeretang 200, Mol 2400 (Belgium); Bosmans, Hilde [Department of Imaging and Pathology, Medical Physics and Quality Assessment, KUL, Herestraat 49, Leuven B-3000, Belgium and Department of Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium); Struelens, Lara [SCK-CEN, Boeretang 200, Mol 2400 (Belgium); Marshall, Nicholas W. [Department of Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium)

    2015-07-15

    Purpose: The automatic exposure control (AEC) modes of most full field digital mammography (FFDM) systems are set up to hold pixel value (PV) constant as breast thickness changes. This paper proposes an alternative AEC mode, set up to maintain some minimum detectability level, with the ultimate goal of improving object detectability at larger breast thicknesses. Methods: The default “OPDOSE” AEC mode of a Siemens MAMMOMAT Inspiration FFDM system was assessed using poly(methyl methacrylate) (PMMA) of thickness 20, 30, 40, 50, 60, and 70 mm to find the tube voltage and anode/filter combination programmed for each thickness; these beam quality settings were used for the modified AEC mode. Detectability index (d′), in terms of a non-prewhitened model observer with eye filter, was then calculated as a function of tube current-time product (mAs) for each thickness. A modified AEC could then be designed in which detectability never fell below some minimum setting for any thickness in the operating range. In this study, the value was chosen such that the system met the achievable threshold gold thickness (T{sub t}) in the European guidelines for the 0.1 mm diameter disc (i.e., T{sub t} ≤ 1.10 μm gold). The default and modified AEC modes were compared in terms of contrast-detail performance (T{sub t}), calculated detectability (d′), signal-difference-to-noise ratio (SDNR), and mean glandular dose (MGD). The influence of a structured background on object detectability for both AEC modes was examined using a CIRS BR3D phantom. Computer-based CDMAM reading was used for the homogeneous case, while the images with the BR3D background were scored by human observers. Results: The default OPDOSE AEC mode maintained PV constant as PMMA thickness increased, leading to a reduction in SDNR for the homogeneous background 39% and d′ 37% in going from 20 to 70 mm; introduction of the structured BR3D plate changed these figures to 22% (SDNR) and 6% (d′), respectively

  5. Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

    International Nuclear Information System (INIS)

    Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

    2007-01-01

    Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

  6. Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

    International Nuclear Information System (INIS)

    Marghany, Maged

    2014-01-01

    Highlights: • An oil platform located 70 km from the coast of Louisiana sank on Thursday. • Oil spill has backscatter values of −25 dB in RADARSAT-2 SAR. • Oil spill is portrayed in SCNB mode by shallower incidence angle. • Ideal detection of oil spills in SAR images requires moderate wind speeds. • Genetic algorithm is excellent tool for automatic detection of oil spill in RADARSAT-2 SAR data. - Abstract: In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey

  7. Automatic detection of surface changes on Mars - a status report

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2016-10-01

    Orbiter missions have acquired approximately 500,000 high-resolution visible images of the Martian surface, covering an area approximately 6 times larger than the overall area of Mars. This data abundance allows the scientific community to examine the Martian surface thoroughly and potentially make exciting new discoveries. However, the increased data volume, as well as its complexity, generate problems at the data processing stages, which are mainly related to a number of unresolved issues that batch-mode planetary data processing presents. As a matter of fact, the scientific community is currently struggling to scale the common ("one-at-a-time" processing of incoming products by expert scientists) paradigm to tackle the large volumes of input data. Moreover, expert scientists are more or less forced to use complex software in order to extract input information for their research from raw data, even though they are not data scientists themselves.Our work within the STFC and EU FP7 i-Mars projects aims at developing automated software that will process all of the acquired data, leaving domain expert planetary scientists to focus on their final analysis and interpretation. Moreover, after completing the development of a fully automated pipeline that processes automatically the co-registration of high-resolution NASA images to ESA/DLR HRSC baseline, our main goal has shifted to the automated detection of surface changes on Mars. In particular, we are developing a pipeline that uses as an input multi-instrument image pairs, which are processed by an automated pipeline, in order to identify changes that are correlated with Mars surface dynamic phenomena. The pipeline has currently been tested in anger on 8,000 co-registered images and by the time of DPS/EPSC we expect to have processed many tens of thousands of image pairs, producing a set of change detection results, a subset of which will be shown in the presentation.The research leading to these results has received

  8. Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Yazan M. Alomari

    2014-01-01

    Full Text Available Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs and red blood cells (RBCs in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD algorithm to solve the initialization problem, detecting irregular circles (cells, selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.

  9. Automatic Detection of Childhood Absence Epilepsy Seizures: Toward a Monitoring Device

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Madsen, Rasmus E.; Remvig, Line S.

    2012-01-01

    Automatic detections of paroxysms in patients with childhood absence epilepsy have been neglected for several years. We acquire reliable detections using only a single-channel brainwave monitor, allowing for unobtrusive monitoring of antiepileptic drug effects. Ultimately we seek to obtain optimal...... long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection. We implemented a state-of-the-art algorithm...

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

    Science.gov (United States)

    Shieh, Wann-Yun; Huang, Ju-Chin

    2012-09-01

    For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

  11. Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count

    NARCIS (Netherlands)

    Kamphuis, C.; Sherlock, R.; Jago, J.; Mein, G.; Hogeveen, H.

    2008-01-01

    This study explored the potential value of in-line composite somatic cell count (ISCC) sensing as a sole criterion or in combination with quarter-based electrical conductivity (EC) of milk, for automatic detection of clinical mastitis (CM) during automatic milking. Data generated from a New Zealand

  12. Automatic QRS complex detection algorithm designed for a novel wearable, wireless electrocardiogram recording device

    DEFF Research Database (Denmark)

    Saadi, Dorthe Bodholt; Egstrup, Kenneth; Branebjerg, Jens

    2012-01-01

    We have designed and optimized an automatic QRS complex detection algorithm for electrocardiogram (ECG) signals recorded with the DELTA ePatch platform. The algorithm is able to automatically switch between single-channel and multi-channel analysis mode. This preliminary study includes data from ...

  13. Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

    Science.gov (United States)

    Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

    2013-09-01

    Diabetic retinopathy (DR) is one of the major complications of diabetes that changes the blood vessels of the retina and distorts patient vision that finally in high stages can lead to blindness. Micro-aneurysms (MAs) are one of the first pathologies associated with DR. The number and the location of MAs are very important in grading of DR. Early diagnosis of micro-aneurysms (MAs) can reduce the incidence of blindness. As MAs are tiny area of blood protruding from vessels in the retina and their size is about 25 to 100 microns, automatic detection of these tiny lesions is still challenging. MAs occurring in the macula can lead to visual loss. Also the position of a lesion such as MAs relative to the macula is a useful feature for analysis and classification of different stages of DR. Because MAs are more distinguishable in fundus fluorescin angiography (FFA) compared to color fundus images, we introduce a new method based on curvelet transform and morphological operations for MAs detection in FFA images. As vessels and MAs are the bright parts of FFA image, firstly extracted vessels by curvelet transform are removed from image. Then morphological operations are applied on resulted image for detecting MAs.

  14. Automatic centroid detection and surface measurement with a digital Shack–Hartmann wavefront sensor

    International Nuclear Information System (INIS)

    Yin, Xiaoming; Zhao, Liping; Li, Xiang; Fang, Zhongping

    2010-01-01

    With the breakthrough of manufacturing technologies, the measurement of surface profiles is becoming a big issue. A Shack–Hartmann wavefront sensor (SHWS) provides a promising technology for non-contact surface measurement with a number of advantages over interferometry. The SHWS splits the incident wavefront into many subsections and transfers the distorted wavefront detection into the centroid measurement. So the accuracy of the centroid measurement determines the accuracy of the SHWS. In this paper, we have presented a new centroid measurement algorithm based on an adaptive thresholding and dynamic windowing method by utilizing image-processing techniques. Based on this centroid detection method, we have developed a digital SHWS system which can automatically detect centroids of focal spots, reconstruct the wavefront and measure the 3D profile of the surface. The system has been tested with various simulated and real surfaces such as flat surfaces, spherical and aspherical surfaces as well as deformable surfaces. The experimental results demonstrate that the system has good accuracy, repeatability and immunity to optical misalignment. The system is also suitable for on-line applications of surface measurement

  15. Incident detection and isolation in drilling using analytical redundancy relations

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian - they follow a multivariate t-distribution - hence, a dedicated generalized likelihood...... measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability...

  16. Automatic Detection and Resolution of Lexical Ambiguity in Process Models

    NARCIS (Netherlands)

    Pittke, F.; Leopold, H.; Mendling, J.

    2015-01-01

    System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal

  17. On advisability of developing automatic complexes of radiation flow detection

    International Nuclear Information System (INIS)

    Akopov, V.S.; Voronin, S.A.; Meshalkin, I.A.

    1976-01-01

    On the basis of mathematical treatment of statistical data obtained by inquest of specialists from a number of factories, problems associated with the determination of the most acceptable efficiency of radiation defectoscopy automatized complexes are considered. Production requirements for radiation control sensitivity are generalized. The use of providing the complexes with computer technique is substantiated

  18. Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

    Science.gov (United States)

    Osmanoglu, B.; Ozkan, C.; Sunar, F.

    2013-10-01

    After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

  19. Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

    Energy Technology Data Exchange (ETDEWEB)

    2018-03-25

    A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

  20. Automatic cough episode detection using a vibroacoustic sensor.

    Science.gov (United States)

    Mlynczak, Marcel; Pariaszewska, Katarzyna; Cybulski, Gerard

    2015-08-01

    Cough monitoring is an important element of the diagnostics of respiratory diseases. The European Respiratory Society recommends objective assessment of cough episodes and the search for methods of automatic analysis to make obtaining the quantitative parameters possible. The cough "events" could be classified by a microphone and a sensor that measures the vibrations of the chest. Analysis of the recorded signals consists of calculating the features vectors for selected episodes and of performing automatic classification using them. The aim of the study was to assess the accuracy of classification based on an artificial neural networks using vibroacoustic signals collected from chest. Six healthy, young men and eight healthy, young women carried out an imitated cough, hand clapping, speech and shouting. Three methods of parametrization were used to prepare the vectors of episode features - time domain, time-frequency domain and spectral modeling. We obtained the accuracy of 95% using artificial neural networks.

  1. Automatic detection of frequency changes depends on auditory stimulus intensity.

    Science.gov (United States)

    Salo, S; Lang, A H; Aaltonen, O; Lertola, K; Kärki, T

    1999-06-01

    A cortical cognitive auditory evoked potential, mismatch negativity (MMN), reflects automatic discrimination and echoic memory functions of the auditory system. For this study, we examined whether this potential is dependent on the stimulus intensity. The MMN potentials were recorded from 10 subjects with normal hearing using a sine tone of 1000 Hz as the standard stimulus and a sine tone of 1141 Hz as the deviant stimulus, with probabilities of 90% and 10%, respectively. The intensities were 40, 50, 60, 70, and 80 dB HL for both standard and deviant stimuli in separate blocks. Stimulus intensity had a statistically significant effect on the mean amplitude, rise time parameter, and onset latency of the MMN. Automatic auditory discrimination seems to be dependent on the sound pressure level of the stimuli.

  2. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Dawei Li

    2017-01-01

    Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

  3. Automatic pitch detection for a computer game interface

    International Nuclear Information System (INIS)

    Fonseca Solis, Juan M.

    2015-01-01

    A software able to recognize notes played by musical instruments is created through automatic pitch recognition. A pitch recognition algorithm is embedded into a software project, using the C implementation of SWIPEP. A memory game is chosen for project. A sequence of notes is listened and played by user to the computer, using a soprano recorder flute. The basic concepts to understand the acoustic phenomena involved are explained. The paper is aimed for all students with basic programming knowledge and want to incorporate sound processing to their projects. (author) [es

  4. Automatic multimodal detection for long-term seizure documentation in epilepsy.

    Science.gov (United States)

    Fürbass, F; Kampusch, S; Kaniusas, E; Koren, J; Pirker, S; Hopfengärtner, R; Stefan, H; Kluge, T; Baumgartner, C

    2017-08-01

    This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  5. Oblique-incidence reflectivity difference application for morphology detection.

    Science.gov (United States)

    Zhan, Honglei; Zhao, Kun; Lü, Huibin; Jin, Kuijuan; Yang, Guozhen; Chen, Xiaohong

    2017-10-20

    Analogous with scanning electron microscopy, we use an oblique-incidence reflectivity difference (OIRD) approach for morphology detection. By scanning the active carbon clusters in a one-dimensional way and the reservoir rocks in a two-dimensional way, the morphology of the samples' surface can be revealed in OIRD signal images. High OIRD signals of active carbon samples refer to the centralized distribution areas of carbon, and the fluctuations are caused by the uneven distribution of carbon pellets. OIRD intensity is proportional to the thickness of materials. In terms of rocks, the trough areas with smaller values refer to the low-lying fields. The areas with relatively large OIRD intensities correspond to the protuberance areas of rocks. Consequently, OIRD is a sensitive yet rapid measure of surface detection in material and petrogeology science.

  6. Automatic hearing loss detection system based on auditory brainstem response

    International Nuclear Information System (INIS)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R

    2007-01-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory

  7. An automatized frequency analysis for vine plot detection and delineation in remote sensing

    OpenAIRE

    Delenne , Carole; Rabatel , G.; Deshayes , M.

    2008-01-01

    The availability of an automatic tool for vine plot detection, delineation, and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundary and accurate estimation of interrow width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard ver...

  8. Automatic welding detection by an intelligent tool pipe inspection

    Science.gov (United States)

    Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

    2015-07-01

    This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

  9. BgCut: Automatic Ship Detection from UAV Images

    Directory of Open Access Journals (Sweden)

    Chao Xu

    2014-01-01

    foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

  10. Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering.

    Science.gov (United States)

    Shi, Lin; Wang, Defeng; Liu, Wen; Fang, Kui; Wang, Yi-Xiang J; Huang, Wenhua; King, Ann D; Heng, Pheng Ann; Ahuja, Anil T

    2014-05-01

    To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37-92.10% on rat dataset and 73.18-90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency. Copyright © 2013 Wiley Periodicals, Inc.

  11. Differences between automatically detected and steady-state fractional flow reserve.

    Science.gov (United States)

    Härle, Tobias; Meyer, Sven; Vahldiek, Felix; Elsässer, Albrecht

    2016-02-01

    Measurement of fractional flow reserve (FFR) has become a standard diagnostic tool in the catheterization laboratory. FFR evaluation studies were based on pressure recordings during steady-state maximum hyperemia. Commercially available computer systems detect the lowest Pd/Pa ratio automatically, which might not always be measured during steady-state hyperemia. We sought to compare the automatically detected FFR and true steady-state FFR. Pressure measurement traces of 105 coronary lesions from 77 patients with intermediate coronary lesions or multivessel disease were reviewed. In all patients, hyperemia had been achieved by intravenous adenosine administration using a dosage of 140 µg/kg/min. In 42 lesions (40%) automatically detected FFR was lower than true steady-state FFR. Mean bias was 0.009 (standard deviation 0.015, limits of agreement -0.02, 0.037). In 4 lesions (3.8%) both methods lead to different treatment recommendations, in all 4 cases instantaneous wave-free ratio confirmed steady-state FFR. Automatically detected FFR was slightly lower than steady-state FFR in more than one-third of cases. Consequently, interpretation of automatically detected FFR values closely below the cutoff value requires special attention.

  12. Usage of polarisation features of landmines for improved automatic detection

    NARCIS (Netherlands)

    Jong, W. de; Cremer, F.; Schutte, K.; Storm, J.

    2000-01-01

    In this paper the landmine detection performance of an infrared and a visual light camera both equipped with a polarisation filter are compared with the detection performance of these cameras without polarisation filters. Sequences of images have been recorded with in front of these cameras a

  13. Automatic fog detection for public safety by using camera images

    Science.gov (United States)

    Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

    2017-04-01

    Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

  14. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David; Gereige, Issam; Gourgon, Cé cile

    2013-01-01

    patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications

  15. Automatic Detection of Cyberbullying in Social Media Text

    NARCIS (Netherlands)

    Van Hee, Cynthia; Jacobs, Gilles; Emmery, Chris; Desmet, Bart; Lefever, Els; Verhoeven, Ben; De Pauw, Guy; Daelemans, W.M.P.; Hoste, Veronique

    2018-01-01

    While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of

  16. Automatic Gap Detection in Friction Stir Welding Processes (Preprint)

    National Research Council Canada - National Science Library

    Yang, Yu; Kalya, Prabhanjana; Landers, Robert G; Krishnamurthy, K

    2006-01-01

    .... This paper develops a monitoring algorithm to detect gaps in Friction Stir Welding (FSW) processes. Experimental studies are conducted to determine how the process parameters and the gap width affect the welding process...

  17. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong; Nan, Liangliang; Yan, Dongming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2015-01-01

    plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing

  18. Improving Automation Routines for Automatic Heating Load Detection in Buildings

    Directory of Open Access Journals (Sweden)

    Stephen Timlin

    2012-11-01

    Full Text Available Energy managers use weather compensation data and heating system cut off routines to reduce heating energy consumption in buildings and improve user comfort. These routines are traditionally based on the calculation of an estimated building load that is inferred from the external dry bulb temperature at any point in time. While this method does reduce heating energy consumption and accidental overheating, it can be inaccurate under some weather conditions and therefore has limited effectiveness. There remains considerable scope to improve on the accuracy and relevance of the traditional method by expanding the calculations used to include a larger range of environmental metrics. It is proposed that weather compensation and automatic shut off routines that are commonly used could be improved notably with little additional cost by the inclusion of additional weather metrics. This paper examines the theoretical relationship between various external metrics and building heating loads. Results of the application of an advanced routine to a recently constructed building are examined, and estimates are made of the potential savings that can be achieved through the use of the routines proposed.

  19. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

    Full Text Available The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step, is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies.

  20. Automatic gender detection of dream reports: A promising approach.

    Science.gov (United States)

    Wong, Christina; Amini, Reza; De Koninck, Joseph

    2016-08-01

    A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ=0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (κ=0.492), both of which were higher than chance of 50% (κ=0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Automatic Change Detection to Facial Expressions in Adolescents: Evidence from Visual Mismatch Negativity Responses

    Directory of Open Access Journals (Sweden)

    Tongran eLiu

    2016-03-01

    Full Text Available Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group were recruited to complete an emotional oddball task featuring on happy and one fearful condition. The measurement of event-related potential (ERP was carried out via electroencephalography (EEG and electrooculography (EOG recording, to detect visual mismatch negativity (vMMN with regard to the automatic detection of changes in facial expressions between the two age groups. The current findings demonstrated that the adolescent group featured more negative vMMN amplitudes than the adult group in the fronto-central region during the 120-200 ms interval. During the time window of 370-450 ms, only the adult group showed better automatic processing on fearful faces than happy faces. The present study indicated that adolescents posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information.

  2. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  3. Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data.

    Science.gov (United States)

    Yu, Yong-Jie; Xia, Qiao-Ling; Wang, Sheng; Wang, Bing; Xie, Fu-Wei; Zhang, Xiao-Bing; Ma, Yun-Ming; Wu, Hai-Long

    2014-09-12

    Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Automatic detection of regions of interest in mammographic images

    Science.gov (United States)

    Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

    2011-03-01

    This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

  5. Study on the Automatic Detection Method and System of Multifunctional Hydrocephalus Shunt

    Science.gov (United States)

    Sun, Xuan; Wang, Guangzhen; Dong, Quancheng; Li, Yuzhong

    2017-07-01

    Aiming to the difficulty of micro pressure detection and the difficulty of micro flow control in the testing process of hydrocephalus shunt, the principle of the shunt performance detection was analyzed.In this study, the author analyzed the principle of several items of shunt performance detection,and used advanced micro pressure sensor and micro flow peristaltic pump to overcome the micro pressure detection and micro flow control technology.At the same time,This study also puted many common experimental projects integrated, and successfully developed the automatic detection system for a shunt performance detection function, to achieve a test with high precision, high efficiency and automation.

  6. Automatic detection and analysis of nuclear plant malfunctions

    International Nuclear Information System (INIS)

    Bruschi, R.; Di Porto, P.; Pallottelli, R.

    1985-01-01

    In this paper a system is proposed, which performs dynamically the detection and analysis of malfunctions in a nuclear plant. The proposed method was developed and implemented on a Reactor Simulator, instead of on a real one, thus allowing a wide range of tests. For all variables under control, a simulation module was identified and implemented on the reactor on-line computer. In the malfunction identification phase all modules run separately, processing plant input variables and producing their output variable in Real-Time; continuous comparison of the computed variables with plant variables allows malfunction's detection. At this moment the second phase can occur: when a malfunction is detected, all modules are connected, except the module simulating the wrong variable, and a fast simulation is carried on, to analyse the consequences. (author)

  7. AUTOMATIC TREE-CROWN DETECTION IN CHALLENGING SCENARIOS

    Directory of Open Access Journals (Sweden)

    D. Bulatov

    2016-06-01

    Full Text Available In this paper, a new procedure for individual tree detection and modeling is presented. The input of this procedure consists of a normalized digital surface model NDSM, and a possibly error-prone classification result. The procedure is modular so that the functionality, the advantages and the disadvantages for every single module will be explained. The most important technical contributions of the paper are: Employing watershed transformation combined with classification results, applying hotspots detectors for identifying treetops in groups of trees, and correcting NDSM by detecting and geometric reconstruction of small anomalies, such as earth walls. Two minor contributions are made up by a detailed literature research on available methods for individual tree detection and estimation of tree-crowns for clearly identified trees in order to reduce arbitrariness by assigning trees to one of the few types in the output model.

  8. Multislice CT coronary angiography: evaluation of an automatic vessel detection tool

    International Nuclear Information System (INIS)

    Dewey, M.; Schnapauff, D.; Lembcke, A.; Hamm, B.; Rogalla, P.; Laule, M.; Borges, A.C.; Rutsch, W.

    2004-01-01

    Purpose: To investigate the potential of a new detection tool for multisliceCT (MSCT) coronary angiography with automatic display of curved multiplanar reformations and orthogonal cross-sections. Materials and Methods: Thirty-five patients were consecutively enrolled in a prospective intention-to-diagnose study and examined using a MSCT scanner with 16 x 0.5 mm detector collimation and 400 ms gantry rotation time (Aquilion, Toshiba). A multisegment algorithm using up to four segments was applied for ECG-gated reconstruction. Automatic and manual detection of coronary arteries was conducted using the coronary artery CT protocol of a workstation (Vitrea 2, Version 3.3, Vital Images) to detect significant stenoses (≥50%) in all segments of ≥1.5 mm in diameter. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of conventional coronary angiography. Results: The overall sensitivity, specificity, nondiagnostic rate, and accuracy of the automatic and manual approach were 90 vs. 94%, 89 vs. 84%, 6 vs. 6%, and 89 vs. 88%, respectively (p=n.s.). The vessel length detected with the automatic and manual approach were highly correlated for the left main/left anterior descending (143±30 vs. 146±24 mm, r=0.923, p [de

  9. Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

    Directory of Open Access Journals (Sweden)

    Maíla de Lima Claro

    2016-08-01

    Full Text Available The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

  10. Automatic burst detection for the EEG of the preterm infant

    NARCIS (Netherlands)

    Jennekens, W.; Ruijs, L.S.; Lommen, Ch.M.L.; Niemarkt, H.J.; Pasman, J.W.; van Kranen-Mastenbroek, V.H.J.M.; Wijn, P.F.F.; van Pul, C.; Andriessen, P.

    2011-01-01

    To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological

  11. Automatic Data Collection Design for Neural Networks Detection of ...

    African Journals Online (AJOL)

    Automated data collection is necessary to alleviate problems inherent in data collection for investigation of management frauds. Once we have gathered a realistic data, several methods then exist for proper analysis and detection of anomalous transactions. However, in Nigeria, collecting fraudulent data is relatively difficult ...

  12. Automated Incident Detection Using Real-Time Floating Car Data

    Directory of Open Access Journals (Sweden)

    Maarten Houbraken

    2017-01-01

    Full Text Available The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID system using only Floating Car Data (FCD in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

  13. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

    Science.gov (United States)

    Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

    2017-06-26

    We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

  14. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David

    2013-12-01

    Nanoimprint Lithography (NIL) is a promising technology for low cost and large scale nanostructure fabrication. This technique is based on a contact molding-demolding process, that can produce number of defects such as incomplete filling, negative patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications are presented. Results are independent on the device which captures the image (optical, confocal or electron microscope). The use of numerical images allows the possibility to automate the detection and to compute a statistical analysis of defects. This method provides a fast analysis of printed gratings and could be used to monitor the production of such structures. © 2013 Elsevier B.V. All rights reserved.

  15. CHLOE: A tool for automatic detection of peculiar galaxies

    Science.gov (United States)

    Shamir, Lior; Manning, Saundra; Wallin, John

    2014-09-01

    CHLOE is an image analysis unsupervised learning algorithm that detects peculiar galaxies in datasets of galaxy images. The algorithm first computes a large set of numerical descriptors reflecting different aspects of the visual content, and then weighs them based on the standard deviation of the values computed from the galaxy images. The weighted Euclidean distance of each galaxy image from the median is measured, and the peculiarity of each galaxy is determined based on that distance.

  16. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    International Nuclear Information System (INIS)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing

    2016-01-01

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  17. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    Energy Technology Data Exchange (ETDEWEB)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing, E-mail: jzeng13@uw.edu [Department of Radiation Oncology, University of Washington Medical Center, 1959 NE Pacific Street, Campus Box 356043, Seattle, Washington 98195 (United States)

    2016-05-15

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  18. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

    Tomei-Le-Digarcher, Sandrine

    2009-01-01

    Therapeutic follow-up of patients with cancer is nowadays of main interest in research. Positron Emission Tomography (PET) appears to become a reference exam for monitoring treatment of cancers, particular in lymphoma. This PhD thus deals on the development of a computer aided detection (CAD) tool focused on hardly visible tumors for whole-body 3D PET images. To achieve such a goal, we proposed an approach based on the combination of two classifiers, the Linear Discriminant Analysis (LDA) and the Support Vector Machines, associated with wavelet image features. Each classifier gives a 3D score map quantifying the probability of its voxels to correspond to a tumor. We proposed a 3D evaluation strategy based on the use of simulated images giving the targeted tumor characteristic gold standard. Such database was developed in this PhD from hundred Monte Carlo simulations of the Zuba phantom. It includes hundred images presenting 375 spherical tumors of calibrated contrasts. Results of the CAD obtained from the binary detection maps are promising. They open the perspective of enriching the binary information generally given to the clinician with parametric indices quantifying the pertinence of each detected tumor. (author)

  19. Automatic car driving detection using raw accelerometry data.

    Science.gov (United States)

    Strączkiewicz, M; Urbanek, J K; Fadel, W F; Crainiceanu, C M; Harezlak, J

    2016-09-21

    Measuring physical activity using wearable devices has become increasingly popular. Raw data collected from such devices is usually summarized as 'activity counts', which combine information of human activity with environmental vibrations. Driving is a major sedentary activity that artificially increases the activity counts due to various car and body vibrations that are not connected to human movement. Thus, it has become increasingly important to identify periods of driving and quantify the bias induced by driving in activity counts. To address these problems, we propose a detection algorithm of driving via accelerometry (DADA), designed to detect time periods when an individual is driving a car. DADA is based on detection of vibrations generated by a moving vehicle and recorded by an accelerometer. The methodological approach is based on short-time Fourier transform (STFT) applied to the raw accelerometry data and identifies and focuses on frequency vibration ranges that are specific to car driving. We test the performance of DADA on data collected using wrist-worn ActiGraph devices in a controlled experiment conducted on 24 subjects. The median area under the receiver-operating characteristic curve (AUC) for predicting driving periods was 0.94, indicating an excellent performance of the algorithm. We also quantify the size of the bias induced by driving and obtain that per unit of time the activity counts generated by driving are, on average, 16% of the average activity counts generated during walking.

  20. Automatic burst detection for the EEG of the preterm infant.

    Science.gov (United States)

    Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

    2011-10-01

    To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

  1. Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

    Science.gov (United States)

    Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

    2007-11-01

    Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

  2. Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods

    Directory of Open Access Journals (Sweden)

    Dorothée Coppieters ’t Wallant

    2016-01-01

    Full Text Available Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation and individual characteristics (intellectual quotient. Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.

  3. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  4. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    Science.gov (United States)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  5. Automatic detection of coronary arterial branches from X-ray angiograms

    International Nuclear Information System (INIS)

    Lu, Shan; Eiho, Shigeru

    1992-01-01

    This paper describes a method to trace the coronary arterial boundaries automatically from x-ray angiograms. We developed an automatic procedure to detect the edges of an artery with its branches. The edge point is evaluated by a function based on smoothing differential operator on a searching line which is obtained by using the continuous properties of the arterial edges. Thus the boundary points along the artery are detected automatically. If there exists a branch on the boundary, it can be detected automatically. This information about the branch is stored on the stack of the search information and will be used to detect the branch artery. In our edge detection process, the required user interaction is only the manual definition of a starting point for the search, the direction of the search and the range for search. We tested this method on some images generated by a computer with different stenoses and on a coronary angiogram. These results show that this method is useful for analyzing coronary angiograms. (author)

  6. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Washington University in St Louis, Taian, Shandong (China); Yang, D [Washington University School of Medicine, St Louis, MO (United States)

    2015-06-15

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  7. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    International Nuclear Information System (INIS)

    Qiu, J; Yang, D

    2015-01-01

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  8. Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

    Science.gov (United States)

    Sa, Qila; Wang, Zhihui

    2018-03-01

    At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

  9. A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

    International Nuclear Information System (INIS)

    Lu Wei; Nystrom, Michelle M.; Parikh, Parag J.; Fooshee, David R.; Hubenschmidt, James P.; Bradley, Jeffrey D.; Low, Daniel A.

    2006-01-01

    The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations

  10. Automatic detection of mycobacterium tuberculosis using artificial intelligence

    Science.gov (United States)

    Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen

    2018-01-01

    Background Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. Methods We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Results Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. Conclusions TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified. PMID:29707349

  11. Automatic detection of mycobacterium tuberculosis using artificial intelligence.

    Science.gov (United States)

    Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen; Li, Ting

    2018-03-01

    Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified.

  12. Automatic detection of service initiation signals used in bars

    Directory of Open Access Journals (Sweden)

    Sebastian eLoth

    2013-08-01

    Full Text Available Recognising the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognise the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesised candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that a these two easily recognisable actions are sufficient for recognising the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and b the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.

  13. Automatic detection of service initiation signals used in bars.

    Science.gov (United States)

    Loth, Sebastian; Huth, Kerstin; De Ruiter, Jan P

    2013-01-01

    Recognizing the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognize the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesized candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that (a) these two easily recognizable actions are sufficient for recognizing the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and (b) the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.

  14. Exploiting ensemble learning for automatic cataract detection and grading.

    Science.gov (United States)

    Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

    2016-02-01

    Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  15. Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Benabbas Yassine

    2011-01-01

    Full Text Available Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.

  16. Application of image recognition-based automatic hyphae detection in fungal keratitis.

    Science.gov (United States)

    Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

    2018-03-01

    The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal

  17. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

    International Nuclear Information System (INIS)

    Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A.; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Greer, Peter B.

    2016-01-01

    Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

  18. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

    Energy Technology Data Exchange (ETDEWEB)

    Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli [Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106 and CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A. [CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Stanwell, Peter [School of health sciences, The University of Newcastle, Newcastle, NSW 2308 (Australia); Pichler, Peter [Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia); Sun, Jidi; Greer, Peter B. [School of Mathematical and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia and Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia)

    2016-05-15

    Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

  19. Automatic Detection of Retinal Exudates using a Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Nualsawat HIRANSAKOLWONG

    2013-02-01

    Full Text Available Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. Unfortunately, it is a normal situation that retinal images in Thailand are poor quality images. In this paper, we present a series of experiments on feature selection and exudates classification using the support vector machine classifiers. The retinal images are segmented following key preprocessing steps, i.e., color normalization, contrast enhancement, noise removal and color space selection. On data sets of poor quality images, sensitivity, specificity and accuracy is 94.46%, 89.52% and 92.14%, respectively.

  20. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  1. Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

    Science.gov (United States)

    Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

  2. Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

    NARCIS (Netherlands)

    Chen, Junwen; Liu, Zhigang; Wang, H.; Nunez Vicencio, Alfredo; Han, Zhiwei

    2018-01-01

    The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary

  3. Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

    DEFF Research Database (Denmark)

    Bruun, Iben H.; Hissabu, Semira M. S.; Poulsen, Erik S.

    2017-01-01

    be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed...

  4. Automatic detection of frequent pronunciation errors made by L2-learners

    NARCIS (Netherlands)

    Truong, K.P.; Neri, A.; Wet, F. de; Cucchiarini, C.; Strik, H.

    2005-01-01

    In this paper, we present an acoustic-phonetic approach to automatic pronunciation error detection. Classifiers using techniques such as Linear Discriminant Analysis and Decision Trees were developed for three sounds that are frequently pronounced incorrectly by L2-learners of Dutch: /a/, /y/ and

  5. Automatic detection of children's engagement using non-verbal features and ordinal learning

    NARCIS (Netherlands)

    Kim, Jaebok; Truong, Khiet Phuong; Evers, Vanessa

    In collaborative play, young children can exhibit different types of engagement. Some children are engaged with other children in the play activity while others are just looking. In this study, we investigated methods to automatically detect the children's levels of engagement in play settings using

  6. Mastitis therapy and control - Automatic on-line detection of abnormal milk.

    NARCIS (Netherlands)

    Hogeveen, H.

    2011-01-01

    Automated online detection of mastitis and abnormal milk is an important subject in the dairy industry, especially because of the introduction of automatic milking systems and the growing farm sizes with consequently less labor available per cow. Demands for performance, which is expressed as

  7. Automatic processing of isotopic dilution curves obtained by precordial detection

    International Nuclear Information System (INIS)

    Verite, J.C.

    1973-01-01

    Dilution curves pose two distinct problems: that of their acquisition and that of their processing. A study devoted to the latter aspect only was presented. It was necessary to satisfy two important conditions: the treatment procedure, although applied to a single category of curves (isotopic dilution curves obtained by precordial detection), had to be as general as possible; to allow dissemination of the method the equipment used had to be relatively modest and inexpensive. A simple method, considering the curve processing as a process identification, was developed and should enable the mean heart cavity volume and certain pulmonary circulation parameters to be determined. Considerable difficulties were encountered, limiting the value of the results obtained though not condemning the method itself. The curve processing question raised the problem of their acquisition, i.e. the number of these curves and their meaning. A list of the difficulties encountered is followed by a set of possible solutions, a solution being understood to mean a curve processing combination where the overlapping between the two aspects of the problem is accounted for [fr

  8. Fully automatic detection of corresponding anatomical landmarks in volume scans of different respiratory state

    International Nuclear Information System (INIS)

    Berlinger, Kajetan; Roth, Michael; Sauer, Otto; Vences, Lucia; Schweikard, Achim

    2006-01-01

    A method is described which provides fully automatic detection of corresponding anatomical landmarks in volume scans taken at different respiratory states. The resulting control points are needed for creating a volumetric deformation model for motion compensation in radiotherapy. Prior to treatment two CT volumes are taken, one scan during inhalation, one during exhalation. These scans and the detected control point pairs are taken as input for creating the four-dimensional model by using thin-plate splines

  9. Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements

    OpenAIRE

    Martini, Alberto; Troncossi, Marco; Rivola, Alessandro

    2015-01-01

    The implementation of strategies for controlling water leaks is essential in order to reduce losses affecting distribution networks of drinking water. This paper focuses on leak detection by using vibration monitoring techniques. The long-term goal is the development of a system for automatic early detection of burst leaks in service pipes. An experimental campaign was started to measure vibrations transmitted along water pipes by real burst leaks occurring in actual water supply networks. Th...

  10. Automatic detection of breast lesions with MIBI-Tc99m scintimammography using a novelty filter

    International Nuclear Information System (INIS)

    Costa, M.; Moura, L.

    1996-01-01

    An automatic method for detecting breast lesion in scintimammography is described. It is reported that the proposed method not only detects lesions but also classifies them as benign or malignant. The detection method makes use of Kohonen's novelty filter and the classification method is obtained by the analysis of an identified lesion mean profile. The method was able to detect all lesions presented in the scintimammogram and to correctly classify 16 out of 17 malignant lesions and 15 out of 17 benign lesions. The sensitivity of the method was 94,12% and specificity was 88,24%

  11. Automatic construction of a recurrent neural network based classifier for vehicle passage detection

    Science.gov (United States)

    Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

    2017-03-01

    Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

  12. Automatic QRS complex detection using two-level convolutional neural network.

    Science.gov (United States)

    Xiang, Yande; Lin, Zhitao; Meng, Jianyi

    2018-01-29

    The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

  13. Aircraft noise effects on sleep: a systematic comparison of EEG awakenings and automatically detected cardiac activations

    International Nuclear Information System (INIS)

    Basner, Mathias; Müller, Uwe; Elmenhorst, Eva-Maria; Kluge, Götz; Griefahn, Barbara

    2008-01-01

    Polysomnography is the gold standard for investigating noise effects on sleep, but data collection and analysis are sumptuous and expensive. We recently developed an algorithm for the automatic identification of cardiac activations associated with cortical arousals, which uses heart rate information derived from a single electrocardiogram (ECG) channel. We hypothesized that cardiac activations can be used as estimates for EEG awakenings. Polysomnographic EEG awakenings and automatically detected cardiac activations were systematically compared using laboratory data of 112 subjects (47 male, mean ± SD age 37.9 ± 13 years), 985 nights and 23 855 aircraft noise events (ANEs). The probability of automatically detected cardiac activations increased monotonically with increasing maximum sound pressure levels of ANEs, exceeding the probability of EEG awakenings by up to 18.1%. If spontaneous reactions were taken into account, exposure–response curves were practically identical for EEG awakenings and cardiac activations. Automatically detected cardiac activations may be used as estimates for EEG awakenings. More investigations are needed to further validate the ECG algorithm in the field and to investigate inter-individual differences in its ability to predict EEG awakenings. This inexpensive, objective and non-invasive method facilitates large-scale field studies on the effects of traffic noise on sleep

  14. Using activity-related behavioural features towards more effective automatic stress detection.

    Directory of Open Access Journals (Sweden)

    Dimitris Giakoumis

    Full Text Available This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.

  15. Long Baseline Stereovision for Automatic Detection and Ranging of Moving Objects in the Night Sky

    Directory of Open Access Journals (Sweden)

    Vlad Turcu

    2012-09-01

    Full Text Available As the number of objects in Earth’s atmosphere and in low Earth orbit is continuously increasing; accurate surveillance of these objects has become important. This paper presents a generic, low cost sky surveillance system based on stereovision. Two cameras are placed 37 km apart and synchronized by a GPS-controlled external signal. The intrinsic camera parameters are calibrated before setup in the observation position, the translation vectors are determined from the GPS coordinates and the rotation matrices are continuously estimated using an original automatic calibration methodology based on following known stars. The moving objects in the sky are recognized as line segments in the long exposure images, using an automatic detection and classification algorithm based on image processing. The stereo correspondence is based on the epipolar geometry and is performed automatically using the image detection results. The resulting experimental system is able to automatically detect moving objects such as planes, meteors and Low Earth Orbit satellites, and measure their 3D position in an Earth-bound coordinate system.

  16. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

    Full Text Available This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i automatic camera calibration using both moving objects and a background structure; (ii object depth estimation; and (iii detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

  17. Automatic REM Sleep Detection Associated with Idiopathic REM Sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sørensen, Gertrud Laura; Sørensen, Helge Bjarup Dissing

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG......, an automatic computerized REM detection algorithm has been implemented, using wavelet packet combined with artificial neural network. Results: When using the EEG, EOG and EMG modalities, it was possible to correctly classify REM sleep with an average Area Under Curve (AUC) equal to 0:900:03 for normal subjects...

  18. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

  19. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

    Science.gov (United States)

    Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

    2018-01-01

    Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Generating Impact Maps from Automatically Detected Bomb Craters in Aerial Wartime Images Using Marked Point Processes

    Science.gov (United States)

    Kruse, Christian; Rottensteiner, Franz; Hoberg, Thorsten; Ziems, Marcel; Rebke, Julia; Heipke, Christian

    2018-04-01

    The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

  1. Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

    Science.gov (United States)

    Litkey, P.; Nurminen, K.; Honkavaara, E.

    2013-05-01

    The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

  2. Automatic supervision and fault detection of PV systems based on power losses analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chouder, A.; Silvestre, S. [Electronic Engineering Department, Universitat Politecnica de Catalunya, C/Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona (Spain)

    2010-10-15

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L{sub ct}) and miscellaneous capture losses (L{sub cm}). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R{sub C} and R{sub V}. Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally. (author)

  3. Automatic supervision and fault detection of PV systems based on power losses analysis

    International Nuclear Information System (INIS)

    Chouder, A.; Silvestre, S.

    2010-01-01

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L ct ) and miscellaneous capture losses (L cm ). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R C and R V . Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally.

  4. Automatic laser beam alignment using blob detection for an environment monitoring spectroscopy

    Science.gov (United States)

    Khidir, Jarjees; Chen, Youhua; Anderson, Gary

    2013-05-01

    This paper describes a fully automated system to align an infra-red laser beam with a small retro-reflector over a wide range of distances. The component development and test were especially used for an open-path spectrometer gas detection system. Using blob detection under OpenCV library, an automatic alignment algorithm was designed to achieve fast and accurate target detection in a complex background environment. Test results are presented to show that the proposed algorithm has been successfully applied to various target distances and environment conditions.

  5. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

    Science.gov (United States)

    Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

  6. Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation.

    Science.gov (United States)

    Vitikainen, Anne-Mari; Mäkelä, Elina; Lioumis, Pantelis; Jousmäki, Veikko; Mäkelä, Jyrki P

    2015-09-30

    The use of navigated repetitive transcranial magnetic stimulation (rTMS) in mapping of speech-related brain areas has recently shown to be useful in preoperative workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver variability and non-optimal replicability of the rTMS results have been reported, and a need for additional development of the methodology is recognized. In TMS motor cortex mappings the evoked responses can be quantitatively monitored by electromyographic recordings; however, no such easily available setup exists for speech mappings. We present an accelerometer-based setup for detection of vocalization-related larynx vibrations combined with an automatic routine for voice onset detection for rTMS speech mapping applying naming. The results produced by the automatic routine were compared with the manually reviewed video-recordings. The new method was applied in the routine navigated rTMS speech mapping for 12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity and 71% specificity. Majority (63%) of the misdetections were related to visible throat movements, extra voices before the response, or delayed naming of the previous stimuli. The no-response errors were correctly detected in 88% of events. The proposed setup for automatic detection of voice onsets provides quantitative additional data for analysis of the rTMS-induced speech response modifications. The objectively defined speech response latencies increase the repeatability, reliability and stratification of the rTMS results. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. The design of a new criticality incident detection and alarm system

    International Nuclear Information System (INIS)

    Nobes, T.S.

    1999-01-01

    This paper presents a general review of criticality and its detection. After a brief description of what a criticality incident involves, an outline is given of detection methods and warning systems. (author)

  8. [An automatic peak detection method for LIBS spectrum based on continuous wavelet transform].

    Science.gov (United States)

    Chen, Peng-Fei; Tian, Di; Qiao, Shu-Jun; Yang, Guang

    2014-07-01

    Spectrum peak detection in the laser-induced breakdown spectroscopy (LIBS) is an essential step, but the presence of background and noise seriously disturb the accuracy of peak position. The present paper proposed a method applied to automatic peak detection for LIBS spectrum in order to enhance the ability of overlapping peaks searching and adaptivity. We introduced the ridge peak detection method based on continuous wavelet transform to LIBS, and discussed the choice of the mother wavelet and optimized the scale factor and the shift factor. This method also improved the ridge peak detection method with a correcting ridge method. The experimental results show that compared with other peak detection methods (the direct comparison method, derivative method and ridge peak search method), our method had a significant advantage on the ability to distinguish overlapping peaks and the precision of peak detection, and could be be applied to data processing in LIBS.

  9. Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients

    Science.gov (United States)

    Liu, Jiamin; Hua, Jeremy; Chellappa, Vivek; Petrick, Nicholas; Sahiner, Berkman; Farooqui, Mohammed; Marti, Gerald; Wiestner, Adrian; Summers, Ronald M.

    2012-03-01

    Patients with chronic lymphocytic leukemia (CLL) have an increased frequency of axillary lymphadenopathy. Pretreatment CT scans can be used to upstage patients at the time of presentation and post-treatment CT scans can reduce the number of complete responses. In the current clinical workflow, the detection and diagnosis of lymph nodes is usually performed manually by examining all slices of CT images, which can be time consuming and highly dependent on the observer's experience. A system for automatic lymph node detection and measurement is desired. We propose a computer aided detection (CAD) system for axillary lymph nodes on CT scans in CLL patients. The lung is first automatically segmented and the patient's body in lung region is extracted to set the search region for lymph nodes. Multi-scale Hessian based blob detection is then applied to detect potential lymph nodes within the search region. Next, the detected potential candidates are segmented by fast level set method. Finally, features are calculated from the segmented candidates and support vector machine (SVM) classification is utilized for false positive reduction. Two blobness features, Frangi's and Li's, are tested and their free-response receiver operating characteristic (FROC) curves are generated to assess system performance. We applied our detection system to 12 patients with 168 axillary lymph nodes measuring greater than 10 mm. All lymph nodes are manually labeled as ground truth. The system achieved sensitivities of 81% and 85% at 2 false positives per patient for Frangi's and Li's blobness, respectively.

  10. Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions

    Science.gov (United States)

    Montazeri, Sina; Gisinger, Christoph; Eineder, Michael; Zhu, Xiao xiang

    2018-05-01

    Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

  11. An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

    Directory of Open Access Journals (Sweden)

    Hai Guo

    2015-01-01

    Full Text Available An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA. So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine, NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

  12. Automatic defect detection in video archives: application to Montreux Jazz Festival digital archives

    Science.gov (United States)

    Hanhart, Philippe; Rerabek, Martin; Ivanov, Ivan; Dufaux, Alain; Jones, Caryl; Delidais, Alexandre; Ebrahimi, Touradj

    2013-09-01

    Archival of audio-visual databases has become an important discipline in multimedia. Various defects are typ- ically present in such archives. Among those, one can mention recording related defects such as interference between audio and video signals, optical related artifacts, recording and play out artifacts such as horizontal lines, and dropouts, as well as those due to digitization such as diagonal lines. An automatic or semi-automatic detection to identify such defects is useful, especially for large databases. In this paper, we propose two auto- matic algorithms for detection of horizontal and diagonal lines, as well as dropouts that are among the most typical artifacts encountered. We then evaluate the performance of these algorithms by making use of ground truth scores obtained by human subjects.

  13. Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?

    DEFF Research Database (Denmark)

    Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk

    1995-01-01

    of relevant pressure peaks at the various recording levels. Until now, this selection has been performed entirely by rule-based systems, requiring each pressure deflection to fit within predefined rigid numerical limits in order to be detected. However, due to great variations in the shapes of the pressure...... curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...

  14. Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets

    Directory of Open Access Journals (Sweden)

    B. Téllez

    2008-04-01

    Full Text Available The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation. The resulting maps are then used in the automatic outlier detection in meteorological datasets.

  15. Automatic collection of the rare-earths with post chromatography column detection

    International Nuclear Information System (INIS)

    David, P.; Metzger, G.; Repellin, M.

    1987-01-01

    The complete separation of rare-earths (in the aim of radio-isotopes measurement) requires High Performance Liquid Chromatography with ternary elution gradient. To automatize their collection with satisfying conditions, we have realized a non polluting, reliable and easy to operate detection method. This one is based on a derivation colorimetric system with arsenazo I (3 -(2 arsophenylazo 4.5) - dihydroxy - 2.7 naphtalene disulfonic acid)

  16. Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

    Science.gov (United States)

    Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

    2012-09-01

    The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

  17. Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

    Science.gov (United States)

    Marghany, Maged

    2016-10-01

    In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

  18. Presentation of the results of a Bayesian automatic event detection and localization program to human analysts

    Science.gov (United States)

    Kushida, N.; Kebede, F.; Feitio, P.; Le Bras, R.

    2016-12-01

    The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has been developing and testing NET-VISA (Arora et al., 2013), a Bayesian automatic event detection and localization program, and evaluating its performance in a realistic operational mode. In our preliminary testing at the CTBTO, NET-VISA shows better performance than its currently operating automatic localization program. However, given CTBTO's role and its international context, a new technology should be introduced cautiously when it replaces a key piece of the automatic processing. We integrated the results of NET-VISA into the Analyst Review Station, extensively used by the analysts so that they can check the accuracy and robustness of the Bayesian approach. We expect the workload of the analysts to be reduced because of the better performance of NET-VISA in finding missed events and getting a more complete set of stations than the current system which has been operating for nearly twenty years. The results of a series of tests indicate that the expectations born from the automatic tests, which show an overall overlap improvement of 11%, meaning that the missed events rate is cut by 42%, hold for the integrated interactive module as well. New events are found by analysts, which qualify for the CTBTO Reviewed Event Bulletin, beyond the ones analyzed through the standard procedures. Arora, N., Russell, S., and Sudderth, E., NET-VISA: Network Processing Vertically Integrated Seismic Analysis, 2013, Bull. Seismol. Soc. Am., 103, 709-729.

  19. Forming and detection of digital watermarks in the System for Automatic Identification of VHF Transmissions

    Directory of Open Access Journals (Sweden)

    О. В. Шишкін

    2013-07-01

    Full Text Available Forming and detection algorithms for digital watermarks are designed for automatic identification of VHF radiotelephone transmissions in the maritime and aeronautical mobile services. An audible insensitivity and interference resistance of embedded digital data are provided by means of OFDM technology jointly with normalized distortions distribution and data packet detection by the hash-function. Experiments were carried out on the base of ship’s radio station RT-2048 Sailor and USB ADC-DAC module of type Е14-140M L-CARD in the off-line processing regime in Matlab medium

  20. Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, J; Sørensen, Gertrud Laura; Sorensen, H B D

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG....

  1. Automatic Pedestrian Crossing Detection and Impairment Analysis Based on Mobile Mapping System

    Science.gov (United States)

    Liu, X.; Zhang, Y.; Li, Q.

    2017-09-01

    Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  2. Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings

    Directory of Open Access Journals (Sweden)

    David Hewson

    2007-01-01

    Full Text Available CUSUM (cumulative sum is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS algorithm, previously developed for application to long-term electromyography (EMG recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold acted to stop the estimation process, the other one (upper threshold was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events. Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm.

  3. Learning-based automatic detection of severe coronary stenoses in CT angiographies

    Science.gov (United States)

    Melki, Imen; Cardon, Cyril; Gogin, Nicolas; Talbot, Hugues; Najman, Laurent

    2014-03-01

    3D cardiac computed tomography angiography (CCTA) is becoming a standard routine for non-invasive heart diseases diagnosis. Thanks to its high negative predictive value, CCTA is increasingly used to decide whether or not the patient should be considered for invasive angiography. However, an accurate assessment of cardiac lesions using this modality is still a time consuming task and needs a high degree of clinical expertise. Thus, providing automatic tool to assist clinicians during the diagnosis task is highly desirable. In this work, we propose a fully automatic approach for accurate severe cardiac stenoses detection. Our algorithm uses the Random Forest classi cation to detect stenotic areas. First, the classi er is trained on 18 CT cardiac exams with CTA reference standard. Then, then classi cation result is used to detect severe stenoses (with a narrowing degree higher than 50%) in a 30 cardiac CT exam database. Features that best captures the di erent stenoses con guration are extracted along the vessel centerlines at di erent scales. To ensure the accuracy against the vessel direction and scale changes, we extract features inside cylindrical patterns with variable directions and radii. Thus, we make sure that the ROIs contains only the vessel walls. The algorithm is evaluated using the Rotterdam Coronary Artery Stenoses Detection and Quantication Evaluation Framework. The evaluation is performed using reference standard quanti cations obtained from quantitative coronary angiography (QCA) and consensus reading of CTA. The obtained results show that we can reliably detect severe stenosis with a sensitivity of 64%.

  4. AUTOMATIC PEDESTRIAN CROSSING DETECTION AND IMPAIRMENT ANALYSIS BASED ON MOBILE MAPPING SYSTEM

    Directory of Open Access Journals (Sweden)

    X. Liu

    2017-09-01

    Full Text Available Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  5. Farmers' preferences for automatic lameness-detection systems in dairy cattle.

    Science.gov (United States)

    Van De Gucht, T; Saeys, W; Van Nuffel, A; Pluym, L; Piccart, K; Lauwers, L; Vangeyte, J; Van Weyenberg, S

    2017-07-01

    As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic

  6. Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT

    Energy Technology Data Exchange (ETDEWEB)

    Baum, Thomas; Dobritz, Martin; Rummeny, Ernst J.; Noel, Peter B. [Technische Universitaet Muenchen, Institut fuer Radiologie, Klinikum rechts der Isar, Muenchen (Germany); Bauer, Jan S. [Technische Universitaet Muenchen, Abteilung fuer Neuroradiologie, Klinikum rechts der Isar, Muenchen (Germany); Klinder, Tobias; Lorenz, Cristian [Philips Research Laboratories, Hamburg (Germany)

    2014-04-15

    To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures. Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus. Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p < 0.001). Longitudinal changes in APR and MPR were significantly different between vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p < 0.001). This prototype algorithm may support radiologists in reporting currently underdiagnosed osteoporotic vertebral fractures so that appropriate therapy can be initiated. circle This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. (orig.)

  7. Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

    Science.gov (United States)

    Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

    2018-03-01

    Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2010-01-01

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

  9. Automatic concrete cracks detection and mapping of terrestrial laser scan data

    Directory of Open Access Journals (Sweden)

    Mostafa Rabah

    2013-12-01

    The current paper submits a method for automatic concrete cracks detection and mapping from the data that was obtained during laser scanning survey. The method of cracks detection and mapping is achieved by three steps, namely the step of shading correction in the original image, step of crack detection and finally step of crack mapping and processing steps. The detected crack is defined in a pixel coordinate system. To remap the crack into the referred coordinate system, a reverse engineering is used. This is achieved by a hybrid concept of terrestrial laser-scanner point clouds and the corresponding camera image, i.e. a conversion from the pixel coordinate system to the terrestrial laser-scanner or global coordinate system. The results of the experiment show that the mean differences between terrestrial laser scan and the total station are about 30.5, 16.4 and 14.3 mms in x, y and z direction, respectively.

  10. Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements

    Directory of Open Access Journals (Sweden)

    Alberto Martini

    2015-01-01

    Full Text Available The implementation of strategies for controlling water leaks is essential in order to reduce losses affecting distribution networks of drinking water. This paper focuses on leak detection by using vibration monitoring techniques. The long-term goal is the development of a system for automatic early detection of burst leaks in service pipes. An experimental campaign was started to measure vibrations transmitted along water pipes by real burst leaks occurring in actual water supply networks. The first experimental data were used for assessing the leak detection performance of a prototypal algorithm based on the calculation of the standard deviation of acceleration signals. The experimental campaign is here described and discussed. The proposed algorithm, enhanced by means of proper signal filtering techniques, was successfully tested on all monitored leaks, thus proving effective for leak detection purpose.

  11. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.

    Science.gov (United States)

    Raja, D Siva Sundhara; Vasuki, S

    2015-01-01

    Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

  12. Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches

    Science.gov (United States)

    Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Thomas

    To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

  13. Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

    Science.gov (United States)

    Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

    2015-01-01

    It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

  14. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  15. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    International Nuclear Information System (INIS)

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde

    2017-01-01

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

  16. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde [Santa María Tonantzintla, Puebla (Mexico)

    2017-06-15

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

  17. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

    Directory of Open Access Journals (Sweden)

    Kemal Akyol

    2016-01-01

    Full Text Available With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

  18. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

    Science.gov (United States)

    Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

    2013-11-01

    A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

  19. Planning an Automatic Fire Detection, Alarm, and Extinguishing System for Research Laboratories

    Directory of Open Access Journals (Sweden)

    Rostam Golmohamadi

    2014-04-01

    Full Text Available Background & Objectives: Educational and research laboratories in universities have a high risk of fire, because they have a variety of materials and equipment. The aim of this study was to provide a technical plan for safety improvement in educational and research laboratories of a university based on the design of automatic detection, alarm, and extinguishing systems . Methods : In this study, fire risk assessment was performed based on the standard of Military Risk Assessment method (MIL-STD-882. For all laboratories, detection and fire alarm systems and optimal fixed fire extinguishing systems were designed. Results : Maximum and minimum risks of fire were in chemical water and wastewater (81.2% and physical agents (62.5% laboratories, respectively. For studied laboratories, we designed fire detection systems based on heat and smoke detectors. Also in these places, fire-extinguishing systems based on CO2 were designed . Conclusion : Due to high risk of fire in studied laboratories, the best control method for fire prevention and protection based on special features of these laboratories is using automatic detection, warning and fire extinguishing systems using CO2 .

  20. Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis

    Science.gov (United States)

    Spies, Lothar; Tewes, Anja; Suppa, Per; Opfer, Roland; Buchert, Ralph; Winkler, Gerhard; Raji, Alaleh

    2013-12-01

    A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (≥0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.

  1. Automatic detection and quantitative analysis of cells in the mouse primary motor cortex

    Science.gov (United States)

    Meng, Yunlong; He, Yong; Wu, Jingpeng; Chen, Shangbin; Li, Anan; Gong, Hui

    2014-09-01

    Neuronal cells play very important role on metabolism regulation and mechanism control, so cell number is a fundamental determinant of brain function. Combined suitable cell-labeling approaches with recently proposed three-dimensional optical imaging techniques, whole mouse brain coronal sections can be acquired with 1-μm voxel resolution. We have developed a completely automatic pipeline to perform cell centroids detection, and provided three-dimensional quantitative information of cells in the primary motor cortex of C57BL/6 mouse. It involves four principal steps: i) preprocessing; ii) image binarization; iii) cell centroids extraction and contour segmentation; iv) laminar density estimation. Investigations on the presented method reveal promising detection accuracy in terms of recall and precision, with average recall rate 92.1% and average precision rate 86.2%. We also analyze laminar density distribution of cells from pial surface to corpus callosum from the output vectorizations of detected cell centroids in mouse primary motor cortex, and find significant cellular density distribution variations in different layers. This automatic cell centroids detection approach will be beneficial for fast cell-counting and accurate density estimation, as time-consuming and error-prone manual identification is avoided.

  2. vMMN for schematic faces: automatic detection of change in emotional expression

    Directory of Open Access Journals (Sweden)

    Kairi eKreegipuu

    2013-10-01

    Full Text Available Our brain is able to automatically detect changes in sensory stimulation, including in vision. A large variety of changes of features in stimulation elicit a deviance-reflecting ERP component known as the mismatch negativity (MMN. The present study has three main goals: (1 to register vMMN using a rapidly presented stream of schematic faces (neutral, happy, angry; adapted from Öhman et al., 2001; (2 to compare elicited vMMNs to angry and happy schematic faces in two different paradigms, in a traditional oddball design with frequent standard and rare target and deviant stimuli (12.5% each and in an version of an optimal multi-feature paradigm with several deviant stimuli (altogether 37.5% in the stimulus block; (3 to compare vMMNs to subjective ratings of valence, arousal and attention capture for happy and angry schematic faces, i.e., to estimate the effect of affective value of stimuli on their automatic detection. Eleven observers (19-32 years, 6 women took part in both experiments, an oddball and optimum paradigm. Stimuli were rapidly presented schematic faces and an object with face-features that served as the target stimulus to be detected by a button-press. Results show that a vMMN-type response at posterior sites was equally elicited in both experiments. Post-experimental reports confirmed that the angry face attracted more automatic attention than the happy face but the difference did not emerge directly at the ERP level. Thus, when interested in studying change detection in facial expressions we encourage the use of the optimum (multi-feature design in order to save time and other experimental resources.

  3. Automatic polymerase chain reaction product detection system for food safety monitoring using zinc finger protein fused to luciferase

    International Nuclear Information System (INIS)

    Yoshida, Wataru; Kezuka, Aki; Murakami, Yoshiyuki; Lee, Jinhee; Abe, Koichi; Motoki, Hiroaki; Matsuo, Takafumi; Shimura, Nobuaki; Noda, Mamoru; Igimi, Shizunobu; Ikebukuro, Kazunori

    2013-01-01

    Graphical abstract: -- Highlights: •Zif268 fused to luciferase was used for E. coli O157, Salmonella and coliform detection. •Artificial zinc finger protein fused to luciferase was constructed for Norovirus detection. •An analyzer that automatically detects PCR products by zinc finger protein fused to luciferase was developed. •Target pathogens were specifically detected by the automatic analyzer with zinc finger protein fused to luciferase. -- Abstract: An automatic polymerase chain reaction (PCR) product detection system for food safety monitoring using zinc finger (ZF) protein fused to luciferase was developed. ZF protein fused to luciferase specifically binds to target double stranded DNA sequence and has luciferase enzymatic activity. Therefore, PCR products that comprise ZF protein recognition sequence can be detected by measuring the luciferase activity of the fusion protein. We previously reported that PCR products from Legionella pneumophila and Escherichia coli (E. coli) O157 genomic DNA were detected by Zif268, a natural ZF protein, fused to luciferase. In this study, Zif268–luciferase was applied to detect the presence of Salmonella and coliforms. Moreover, an artificial zinc finger protein (B2) fused to luciferase was constructed for a Norovirus detection system. In the luciferase activity detection assay, several bound/free separation process is required. Therefore, an analyzer that automatically performed the bound/free separation process was developed to detect PCR products using the ZF–luciferase fusion protein. By means of the automatic analyzer with ZF–luciferase fusion protein, target pathogenic genomes were specifically detected in the presence of other pathogenic genomes. Moreover, we succeeded in the detection of 10 copies of E. coli BL21 without extraction of genomic DNA by the automatic analyzer and E. coli was detected with a logarithmic dependency in the range of 1.0 × 10 to 1.0 × 10 6 copies

  4. Automatic polymerase chain reaction product detection system for food safety monitoring using zinc finger protein fused to luciferase

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Wataru; Kezuka, Aki; Murakami, Yoshiyuki; Lee, Jinhee; Abe, Koichi [Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588 (Japan); Motoki, Hiroaki; Matsuo, Takafumi; Shimura, Nobuaki [System Instruments Co., Ltd., 776-2 Komiya-cho, Hachioji, Tokyo 192-0031 (Japan); Noda, Mamoru; Igimi, Shizunobu [Division of Biomedical Food Research, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501 (Japan); Ikebukuro, Kazunori, E-mail: ikebu@cc.tuat.ac.jp [Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588 (Japan)

    2013-11-01

    Graphical abstract: -- Highlights: •Zif268 fused to luciferase was used for E. coli O157, Salmonella and coliform detection. •Artificial zinc finger protein fused to luciferase was constructed for Norovirus detection. •An analyzer that automatically detects PCR products by zinc finger protein fused to luciferase was developed. •Target pathogens were specifically detected by the automatic analyzer with zinc finger protein fused to luciferase. -- Abstract: An automatic polymerase chain reaction (PCR) product detection system for food safety monitoring using zinc finger (ZF) protein fused to luciferase was developed. ZF protein fused to luciferase specifically binds to target double stranded DNA sequence and has luciferase enzymatic activity. Therefore, PCR products that comprise ZF protein recognition sequence can be detected by measuring the luciferase activity of the fusion protein. We previously reported that PCR products from Legionella pneumophila and Escherichia coli (E. coli) O157 genomic DNA were detected by Zif268, a natural ZF protein, fused to luciferase. In this study, Zif268–luciferase was applied to detect the presence of Salmonella and coliforms. Moreover, an artificial zinc finger protein (B2) fused to luciferase was constructed for a Norovirus detection system. In the luciferase activity detection assay, several bound/free separation process is required. Therefore, an analyzer that automatically performed the bound/free separation process was developed to detect PCR products using the ZF–luciferase fusion protein. By means of the automatic analyzer with ZF–luciferase fusion protein, target pathogenic genomes were specifically detected in the presence of other pathogenic genomes. Moreover, we succeeded in the detection of 10 copies of E. coli BL21 without extraction of genomic DNA by the automatic analyzer and E. coli was detected with a logarithmic dependency in the range of 1.0 × 10 to 1.0 × 10{sup 6} copies.

  5. Automatic detection of lexical change: an auditory event-related potential study.

    Science.gov (United States)

    Muller-Gass, Alexandra; Roye, Anja; Kirmse, Ursula; Saupe, Katja; Jacobsen, Thomas; Schröger, Erich

    2007-10-29

    We investigated the detection of rare task-irrelevant changes in the lexical status of speech stimuli. Participants performed a nonlinguistic task on word and pseudoword stimuli that occurred, in separate conditions, rarely or frequently. Task performance for pseudowords was deteriorated relative to words, suggesting unintentional lexical analysis. Furthermore, rare word and pseudoword changes had a similar effect on the event-related potentials, starting as early as 165 ms. This is the first demonstration of the automatic detection of change in lexical status that is not based on a co-occurring acoustic change. We propose that, following lexical analysis of the incoming stimuli, a mental representation of the lexical regularity is formed and used as a template against which lexical change can be detected.

  6. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    Science.gov (United States)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  7. Automatic detection and classification of obstacles with applications in autonomous mobile robots

    Science.gov (United States)

    Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

    2016-04-01

    Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

  8. Automatic Detection of Microcalcifications in a Digital Mammography Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Carlos A. Madrigal-González

    2013-11-01

    Full Text Available Breast cancer is one of the cancers that has a higher mortality rate among women and early detection increases the possibilities of cure, so its early detection is one of the best treatments for this serious disease. Microcalcifications are a type of lesion in the breast and its presence is highly correlated with the presence of cancer. In this paper we present a method for automatic detection of microcalcifications using digital image processing using a Gaussian filtering approach, which can enhance the contrast between microcalcifications and normal tissue present in a mammography, then apply a local thresholding algorithm witch allow the identification of suspicious microcalcifications. The classifier used to determine the degree of benign or malignant microcalcifications is the K-Nearest Neighbours (KNN and the validation of the results was done using ROC curves.

  9. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    Science.gov (United States)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  10. Automatic Echographic Detection of Halloysite Clay Nanotubes in a Low Concentration Range.

    Science.gov (United States)

    Conversano, Francesco; Pisani, Paola; Casciaro, Ernesto; Di Paola, Marco; Leporatti, Stefano; Franchini, Roberto; Quarta, Alessandra; Gigli, Giuseppe; Casciaro, Sergio

    2016-04-11

    Aim of this work was to investigate the automatic echographic detection of an experimental drug delivery agent, halloysite clay nanotubes (HNTs), by employing an innovative method based on advanced spectral analysis of the corresponding "raw" radiofrequency backscatter signals. Different HNT concentrations in a low range (5.5-66 × 10 10 part/mL, equivalent to 0.25-3.00 mg/mL) were dispersed in custom-designed tissue-mimicking phantoms and imaged through a clinically-available echographic device at a conventional ultrasound diagnostic frequency (10 MHz). The most effective response (sensitivity = 60%, specificity = 95%), was found at a concentration of 33 × 10 10 part/mL (1.5 mg/mL), representing a kind of best compromise between the need of enough particles to introduce detectable spectral modifications in the backscattered signal and the necessity to avoid the losses of spectral peculiarity associated to higher HNT concentrations. Based on theoretical considerations and quantitative comparisons with literature-available results, this concentration could also represent an optimal concentration level for the automatic echographic detection of different solid nanoparticles when employing a similar ultrasound frequency. Future dedicated studies will assess the actual clinical usefulness of the proposed approach and the potential of HNTs for effective theranostic applications.

  11. Automatic vs. Human Detection of Bipolar Magnetic Regions: Using the Best of Both Worlds

    Science.gov (United States)

    Munoz-Jaramillo, A.; DeLuca, M. D.; Windmueller, J. C.; Longcope, D. W.

    2014-12-01

    The solar cycle can be understood as a process that alternates the large-scale magnetic field of the Sun between poloidal and toroidal configurations. Although the process that transitions the solar cycle between toroidal and poloidal phases is still not fully understood, theoretical studies, and observational evidence, suggest that this process is driven by the emergence and decay of bipolar magnetic regions (BMRs) at the photosphere. Furthermore, the emergence of BMRs at the photosphere is the main driver behind solar variability and solar activity in general; making the study of their properties doubly important for heliospheric physics. However, in spite of their critical role, there is still no unified catalog of BMRs spanning multiple instruments and covering the entire period of systematic measurement of the solar magnetic field (i.e. 1975 to present).One of the interesting aspects of the detection of BMRs is that, due to the time and spatial scales of interest, it is tractable for both human observers and automatic detection algorithms. This makes it ideal for comparative studies of the advantages and failing of both approaches. In this presentation we will compare three different BMR catalogs, reduced from magnetograms taken by SOHO/MDI, using human, automatic, and hybrid methods of detection. The focus will be the comparative performance between the three methods, their merits, and disadvantages, and the lessons that can be applied to other imaging data sets.

  12. Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

    Science.gov (United States)

    Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

    2018-05-06

    In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

  13. On-line automatic detection of wood pellets in pneumatically conveyed wood dust flow

    Science.gov (United States)

    Sun, Duo; Yan, Yong; Carter, Robert M.; Gao, Lingjun; Qian, Xiangchen; Lu, Gang

    2014-04-01

    This paper presents a piezoelectric transducer based system for on-line automatic detection of wood pellets in wood dust flow in pneumatic conveying pipelines. The piezoelectric transducer senses non-intrusively the collisions between wood pellets and the pipe wall. Wavelet-based denoising is adopted to eliminate environmental noise and recover the collision events. Then the wood pellets are identified by sliding a time window through the denoised signal with a suitable threshold. Experiments were carried out on a laboratory test rig and on an industrial pneumatic conveying pipeline to assess the effectiveness and operability of the system.

  14. Development and testing of incident detection algorithms. Vol. 2, research methodology and detailed results.

    Science.gov (United States)

    1976-04-01

    The development and testing of incident detection algorithms was based on Los Angeles and Minneapolis freeway surveillance data. Algorithms considered were based on times series and pattern recognition techniques. Attention was given to the effects o...

  15. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

    Directory of Open Access Journals (Sweden)

    Tao Ye

    2018-06-01

    Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  16. Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery.

    Science.gov (United States)

    Sandino, Juan; Wooler, Adam; Gonzalez, Felipe

    2017-09-24

    The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

  17. Automatic Detection and Classification of Audio Events for Road Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Noor Almaadeed

    2018-06-01

    Full Text Available This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

  18. Section based traffic detection on motorways for incident management

    NARCIS (Netherlands)

    Noort, M. van; Klunder, G.

    2007-01-01

    Current vehicle detection on motorways is based generally on either inductive loop systems or various alternatives such as video cameras. Recently, we encountered two new developments that take a different approach: one from The Netherlands using microwave sensors, and the other from Sweden using

  19. Association of standing and lying behavior patterns and incidence of intramammary infection in dairy cows milked with an automatic milking system.

    Science.gov (United States)

    Devries, T J; Deming, J A; Rodenburg, J; Seguin, G; Leslie, K E; Barkema, H W

    2011-08-01

    The standing and lying behavior patterns of dairy cows, particularly the length of time cows spend standing after milking, have the potential to influence the incidence of intramammary infection (IMI). The objectives were to describe the standing and lying behavior patterns of cows milked with an automatic milking system (AMS) and to determine how these patterns relate to the incidence of IMI. One hundred and eleven lactating Holstein dairy cows were monitored over a 4-mo period. These cows were kept in a sand-bedded freestall barn with 2 pens, each with a free cow traffic AMS. Feed was delivered once daily, and pushed up 2 to 3 times daily. Quarter milk samples were collected for bacteriological culture from each cow once every 4 wk. A new IMI was defined as a positive culture sample following a negative culture. For 7 d before each of the last 3 milk samplings, standing and lying behavior, and times of milking and feed manipulation (feed delivery and push up) were recorded. Daily lying time and lying bout length were negatively related with milk yield (r=-0.23 and -0.20, respectively) and milking frequency (r=-0.32 and -0.20, respectively); milk yield was positively related to milking frequency (r=0.58). Feed manipulation near the time cows were milked (1h before 2h after) resulted in the longest post-milking standing times (mean=86 min; 95% confidence interval=78, 94 min), whereas feed manipulation occurring outside that time frame resulted in shorter post-milking standing times. Over the study period, 171 new IMI were detected. Of these new IMI detected, those caused by coagulase-negative staphylococci were the only ones associated with post-milking standing time; as post-milking standing time increased past 2.5h after milking, the odds of acquiring a new IMI tended to also increase. In summary, standing and lying behavior patterns of cows milked with an AMS were affected by both feed manipulation and their milking activity. Further, the post-milking standing

  20. Automatic detection of subglacial lakes in radar sounder data acquired in Antarctica

    Science.gov (United States)

    Ilisei, Ana-Maria; Khodadadzadeh, Mahdi; Dalsasso, Emanuele; Bruzzone, Lorenzo

    2017-10-01

    Subglacial lakes decouple the ice sheet from the underlying bedrock, thus facilitating the sliding of the ice masses towards the borders of the continents, consequently raising the sea level. This motivated increasing attention in the detection of subglacial lakes. So far, about 70% of the total number of subglacial lakes in Antarctica have been detected by analysing radargrams acquired by radar sounder (RS) instruments. Although the amount of radargrams is expected to drastically increase, from both airborne and possible future Earth observation RS missions, currently the main approach to the detection of subglacial lakes in radargrams is by visual interpretation. This approach is subjective and extremely time consuming, thus difficult to apply to a large amount of radargrams. In order to address the limitations of the visual interpretation and to assist glaciologists in better understanding the relationship between the subglacial environment and the climate system, in this paper, we propose a technique for the automatic detection of subglacial lakes. The main contribution of the proposed technique is the extraction of features for discriminating between lake and non-lake basal interfaces. In particular, we propose the extraction of features that locally capture the topography of the basal interface, the shape and the correlation of the basal waveforms. Then, the extracted features are given as input to a supervised binary classifier based on Support Vector Machine to perform the automatic subglacial lake detection. The effectiveness of the proposed method is proven both quantitatively and qualitatively by applying it to a large dataset acquired in East Antarctica by the MultiChannel Coherent Radar Depth Sounder.

  1. Automatic detection of referral patients due to retinal pathologies through data mining.

    Science.gov (United States)

    Quellec, Gwenolé; Lamard, Mathieu; Erginay, Ali; Chabouis, Agnès; Massin, Pascale; Cochener, Béatrice; Cazuguel, Guy

    2016-04-01

    With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully

  2. Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy

    Science.gov (United States)

    Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

    2015-03-01

    Stereotactic ablative radiotherapy (SABR) is a treatment for early-stage lung cancer with local control rates comparable to surgery. After SABR, benign radiation induced lung injury (RILI) results in tumour-mimicking changes on computed tomography (CT) imaging. Distinguishing recurrence from RILI is a critical clinical decision determining the need for potentially life-saving salvage therapies whose high risks in this population dictate their use only for true recurrences. Current approaches do not reliably detect recurrence within a year post-SABR. We measured the detection accuracy of texture features within automatically determined regions of interest, with the only operator input being the single line segment measuring tumour diameter, normally taken during the clinical workflow. Our leave-one-out cross validation on images taken 2-5 months post-SABR showed robustness of the entropy measure, with classification error of 26% and area under the receiver operating characteristic curve (AUC) of 0.77 using automatic segmentation; the results using manual segmentation were 24% and 0.75, respectively. AUCs for this feature increased to 0.82 and 0.93 at 8-14 months and 14-20 months post SABR, respectively, suggesting even better performance nearer to the date of clinical diagnosis of recurrence; thus this system could also be used to support and reinforce the physician's decision at that time. Based on our ongoing validation of this automatic approach on a larger sample, we aim to develop a computer-aided diagnosis system which will support the physician's decision to apply timely salvage therapies and prevent patients with RILI from undergoing invasive and risky procedures.

  3. An automatic system for the detection of dairy cows lying behaviour in free-stall barns

    Directory of Open Access Journals (Sweden)

    Simona M.C. Porto

    2013-09-01

    Full Text Available In this paper, a method for the automatic detection of dairy cow lying behaviour in free-stall barns is proposed. A computer visionbased system (CVBS composed of a video-recording system and a cow lying behaviour detector based on the Viola Jones algorithm was developed. The CVBS performance was tested in a head-to-head free stall barn. Two classifiers were implemented in the software component of the CVBS to obtain the cow lying behaviour detector. The CVBS was validated by comparing its detection results with those generated from visual recognition. This comparison allowed the following accuracy indices to be calculated: the branching factor (BF, the miss factor (MF, the sensitivity, and the quality percentage (QP. The MF value of approximately 0.09 showed that the CVBS missed one cow every 11 well detected cows. Conversely, the BF value of approximately 0.08 indicated that one false positive was detected every 13 well detected cows. The high value of approximately 0.92 obtained for the sensitivity index and that obtained for QP of about 0.85 revealed the ability of the proposed system to detect cows lying in the stalls.

  4. DESIGN AND DEVELOP A COMPUTER AIDED DESIGN FOR AUTOMATIC EXUDATES DETECTION FOR DIABETIC RETINOPATHY SCREENING

    Directory of Open Access Journals (Sweden)

    C. A. SATHIYAMOORTHY

    2016-04-01

    Full Text Available Diabetic Retinopathy is a severe and widely spread eye disease which can lead to blindness. One of the main symptoms for vision loss is Exudates and it could be prevented by applying an early screening process. In the Existing systems, a Fuzzy C-Means Clustering technique is used for detecting the exudates for analyzation. The main objective of this paper is, to improve the efficiency of the Exudates detection in diabetic retinopathy images. To do this, a three Stage – [TS] approach is introduced for detecting and extracting the exudates automatically from the retinal images for screening the Diabetic retinopathy. TS functions on the image in three levels such as Pre-processing the image, enhancing the image and detecting the Exudates accurately. After successful detection, the detected exudates are classified using GLCM method for finding the accuracy. The TS approach is experimented using MATLAB software and the performance evaluation can be proved by comparing the results with the existing approach’s result and with the hand-drawn ground truths images from the expert ophthalmologist.

  5. A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

    Science.gov (United States)

    Salehi, Leila; Azmi, Reza

    2014-07-01

    Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

  6. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  7. Automatic detection of ECG electrode misplacement: a tale of two algorithms

    International Nuclear Information System (INIS)

    Xia, Henian; Garcia, Gabriel A; Zhao, Xiaopeng

    2012-01-01

    Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records. (paper)

  8. A FUZZY AUTOMATIC CAR DETECTION METHOD BASED ON HIGH RESOLUTION SATELLITE IMAGERY AND GEODESIC MORPHOLOGY

    Directory of Open Access Journals (Sweden)

    N. Zarrinpanjeh

    2017-09-01

    Full Text Available Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  9. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

    Directory of Open Access Journals (Sweden)

    Wenyu Zhang

    2014-10-01

    Full Text Available Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

  10. Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

    Science.gov (United States)

    Rahnemoonfar, Maryam; Foster, Jamie; Starek, Michael J.

    2017-05-01

    Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

  11. a Fuzzy Automatic CAR Detection Method Based on High Resolution Satellite Imagery and Geodesic Morphology

    Science.gov (United States)

    Zarrinpanjeh, N.; Dadrassjavan, F.

    2017-09-01

    Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  12. Automatic crack detection and classification method for subway tunnel safety monitoring.

    Science.gov (United States)

    Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

    2014-10-16

    Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

  13. Automatic multiresolution age-related macular degeneration detection from fundus images

    Science.gov (United States)

    Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

    2014-03-01

    Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

  14. Automatic Detection and Evaluation of Solar Cell Micro-Cracks in Electroluminescence Images Using Matched Filters

    Energy Technology Data Exchange (ETDEWEB)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2016-11-21

    A method for detecting micro-cracks in solar cells using two dimensional matched filters was developed, derived from the electroluminescence intensity profile of typical micro-cracks. We describe the image processing steps to obtain a binary map with the location of the micro-cracks. Finally, we show how to automatically estimate the total length of each micro-crack from these maps, and propose a method to identify severe types of micro-cracks, such as parallel, dendritic, and cracks with multiple orientations. With an optimized threshold parameter, the technique detects over 90 % of cracks larger than 3 cm in length. The method shows great potential for quantifying micro-crack damage after manufacturing or module transportation for the determination of a module quality criterion for cell cracking in photovoltaic modules.

  15. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  16. Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve

    Science.gov (United States)

    Xu, Lili; Luo, Shuqian

    2010-11-01

    Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

  17. Automatic optical detection and classification of marine animals around MHK converters using machine vision

    Energy Technology Data Exchange (ETDEWEB)

    Brunton, Steven [Univ. of Washington, Seattle, WA (United States)

    2018-01-15

    Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

  18. NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER’S DISEASE: A REVIEW

    Directory of Open Access Journals (Sweden)

    Rupali Kamathe

    2017-08-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

  19. Automatic Detection of P and S Phases by Support Vector Machine

    Science.gov (United States)

    Jiang, Y.; Ning, J.; Bao, T.

    2017-12-01

    Many methods in seismology rely on accurately picked phases. A well performed program on automatically phase picking will assure the application of these methods. Related researches before mostly focus on finding different characteristics between noise and phases, which are all not enough successful. We have developed a new method which mainly based on support vector machine to detect P and S phases. In it, we first input some waveform pieces into the support vector machine, then employ it to work out a hyper plane which can divide the space into two parts: respectively noise and phase. We further use the same method to find a hyper plane which can separate the phase space into P and S parts based on the three components' cross-correlation matrix. In order to further improve the ability of phase detection, we also employ array data. At last, we show that the overall effect of our method is robust by employing both synthetic and real data.

  20. A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Bohui Zhu

    2013-01-01

    Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

  1. Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

    Science.gov (United States)

    Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

    2017-07-01

    Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

  2. Automatic detection of non-convulsive seizures: A reduced complexity approach

    Directory of Open Access Journals (Sweden)

    Tazeem Fatma

    2016-10-01

    Full Text Available Detection of non-convulsive seizures (NCSz is a challenging task because they lack convulsions, meaning no physical visible symptoms are there to detect the presence of a seizure activity. Hence their diagnosis is not easy, also continuous observation of full length EEG for the detection of non-convulsive seizures (NCSz by an expert or a technician is a very exhaustive, time consuming job. A technique for the automatic detection of NCSz is proposed in this paper. The database used in this research was recorded at the All India Institute of Medical Sciences (AIIMS, New Delhi. 13 EEG recordings of 9 subjects consisting of a total 23 seizures of 29.42 min duration were used for analysis. Normalized modified Wilson amplitude is used as a key feature to classify between normal and seizure activity. The main advantage of this study lies in the fact that no classifier is used here and hence algorithm is very simple and computationally fast. With the use of only one feature, all of the seizures under test were detected correctly, and hence the median sensitivity and specificity of 100% and 99.21% were achieved respectively.

  3. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data.

    Science.gov (United States)

    Brunner, Gerd; Chittajallu, Deepak R; Kurkure, Uday; Kakadiaris, Ioannis A

    2010-10-01

    Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (± 15.80)%, 93.54 (± 1.98)%, and 85.27 (± 14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (± 7.78)%, 96.57 (± 4.90)%, and 73.58 (± 8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.

  4. Deep Learning-Based Data Forgery Detection in Automatic Generation Control

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Fengli [Univ. of Arkansas, Fayetteville, AR (United States); Li, Qinghua [Univ. of Arkansas, Fayetteville, AR (United States)

    2017-10-09

    Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

  5. Towards an automatic lab-on-valve-ion mobility spectrometric system for detection of cocaine abuse.

    Science.gov (United States)

    Cocovi-Solberg, David J; Esteve-Turrillas, Francesc A; Armenta, Sergio; de la Guardia, Miguel; Miró, Manuel

    2017-08-25

    A lab-on-valve miniaturized system integrating on-line disposable micro-solid phase extraction has been interfaced with ion mobility spectrometry for the accurate and sensitive determination of cocaine and ecgonine methyl ester in oral fluids. The method is based on the automatic loading of 500μL of oral fluid along with the retention of target analytes and matrix clean-up by mixed-mode cationic/reversed-phase solid phase beads, followed by elution with 100μL of 2-propanol containing (3% v/v) ammonia, which are online injected into the IMS. The sorptive particles are automatically discarded after every individual assay inasmuch as the sorptive capacity of the sorbent material is proven to be dramatically deteriorated with reuse. The method provided a limit of detection of 0.3 and 0.14μgL -1 for cocaine and ecgonine methyl ester, respectively, with relative standard deviation values from 8 till 14% with a total analysis time per sample of 7.5min. Method trueness was evaluated by analyzing oral fluid samples spiked with cocaine at different concentration levels (1, 5 and 25μgL -1 ) affording relative recoveries within the range of 85±24%. Fifteen saliva samples were collected from volunteers and analysed following the proposed automatic procedure, showing a 40% cocaine occurrence with concentrations ranging from 1.3 to 97μgL -1 . Field saliva samples were also analysed by reference methods based on lateral flow immunoassay and gas chromatography-mass spectrometry. The application of this procedure to the control of oral fluids of cocaine consumers represents a step forward towards the development of a point-of-care cocaine abuse sensing system. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    Science.gov (United States)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  7. Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method

    Directory of Open Access Journals (Sweden)

    Hamid Reza Pourreza

    2009-03-01

    Full Text Available Introduction: Diabetic retinopathy (DR is one of the most serious and most frequent eye diseases in the world and the most common cause of blindness in adults between 20 and 60 years of age. Following 15 years of diabetes, about 2% of the diabetic patients are blind and 10% suffer from vision impairment due to DR complications. This paper addresses the automatic detection of microaneurysms (MA in color fundus images, which plays a key role in computer-assisted early diagnosis of diabetic retinopathy. Materials and Methods: The algorithm can be divided into three main steps. The purpose of the first step or pre-processing is background normalization and contrast enhancement of the images. The second step aims to detect candidates, i.e., all patterns possibly corresponding to MA, which is achieved using a local radon transform, Then, features are extracted, which are used in the last step to automatically classify the candidates into real MA or other objects using the SVM method. A database of 100 annotated images was used to test the algorithm. The algorithm was compared to manually obtained gradings of these images. Results: The sensitivity of diagnosis for DR was 100%, with specificity of 90% and the sensitivity of precise MA localization was 97%, at an average number of 5 false positives per image. Discussion and Conclusion: Sensitivity and specificity of this algorithm make it one of the best methods in this field. Using the local radon transform in this algorithm eliminates the noise sensitivity for MA detection in retinal image analysis.

  8. Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

    Science.gov (United States)

    Kage, Andreas; Legal, Wolfgang; Kelm, Peter; Simon, Jörg; Bergen, Tobias; Münzenmayer, Christian; Benz, Michaela

    2012-03-01

    Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

  9. Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images.

    Science.gov (United States)

    Protonotarios, Nicholas E; Spyrou, George M; Kastis, George A

    2017-06-01

    The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

    Science.gov (United States)

    Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

    2017-06-01

    The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

  11. CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

    Science.gov (United States)

    Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

    2018-01-01

    Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

  12. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  13. Automatic detection of the macula in retinal fundus images using seeded mode tracking approach.

    Science.gov (United States)

    Wong, Damon W K; Liu, Jiang; Tan, Ngan-Meng; Yin, Fengshou; Cheng, Xiangang; Cheng, Ching-Yu; Cheung, Gemmy C M; Wong, Tien Yin

    2012-01-01

    The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

  14. Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

    Science.gov (United States)

    Tabrizi, Pooneh R.; Mansoor, Awais; Biggs, Elijah; Jago, James; Linguraru, Marius George

    2018-02-01

    Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 +/- 0.9 mm, 6.34 +/- 4.32 degrees, and 1.73 +/- 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

  15. Automatic detection of optic disc based on PCA and mathematical morphology.

    Science.gov (United States)

    Morales, Sandra; Naranjo, Valery; Angulo, Us; Alcaniz, Mariano

    2013-04-01

    The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.

  16. Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

  17. Automatic detection of measurement points for non-contact vibrometer-based diagnosis of cardiac arrhythmias

    Science.gov (United States)

    Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter

    2017-03-01

    Monitoring of the heart rhythm is the cornerstone of the diagnosis of cardiac arrhythmias. It is done by means of electrocardiography which relies on electrodes attached to the skin of the patient. We present a new system approach based on the so-called vibrocardiogram that allows an automatic non-contact registration of the heart rhythm. Because of the contactless principle, the technique offers potential application advantages in medical fields like emergency medicine (burn patient) or premature baby care where adhesive electrodes are not easily applicable. A laser-based, mobile, contactless vibrometer for on-site diagnostics that works with the principle of laser Doppler vibrometry allows the acquisition of vital functions in form of a vibrocardiogram. Preliminary clinical studies at the Klinikum Karlsruhe have shown that the region around the carotid artery and the chest region are appropriate therefore. However, the challenge is to find a suitable measurement point in these parts of the body that differs from person to person due to e. g. physiological properties of the skin. Therefore, we propose a new Microsoft Kinect-based approach. When a suitable measurement area on the appropriate parts of the body are detected by processing the Kinect data, the vibrometer is automatically aligned on an initial location within this area. Then, vibrocardiograms on different locations within this area are successively acquired until a sufficient measuring quality is achieved. This optimal location is found by exploiting the autocorrelation function.

  18. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  19. Statistical Analysis of Automatic Seed Word Acquisition to Improve Harmful Expression Extraction in Cyberbullying Detection

    Directory of Open Access Journals (Sweden)

    Suzuha Hatakeyama

    2016-04-01

    Full Text Available We study the social problem of cyberbullying, defined as a new form of bullying that takes place in the Internet space. This paper proposes a method for automatic acquisition of seed words to improve performance of the original method for the cyberbullying detection by Nitta et al. [1]. We conduct an experiment exactly in the same settings to find out that the method based on a Web mining technique, lost over 30% points of its performance since being proposed in 2013. Thus, we hypothesize on the reasons for the decrease in the performance and propose a number of improvements, from which we experimentally choose the best one. Furthermore, we collect several seed word sets using different approaches, evaluate and their precision. We found out that the influential factor in extraction of harmful expressions is not the number of seed words, but the way the seed words were collected and filtered.

  20. Automatic detection and classification of artifacts in single-channel EEG

    DEFF Research Database (Denmark)

    Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W.

    2014-01-01

    Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single......-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different...... artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated...

  1. AUTOMATIC DETECTION ALGORITHM OF DYNAMIC PRESSURE PULSES IN THE SOLAR WIND

    International Nuclear Information System (INIS)

    Zuo, Pingbing; Feng, Xueshang; Wang, Yi; Xie, Yanqiong; Li, Huijun; Xu, Xiaojun

    2015-01-01

    Dynamic pressure pulses (DPPs) in the solar wind are a significant phenomenon closely related to the solar-terrestrial connection and physical processes of solar wind dynamics. In order to automatically identify DPPs from solar wind measurements, we develop a procedure with a three-step detection algorithm that is able to rapidly select DPPs from the plasma data stream and simultaneously define the transition region where large dynamic pressure variations occur and demarcate the upstream and downstream region by selecting the relatively quiet status before and after the abrupt change in dynamic pressure. To demonstrate the usefulness, efficiency, and accuracy of this procedure, we have applied it to the Wind observations from 1996 to 2008 by successfully obtaining the DPPs. The procedure can also be applied to other solar wind spacecraft observation data sets with different time resolutions

  2. Automatic detection of photoresist residual layer in lithography using a neural classification approach

    KAUST Repository

    Gereige, Issam

    2012-09-01

    Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

  3. NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    G. Máttyus

    2013-05-01

    Full Text Available Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation. Optical satellite images (OSI can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

  4. Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

    Science.gov (United States)

    Máttyus, G.

    2013-05-01

    Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

  5. Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

    Directory of Open Access Journals (Sweden)

    Alemi Farrokh

    2010-01-01

    Full Text Available Abstract Background The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level. Methods Influenza-Like Illness (ILI was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes. Results Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters. Conclusions Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual

  6. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

    Science.gov (United States)

    Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

    2014-03-01

    This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative

  7. AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    Elaheh Aghabalaei Khordehchi

    2017-06-01

    Full Text Available Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs are extracted from the smoothed images. The Statistical Region Merging (SRM algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

  8. Rheticus Displacement: an Automatic Geo-Information Service Platform for Ground Instabilities Detection and Monitoring

    Science.gov (United States)

    Chiaradia, M. T.; Samarelli, S.; Agrimano, L.; Lorusso, A. P.; Nutricato, R.; Nitti, D. O.; Morea, A.; Tijani, K.

    2016-12-01

    Rheticus® is an innovative cloud-based data and services hub able to deliver Earth Observation added-value products through automatic complex processes and a minimum interaction with human operators. This target is achieved by means of programmable components working as different software layers in a modern enterprise system which relies on SOA (service-oriented-architecture) model. Due to its architecture, where every functionality is well defined and encapsulated in a standalone component, Rheticus is potentially highly scalable and distributable allowing different configurations depending on the user needs. Rheticus offers a portfolio of services, ranging from the detection and monitoring of geohazards and infrastructural instabilities, to marine water quality monitoring, wildfires detection or land cover monitoring. In this work, we outline the overall cloud-based platform and focus on the "Rheticus Displacement" service, aimed at providing accurate information to monitor movements occurring across landslide features or structural instabilities that could affect buildings or infrastructures. Using Sentinel-1 (S1) open data images and Multi-Temporal SAR Interferometry techniques (i.e., SPINUA), the service is complementary to traditional survey methods, providing a long-term solution to slope instability monitoring. Rheticus automatically browses and accesses (on a weekly basis) the products of the rolling archive of ESA S1 Scientific Data Hub; S1 data are then handled by a mature running processing chain, which is responsible of producing displacement maps immediately usable to measure with sub-centimetric precision movements of coherent points. Examples are provided, concerning the automatic displacement map generation process, as well as the integration of point and distributed scatterers, the integration of multi-sensors displacement maps (e.g., Sentinel-1 IW and COSMO-SkyMed HIMAGE), the combination of displacement rate maps acquired along both ascending

  9. Efficient and automatic image reduction framework for space debris detection based on GPU technology

    Science.gov (United States)

    Diprima, Francesco; Santoni, Fabio; Piergentili, Fabrizio; Fortunato, Vito; Abbattista, Cristoforo; Amoruso, Leonardo

    2018-04-01

    In the last years, the increasing number of space debris has triggered the need of a distributed monitoring system for the prevention of possible space collisions. Space surveillance based on ground telescope allows the monitoring of the traffic of the Resident Space Objects (RSOs) in the Earth orbit. This space debris surveillance has several applications such as orbit prediction and conjunction assessment. In this paper is proposed an optimized and performance-oriented pipeline for sources extraction intended to the automatic detection of space debris in optical data. The detection method is based on the morphological operations and Hough Transform for lines. Near real-time detection is obtained using General Purpose computing on Graphics Processing Units (GPGPU). The high degree of processing parallelism provided by GPGPU allows to split data analysis over thousands of threads in order to process big datasets with a limited computational time. The implementation has been tested on a large and heterogeneous images data set, containing both imaging satellites from different orbit ranges and multiple observation modes (i.e. sidereal and object tracking). These images were taken during an observation campaign performed from the EQUO (EQUatorial Observatory) observatory settled at the Broglio Space Center (BSC) in Kenya, which is part of the ASI-Sapienza Agreement.

  10. Infrared machine vision system for the automatic detection of olive fruit quality.

    Science.gov (United States)

    Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

    2013-11-15

    External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

  11. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features.

    Science.gov (United States)

    Su, Yanni; Wang, Yuanyuan; Jiao, Jing; Guo, Yi

    2011-01-01

    Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

  12. Results of automatic system implementation for Cofrentes power plant detection system LPRM inspection execution

    Energy Technology Data Exchange (ETDEWEB)

    Palomo, M., E-mail: mpalomo@iqn.upv.es [Departamento de Ingenieria Quimica y Nuclear, Universidad Politecnica de Valencia (Spain); Urrea, M., E-mail: matias.urrea@iberdrola.es [C.N.Cofrentes - Iberdrola Generacion S.A., Valencia (Spain); Curiel, M., E-mail: m.curiel@lainsa.com [LAINSA, Grupo Dominguis, Valencia (Brazil); Arnaldos, A., E-mail: a.arnaldos@titaniast.com [TITANIA Servicios Teconologicos, Valencia (Spain)

    2011-07-01

    During this presentation we are going to introduce the results of Cofrentes nuclear power plant automation of the detection system LPRM (Local Power Range Monitor) inspection procedure. An LPRM's test system has been developed and it consists in a software application and data acquisition hardware that performs automatically the complete detection system process: refueling, storage and operation inspection: Ramp voltage generation, measured voltage Plateaux evaluation, qualification report emission; historical analysis to scan burn evolution. The inspections differentiations are developed by the different specifications that it has to fulfil: operation inspection: it is made to check the fission bolt wearing, the detection system functioning and to analyse malfunctioning. From technical specifications and curves analyses it can be determined each LPRM's substitution. Storage inspection: it is made to check the correct functioning and isolation losses before being installed in the core during refueling. Refueling inspection: it is checked that storage LPRM's installation is correct and that they are ready for new fuel cycle. The software application LPRM's Test has been developed by National Instruments LabVIEW, and it performs the following actions: Protocol IEEE-488 (GPIB) control of the source KEITHLEY 237. This source generates the ramp voltage and measure voltage; information acquisition of storage, process and source, identifying LPRM and realization conditions of the same; data analysis and conditions report, historical comparative analysis. (author)

  13. Automatic Detection and Visualization of Qualitative Hemodynamic Characteristics in Cerebral Aneurysms.

    Science.gov (United States)

    Gasteiger, R; Lehmann, D J; van Pelt, R; Janiga, G; Beuing, O; Vilanova, A; Theisel, H; Preim, B

    2012-12-01

    Cerebral aneurysms are a pathological vessel dilatation that bear a high risk of rupture. For the understanding and evaluation of the risk of rupture, the analysis of hemodynamic information plays an important role. Besides quantitative hemodynamic information, also qualitative flow characteristics, e.g., the inflow jet and impingement zone are correlated with the risk of rupture. However, the assessment of these two characteristics is currently based on an interactive visual investigation of the flow field, obtained by computational fluid dynamics (CFD) or blood flow measurements. We present an automatic and robust detection as well as an expressive visualization of these characteristics. The detection can be used to support a comparison, e.g., of simulation results reflecting different treatment options. Our approach utilizes local streamline properties to formalize the inflow jet and impingement zone. We extract a characteristic seeding curve on the ostium, on which an inflow jet boundary contour is constructed. Based on this boundary contour we identify the impingement zone. Furthermore, we present several visualization techniques to depict both characteristics expressively. Thereby, we consider accuracy and robustness of the extracted characteristics, minimal visual clutter and occlusions. An evaluation with six domain experts confirms that our approach detects both hemodynamic characteristics reasonably.

  14. Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach.

    Science.gov (United States)

    Yuan, Yixuan; Li, Dengwang; Meng, Max Q-H

    2017-07-31

    In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast based saliency and object-center based saliency together. The contrast based saliency map highlights image parts that show different appearances compared with surrounding areas while the object-center based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with Multiple Kernel Boosting (MKB) is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multi-level WBU saliency maps. We finally integrate these two stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs favorably against state-of-the-art saliency methods to detect polyps.

  15. A Robust Vision-based Runway Detection and Tracking Algorithm for Automatic UAV Landing

    KAUST Repository

    Abu Jbara, Khaled F.

    2015-05-01

    This work presents a novel real-time algorithm for runway detection and tracking applied to the automatic takeoff and landing of Unmanned Aerial Vehicles (UAVs). The algorithm is based on a combination of segmentation based region competition and the minimization of a specific energy function to detect and identify the runway edges from streaming video data. The resulting video-based runway position estimates are updated using a Kalman Filter, which can integrate other sensory information such as position and attitude angle estimates to allow a more robust tracking of the runway under turbulence. We illustrate the performance of the proposed lane detection and tracking scheme on various experimental UAV flights conducted by the Saudi Aerospace Research Center. Results show an accurate tracking of the runway edges during the landing phase under various lighting conditions. Also, it suggests that such positional estimates would greatly improve the positional accuracy of the UAV during takeoff and landing phases. The robustness of the proposed algorithm is further validated using Hardware in the Loop simulations with diverse takeoff and landing videos generated using a commercial flight simulator.

  16. Automatic Detection of Changes on Mars Surface from High-Resolution Orbital Images

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2017-04-01

    Over the last 40 years Mars has been extensively mapped by several NASA and ESA orbital missions, generating a large image dataset comprised of approximately 500,000 high-resolution images (of citizen science can be employed for training and verification it is unsuitable for planetwide systematic change detection. In this work, we introduce a novel approach in planetary image change detection, which involves a batch-mode automatic change detection pipeline that identifies regions that have changed. This is tested in anger, on tens of thousands of high-resolution images over the MC11 quadrangle [5], acquired by CTX, HRSC, THEMIS-VIS and MOC-NA instruments [1]. We will present results which indicate a substantial level of activity in this region of Mars, including instances of dynamic natural phenomena that haven't been cataloged in the planetary science literature before. We will demonstrate the potential and usefulness of such an automatic approach in planetary science change detection. Acknowledgments: The research leading to these results has received funding from the STFC "MSSL Consolidated Grant" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n° 607379. References: [1] P. Sidiropoulos and J. - P. Muller (2015) On the status of orbital high-resolution repeat imaging of Mars for the observation of dynamic surface processes. Planetary and Space Science, 117: 207-222. [2] O. Aharonson, et al. (2003) Slope streak formation and dust deposition rates on Mars. Journal of Geophysical Research: Planets, 108(E12):5138 [3] A. McEwen, et al. (2011) Seasonal flows on warm martian slopes. Science, 333 (6043): 740-743. [4] S. Byrne, et al. (2009) Distribution of mid-latitude ground ice on mars from new impact craters. Science, 325(5948):1674-1676. [5] K. Gwinner, et al (2016) The High Resolution Stereo Camera (HRSC) of Mars Express and its approach to science analysis and mapping for Mars and

  17. Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    Downhole abnormal incidents during oil and gas drilling causes costly delays, any may also potentially lead to dangerous scenarios. Dierent incidents willcause changes to dierent parts of the physics of the process. Estimating thechanges in physical parameters, and correlating these with changes ...... expectedfrom various defects, can be used to diagnose faults while in development.This paper shows how estimated friction parameters and ow rates can de-tect and isolate the type of incident, as well as isolating the position of adefect. Estimates are shown to be subjected to non......-Gaussian,t-distributednoise, and a dedicated multivariate statistical change detection approach isused that detects and isolates faults by detecting simultaneous changes inestimated parameters and ow rates. The properties of the multivariate di-agnosis method are analyzed, and it is shown how detection and false alarmprobabilities...... are assessed and optimized using data-based learning to obtainthresholds for hypothesis testing. Data from a 1400 m horizontal ow loop isused to test the method, and successful diagnosis of the incidents drillstringwashout (pipe leakage), lost circulation, gas in ux, and drill bit plugging aredemonstrated....

  18. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  19. AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2016-06-01

    Full Text Available Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8

  20. Usefulness of Cone-Beam Computed Tomography and Automatic Vessel Detection Software in Emergency Transarterial Embolization

    Energy Technology Data Exchange (ETDEWEB)

    Carrafiello, Gianpaolo, E-mail: gcarraf@gmail.com; Ierardi, Anna Maria, E-mail: amierardi@yahoo.it; Duka, Ejona, E-mail: ejonaduka@hotmail.com [Insubria University, Department of Radiology, Interventional Radiology (Italy); Radaelli, Alessandro, E-mail: alessandro.radaelli@philips.com [Philips Healthcare (Netherlands); Floridi, Chiara, E-mail: chiara.floridi@gmail.com [Insubria University, Department of Radiology, Interventional Radiology (Italy); Bacuzzi, Alessandro, E-mail: alessandro.bacuzzi@ospedale.varese.it [University of Insubria, Anaesthesia and Palliative Care (Italy); Bucourt, Maximilian de, E-mail: maximilian.de-bucourt@charite.de [Charité - University Medicine Berlin, Department of Radiology (Germany); Marchi, Giuseppe De, E-mail: giuseppedemarchi@email.it [Insubria University, Department of Radiology, Interventional Radiology (Italy)

    2016-04-15

    BackgroundThis study was designed to evaluate the utility of dual phase cone beam computed tomography (DP-CBCT) and automatic vessel detection (AVD) software to guide transarterial embolization (TAE) of angiographically challenging arterial bleedings in emergency settings.MethodsTwenty patients with an arterial bleeding at computed tomography angiography and an inconclusive identification of the bleeding vessel at the initial 2D angiographic series were included. Accuracy of DP-CBCT and AVD software were defined as the ability to detect the bleeding site and the culprit arterial bleeder, respectively. Technical success was defined as the correct positioning of the microcatheter using AVD software. Clinical success was defined as the successful embolization. Total volume of iodinated contrast medium and overall procedure time were registered.ResultsThe bleeding site was not detected by initial angiogram in 20 % of cases, while impossibility to identify the bleeding vessel was the reason for inclusion in the remaining cases. The bleeding site was detected by DP-CBCT in 19 of 20 (95 %) patients; in one case CBCT-CT fusion was required. AVD software identified the culprit arterial branch in 18 of 20 (90 %) cases. In two cases, vessel tracking required manual marking of the candidate arterial bleeder. Technical success was 95 %. Successful embolization was achieved in all patients. Mean contrast volume injected for each patient was 77.5 ml, and mean overall procedural time was 50 min.ConclusionsC-arm CBCT and AVD software during TAE of angiographically challenging arterial bleedings is feasible and may facilitate successful embolization. Staff training in CBCT imaging and software manipulation is necessary.

  1. Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

    Science.gov (United States)

    Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

    2014-01-01

    Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

  2. Attributed graph distance measure for automatic detection of Attention Deficit Hyperactive Disordered subjects

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2014-06-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49% and test data sets (73.55%. Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

  3. Semi-automatic detection and correction of body organ motion, particularly cardiac motion in SPECT studies

    International Nuclear Information System (INIS)

    Quintana, J.C.; Caceres, F.; Vargas, P.

    2002-01-01

    patient and artificially imposed). The method is fast (<20s) and robust as compared with manual or other semi-automatic detection of body organ motions in nuclear medicine studies. Conclusion: A fast and robust semi-automatic patient motion detection and correction for SPECT studies has been developed

  4. Long term Suboxone™ emotional reactivity as measured by automatic detection in speech.

    Directory of Open Access Journals (Sweden)

    Edward Hill

    Full Text Available Addictions to illicit drugs are among the nation's most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (Suboxone®; SUBX as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states ("true ground emotionality" in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of "true" emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP and 33 members of Alcoholics Anonymous (AA. Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate "true ground" emotionality in long-term buprenorphine/naloxone combination (Suboxone™. We found in long-term SUBX patients a significantly flat affect (p<0.01, and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction.

  5. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  6. Study on Classification Accuracy Inspection of Land Cover Data Aided by Automatic Image Change Detection Technology

    Science.gov (United States)

    Xie, W.-J.; Zhang, L.; Chen, H.-P.; Zhou, J.; Mao, W.-J.

    2018-04-01

    The purpose of carrying out national geographic conditions monitoring is to obtain information of surface changes caused by human social and economic activities, so that the geographic information can be used to offer better services for the government, enterprise and public. Land cover data contains detailed geographic conditions information, thus has been listed as one of the important achievements in the national geographic conditions monitoring project. At present, the main issue of the production of the land cover data is about how to improve the classification accuracy. For the land cover data quality inspection and acceptance, classification accuracy is also an important check point. So far, the classification accuracy inspection is mainly based on human-computer interaction or manual inspection in the project, which are time consuming and laborious. By harnessing the automatic high-resolution remote sensing image change detection technology based on the ERDAS IMAGINE platform, this paper carried out the classification accuracy inspection test of land cover data in the project, and presented a corresponding technical route, which includes data pre-processing, change detection, result output and information extraction. The result of the quality inspection test shows the effectiveness of the technical route, which can meet the inspection needs for the two typical errors, that is, missing and incorrect update error, and effectively reduces the work intensity of human-computer interaction inspection for quality inspectors, and also provides a technical reference for the data production and quality control of the land cover data.

  7. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2017-08-01

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  8. Automatic detection of end-diastole and end-systole from echocardiography images using manifold learning

    International Nuclear Information System (INIS)

    Gifani, Parisa; Behnam, Hamid; Shalbaf, Ahmad; Sani, Zahra Alizadeh

    2010-01-01

    The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold. There are three dense regions on the manifold which correspond to the three phases of cardiac cycle ('isovolumetric contraction', 'isovolumetric relaxation', 'reduced filling'), wherein there is no prominent change in ventricular volume. By the fact that the end-systolic and end-diastolic frames are in isovolumic phases of the cardiac cycle, the dense regions can be used to find these frames. By calculating the distance between consecutive points in the manifold, the isovolumic frames are mapped on the three minimums of the distance diagrams which were used to select the corresponding images. The minimum correlation between these images leads to detection of end-systole and end-diastole frames. The results on six healthy volunteers have been validated by an experienced echo cardiologist and depict the usefulness of the presented method

  9. Automatic crack detection method for loaded coal in vibration failure process.

    Directory of Open Access Journals (Sweden)

    Chengwu Li

    Full Text Available In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM. A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.

  10. Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Till F. Sonnemann

    2017-12-01

    Full Text Available Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.

  11. Automatic facial pore analysis system using multi-scale pore detection.

    Science.gov (United States)

    Sun, J Y; Kim, S W; Lee, S H; Choi, J E; Ko, S J

    2017-08-01

    As facial pore widening and its treatments have become common concerns in the beauty care field, the necessity for an objective pore-analyzing system has been increased. Conventional apparatuses lack in usability requiring strong light sources and a cumbersome photographing process, and they often yield unsatisfactory analysis results. This study was conducted to develop an image processing technique for automatic facial pore analysis. The proposed method detects facial pores using multi-scale detection and optimal scale selection scheme and then extracts pore-related features such as total area, average size, depth, and the number of pores. Facial photographs of 50 subjects were graded by two expert dermatologists, and correlation analyses between the features and clinical grading were conducted. We also compared our analysis result with those of conventional pore-analyzing devices. The number of large pores and the average pore size were highly correlated with the severity of pore enlargement. In comparison with the conventional devices, the proposed analysis system achieved better performance showing stronger correlation with the clinical grading. The proposed system is highly accurate and reliable for measuring the severity of skin pore enlargement. It can be suitably used for objective assessment of the pore tightening treatments. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

    Science.gov (United States)

    LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

    2017-12-01

    With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

  13. Automatic RST-based system for a rapid detection of man-made disasters

    Science.gov (United States)

    Tramutoli, Valerio; Corrado, Rosita; Filizzola, Carolina; Livia Grimaldi, Caterina Sara; Mazzeo, Giuseppe; Marchese, Francesco; Pergola, Nicola

    2010-05-01

    Man-made disasters may cause injuries to citizens and damages to critical infrastructures. When it is not possible to prevent or foresee such disasters it is hoped at least to rapidly detect the accident in order to intervene as soon as possible to minimize damages. In this context, the combination of a Robust Satellite Technique (RST), able to identify for sure actual (i.e. no false alarm) accidents, and satellite sensors with high temporal resolution seems to assure both a reliable and a timely detection of abrupt Thermal Infrared (TIR) transients related to dangerous explosions. A processing chain, based on the RST approach, has been developed in the framework of the GMOSS and G-MOSAIC projects by DIFA-UNIBAS team, suitable for automatically identify on MSG-SEVIRI images harmful events. Maps of thermal anomalies are generated every 15 minutes (i.e. SEVIRI temporal repetition rate) over a selected area together with kml files (containing information on latitude and longitude of "thermally" anomalous SEVIRI pixel centre, time of image acquisition, relative intensity of anomalies, etc.) for a rapid visualization of the accident position even on Google Earth. Results achieved in the cases of gas pipelines recently exploded or attacked in Russia and in Iraq will be presented in this work.

  14. Early Detection and Localization of Downhole Incidents in Managed Pressure Drilling

    DEFF Research Database (Denmark)

    Willersrud, Anders; Imsland, Lars; Blanke, Mogens

    2015-01-01

    Downhole incidents such as kick, lost circulation, pack-off, and hole cleaning issues are important contributors to downtime in drilling. In managed pressure drilling (MPD), operations margins are typically narrower, implying more frequent incidents and more severe consequences. Detection...... and handling of symptoms of downhole drilling contingencies at an early stage are therefore crucial for the reliability and safety of MPD operations. In this paper we describe a method for early detection and localization of such incidents, based on a fit for purpose model of the downhole pressure hydraulics...... successfully been tested on experimental data from a medium-scale horizontal flow loop in Sta- vanger, Norway. The flow loop represents a 700 m borehole with emulation of the following downhole contingencies: drillstring washout, drill bit nozzle plugging, gas influx and fluid loss. In the tests...

  15. Influence on ultrasonic incident angle and defect detection sensitivity by cast stainless steel structure

    International Nuclear Information System (INIS)

    Kurozumi, Y.

    2004-01-01

    It is well known that ultrasonic waves are affected strongly by macro-structures in cast stainless steel, as in the primary pipe or other components in pressurized water reactors (PWRs). In this work, ultrasonic refractive angles and defect detection sensitivities are investigated at different incident angles to cast stainless steel. The aims of the investigation are to clarify the transmission of ultrasonic waves in cast stainless steel and to contribute to the transducer design. The results are that ultrasonic refractive angles in cast stainless steel shift towards the 45-degree direction with respect to the direction of dendritic structures by 11.8 degrees at the maximum and that the sensitivity of transducer for inner surface breaking cracks increases with decreasing incident angle. However, in an ultrasonic inspection of actual welds at smaller incident angles, a trade-off occurs between increased defect detection sensitivity and decreased defect discrimination capability due to intense false signals produced by non-defective features. (orig.)

  16. Novel Automatic Detection of Pleura and B-lines (Comet-Tail Artifacts) on In-Vivo Lung Ultrasound Scans

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hansen, Kristoffer Lindskov; Møller-Sørensen, Hasse

    2016-01-01

    This paper presents a novel automatic method for detection of B-lines (comet-tail artifacts) in lung ultrasound scans. B-lines are the most commonly used artifacts for analyzing the pulmonary edema. They appear as laser-like vertical beams, which arise from the pleural line and spread down without...

  17. A Feasibility Study on the Automatic Detection of Atrial Fibrillations using an Unobtrusive Bed-Mounted Sensor

    NARCIS (Netherlands)

    Brueser, C.; Zink, M.D.H.; Winter, S.; Schauerte, P.; Leonhardt, S.

    2012-01-01

    We present a feasibility study on the automatic detection of atrialfibrillations (AF) from a cardiac vibration signal (ballistocardiogram). Signals were recorded by means of an electromechanical foil attached to a bed’s mattress. A clinical study with 10 AF patients wasconducted to assess whether

  18. Automatic trip and mode detection with MoveSmarter: first results from the Dutch Mobile Mobility Panel

    NARCIS (Netherlands)

    Geurs, Karst Teunis; Thomas, Tom; Bijlsma, Marcel; Douhou, Salima

    2015-01-01

    This paper describes the performance of a smartphone app called MoveSmarter to automatically detect departure and arrival times, trip origins and destinations, transport modes, and travel purposes. The app is used in a three-year smartphone-based prompted-recall panel survey in which about 600

  19. Building a Global Catalog of Nonvolcanic Tremor Events Using an Automatic Detection Algorithm

    Science.gov (United States)

    Bagley, B. C.; Revenaugh, J.

    2009-12-01

    Nonvolcanic tremor is characterized by a long-period seismic event containing a series of low-frequency earthquakes (LFEs). Tremor has been detected in regions of subduction (e.g. Kao et. al. 2007, 2008; Shelly 2006) and beneath the San Andreas fault near Cholame, California (e.g. Nadeau and Dolenc, 2005). In some cases tremor events seem to have periodicity, and these are often referred to as episodic tremor and slip (ETS). The origin of nonvolcanic tremor has been ascribed to shear slip along plate boundaries and/or high pore-fluid pressure. The apparent periodicity and tectonic setting associated with ETS has led to the suggestion that there may be a link between ETS and megathrust earthquakes. Until recently tremor detection has been a manual process requiring visual inspection of seismic data. In areas that have dense seismic arrays (e.g. Japan) waveform cross correlation techniques have been successfully employed (e.g. Obara, 2002). Kao et al. (2007) developed an algorithm for automatic detection of seismic tremor that can be used in regions without dense arrays. This method has been used to create the Tremor Activity Monitoring System (TAMS), which is used by the Geologic Survey of Canada to monitor northern Cascadia. So far the study of nonvolcanic tremor has been limited to regions of subduction or along major transform faults. It is unknown if tremor events occur in other tectonic settings, or if the current detection schemes will be useful for finding them. We propose to look for tremor events in non-subduction regions. It is possible that if tremor exists in other regions it will have different characteristics and may not trigger the TAMS system or be amenable to other existing detection schemes. We are developing algorithms for searching sparse array data sets for quasi-harmonic energy bursts in hopes of recognizing and cataloging nonvolcanic tremor in an expanded tectonic setting. Statistical comparisons against the TAMS algorithm will be made if

  20. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Min-Yin Liu

    2017-05-01

    Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

  1. Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system

    International Nuclear Information System (INIS)

    Wormanns, Dag; Fiebich, Martin; Saidi, Mustafa; Diederich, Stefan; Heindel, Walter

    2002-01-01

    The aim of this study was to evaluate a computer-aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Eighty-eight consecutive spiral-CT examinations were reported by two radiologists in consensus. All examinations were reviewed using a CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm is designed to detect nodules with diameters of at least 5 mm. A total of 153 nodules were detected with at least one modality (radiologists in consensus, CAD, 85 nodules with diameter <5 mm, 68 with diameter ≥5 mm). The results of automatic nodule detection were compared to nodules detected with any modality as gold standard. Computer-aided diagnosis correctly identified 26 of 59 (38%) nodules with diameters ≥5 mm detected by visual assessment by the radiologists; of these, CAD detected 44% (24 of 54) nodules without pleural contact. In addition, 12 nodules ≥5 mm were detected which were not mentioned in the radiologist's report but represented real nodules. Sensitivity for detection of nodules ≥5 mm was 85% (58 of 68) for radiologists and 38% (26 of 68) for CAD. There were 5.8±3.6 false-positive results of CAD per CT study. Computer-aided diagnosis improves detection of pulmonary nodules at spiral CT and is a valuable second opinion in a clinical setting for lung cancer screening despite of its still limited sensitivity. (orig.)

  2. Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark.

    Science.gov (United States)

    Singha, Suman; Vespe, Michele; Trieschmann, Olaf

    2013-08-15

    Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and 'look-alike' classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Automatic Detection and Reproduction of Natural Head Position in Stereo-Photogrammetry.

    Science.gov (United States)

    Hsung, Tai-Chiu; Lo, John; Li, Tik-Shun; Cheung, Lim-Kwong

    2015-01-01

    The aim of this study was to develop an automatic orientation calibration and reproduction method for recording the natural head position (NHP) in stereo-photogrammetry (SP). A board was used as the physical reference carrier for true verticals and NHP alignment mirror orientation. Orientation axes were detected and saved from the digital mesh model of the board. They were used for correcting the pitch, roll and yaw angles of the subsequent captures of patients' facial surfaces, which were obtained without any markings or sensors attached onto the patient. We tested the proposed method on two commercial active (3dMD) and passive (DI3D) SP devices. The reliability of the pitch, roll and yaw for the board placement were within ±0.039904°, ±0.081623°, and ±0.062320°; where standard deviations were 0.020234°, 0.045645° and 0.027211° respectively. Orientation-calibrated stereo-photogrammetry is the most accurate method (angulation deviation within ±0.1°) reported for complete NHP recording with insignificant clinical error.

  4. AN AUTOMATIC DETECTION METHOD FOR EXTREME-ULTRAVIOLET DIMMINGS ASSOCIATED WITH SMALL-SCALE ERUPTION

    Energy Technology Data Exchange (ETDEWEB)

    Alipour, N.; Safari, H. [Department of Physics, University of Zanjan, P.O. Box 45195-313, Zanjan (Iran, Islamic Republic of); Innes, D. E. [Max-Planck Institut fuer Sonnensystemforschung, 37191 Katlenburg-Lindau (Germany)

    2012-02-10

    Small-scale extreme-ultraviolet (EUV) dimming often surrounds sites of energy release in the quiet Sun. This paper describes a method for the automatic detection of these small-scale EUV dimmings using a feature-based classifier. The method is demonstrated using sequences of 171 Angstrom-Sign images taken by the STEREO/Extreme UltraViolet Imager (EUVI) on 2007 June 13 and by Solar Dynamics Observatory/Atmospheric Imaging Assembly on 2010 August 27. The feature identification relies on recognizing structure in sequences of space-time 171 Angstrom-Sign images using the Zernike moments of the images. The Zernike moments space-time slices with events and non-events are distinctive enough to be separated using a support vector machine (SVM) classifier. The SVM is trained using 150 events and 700 non-event space-time slices. We find a total of 1217 events in the EUVI images and 2064 events in the AIA images on the days studied. Most of the events are found between latitudes -35 Degree-Sign and +35 Degree-Sign . The sizes and expansion speeds of central dimming regions are extracted using a region grow algorithm. The histograms of the sizes in both EUVI and AIA follow a steep power law with slope of about -5. The AIA slope extends to smaller sizes before turning over. The mean velocity of 1325 dimming regions seen by AIA is found to be about 14 km s{sup -1}.

  5. 3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

    Science.gov (United States)

    Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

    2017-01-01

    Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

  6. Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

    Science.gov (United States)

    Ebert, Lars C; Heimer, Jakob; Schweitzer, Wolf; Sieberth, Till; Leipner, Anja; Thali, Michael; Ampanozi, Garyfalia

    2017-12-01

    Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT. For deep learning image analysis software, we used the ViDi Suite 2.0. We retrospectively selected 28 cases with, and 24 cases without, hemopericardium. Based on these data, we trained two separate deep learning networks. The first one classified images into hemopericardium/not hemopericardium, and the second one segmented the blood content. We randomly selected 50% of the data for training and 50% for validation. This process was repeated 20 times. The best performing classification network classified all cases of hemopericardium from the validation images correctly with only a few false positives. The best performing segmentation network would tend to underestimate the amount of blood in the pericardium, which is the case for most networks. This is the first study that shows that deep learning has potential for automated image analysis of radiological images in forensic medicine.

  7. Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results

    Directory of Open Access Journals (Sweden)

    Alexandru Florin Badea

    2013-01-01

    Full Text Available The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD. It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of “real-time” elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology.

  8. An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

    Science.gov (United States)

    Hajimani, Elmira; Ruano, M G; Ruano, A E

    2017-07-01

    This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

  9. Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

    Science.gov (United States)

    Palmieri, Roberta; Bonifazi, Giuseppe; Serranti, Silvia

    2014-05-01

    The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

  10. Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

    Science.gov (United States)

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

    2015-02-01

    Early identification of diabetic foot complications and their precursors is essential in preventing their devastating consequences, such as foot infection and amputation. Frequent, automatic risk assessment by an intelligent telemedicine system might be feasible and cost effective. Infrared thermography is a promising modality for such a system. The temperature differences between corresponding areas on contralateral feet are the clinically significant parameters. This asymmetric analysis is hindered by (1) foot segmentation errors, especially when the foot temperature and the ambient temperature are comparable, and by (2) different shapes and sizes between contralateral feet due to deformities or minor amputations. To circumvent the first problem, we used a color image and a thermal image acquired synchronously. Foot regions, detected in the color image, were rigidly registered to the thermal image. This resulted in 97.8%±1.1% sensitivity and 98.4%±0.5% specificity over 76 high-risk diabetic patients with manual annotation as a reference. Nonrigid landmark-based registration with B-splines solved the second problem. Corresponding points in the two feet could be found regardless of the shapes and sizes of the feet. With that, the temperature difference of the left and right feet could be obtained.

  11. Automatic detection of the hippocampal region associated with Alzheimer's disease from microscopic images of mice brain

    Science.gov (United States)

    Albaidhani, Tahseen; Hawkes, Cheryl; Jassim, Sabah; Al-Assam, Hisham

    2016-05-01

    The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer's disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.

  12. Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

    Science.gov (United States)

    Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

    2018-02-01

    Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

  13. EMS Response to Mass Casualty Incidents: The Critical Importance of Automatic Statewide Mutual Aid and MCI Training

    Science.gov (United States)

    2008-09-01

    incidents, dam failure, earthquakes, fires and wild fires, thunderstorms, tsunamis, volcanoes , winter storms, heat, landslides , nuclear power plant...President Bush reorganized the federal government and created the U.S. Department of Homeland Security, which produced the National Strategy for...to produce the best outcome and ensure personnel safety. This discipline prevented units from treating the first victim they came upon and ensured

  14. What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit, detected by staff and reported to an incident system.

    Science.gov (United States)

    Westbrook, Johanna I; Li, Ling; Lehnbom, Elin C; Baysari, Melissa T; Braithwaite, Jeffrey; Burke, Rosemary; Conn, Chris; Day, Richard O

    2015-02-01

    To (i) compare medication errors identified at audit and observation with medication incident reports; (ii) identify differences between two hospitals in incident report frequency and medication error rates; (iii) identify prescribing error detection rates by staff. Audit of 3291 patient records at two hospitals to identify prescribing errors and evidence of their detection by staff. Medication administration errors were identified from a direct observational study of 180 nurses administering 7451 medications. Severity of errors was classified. Those likely to lead to patient harm were categorized as 'clinically important'. Two major academic teaching hospitals in Sydney, Australia. Rates of medication errors identified from audit and from direct observation were compared with reported medication incident reports. A total of 12 567 prescribing errors were identified at audit. Of these 1.2/1000 errors (95% CI: 0.6-1.8) had incident reports. Clinically important prescribing errors (n = 539) were detected by staff at a rate of 218.9/1000 (95% CI: 184.0-253.8), but only 13.0/1000 (95% CI: 3.4-22.5) were reported. 78.1% (n = 421) of clinically important prescribing errors were not detected. A total of 2043 drug administrations (27.4%; 95% CI: 26.4-28.4%) contained ≥ 1 errors; none had an incident report. Hospital A had a higher frequency of incident reports than Hospital B, but a lower rate of errors at audit. Prescribing errors with the potential to cause harm frequently go undetected. Reported incidents do not reflect the profile of medication errors which occur in hospitals or the underlying rates. This demonstrates the inaccuracy of using incident frequency to compare patient risk or quality performance within or across hospitals. New approaches including data mining of electronic clinical information systems are required to support more effective medication error detection and mitigation. © The Author 2015. Published by Oxford University Press in association

  15. Computerized Diagnostic Assistant for the Automatic Detection of Pneumothorax on Ultrasound: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Shane M. Summers, MD, RDMS

    2016-03-01

    Full Text Available Introduction: Bedside thoracic ultrasound (US can rapidly diagnose pneumothorax (PTX with improved accuracy over the physical examination and without the need for chest radiography (CXR; however, US is highly operator dependent. A computerized diagnostic assistant was developed by the United States Army Institute of Surgical Research to detect PTX on standard thoracic US images. This computer algorithm is designed to automatically detect sonographic signs of PTX by systematically analyzing B-mode US video clips for pleural sliding and M-mode still images for the seashore sign. This was a pilot study to estimate the diagnostic accuracy of the PTX detection computer algorithm when compared to an expert panel of US trained physicians. Methods: This was a retrospective study using archived thoracic US obtained on adult patients presenting to the emergency department (ED between 5/23/2011 and 8/6/2014. Emergency medicine residents, fellows, attending physicians, physician assistants, and medical students performed the US examinations and stored the images in the picture archive and communications system (PACS. The PACS was queried for all ED bedside US examinations with reported positive PTX during the study period along with a random sample of negatives. The computer algorithm then interpreted the images, and we compared the results to an independent, blinded expert panel of three physicians, each with experience reviewing over 10,000 US examinations. Results: Query of the PACS system revealed 146 bedside thoracic US examinations for analysis. Thirteen examinations were indeterminate and were excluded. There were 79 true negatives, 33 true positives, 9 false negatives, and 12 false positives. The test characteristics of the algorithm when compared to the expert panel were sensitivity 79% (95 % CI [63-89] and specificity 87% (95% CI [77-93]. For the 20 images scored as highest quality by the expert panel, the algorithm demonstrated 100% sensitivity

  16. A radial basis classifier for the automatic detection of aspiration in children with dysphagia

    Directory of Open Access Journals (Sweden)

    Blain Stefanie

    2006-07-01

    Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion

  17. Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

    Science.gov (United States)

    López-Linares, Karen; Aranjuelo, Nerea; Kabongo, Luis; Maclair, Gregory; Lete, Nerea; Ceresa, Mario; García-Familiar, Ainhoa; Macía, Iván; González Ballester, Miguel A

    2018-05-01

    Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. DEVELOPING INCIDENT DETECTION ALGORITHM BASED ON THE MAMDANI FUZZY INFERENCE ALGORITHM

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available Application of fuzzy logic in the incident detection system allows making a decision under uncertainty. The phase of incident detection is a process of finding difficulties in traffic. The difficulty in traffic is the main sign that there was a road accident and requires a reaction for its elimination. This leads to the use of input data that must be relevant to the vehicles and the road. These data must be considered together, and should be compared with the corresponding values for further analysis. The main parameters of the traffic flow, which can characterize its current state, are a flow rate, a volume flow. Necessary to analyze the input data received from the sensors. After processing the input data, using the previously entered fuzzy rules, will be taken action that will improve the situation in traffic or at least not allow it worse.

  19. Detection and segmentation of virus plaque using HOG and SVM: toward automatic plaque assay.

    Science.gov (United States)

    Mao, Yihao; Liu, Hong; Ye, Rong; Shi, Yonghong; Song, Zhijian

    2014-01-01

    Plaque assaying, measurement of the number, diameter, and area of plaques in a Petri dish image, is a standard procedure gauging the concentration of phage in biology. This paper presented a novel and effective method for implementing automatic plaque assaying. The method was mainly comprised of the following steps: In the training stage, after pre-processing the images for noise suppression, an initial training set was readied by sampling positive (with a plaque at the center) and negative (plaque-free) patches from the training images, and extracting the HOG features from each patch. The linear SVM classifier was trained in a self-learnt supervised learning strategy to avoid possible missing detection. Specifically, the training set which contained positive and negative patches sampled manually from training images was used to train the preliminary classifier which exhaustively searched the training images to predict the label for the unlabeled patches. The mislabeled patches were evaluated by experts and relabeled. And all the newly labeled patches and their corresponding HOG features were added to the initial training set to train the final classifier. In the testing stage, a sliding-window technique was first applied to the unseen image for obtaining HOG features, which were inputted into the classifier to predict whether the patch was positive. Second, a locally adaptive Otsu method was performed on the positive patches to segment the plaques. Finally, after removing the outliers, the parameters of the plaques were measured in the segmented plaques. The experimental results demonstrated that the accuracy of the proposed method was similar to the one measured manually by experts, but it took less than 30 seconds.

  20. Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

    Science.gov (United States)

    Buteneers, Pieter; Verstraeten, David; van Mierlo, Pieter; Wyckhuys, Tine; Stroobandt, Dirk; Raedt, Robrecht; Hallez, Hans; Schrauwen, Benjamin

    2011-11-01

    In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG. Copyright © 2011 Elsevier B.V. All rights reserved.

  1. Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

    Science.gov (United States)

    Chen, Po-Hsiung; Shimada, Rintaro; Yabumoto, Sohshi; Okajima, Hajime; Ando, Masahiro; Chang, Chiou-Tzu; Lee, Li-Tzu; Wong, Yong-Kie; Chiou, Arthur; Hamaguchi, Hiro-O.

    2016-01-01

    We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin.

  2. A framework for automatic feature extraction from airborne light detection and ranging data

    Science.gov (United States)

    Yan, Jianhua

    Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly

  3. Automatic earthquake detection and classification with continuous hidden Markov models: a possible tool for monitoring Las Canadas caldera in Tenerife

    Energy Technology Data Exchange (ETDEWEB)

    Beyreuther, Moritz; Wassermann, Joachim [Department of Earth and Environmental Sciences (Geophys. Observatory), Ludwig Maximilians Universitaet Muenchen, D-80333 (Germany); Carniel, Roberto [Dipartimento di Georisorse e Territorio Universitat Degli Studi di Udine, I-33100 (Italy)], E-mail: roberto.carniel@uniud.it

    2008-10-01

    A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

  4. Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery.

    Science.gov (United States)

    Wein, Wolfgang; Karamalis, Athanasios; Baumgartner, Adrian; Navab, Nassir

    2015-06-01

    The transfer of preoperative CT data into the tracking system coordinates within an operating room is of high interest for computer-aided orthopedic surgery. In this work, we introduce a solution for intra-operative ultrasound-CT registration of bones. We have developed methods for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a novel bone-specific feature descriptor and was thoroughly evaluated on both in-vivo and ex-vivo data. A global optimization strategy aligns the bone surface, followed by a soft tissue aware intensity-based registration to provide higher local registration accuracy. We evaluated the system on femur, tibia and fibula anatomy in a cadaver study with human legs, where magnetically tracked bone markers were implanted to yield ground truth information. An overall median system error of 3.7 mm was achieved on 11 datasets. Global and fully automatic registration of bones aquired with ultrasound to CT is feasible, with bone detection and tracking operating in real time for immediate feedback to the surgeon.

  5. Robustness and precision of an automatic marker detection algorithm for online prostate daily targeting using a standard V-EPID.

    Science.gov (United States)

    Aubin, S; Beaulieu, L; Pouliot, S; Pouliot, J; Roy, R; Girouard, L M; Martel-Brisson, N; Vigneault, E; Laverdière, J

    2003-07-01

    An algorithm for the daily localization of the prostate using implanted markers and a standard video-based electronic portal imaging device (V-EPID) has been tested. Prior to planning, three gold markers were implanted in the prostate of seven patients. The clinical images were acquired with a BeamViewPlus 2.1 V-EPID for each field during the normal course radiotherapy treatment and are used off-line to determine the ability of the automatic marker detection algorithm to adequately and consistently detect the markers. Clinical images were obtained with various dose levels from ranging 2.5 to 75 MU. The algorithm is based on marker attenuation characterization in the portal image and spatial distribution. A total of 1182 clinical images were taken. The results show an average efficiency of 93% for the markers detected individually and 85% for the group of markers. This algorithm accomplishes the detection and validation in 0.20-0.40 s. When the center of mass of the group of implanted markers is used, then all displacements can be corrected to within 1.0 mm in 84% of the cases and within 1.5 mm in 97% of cases. The standard video-based EPID tested provides excellent marker detection capability even with low dose levels. The V-EPID can be used successfully with radiopaque markers and the automatic detection algorithm to track and correct the daily setup deviations due to organ motions.

  6. Automatic detection of ''bore slug'' in tubes; Detection automatique des manques de metal internes sur tubes

    Energy Technology Data Exchange (ETDEWEB)

    Bisiaux, B.; Deutsch, S.; Tailleux, O.; Mette, F. [CEV Vallourec, Aulnoye (France)

    2001-07-01

    During the tube manufacturing for the petroleum industry, the lacks of internal metal (called Bore Slug) can be created during the hot rolling. These large defects are not good detected by the classic UT and by the wall thickness measurement. That's why VALLOUREC developed an automatic UT device which works by transmission. Nevertheless, this system is too little selective and can cause no doubtful pipes (tubes good detected bad). We adapted a Bore Slug control system on the VMOG UK RP20 at the end of August. The results are rather good and showed a good detection of the Bore Slug and very little no doubtful pipes. (authors)

  7. A low cost automatic detection and ranging system for space surveillance in the medium Earth orbit region and beyond.

    Science.gov (United States)

    Danescu, Radu; Ciurte, Anca; Turcu, Vlad

    2014-02-11

    The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth's orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image.

  8. A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

    Science.gov (United States)

    Panicker, Rani Oomman; Soman, Biju; Saini, Gagan; Rajan, Jeny

    2016-01-01

    Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

  9. Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

    Science.gov (United States)

    Mencarelli, A.; van Beek, S.; Zijp, L. J.; Rasch, C.; van Herk, M.; Sonke, J.-J.

    2014-04-01

    Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland-Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (-1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically implemented in

  10. A quantification of the effectiveness of EPID dosimetry and software-based plan verification systems in detecting incidents in radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Bojechko, Casey; Phillps, Mark; Kalet, Alan; Ford, Eric C., E-mail: eford@uw.edu [Department of Radiation Oncology, University of Washington, 1959 N. E. Pacific Street, Seattle, Washington 98195 (United States)

    2015-09-15

    Purpose: Complex treatments in radiation therapy require robust verification in order to prevent errors that can adversely affect the patient. For this purpose, the authors estimate the effectiveness of detecting errors with a “defense in depth” system composed of electronic portal imaging device (EPID) based dosimetry and a software-based system composed of rules-based and Bayesian network verifications. Methods: The authors analyzed incidents with a high potential severity score, scored as a 3 or 4 on a 4 point scale, recorded in an in-house voluntary incident reporting system, collected from February 2012 to August 2014. The incidents were categorized into different failure modes. The detectability, defined as the number of incidents that are detectable divided total number of incidents, was calculated for each failure mode. Results: In total, 343 incidents were used in this study. Of the incidents 67% were related to photon external beam therapy (EBRT). The majority of the EBRT incidents were related to patient positioning and only a small number of these could be detected by EPID dosimetry when performed prior to treatment (6%). A large fraction could be detected by in vivo dosimetry performed during the first fraction (74%). Rules-based and Bayesian network verifications were found to be complimentary to EPID dosimetry, able to detect errors related to patient prescriptions and documentation, and errors unrelated to photon EBRT. Combining all of the verification steps together, 91% of all EBRT incidents could be detected. Conclusions: This study shows that the defense in depth system is potentially able to detect a large majority of incidents. The most effective EPID-based dosimetry verification is in vivo measurements during the first fraction and is complemented by rules-based and Bayesian network plan checking.

  11. Detection of the specific binding on protein microarrays by oblique-incidence reflectivity difference method

    International Nuclear Information System (INIS)

    Lu, Heng; Wen, Juan; Wang, Xu; Yuan, Kun; Lu, Huibin; Zhou, Yueliang; Jin, Kuijuan; Yang, Guozhen; Li, Wei; Ruan, Kangcheng

    2010-01-01

    The specific binding between Cy5-labeled goat anti-mouse Immunoglobulin G (IgG) and mouse IgG with a concentration range from 625 to 10 4 µg ml −1 has been detected successfully by the oblique-incidence reflectivity difference (OI-RD) method in each procedure of microarray fabrication. The experimental data prove that the OI-RD method can be employed not only to distinguish the different concentrations in label-free fashion but also to detect the antibody–antigen capture. In addition, the differential treatment of the OI-RD signals can decrease the negative influences of glass slide as the microarray upholder. Therefore the OI-RD technique has promising applications for the label-free and high-throughput detection of protein microarrays

  12. A grey incidence algorithm to detect high-Z material using cosmic ray muons

    Science.gov (United States)

    He, W.; Xiao, S.; Shuai, M.; Chen, Y.; Lan, M.; Wei, M.; An, Q.; Lai, X.

    2017-10-01

    Muon scattering tomography (MST) is a method for using cosmic muons to scan cargo containers and vehicles for special nuclear materials. However, the flux of cosmic ray muons is low, in the real life application, the detection has to be done a short timescale with small numbers of muons. In this paper, we present a novel approach to detection of special nuclear material by using cosmic ray muons. We use the degree of grey incidence to distinguish typical waste fuel material, uranium, from low-Z material, medium-Z material and other high-Z materials of tungsten and lead. The result shows that using this algorithm, it is possible to detect high-Z materials with an acceptable timescale.

  13. A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment.

    Science.gov (United States)

    Raboshchuk, Ganna; Nadeu, Climent; Jancovic, Peter; Lilja, Alex Peiro; Kokuer, Munevver; Munoz Mahamud, Blanca; Riverola De Veciana, Ana

    2018-01-01

    A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

  14. A new methodology for automatic detection of reference points in 3D cephalometry: A pilot study.

    Science.gov (United States)

    Ed-Dhahraouy, Mohammed; Riri, Hicham; Ezzahmouly, Manal; Bourzgui, Farid; El Moutaoukkil, Abdelmajid

    2018-04-05

    The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32mm. In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric (reference) points. A larger sample will be implemented in the future to assess the method validity and reliability. Copyright © 2018 CEO. Published by Elsevier Masson SAS. All rights reserved.

  15. Breast implants and breast cancer: a review of incidence, detection, mortality, and survival.

    Science.gov (United States)

    Deapen, Dennis

    2007-12-01

    Soon after breast implants were commercially introduced over 30 years ago, questions about potential carcinogenicity were raised. Animal experiments dating back to the mid-twentieth century demonstrated that foreign body implantation of many materials, including silicone, can induce sarcomas. Indeed, female breast cancer incidence rates in the United States have increased substantially over that period. Of the several published studies from various countries that have formally investigated the risk of breast cancer among augmentation mammaplasty patients, none show any evidence of increased risk. In fact, most find lower than expected risk, some with statistically significant reductions. Similarly, breast cancer mortality among these patients is generally found to be below that expected of other similar women. Delayed detection of breast cancer is a concern for these patients because implants can interfere with mammography. However, using indicators such as stage at diagnosis and tumor size, current research shows that augmentation patients do not experience delayed detection. Furthermore, several comparisons of post-breast cancer survival of augmented versus nonaugmented patients have found no significant differences. In summary, breast implants are not associated with an increased risk of breast cancer incidence or death, and these patients do not experience delayed detection or poorer post-breast cancer survival.

  16. An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters.

    Science.gov (United States)

    Behrens, F; Mackeben, M; Schröder-Preikschat, W

    2010-08-01

    This analysis of time series of eye movements is a saccade-detection algorithm that is based on an earlier algorithm. It achieves substantial improvements by using an adaptive-threshold model instead of fixed thresholds and using the eye-movement acceleration signal. This has four advantages: (1) Adaptive thresholds are calculated automatically from the preceding acceleration data for detecting the beginning of a saccade, and thresholds are modified during the saccade. (2) The monotonicity of the position signal during the saccade, together with the acceleration with respect to the thresholds, is used to reliably determine the end of the saccade. (3) This allows differentiation between saccades following the main-sequence and non-main-sequence saccades. (4) Artifacts of various kinds can be detected and eliminated. The algorithm is demonstrated by applying it to human eye movement data (obtained by EOG) recorded during driving a car. A second demonstration of the algorithm detects microsleep episodes in eye movement data.

  17. A semi-automatic traffic sign detection, classification and positioning system

    NARCIS (Netherlands)

    Creusen, I.M.; Hazelhoff, L.; With, de P.H.N.; Said, A.; Guleryuz, O.G.; Stevenson, R.L.

    2012-01-01

    The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental

  18. Making sense of sensor data : detecting clinical mastitis in automatic milking systems

    NARCIS (Netherlands)

    Kamphuis, C.

    2010-01-01

    Farmers milking dairy cows are obliged to exclude milk with abnormal homogeneity or color for human consumption (e.g., Regulation (EC) No 853/2004), where most abnormal milk is caused by clinical mastitis (CM). With automatic milking (AM), farmers are no longer physically present during the milking

  19. Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

    Science.gov (United States)

    Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

    2016-01-01

    This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

  20. Automatic landmark detection and face recognition for side-view face images

    NARCIS (Netherlands)

    Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Broemme, Arslan; Busch, Christoph

    2013-01-01

    In real-life scenarios where pose variation is up to side-view positions, face recognition becomes a challenging task. In this paper we propose an automatic side-view face recognition system designed for home-safety applications. Our goal is to recognize people as they pass through doors in order to

  1. Automatic detection of prominence (as defined by listeners' judgements) in read aloud Dutch sentences

    NARCIS (Netherlands)

    Streefkerk, B.M.; Pols, L.C.W.; ten Bosch, L.F.M.

    1998-01-01

    This paper describes a first step towards the automatic classification of prominence (as defined by native listeners). As a result of a listening experiment each word in 500 sentences was marked with a rating scale between `0' (non-prominent) and `10' (very prominent). These prominence labels are

  2. Detection technology research on the one-way clutch of automatic brake adjuster

    Science.gov (United States)

    Jiang, Wensong; Luo, Zai; Lu, Yi

    2013-10-01

    In this article, we provide a new testing method to evaluate the acceptable quality of the one-way clutch of automatic brake adjuster. To analysis the suitable adjusting brake moment which keeps the automatic brake adjuster out of failure, we build a mechanical model of one-way clutch according to the structure and the working principle of one-way clutch. The ranges of adjusting brake moment both clockwise and anti-clockwise can be calculated through the mechanical model of one-way clutch. Its critical moment, as well, are picked up as the ideal values of adjusting brake moment to evaluate the acceptable quality of one-way clutch of automatic brake adjuster. we calculate the ideal values of critical moment depending on the different structure of one-way clutch based on its mechanical model before the adjusting brake moment test begin. In addition, an experimental apparatus, which the uncertainty of measurement is ±0.1Nm, is specially designed to test the adjusting brake moment both clockwise and anti-clockwise. Than we can judge the acceptable quality of one-way clutch of automatic brake adjuster by comparing the test results and the ideal values instead of the EXP. In fact, the evaluation standard of adjusting brake moment applied on the project are still using the EXP provided by manufacturer currently in China, but it would be unavailable when the material of one-way clutch changed. Five kinds of automatic brake adjusters are used in the verification experiment to verify the accuracy of the test method. The experimental results show that the experimental values of adjusting brake moment both clockwise and anti-clockwise are within the ranges of theoretical results. The testing method provided by this article vividly meet the requirements of manufacturer's standard.

  3. DESIGN A FILTER TO DETECT AND REMOVE VEGETATION FROM ULTRA-CAM-X AERIAL IMAGES’ POINT CLOUD TO PRODUCE AUTOMATICALLY DIGITAL ELEVATION MODEL

    Directory of Open Access Journals (Sweden)

    H. Enayati

    2015-12-01

    segmented image is added to raster of elevation and vegetation elevation is detected. Results is showing that point clouds’ texture is a good data for filtering vegetation and generating DEM automatically.

  4. A Portable Automatic Endpoint Detection System for Amplicons of Loop Mediated Isothermal Amplification on Microfluidic Compact Disk Platform

    Directory of Open Access Journals (Sweden)

    Shah Mukim Uddin

    2015-03-01

    Full Text Available In recent years, many improvements have been made in foodborne pathogen detection methods to reduce the impact of food contamination. Several rapid methods have been developed with biosensor devices to improve the way of performing pathogen detection. This paper presents an automated endpoint detection system for amplicons generated by loop mediated isothermal amplification (LAMP on a microfluidic compact disk platform. The developed detection system utilizes a monochromatic ultraviolet (UV emitter for excitation of fluorescent labeled LAMP amplicons and a color sensor to detect the emitted florescence from target. Then it processes the sensor output and displays the detection results on liquid crystal display (LCD. The sensitivity test has been performed with detection limit up to 2.5 × 10−3 ng/µL with different DNA concentrations of Salmonella bacteria. This system allows a rapid and automatic endpoint detection which could lead to the development of a point-of-care diagnosis device for foodborne pathogens detection in a resource-limited environment.

  5. Automatic Detection of Mitosis and Nuclei From Cytogenetic Images by CellProfiler Software for Mitotic Index Estimation.

    Science.gov (United States)

    González, Jorge Ernesto; Radl, Analía; Romero, Ivonne; Barquinero, Joan Francesc; García, Omar; Di Giorgio, Marina

    2016-12-01

    Mitotic Index (MI) estimation expressed as percentage of mitosis plays an important role as quality control endpoint. To this end, MI is applied to check the lot of media and reagents to be used throughout the assay and also to check cellular viability after blood sample shipping, indicating satisfactory/unsatisfactory conditions for the progression of cell culture. The objective of this paper was to apply the CellProfiler open-source software for automatic detection of mitotic and nuclei figures from digitized images of cultured human lymphocytes for MI assessment, and to compare its performance to that performed through semi-automatic and visual detection. Lymphocytes were irradiated and cultured for mitosis detection. Sets of images from cultures were analyzed visually and findings were compared with those using CellProfiler software. The CellProfiler pipeline includes the detection of nuclei and mitosis with 80% sensitivity and more than 99% specificity. We conclude that CellProfiler is a reliable tool for counting mitosis and nuclei from cytogenetic images, saves considerable time compared to manual operation and reduces the variability derived from the scoring criteria of different scorers. The CellProfiler automated pipeline achieves good agreement with visual counting workflow, i.e. it allows fully automated mitotic and nuclei scoring in cytogenetic images yielding reliable information with minimal user intervention. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Automatic Detection of Mitosis and Nuclei from Cytogenetic Images by CellProfiler Software for Mitotic Index Estimation

    International Nuclear Information System (INIS)

    Gonzalez, Jorge Ernesto; Romero, Ivonne; Garcia, Omar; Radl, Analia; Di Giorgio, Marina; Barquinero, Joan Francesc

    2016-01-01

    Mitotic Index (MI) estimation expressed as percentage of mitosis plays an important role as quality control endpoint. To this end, MI is applied to check the lot of media and reagents to be used throughout the assay and also to check cellular viability after blood sample shipping, indicating satisfactory/unsatisfactory conditions for the progression of cell culture. The objective of this paper was to apply the CellProfiler open-source software for automatic detection of mitotic and nuclei figures from digitized images of cultured human lymphocytes for MI assessment, and to compare its performance to that performed through semi-automatic and visual detection. Lymphocytes were irradiated and cultured for mitosis detection. Sets of images from cultures were analyzed visually and findings were compared with those using CellProfiler software. The CellProfiler pipeline includes the detection of nuclei and mitosis with 80% sensitivity and more than 99% specificity. We conclude that CellProfiler is a reliable tool for counting mitosis and nuclei from cytogenetic images, saves considerable time compared to manual operation and reduces the variability derived from the scoring criteria of different scorers. The CellProfiler automated pipeline achieves good agreement with visual counting workflow, i.e. it allows fully automated mitotic and nuclei scoring in cytogenetic images yielding reliable information with minimal user intervention. (authors)

  7. Automatic detection of spiculation of pulmonary nodules in computed tomography images

    DEFF Research Database (Denmark)

    Ciompi, F; Jacobs, C; Scholten, E.T.

    2015-01-01

    to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation. © (2015......-up procedure. For this reason, lung cancer screening scenario would benefit from the presence of a fully automatic system for the assessment of spiculation. The presented framework relies on the fact that spiculated nodules mainly differ from non-spiculated ones in their morphology. In order to discriminate....... A library of spectra is created by clustering data via unsupervised learning. The centroids of the clusters are used to label back each spectrum in the sampling pattern. A compact descriptor encoding the nodule morphology is obtained as the histogram of labels along all the spherical surfaces and used...

  8. Control method and device for automatic drift stabilization in radiation detection

    International Nuclear Information System (INIS)

    Berthold, F.; Kubisiak, H.

    1979-01-01

    In the automatic control circuit individual electron peaks in the detectors, e.g. NaI crystals or proportional counters, are used. These peaks exhibit no drift dependence; they may be produced in the detectors in different ways. The control circuit may be applied in nuclear radiation measurement techniques, photometry, gamma cameras and for measuring the X-ray fine structure with proportional counters. (DG) [de

  9. Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment

    OpenAIRE

    Iglesias, Jesus Omana; Stokes, Nicola; Ventresque, Anthony; Murphy, Liam, B.E.; Thorburn, James

    2013-01-01

    peer-reviewed In a heterogeneous cloud environment, the manual grading of computing assets is the first step in the process of configuring IT infrastructures to ensure optimal utilization of resources. Grading the efficiency of computing assets is however, a difficult, subjective and time consuming manual task. Thus, an automatic efficiency grading algorithm is highly desirable. In this paper, we compare the effectiveness of the different criteria used in the manual gr...

  10. Colour transformations and K-means segmentation for automatic cloud detection

    Directory of Open Access Journals (Sweden)

    Martin Blazek

    2015-08-01

    Full Text Available The main aim of this work is to find simple criteria for automatic recognition of several meteorological phenomena using optical digital sensors (e.g., Wide-Field cameras, automatic DSLR cameras or robotic telescopes. The output of those sensors is commonly represented in RGB channels containing information about both colour and luminosity even when normalised. Transformation into other colour spaces (e.g., CIE 1931 xyz, CIE L*a*b*, YCbCr can separate colour from luminosity, which is especially useful in the image processing of automatic cloud boundary recognition. Different colour transformations provide different sectorization of cloudy images. Hence, the analysed meteorological phenomena (cloud types, clear sky project differently into the colour diagrams of each international colour systems. In such diagrams, statistical tools can be applied in search of criteria which could determine clear sky from a covered one and possibly even perform a meteorological classification of cloud types. For the purpose of this work, a database of sky images (both clear and cloudy, with emphasis on a variety of different observation conditions (e.g., time, altitude, solar angle, etc. was acquired. The effectiveness of several colour transformations for meteorological application is discussed and the representation of different clouds (or clear sky in those colour systems is analysed. Utilisation of this algorithm would be useful in all-sky surveys, supplementary meteorological observations, solar cell effectiveness predictions or daytime astronomical solar observations.

  11. Visualization and automatic detection of defect distribution in GaN atomic structure from sampling Moiré phase.

    Science.gov (United States)

    Wang, Qinghua; Ri, Shien; Tsuda, Hiroshi; Kodera, Masako; Suguro, Kyoichi; Miyashita, Naoto

    2017-09-19

    Quantitative detection of defects in atomic structures is of great significance to evaluating product quality and exploring quality improvement process. In this study, a Fourier transform filtered sampling Moire technique was proposed to visualize and detect defects in atomic arrays in a large field of view. Defect distributions, defect numbers and defect densities could be visually and quantitatively determined from a single atomic structure image at low cost. The effectiveness of the proposed technique was verified from numerical simulations. As an application, the dislocation distributions in a GaN/AlGaN atomic structure in two directions were magnified and displayed in Moire phase maps, and defect locations and densities were detected automatically. The proposed technique is able to provide valuable references to material scientists and engineers by checking the effect of various treatments for defect reduction. © 2017 IOP Publishing Ltd.

  12. TU-G-204-02: Automatic Sclerotic Bone Metastases Detection in the Pelvic Region From Dual Energy CT

    Energy Technology Data Exchange (ETDEWEB)

    Fehr, D; Schmidtlein, C; Hwang, S; Deasy, J; Veeraraghavan, H [Memorial Sloan Kettering Cancer Center, New York, NY (United States)

    2015-06-15

    Purpose: To automatically detect sclerotic bone metastases in the pelvic region using dual energy computed tomography (DECT). Methods: We developed a two stage algorithm to automatically detect sclerotic bone metastases in the pelvis from DECT for patients with multiple bone metastatic lesions and with hip implants. The first stage consists of extracting the bone and marrow regions by using a support vector machine (SVM) classifier. We employed a novel representation of the DECT images using multi-material decomposition, which represents each voxel as a mixture of different physical materials (e.g. bone+water+fat). Following the extraction of bone and marrow, in the second stage, a bi -histogram equalization method was employed to enhance the contrast to reveal the bone metastases. Next, meanshift segmentation was performed to separate the voxels by their intensity levels. Finally, shape-based filtering was performed to extract the possible locations of the metastatic lesions using multiple shape criteria. We used the following shape parameters: area, eccentricity, major and minor axis, perimeter and skeleton. Results: A radiologist with several years of experience with DECT manually labeled 64 regions consisting of metastatic lesions from 10 different patients. However, the patients had many more metastasic lesions throughout the pelvis. Our method correctly identified 46 of the marked 64 regions (72%). In addition, our method also identified several other lesions, which can then be validated by the radiologist. The missed lesions were typically very large elongated regions consisting of several islands of very small (<4mm) lesions. Conclusion: We developed an algorithm to automatically detect sclerotic lesions in the pelvic region from DECT. Preliminary assessment shows that our algorithm generated lesions agreeing with the radiologist generated candidate regions. Furthermore, our method reveals additional lesions that can be inspected by the radiologist, thereby

  13. Automatic detection of recoil-proton tracks and background rejection criteria in liquid scintillator-micro-capillary-array fast neutron spectrometer

    Science.gov (United States)

    Mor, Ilan; Vartsky, David; Dangendorf, Volker; Tittelmeier, Kai.; Weierganz, Mathias; Goldberg, Mark Benjamin; Bar, Doron; Brandis, Michal

    2018-06-01

    We describe an analysis procedure for automatic unambiguous detection of fast-neutron-induced recoil proton tracks in a micro-capillary array filled with organic liquid scintillator. The detector is viewed by an intensified CCD camera. This imaging neutron detector possesses the capability to perform high position-resolution (few tens of μm), energy-dispersive transmission-imaging using ns-pulsed beams. However, when operated with CW or DC beams, it also features medium-quality spectroscopic capabilities for incident neutrons in the energy range 2-20 MeV. In addition to the recoil proton events which display a continuous extended track structure, the raw images exhibit complex ion-tracks from nuclear interactions of fast-neutrons in the scintillator, capillaries quartz-matrix and CCD. Moreover, as expected, one also observes a multitude of isolated scintillation spots of varying intensity (henceforth denoted "blobs") that originate from several different sources, such as: fragmented proton tracks, gamma-rays, heavy-ion reactions as well as events and noise that occur in the image-intensifier and CCD. In order to identify the continuous-track recoil proton events and distinguish them from all these background events, a rapid, computerized and automatic track-recognition-procedure was developed. Based on an appropriately weighted analysis of track parameters such as: length, width, area and overall light intensity, the method is capable of distinguishing a single continuous-track recoil proton from typically surrounding several thousands of background events that are found in each CCD frame.

  14. Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching.

    Science.gov (United States)

    Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra; Wells, William

    2018-06-04

    The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

  15. Automatic detection of slow-wave sleep and REM-sleep stages using polysomnographic ECG signals

    International Nuclear Information System (INIS)

    Khemiri, S.; Aloui, K.; Naceur, M. S.

    2011-01-01

    We describe in this paper a new approach of classifying the different sleep stages only by focusing on the polysomnographic ECG signals. We show the pre-processing technique of the ECG signals. At the same time the identifcation and elimination of the different types of artifacts which contain the signal and its reconstruction are shown. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM-sleep consists in extracting physiological indicators that characterize these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a night's sleep.

  16. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    International Nuclear Information System (INIS)

    Althuwaynee, Omar F; Pradhan, Biswajeet; Ahmad, Noordin

    2014-01-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

  17. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    Science.gov (United States)

    Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

    2014-06-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

  18. Precise 3D Lug Pose Detection Sensor for Automatic Robot Welding Using a Structured-Light Vision System

    Directory of Open Access Journals (Sweden)

    Il Jae Lee

    2009-09-01

    Full Text Available In this study, we propose a precise 3D lug pose detection sensor for automatic robot welding of a lug to a huge steel plate used in shipbuilding, where the lug is a handle to carry the huge steel plate. The proposed sensor consists of a camera and four laser line diodes, and its design parameters are determined by analyzing its detectable range and resolution. For the lug pose acquisition, four laser lines are projected on both lug and plate, and the projected lines are detected by the camera. For robust detection of the projected lines against the illumination change, the vertical threshold, thinning, Hough transform and separated Hough transform algorithms are successively applied to the camera image. The lug pose acquisition is carried out by two stages: the top view alignment and the side view alignment. The top view alignment is to detect the coarse lug pose relatively far from the lug, and the side view alignment is to detect the fine lug pose close to the lug. After the top view alignment, the robot is controlled to move close to the side of the lug for the side view alignment. By this way, the precise 3D lug pose can be obtained. Finally, experiments with the sensor prototype are carried out to verify the feasibility and effectiveness of the proposed sensor.

  19. Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

    Science.gov (United States)

    Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

    2015-09-01

    Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  20. Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

    Science.gov (United States)

    Shiffman, Smadar

    2004-01-01

    Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

  1. The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

    Science.gov (United States)

    Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

    2017-12-01

    In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long

  2. Image processing applied to automatic detection of defects during ultrasonic examination

    International Nuclear Information System (INIS)

    Moysan, J.

    1992-10-01

    This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

  3. Machine learning approach for automatic quality criteria detection of health web pages.

    Science.gov (United States)

    Gaudinat, Arnaud; Grabar, Natalia; Boyer, Célia

    2007-01-01

    The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

  4. Behavioral and electrophysiological evidence for early and automatic detection of phonological equivalence in variable speech inputs.

    Science.gov (United States)

    Kharlamov, Viktor; Campbell, Kenneth; Kazanina, Nina

    2011-11-01

    Speech sounds are not always perceived in accordance with their acoustic-phonetic content. For example, an early and automatic process of perceptual repair, which ensures conformity of speech inputs to the listener's native language phonology, applies to individual input segments that do not exist in the native inventory or to sound sequences that are illicit according to the native phonotactic restrictions on sound co-occurrences. The present study with Russian and Canadian English speakers shows that listeners may perceive phonetically distinct and licit sound sequences as equivalent when the native language system provides robust evidence for mapping multiple phonetic forms onto a single phonological representation. In Russian, due to an optional but productive t-deletion process that affects /stn/ clusters, the surface forms [sn] and [stn] may be phonologically equivalent and map to a single phonological form /stn/. In contrast, [sn] and [stn] clusters are usually phonologically distinct in (Canadian) English. Behavioral data from identification and discrimination tasks indicated that [sn] and [stn] clusters were more confusable for Russian than for English speakers. The EEG experiment employed an oddball paradigm with nonwords [asna] and [astna] used as the standard and deviant stimuli. A reliable mismatch negativity response was elicited approximately 100 msec postchange in the English group but not in the Russian group. These findings point to a perceptual repair mechanism that is engaged automatically at a prelexical level to ensure immediate encoding of speech inputs in phonological terms, which in turn enables efficient access to the meaning of a spoken utterance.

  5. Automatic real-time detection of endoscopic procedures using temporal features.

    Science.gov (United States)

    Stanek, Sean R; Tavanapong, Wallapak; Wong, Johnny; Oh, Jung Hwan; de Groen, Piet C

    2012-11-01

    Endoscopy is used for inspection of the inner surface of organs such as the colon. During endoscopic inspection of the colon or colonoscopy, a tiny video camera generates a video signal, which is displayed on a monitor for interpretation in real-time by physicians. In practice, these images are not typically captured, which may be attributed by lack of fully automated tools for capturing, analysis of important contents, and quick and easy retrieval of these contents. This paper presents the description and evaluation results of our novel software that uses new metrics based on image color and motion over time to automatically record all images of an individual endoscopic procedure into a single digitized video file. The software automatically discards out-patient video frames between different endoscopic procedures. We validated our software system on 2464 h of live video (over 265 million frames) from endoscopy units where colonoscopy and upper endoscopy were performed. Our previous classification method achieved a frame-based sensitivity of 100.00%, but only a specificity of 89.22%. Our new method achieved a frame-based sensitivity and specificity of 99.90% and 99.97%, a significant improvement. Our system is robust for day-to-day use in medical practice. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  6. Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest

    Science.gov (United States)

    Olory Agomma, R.; Vázquez, C.; Cresson, T.; De Guise, J.

    2018-02-01

    Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 +/-18 and 30 +/- 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.

  7. Hazard detection in noise-related incidents - the role of driving experience with battery electric vehicles.

    Science.gov (United States)

    Cocron, Peter; Bachl, Veronika; Früh, Laura; Koch, Iris; Krems, Josef F

    2014-12-01

    The low noise emission of battery electric vehicles (BEVs) has led to discussions about how to address potential safety issues for other road users. Legislative actions have already been undertaken to implement artificial sounds. In previous research, BEV drivers reported that due to low noise emission they paid particular attention to pedestrians and bicyclists. For the current research, we developed a hazard detection task to test whether drivers with BEV experience respond faster to incidents, which arise due to the low noise emission, than inexperienced drivers. The first study (N=65) revealed that BEV experience only played a minor role in drivers' response to hazards resulting from low BEV noise. The tendency to respond, reaction times and hazard evaluations were similar among experienced and inexperienced BEV drivers; only small trends in the assumed direction were observed. Still, both groups clearly differentiated between critical and non-critical scenarios and responded accordingly. In the second study (N=58), we investigated additionally if sensitization to low noise emission of BEVs had an effect on hazard perception in incidents where the noise difference is crucial. Again, participants in all groups differentiated between critical and non-critical scenarios. Even though trends in response rates and latencies occurred, experience and sensitization to low noise seemed to only play a minor role in detecting hazards due to low BEV noise. An additional global evaluation of BEV noise further suggests that even after a short test drive, the lack of noise is perceived more as a comfort feature than a safety threat. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Annelies Van Nuffel

    2015-08-01

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

  9. A novel automatic molecular test for detection of multidrug resistance tuberculosis in sputum specimen: A case control study.

    Science.gov (United States)

    Li, Qiang; Ou, Xi C; Pang, Yu; Xia, Hui; Huang, Hai R; Zhao, Bing; Wang, Sheng F; Zhao, Yan L

    2017-07-01

    MiniLab tuberculosis (ML TB) assay is a new automatic diagnostic tool for diagnosis of multidrug resistance tuberculosis (MDR-TB). This study was conducted with aims to know the performance of this assay. Sputum sample from 224 TB suspects was collected from tuberculosis suspects seeking medical care at Beijing Chest hospital. The sputum samples were directly used for smear and ML TB test. The left sputum sample was used to conduct Xpert MTB/RIF, Bactec MGIT culture and drug susceptibility test (DST). All discrepancies between the results from DST, molecular and phenotypic methods were confirmed by DNA Sequencing. The sensitivity and specificity of ML TB test for detecting MTBC from TB suspects were 95.1% and 88.9%, respectively. The sensitivity for smear negative TB suspects was 64.3%. For detection of RIF resistance, the sensitivity and specificity of ML TB test were 89.2% and 95.7%, respectively. For detection of INH resistance, the sensitivity and specificity of ML TB test were 78.3% and 98.1%, respectively. ML TB test showed similar performance to Xpert MTB/RIF for detection of MTBC and RIF resistance. In addition, ML TB also had good performance for INH resistance detection. Copyright © 2017. Published by Elsevier Ltd.

  10. AUTOMATIC FAST VIDEO OBJECT DETECTION AND TRACKING ON VIDEO SURVEILLANCE SYSTEM

    Directory of Open Access Journals (Sweden)

    V. Arunachalam

    2012-08-01

    Full Text Available This paper describes the advance techniques for object detection and tracking in video. Most visual surveillance systems start with motion detection. Motion detection methods attempt to locate connected regions of pixels that represent the moving objects within the scene; different approaches include frame-to-frame difference, background subtraction and motion analysis. The motion detection can be achieved by Principle Component Analysis (PCA and then separate an objects from background using background subtraction. The detected object can be segmented. Segmentation consists of two schemes: one for spatial segmentation and the other for temporal segmentation. Tracking approach can be done in each frame of detected Object. Pixel label problem can be alleviated by the MAP (Maximum a Posteriori technique.

  11. Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

    DEFF Research Database (Denmark)

    Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.

    2013-01-01

    In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...

  12. Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

    Science.gov (United States)

    Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

    2018-02-01

    Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

  13. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs.

    NARCIS (Netherlands)

    Niemeijer, M.; Ginneken, B. van; Cree, M.J.; Mizutani, A.; Quellec, G.; Sanchez, C.I.; Zhang, B.; Hornero, R.; Lamard, M.; Muramatsu, C.; Wu, X.; Cazuguel, G.; You, J.; Mayo, A.; Li, Q.; Hatanaka, Y.; Cochener, B.; Roux, C.; Karray, F.; Garcia, M.; Fujita, H.; Abramoff, M.D.

    2010-01-01

    The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each

  14. A method for unsupervised change detection and automatic radiometric normalization in multispectral data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton John

    2011-01-01

    Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North...... to carry out the analyses is available from the authors' websites....

  15. 3D Face Model Dataset: Automatic Detection of Facial Expressions and Emotions for Educational Environments

    Science.gov (United States)

    Chickerur, Satyadhyan; Joshi, Kartik

    2015-01-01

    Emotion detection using facial images is a technique that researchers have been using for the last two decades to try to analyze a person's emotional state given his/her image. Detection of various kinds of emotion using facial expressions of students in educational environment is useful in providing insight into the effectiveness of tutoring…

  16. Full automatic fiducial marker detection on coil arrays for accurate instrumentation placement during MRI guided breast interventions

    Science.gov (United States)

    Filippatos, Konstantinos; Boehler, Tobias; Geisler, Benjamin; Zachmann, Harald; Twellmann, Thorsten

    2010-02-01

    With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

  17. Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

    Directory of Open Access Journals (Sweden)

    Fernando eWario

    2015-09-01

    Full Text Available The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as durations or angles are extracted manually directly from the observation hive or from video recordings to quantify dance properties, particularly to determine where bees have foraged. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. As a further step, we have developed technologies to track all individuals of a honeybee colony and detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition and decoding of tags and dances, and we discuss potential research directions that may benefit from automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses for a continuous, experimental recording of nine weeks duration.

  18. A Robust Vision-based Runway Detection and Tracking Algorithm for Automatic UAV Landing

    KAUST Repository

    Abu Jbara, Khaled F.

    2015-01-01

    and attitude angle estimates to allow a more robust tracking of the runway under turbulence. We illustrate the performance of the proposed lane detection and tracking scheme on various experimental UAV flights conducted by the Saudi Aerospace Research Center

  19. Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

    Science.gov (United States)

    Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

    2012-01-01

    Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

  20. Precise Automatic Image Coregistration Tools to Enable Pixel-Level Change Detection, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Automated detection of land cover changes between multitemporal images (i.e., images captured at different times) has long been a goal of the remote sensing...

  1. Automatic near-real-time detection of CMEs in Mauna Loa K-Cor coronagraph images

    Science.gov (United States)

    Thompson, W. T.; St Cyr, O. C.; Burkepile, J.; Posner, A.

    2017-12-01

    A simple algorithm has been developed to detect the onset of coronal massejections (CMEs), together with an estimate of their speed, in near-real-timeusing images of the linearly polarized white-light solar corona taken by theK-Cor telescope at the Mauna Loa Solar Observatory (MLSO). The algorithm usedis a variation on the Solar Eruptive Event Detection System (SEEDS) developedat George Mason University. The algorithm was tested against K-Cor data takenbetween 29 April 2014 and 20 February 2017, on days which the MLSO websitemarked as containing CMEs. This resulted in testing of 139 days worth of datacontaining 171 CMEs. The detection rate varied from close to 80% in 2014-2015when solar activity was high, down to as low as 20-30% in 2017 when activitywas low. The difference in effectiveness with solar cycle is attributed to thedifference in relative prevalance of strong CMEs between active and quietperiods. There were also twelve false detections during this time period,leading to an average false detection rate of 8.6% on any given day. However,half of the false detections were clustered into two short periods of a fewdays each when special conditions prevailed to increase the false detectionrate. The K-Cor data were also compared with major Solar Energetic Particle(SEP) storms during this time period. There were three SEP events detectedeither at Earth or at one of the two STEREO spacecraft where K-Cor wasobserving during the relevant time period. The K-Cor CME detection algorithmsuccessfully generated alerts for two of these events, with lead times of 1-3hours before the SEP onset at 1 AU. The third event was not detected by theautomatic algorithm because of the unusually broad width of the CME in positionangle.

  2. Dual-energy X-ray radiography for automatic high-Z material detection

    International Nuclear Information System (INIS)

    Chen Gongyin; Bennett, Gordon; Perticone, David

    2007-01-01

    There is an urgent need for high-Z material detection in cargo. Materials with Z > 74 can indicate the presence of fissile materials or radiation shielding. Dual (high) energy X-ray material discrimination is based on the fact that different materials have different energy dependence in X-ray attenuation coefficients. This paper introduces the basic physics and analyzes the factors that affect dual-energy material discrimination performance. A detection algorithm is also discussed

  3. Lamb wave based automatic damage detection using matching pursuit and machine learning

    International Nuclear Information System (INIS)

    Agarwal, Sushant; Mitra, Mira

    2014-01-01

    In this study, matching pursuit (MP) has been tested with machine learning algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) to automate the process of damage detection in metallic plates. Here, damage detection is done using the Lamb wave response in a thin aluminium plate simulated using a finite element (FE) method. To reduce the complexity of the Lamb wave response, only the A 0 mode is excited and sensed. The procedure adopted for damage detection consists of three major steps, involving signal processing and machine learning (ML). In the first step, MP is used for de-noising and enhancing the sparsity of the database. In the existing literature, MP is used to decompose any signal into a linear combination of waveforms that are selected from a redundant dictionary. In this work, MP is deployed in two stages to make the database sparse as well as to de-noise it. After using MP on the database, it is then passed as input data for ML classifiers. ANN and SVM are used to detect the location of the potential damage from the reduced data. The study demonstrates that the SVM is a robust classifier in the presence of noise and is more efficient than the ANN. Out-of-sample data are used for the validation of the trained and tested classifier. Trained classifiers are found to be successful in the detection of damage with a detection rate of more than 95%. (paper)

  4. Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding

    Directory of Open Access Journals (Sweden)

    Saad N.H.

    2016-01-01

    Full Text Available Quality control is one of important process in semiconductor manufacturing. A lot of issues trying to be solved in semiconductor manufacturing industry regarding the rate of production with respect to time. In most semiconductor assemblies, a lot of wafers from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents automatic defect segmentation of semiconductor wafer image based on multilevel thresholding algorithm which can be further adopted in machine vision system. In this work, the defect image which is in RGB image at first is converted to the gray scale image. Median filtering then is implemented to enhance the gray scale image. Then the modified multilevel thresholding algorithm is performed to the enhanced image. The algorithm worked in three main stages which are determination of the peak location of the histogram, segmentation the histogram between the peak and determination of first global minimum of histogram that correspond to the threshold value of the image. The proposed approach is being evaluated using defected wafer images. The experimental results shown that it can be used to segment the defect correctly and outperformed other thresholding technique such as Otsu and iterative thresholding.

  5. Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

    Science.gov (United States)

    Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

    2010-07-08

    A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

  6. An automatic algorithm for detecting stent endothelialization from volumetric optical coherence tomography datasets

    Energy Technology Data Exchange (ETDEWEB)

    Bonnema, Garret T; Barton, Jennifer K [College of Optical Sciences, University of Arizona, Tucson, AZ (United States); Cardinal, Kristen O' Halloran [Biomedical and General Engineering, California Polytechnic State University (United States); Williams, Stuart K [Cardiovascular Innovation Institute, University of Louisville, Louisville, KY 40292 (United States)], E-mail: barton@u.arizona.edu

    2008-06-21

    Recent research has suggested that endothelialization of vascular stents is crucial to reducing the risk of late stent thrombosis. With a resolution of approximately 10 {mu}m, optical coherence tomography (OCT) may be an appropriate imaging modality for visualizing the vascular response to a stent and measuring the percentage of struts covered with an anti-thrombogenic cellular lining. We developed an image analysis program to locate covered and uncovered stent struts in OCT images of tissue-engineered blood vessels. The struts were found by exploiting the highly reflective and shadowing characteristics of the metallic stent material. Coverage was evaluated by comparing the luminal surface with the depth of the strut reflection. Strut coverage calculations were compared to manual assessment of OCT images and epi-fluorescence analysis of the stented grafts. Based on the manual assessment, the strut identification algorithm operated with a sensitivity of 93% and a specificity of 99%. The strut coverage algorithm was 81% sensitive and 96% specific. The present study indicates that the program can automatically determine percent cellular coverage from volumetric OCT datasets of blood vessel mimics. The program could potentially be extended to assessments of stent endothelialization in native stented arteries.

  7. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

    Science.gov (United States)

    Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

    2010-08-01

    The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100

  8. Detection of incidents and follow-up by the regulatory bodies; La detection des incidents et le suivi des enseignements par les autorites de surete

    Energy Technology Data Exchange (ETDEWEB)

    Gouffon, A

    1990-12-01

    Although France has established a large nuclear program based on PWR type reactors, experience gained up to this are are quite modest. Concept of PWR-900 reactors is based mostly on safety analyses and evaluations done a priori. At the time of their construction and start-up they have been considered as advanced safe systems. No data were available concerning any incidents during operation. The Three Mile Island accident in 1979 has changed the attitude concerning reactor safety. The result is the procedure adopted by Electricite de France for follow-up of incident situations during operation of a number of PWR-900 plants. Standardization of this type of NPPs demands extremely strict standards and control of safety systems. This safety analysis procedure consists of the following: analysis of incidents based on data collected by EDF during operation; every regular annual shutdown of a unit is the subject of detailed analysis; together with the detailed analysis, principal elements of operating experience for the series of PWR units are submitted for analysis to a permanent regulatory body.

  9. Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2011-09-01

    Full Text Available Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

  10. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies

    Energy Technology Data Exchange (ETDEWEB)

    Ganiler, Onur; Oliver, Arnau; Diez, Yago; Freixenet, Jordi; Llado, Xavier [University of Girona, VICOROB Computer Vision and Robotics Group, Girona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Beltran, Brigitte [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, 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)

    2014-05-15

    Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies. The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections. Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher. Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies. (orig.)

  11. Automatic detection and classification of malarial retinopathy- associated retinal whitening in digital retinal images

    International Nuclear Information System (INIS)

    Akram, M.U.; Alvi, A.B.N.; Khan, S.A.

    2017-01-01

    Malarial retinopathy addresses diseases that are characterized by abnormalities in retinal fundus imaging. Macular whitening is one of the distinct signs of cerebral malaria but has hardly been explored as a critical bio-marker. The paper proposes a computerized detection and classification method for malarial retinopathy using retinal whitening as a bio-marker. The paper combines various statistical and color based features to form a sound feature set for accurate detection of retinal whitening. All features are extracted at image level and feature selection is performed to detect most discriminate features. A new method for macula location is also presented. The detected macula location is further used for grading of whitening as macular or peripheral whitening. Support vector machine along with radial basis function is used for classification of normal and malarial retinopathy patients. The evaluation is performed using a locally gathered dataset from malarial patients and it achieves an accuracy of 95% for detection of retinal whitening and 100% accuracy for grading of retinal whitening as macular or non-macular. One of the major contributions of proposed method is grading of retinal whitening into macular or peripheral whitening. (author)

  12. Generalized Linear Models of Home Activity for Automatic Detection of Mild Cognitive Impairment in Older Adults*

    Science.gov (United States)

    Akl, Ahmad; Snoek, Jasper; Mihailidis, Alex

    2015-01-01

    With a globally aging population, the burden of care of cognitively impaired older adults is becoming increasingly concerning. Instances of Alzheimer’s disease and other forms of dementia are becoming ever more frequent. Earlier detection of cognitive impairment offers significant benefits, but remains difficult to do in practice. In this paper, we develop statistical models of the behavior of older adults within their homes using sensor data in order to detect the early onset of cognitive decline. Specifically, we use inhomogenous Poisson processes to model the presence of subjects within different rooms throughout the day in the home using unobtrusive sensing technologies. We compare the distributions learned from cognitively intact and impaired subjects using information theoretic tools and observe statistical differences between the two populations which we believe can be used to help detect the onset of cognitive decline. PMID:25570050

  13. Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults.

    Science.gov (United States)

    Akl, Ahmad; Snoek, Jasper; Mihailidis, Alex

    2014-01-01

    With a globally aging population, the burden of care of cognitively impaired older adults is becoming increasingly concerning. Instances of Alzheimer's disease and other forms of dementia are becoming ever more frequent. Earlier detection of cognitive impairment offers significant benefits, but remains difficult to do in practice. In this paper, we develop statistical models of the behavior of older adults within their homes using sensor data in order to detect the early onset of cognitive decline. Specifically, we use inhomogenous Poisson processes to model the presence of subjects within different rooms throughout the day in the home using unobtrusive sensing technologies. We compare the distributions learned from cognitively intact and impaired subjects using information theoretic tools and observe statistical differences between the two populations which we believe can be used to help detect the onset of cognitive decline.

  14. Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

    Science.gov (United States)

    Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

    2009-02-01

    Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

  15. Automatic calibration of spectrometer's tracts of installation for detecting of explosive materials

    International Nuclear Information System (INIS)

    Lajkin, A.I.; Mozhaev, A.N.; Kozlovskij, S.A.

    2005-01-01

    Calibration of the facility, representing the spectrometer and intended for the explosive substances detection on various objects is considered. The above work is based on the method of the neutron-radiation analysis. Brief description of the operational prototype, the basic characteristics, the capturing radiation background spectrum with a sample of the nitrogen-containing substance and the example of the calibration straight line are presented. The method of control sums and advantages of its application at the facilities for the explosive substances detection are considered [ru

  16. Trends in Thyroid Cancer Incidence in Korean Children (1999-2012) Based on Palpation and Nonpalpation Detection Methods

    Science.gov (United States)

    Cho, Yoon Young; Jang, Hye Won; Joung, Ji Young; Park, Sun-Mi; Jeong, Dae Joon; Kim, Sun Wook; Chung, Jae Hoon

    2015-01-01

    Background The incidence of childhood thyroid cancer is increasing in several populations; however, contributing factors have not been adequately discussed. Objectives Our aim was to identify trends of childhood thyroid cancer based on the Korea Central Cancer Registry (KCCR) database and to elucidate changes in detection methods of cancers using a single-center database. Methods Data from the KCCR and Statistics Korea between 1999 and 2012 were used to calculate the crude incidence of thyroid cancer in children. To analyze detection methods for cancers, pediatric patients (aged 0-19 years, n = 126) who underwent thyroid surgery for thyroid cancers at our institution were identified. Subjects were divided into two groups by detection method: (1) palpation group and (2) screening group. Results The crude incidence of childhood thyroid cancer increased from 0.5 per 100,000 in 1999 to 1.7 in 2012. The proportion of thyroid cancer among total cancers also increased from 4.4% in 1999 to 10.6% in 2012. Among 126 children from our institution, 91 cases (72%) were identified as palpable neck masses, and the remainder were discovered during imaging studies. The numbers in both groups gradually increased during the study period. Conclusions The incidence of childhood thyroid cancer has steadily increased in Korea. Regarding the detection methods of cancers, most tumors are detected by palpation rather than screening, although the rate of masses identified during screening has increased. PMID:26835429

  17. Automatic detection of outlines. Application to the quantitative analysis of renal scintiscanning pictures

    International Nuclear Information System (INIS)

    Morcos-Ghrab, Nadia.

    1979-01-01

    The purpose of the work described is the finalizing of a method making it possible automatically to extract the significant outlines on a renal scintiscanning picture. The algorithms must be simple and of high performance, their routine execution on a mini-computer must be fast enough to compete effectively with human performances. However, the method that has been developed is general enough to be adapted, with slight modifications, to another type of picture. The first chapter is a brief introduction to the principle of scintiscanning, the equipment used and the type of picture obtained therefrom. In the second chapter the various approaches used for form recognition and scene analysis are very briefly described with the help of examples. The third chapter deals with pretreatment techniques (particularly the machine operators) used for segmenting the pictures. Chapter four presents techniques which segment the picture by parallel processing of all its points. In chapter five a description is given of the sequential research techniques of the outline elements, drawing inspiration from the methods used in artificial intelligence for resolving the optimization problem. The sixth chapter shows the difficulties encountered in extracting the renal outlines and the planning technique stages adopted to overcome these difficulties. Chapter seven describes in detail the two research methods employed for generating the plan. In chapter eight, the methods used for extending the areas obtained on the plan and for refining the outlines that bound them are dealt with. Chapter nine is a short presentation of the organization of the programmes and of their data structure. Finally, examples of results are given in chapter ten [fr

  18. Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.

    Science.gov (United States)

    Zolgharni, Massoud; Negoita, Madalina; Dhutia, Niti M; Mielewczik, Michael; Manoharan, Karikaran; Sohaib, S M Afzal; Finegold, Judith A; Sacchi, Stefania; Cole, Graham D; Francis, Darrel P

    2017-07-01

    Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%). An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame. © 2017, Wiley Periodicals, Inc.

  19. Automatic classification and detection of clinically relevant images for diabetic retinopathy

    Science.gov (United States)

    Xu, Xinyu; Li, Baoxin

    2008-03-01

    We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

  20. A new code for automatic detection and analysis of the lineament patterns for geophysical and geological purposes (ADALGEO)

    Science.gov (United States)

    Soto-Pinto, C.; Arellano-Baeza, A.; Sánchez, G.

    2013-08-01

    We present a new numerical method for automatic detection and analysis of changes in lineament patterns caused by seismic and volcanic activities. The method is implemented as a series of modules: (i) normalization of the image contrast, (ii) extraction of small linear features (stripes) through convolution of the part of the image in the vicinity of each pixel with a circular mask or through Canny algorithm, and (iii) posterior detection of main lineaments using the Hough transform. We demonstrate that our code reliably detects changes in the lineament patterns related to the stress evolution in the Earth's crust: specifically, a significant number of new lineaments appear approximately one month before an earthquake, while one month after the earthquake the lineament configuration returns to its initial state. Application of our software to the deformations caused by volcanic activity yields the opposite results: the number of lineaments decreases with the onset of microseismicity. This discrepancy can be explained assuming that the plate tectonic earthquakes are caused by the compression and accumulation of stress in the Earth's crust due to subduction of tectonic plates, whereas in the case of volcanic activity we deal with the inflation of a volcano edifice due to elevation of pressure and magma intrusion and the resulting stretching of the surface.

  1. Comparing detection and disclosure of traffic incidents in social networks: an intelligent approach based on Twitter vs. Waze

    Directory of Open Access Journals (Sweden)

    Sebastián Vallejos

    2018-03-01

    Full Text Available Nowadays, social networks have become  in a  communication  medium widely  used to disseminate any type  of  information. In  particular,  the  shared  information  in  social  networks  usually  includes  a  considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of  topics  are  communicated.  In this  context,  Twitter  is  a  case  in  point  of  a  generic  social  network  in  which  its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos  Aires  (CABA,  Argentina,  as  the  region  of  interest.  The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.

  2. Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

    KAUST Repository

    Salas-Zárate, María del Pilar

    2017-04-24

    In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.

  3. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model.

    Science.gov (United States)

    Mei, Shuang; Wang, Yudan; Wen, Guojun

    2018-04-02

    Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

  4. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

    Directory of Open Access Journals (Sweden)

    Shuang Mei

    2018-04-01

    Full Text Available Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality. Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

  5. Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

    Science.gov (United States)

    Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.

    2018-04-01

    Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

  6. Automatic change detection and quantification of dermatological diseases with an application to psoriasis images

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Butakoff, C.; Ersbøll, Bjarne Kjær

    2007-01-01

    Change monitoring in skin lesion analysis has proven to be a useful adjunct in their assessment. This article presents a comparative study of the available change detection techniques applied to change visualization and quantification in bi-temporal psoriasis images. The chosen methods are evalua...

  7. Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

    KAUST Repository

    Salas-Zá rate, Marí a del Pilar; Paredes-Valverde, Mario André s; Rodriguez-Garcia, Miguel Angel; Valencia-Garcí a, Rafael; Alor-Herná ndez, Giner

    2017-01-01

    In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.

  8. Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients

    Directory of Open Access Journals (Sweden)

    Mahsa Partovi

    2016-05-01

    Full Text Available Introduction: Diabetic retinopathy (DR is the most frequent microvascular complication of diabetes and can lead to several retinal abnormalities including microaneurysms, exudates, dot and blot hemorrhages, and cotton wool spots. Automated early detection of these abnormalities could limit the severity of the disease and assist ophthalmologists in investigating and treating the disease more efficiently. Segmentation of retinal image features provides the basis for automated assessment. In this study, exudates lesion on retinopathy retinal images was segmented by different image processing techniques. The objective of this study is detection of the exudates regions on retinal images of retinopathy patients by different image processing techniques. Methods: A total of 30 color images from retinopathy patients were selected for this study. The images were taken by Topcon TRC-50 IX mydriatic camera and saves with TIFF format with a resolution of 500 × 752 pixels. The morphological function was applied on intensity components of hue saturation intensity (HSI space. To detect the exudates regions, thresholding was performed on all images and the exudates region was segmented. To optimize the detection efficiency, the binary morphological functions were applied. Finally, the exudates regions were quantified and evaluated for further statistical purposes. Results: The average of sensitivity of 76%, specificity of 98%, and accuracy of 97% was obtained. Conclusion: The results showed that our approach can identify the exudate regions in retinopathy images.

  9. [Detection of Brucella with an automatic hemoculture system: Bact/Alert].

    Science.gov (United States)

    Casas, J; Partal, Y; Llosá, J; Leiva, J; Navarro, J M; de la Rosa, M

    1994-12-01

    The ability of in vitro and in vivo detection of Brucella spp. with the Bact/Alert system was studied. Three strains of Brucella melitensis and two of Brucella abortus were used. Different dilutions of the five strains were performed in trypticase soy broth (TSB), achieving concentrations of 1 cfu/ml, 5 cfu/ml, 10 cfu/ml and 100 cfu/ml. Ten ml of each dilution and strain were inoculated into 5 aerobic bottles Bact/Alert and 5 biphasic Hemóline bottles. Furthermore, over a 9 month period, 8,216 bottles of Bact/Alert bottles from hospitalized patients and from the emergency department were processed in the authors' laboratory. The mean detection time for Brucella growth was from 2 to 3 days with the Bact/Alert system, and 14 days in the biphasic bottles. Former bottles processed in the authors' laboratory, 11 aerobic bottles belonged to 5 patients in whom brucelosis was confirmed by bloodculture. The Bact/Alert system detected Brucella melitensis in only on bottle at 2.9 days of incubation. In 7 bottles Bact/Alert detected B. melitensis by a blind pass of these bottles at 10 to 20 days of incubation. These results suggest that the Bact/Alert system does not totally solve the diagnosis of brucellosis. Blind passes of the bloodcultures are required.

  10. A fully automatic microcalcification detection approach based on deep convolution neural network

    Science.gov (United States)

    Cai, Guanxiong; Guo, Yanhui; Zhang, Yaqin; Qin, Genggeng; Zhou, Yuanpin; Lu, Yao

    2018-02-01

    Breast cancer is one of the most common cancers and has high morbidity and mortality worldwide, posing a serious threat to the health of human beings. The emergence of microcalcifications (MCs) is an important signal of early breast cancer. However, it is still challenging and time consuming for radiologists to identify some tiny and subtle individual MCs in mammograms. This study proposed a novel computer-aided MC detection algorithm on the full field digital mammograms (FFDMs) using deep convolution neural network (DCNN). Firstly, a MC candidate detection system was used to obtain potential MC candidates. Then a DCNN was trained using a novel adaptive learning strategy, neutrosophic reinforcement sample learning (NRSL) strategy to speed up the learning process. The trained DCNN served to recognize true MCs. After been classified by DCNN, a density-based regional clustering method was imposed to form MC clusters. The accuracy of the DCNN with our proposed NRSL strategy converges faster and goes higher than the traditional DCNN at same epochs, and the obtained an accuracy of 99.87% on training set, 95.12% on validation set, and 93.68% on testing set at epoch 40. For cluster-based MC cluster detection evaluation, a sensitivity of 90% was achieved at 0.13 false positives (FPs) per image. The obtained results demonstrate that the designed DCNN plays a significant role in the MC detection after being prior trained.

  11. Automatic lameness detection based on consecutive 3D-video recordings

    NARCIS (Netherlands)

    Hertem, van T.; Viazzi, S.; Steensels, M.; Maltz, E.; Antler, A.; Alchanatis, V.; Schlageter-Tello, A.; Lokhorst, C.; Romanini, C.E.B.; Bahr, C.; Berckmans, D.; Halachmi, I.

    2014-01-01

    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive

  12. Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

    Czech Academy of Sciences Publication Activity Database

    Dvořák, Pavel; Kropatsch, W.G.; Bartušek, Karel

    2013-01-01

    Roč. 13, č. 5 (2013), s. 223-230 ISSN 1335-8871 R&D Projects: GA ČR GAP102/12/1104; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : Brain tumor * Brain tumor detection * Symmetry analysis Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.162, year: 2013

  13. Technical improvement and development of automatic detection method for genomic mutation

    International Nuclear Information System (INIS)

    Yamada, Kiyomi; Takai, Setsuo; Togashi, Chikako; Itami, Jun

    1999-01-01

    Fluorescent in situ hybridization (FISH) method was improved to estimate the dose of radiation exposure. Cleavage of DNA molecules in lymphocyte was used as the detection parameter and the procedures for preparation of samples suitable for atomic force microscopy and laser microscopy were developed. When ordinal primers on the market were used for PCR, the products were generally too small (about 200 b.p.) for detection by FISH method. Therefore, dATP and biotin-labeled dUTP were linked to its 3'-end by treatment with TdT for 2 hours, resulting that the mean length of PCR products was ca. 1.5 Kb. After hybridization using this prove, signal amplification was carried out according to biotin-avidin detection method. Thus, fluorescent signals on chromosomes could be easily detected. When three primers, D6S105, D6S291 and D6S282 were used as primer, fluorescent signal was detectable at 3 sites on chromatin fiber. These results indicate that this method is available for the analysis of cleavage of chromosomes. However, the backgrounds were much varied depending on the way to wash the preparation after incubation with fluorescent particle to form biotin-avidin binding. Therefore, further improvement of this method was necessary to apply in practice. When chromosomes 13, 14 and 15 from lymphocytes exposed to X-ray were used as test samples, it was demonstrated that radio-sensitivity was variable depending on the contents of R band in each chromosome. (M.N.)

  14. Sporadic incidence of Fascioliasis detected during hepatobiliary procedures: a study of 18 patients from Sulaimaniyah governorate.

    Science.gov (United States)

    Hawramy, Tahir Abdullah Hussein; Saeed, Kamal Ahmed; Qaradaghy, Seerwan Hama Sharif; Karboli, Taha Ahmed; Nore, Beston Faiek; Bayati, Noora Hisham Abood

    2012-12-21

    Fascioliasis is an often-neglected zoonotic disease and currently is an emerging infection in Iraq. Fascioliasis has two distinct phases, an acute phase, exhibiting the hepatic migratory stage of the fluke's life cycle, and a chronic biliary phase manifested with the presence of the parasite in the bile ducts through hepatic tissue. The incidence of Fascioliasis in Sulaimaniyah governorate was unexpected observation. We believe that shedding light on this disease in our locality will increase our physician awareness and experience in early detection, treatment in order to avoid unnecessary surgeries. We retrospectively evaluated this disease in terms of the demographic features, clinical presentations, and managements by reviewing the medical records of 18 patients, who were admitted to the Sulaimani Teaching Hospital and Kurdistan Centre for Gastroenterology and Hepatology. Patients were complained from hepatobiliary and/or upper gastrointestinal symptoms and diagnosed accidentally with Fascioliasis during hepatobiliary surgeries and ERCP by direct visualization of the flukes and stone analysis. Elevated liver enzymes, white blood cells count and eosinophilia were notable laboratory indices. The dilated CBD, gallstones, liver cysts and abscess were found common in radiological images. Fascioliasis diagnosed during conventional surgical CBD exploration and choledochodoudenostomy, open cholecystectomy, surgical drainage of liver abscess, ERCP and during gallstone analysis. Fascioliasis is indeed an emerging disease in our locality, but it is often underestimated and ignored. We recommend the differential diagnosis of patients suffering from Rt. Hypochondrial pain, fever and eosinophilia. The watercress ingestion was a common factor in patient's history.

  15. Image processing for an automatic detection of defect signals from electromagnetic cartographies

    International Nuclear Information System (INIS)

    Benoist, B.; Marqueste, L.; Birac, C.

    1994-01-01

    As the population of nuclear power plants ages, new defects are appearing in steam generator tubes (stress corrosion, corrosion pitting and intergranular corrosion). For more sophisticated expert appraisal of these defects, tubes can be examined by multifrequency eddy-current testing with an absolute coil (diameter value of 1 mm). A device, consisting of a push-puller mechanism and a motor-driven probe carrying this absolute coil, gives a helical movement to scan the inner surface of the tube. The signals obtained can be represented in the form of cartographies (3D representation in which the coordinates are the circumference, the length and amplitude of the X or Y component at a given frequency). The detection of defect signals by visual examination of these eddy-current cartographies is not always reproducible. The article describes an image processing procedure for the detection of defect signals which leads to a better reproductibility for more safety

  16. Automatic detection of erythemato-squamous diseases using k-means clustering.

    Science.gov (United States)

    Ubeyli, Elif Derya; Doğdu, Erdoğan

    2010-04-01

    A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

  17. Automatic registration of remote sensing images based on SIFT and fuzzy block matching for change detection

    Directory of Open Access Journals (Sweden)

    Cai Guo-Rong

    2011-10-01

    Full Text Available This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT and has two phases. The first phase focuses on SIFT feature extraction and on estimation of image transformation. In the second phase, Structured Local Binary Haar Pattern (SLBHP combined with a fuzzy similarity measure is then used to build a new and effective block similarity measure for change detection. Experimental results obtained on multi-temporal data sets show that compared with three mainstream block matching algorithms, the proposed algorithm is more effective in dealing with scale, rotation and illumination changes.

  18. Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis.

    OpenAIRE

    Zappalá, D.; Tavner, P.J.; Crabtree, C.J.; Sheng, S.

    2014-01-01

    Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is th...

  19. Automatic detection of the unknown number point targets in FMICW radar signals

    Czech Academy of Sciences Publication Activity Database

    Rejfek, L.; Mošna, Zbyšek; Beran, L.; Fišer, O.; Dobrovolný, M.

    2017-01-01

    Roč. 4, č. 11 (2017), s. 116-120 ISSN 2313-626X R&D Projects: GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : FMICW radar * 2D FFT * signal filtration * taraget detection * target parameter estimation Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences http://science-gate.com/IJAAS/Articles/2017-4-11/18%202017-4-11-pp.116-120.pdf

  20. System for Automatic Detection and Analysis of Targets in FMICW Radar Signal

    Czech Academy of Sciences Publication Activity Database

    Rejfek, Luboš; Mošna, Zbyšek; Urbář, Jaroslav; Koucká Knížová, Petra

    2016-01-01

    Roč. 67, č. 1 (2016), s. 36-41 ISSN 1335-3632 R&D Projects: GA ČR(CZ) GAP209/12/2440; GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : power spectral density (PSD) * FMICW radar * Doppler measurement * thresholding * false alert detection Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.483, year: 2016 http://iris.elf.stuba.sk/JEEEC/data/pdf/1_116-05.pdf

  1. Automatic detection of whole night snoring events using non-contact microphone.

    Directory of Open Access Journals (Sweden)

    Eliran Dafna

    Full Text Available OBJECTIVE: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN: Sounds during polysomnography (PSG were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2, m/f 40/27 referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND RESULTS: To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental. A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore and specificity of 98.3% (noise as noise. CONCLUSIONS: Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  2. Automatic recognition of thermographic examinations for early detection of breast cancer

    Science.gov (United States)

    Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz

    2016-09-01

    This article describes the processing and classification of thermographic examinations taken with device developed by Braster SA. The device records the surface temperature of the breast skin using the liquid crystal matrices. Images are analyzed with the use of machine learning algorithms. The result of classification is available after a few minutes and when it detects suspicious changes patient may be referred for detailed examinations.

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

    Science.gov (United States)

    Kuo, Chiao-Ling; Hong, Jung-Hong

    2016-01-01

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

  4. Automatic helmet-wearing detection for law enforcement using CCTV cameras

    Science.gov (United States)

    Wonghabut, P.; Kumphong, J.; Satiennam, T.; Ung-arunyawee, R.; Leelapatra, W.

    2018-04-01

    The objective of this research is to develop an application for enforcing helmet wearing using CCTV cameras. The developed application aims to help law enforcement by police, and eventually resulting in changing risk behaviours and consequently reducing the number of accidents and its severity. Conceptually, the application software implemented using C++ language and OpenCV library uses two different angle of view CCTV cameras. Video frames recorded by the wide-angle CCTV camera are used to detect motorcyclists. If any motorcyclist without helmet is found, then the zoomed (narrow-angle) CCTV is activated to capture image of the violating motorcyclist and the motorcycle license plate in real time. Captured images are managed by database implemented using MySQL for ticket issuing. The results show that the developed program is able to detect 81% of motorcyclists on various motorcycle types during daytime and night-time. The validation results reveal that the program achieves 74% accuracy in detecting the motorcyclist without helmet.

  5. Automatic detection of diseased tomato plants using thermal and stereo visible light images.

    Directory of Open Access Journals (Sweden)

    Shan-e-Ahmed Raza

    Full Text Available Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.

  6. Automatic detection of karstic sinkholes in seismic 3D images using circular Hough transform

    International Nuclear Information System (INIS)

    Parchkoohi, Mostafa Heydari; Farajkhah, Nasser Keshavarz; Delshad, Meysam Salimi

    2015-01-01

    More than 30% of hydrocarbon reservoirs are reported in carbonates that mostly include evidence of fractures and karstification. Generally, the detection of karstic sinkholes prognosticate good quality hydrocarbon reservoirs where looser sediments fill the holes penetrating hard limestone and the overburden pressure on infill sediments is mostly tolerated by their sturdier surrounding structure. They are also useful for the detection of erosional surfaces in seismic stratigraphic studies and imply possible relative sea level fall at the time of establishment. Karstic sinkholes are identified straightforwardly by using seismic geometric attributes (e.g. coherency, curvature) in which lateral variations are much more emphasized with respect to the original 3D seismic image. Then, seismic interpreters rely on their visual skills and experience in detecting roughly round objects in seismic attribute maps. In this paper, we introduce an image processing workflow to enhance selective edges in seismic attribute volumes stemming from karstic sinkholes and finally locate them in a high quality 3D seismic image by using circular Hough transform. Afterwards, we present a case study from an on-shore oilfield in southwest Iran, in which the proposed algorithm is applied and karstic sinkholes are traced. (paper)

  7. A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

    Directory of Open Access Journals (Sweden)

    Mehdi Alilou

    2014-03-01

    Full Text Available Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9. Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

  8. Incidence of postangiographic silent brain infarction detected by diffusion-weighted MR imaging

    International Nuclear Information System (INIS)

    Mori, Harushi; Hayashi, Naoto; Aoki, Shigeki

    2002-01-01

    We surveyed to assess for the incidence of clinically silent brain infarction after cerebral catheter angiography. Diffusion-weighted images were performed shortly after 33 cerebral catheter angiographies. We found totally 11 abnormally high intensity spots in 5 of 33 patients on diffusion-weighted images and, therefore, the incidence was calculated as 15.2%. This incidence is higher than has been estimated based on the incidence of neurological deficits (about 0.5%) after cerebral angiography. Diffusion-weighted MR imaging is suitable to monitor the safety of angiographic procedures and material. (author)

  9. Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

    Science.gov (United States)

    Sun, Shaohui

    Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a "divide-and-conquer" scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected

  10. First tests of a multi-wavelength mini-DIAL system for the automatic detection of greenhouse gases

    Science.gov (United States)

    Parracino, S.; Gelfusa, M.; Lungaroni, M.; Murari, A.; Peluso, E.; Ciparisse, J. F.; Malizia, A.; Rossi, R.; Ventura, P.; Gaudio, P.

    2017-10-01

    Considering the increase of atmospheric pollution levels in our cities, due to emissions from vehicles and domestic heating, and the growing threat of terrorism, it is necessary to develop instrumentation and gather know-how for the automatic detection and measurement of dangerous substances as quickly and far away as possible. The Multi- Wavelength DIAL, an extension of the conventional DIAL technique, is one of the most powerful remote sensing methods for the identification of multiple substances and seems to be a promising solution compared to existing alternatives. In this paper, first in-field tests of a smart and fully automated Multi-Wavelength mini-DIAL will be presented and discussed in details. The recently developed system, based on a long-wavelength infrared (IR-C) CO2 laser source, has the potential of giving an early warning, whenever something strange is found in the atmosphere, followed by identification and simultaneous concentration measurements of many chemical species, ranging from the most important Greenhouse Gases (GHG) to other harmful Volatile Organic Compounds (VOCs). Preliminary studies, regarding the fingerprint of the investigated substances, have been carried out by cross-referencing database of infrared (IR) spectra, obtained using in-cell measurements, and typical Mixing Ratios in the examined region, extrapolated from the literature. First experiments in atmosphere have been performed into a suburban and moderately-busy area of Rome. Moreover, to optimize the automatic identification of the harmful species to be recognized on the basis of in cell measurements of the absorption coefficient spectra, an advanced multivariate statistical method for classification has been developed and tested.

  11. Automatic road traffic safety management system in urban areas

    Directory of Open Access Journals (Sweden)

    Oskarbski Jacek

    2017-01-01

    Full Text Available Traffic incidents and accidents contribute to decreasing levels of transport system reliability and safety. Traffic management and emergency systems on the road, using, among others, automatic detection, video surveillance, communication technologies and institutional solutions improve the organization of the work of various departments involved in traffic and safety management. Automation of incident management helps to reduce the time of a rescue operation as well as of the normalization of the flow of traffic after completion of a rescue operation, which also affects the reduction of the risk of secondary accidents and contributes to reducing their severity. The paper presents the possibility of including city traffic departments in the process of incident management. The results of research on the automatic incident detection in cities are also presented.

  12. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

    Science.gov (United States)

    Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

    2017-06-01

    Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

  13. Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

    Science.gov (United States)

    Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

    2014-01-01

    Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

  14. Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

    Science.gov (United States)

    Poczatek, J. Collin; Turck, Christoph W.; Lechene, Claude

    2012-01-01

    Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php. PMID:22347386

  15. Fault prevention by early stage symptoms detection for automatic vehicle transmission using pattern recognition and curve fitting

    Science.gov (United States)

    Balbin, Jessie R.; Cruz, Febus Reidj G.; Abu, Jon Ervin A.; Siño, Carlo G.; Ubaldo, Paolo E.; Zulueta, Christelle Jianne T.

    2017-06-01

    Automobiles have become essential parts of our everyday lives. It can correlate many factors that may affect a vehicle primarily those which may inconvenient or in some cases harm lives or properties. Thus, focusing on detecting an automatic transmission vehicle engine, body and other parts that cause vibration and sound may help prevent car problems using MATLAB. By using sound, vibration, and temperature sensors to detect the defects of the car and with the help of the transmitter and receiver to gather data wirelessly, it is easy to install on to the vehicle. A technique utilized from Toyota Balintawak Philippines that every car is treated as panels(a, b, c, d, and e) 'a' being from the hood until the front wheel of the car and 'e' the rear shield to the back of the car, this was applied on how to properly place the sensors so that precise data could be gathered. Data gathered would be compared to the normal graph taken from the normal status or performance of a vehicle, data that would surpass 50% of the normal graph would be considered that a problem has occurred. The system is designed to prevent car accidents by determining the current status or performance of the vehicle, also keeping people away from harm.

  16. Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

    Science.gov (United States)

    Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

    2012-12-01

    This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

  17. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

    Directory of Open Access Journals (Sweden)

    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

  18. Fuzzy Algorithm for the Detection of Incidents in the Transport System

    Science.gov (United States)

    Nikolaev, Andrey B.; Sapego, Yuliya S.; Jakubovich, Anatolij N.; Berner, Leonid I.; Stroganov, Victor Yu.

    2016-01-01

    In the paper it's proposed an algorithm for the management of traffic incidents, aimed at minimizing the impact of incidents on the road traffic in general. The proposed algorithm is based on the theory of fuzzy sets and provides identification of accidents, as well as the adoption of appropriate measures to address them as soon as possible. A…

  19. Increased incidence of Mycoplasma pneumoniae infections detected by laboratory-based surveillance in Denmark in 2010

    DEFF Research Database (Denmark)

    Rasmussen, J N; Voldstedlund, M; Andersen, R L

    2010-01-01

    In Denmark recurrent epidemics of Mycoplasma pneumoniae infections have been described since the 1950s at intervals of approximately four to six years. The latest epidemic occurred in 2004/05 followed by two years of high incidence and more than three years of low incidence. Due to a recent incre...

  20. Sporadic incidence of Fascioliasis detected during Hepatobiliary procedures: A study of 18 patients from Sulaimaniyah governorate

    Science.gov (United States)

    2012-01-01

    Background Fascioliasis is an often-neglected zoonotic disease and currently is an emerging infection in Iraq. Fascioliasis has two distinct phases, an acute phase, exhibiting the hepatic migratory stage of the fluke’s life cycle, and a chronic biliary phase manifested with the presence of the parasite in the bile ducts through hepatic tissue. The incidence of Fascioliasis in Sulaimaniyah governorate was unexpected observation. We believe that shedding light on this disease in our locality will increase our physician awareness and experience in early detection, treatment in order to avoid unnecessary surgeries. Findings We retrospectively evaluated this disease in terms of the demographic features, clinical presentations, and managements by reviewing the medical records of 18 patients, who were admitted to the Sulaimani Teaching Hospital and Kurdistan Centre for Gastroenterology and Hepatology. Patients were complained from hepatobiliary and/or upper gastrointestinal symptoms and diagnosed accidentally with Fascioliasis during hepatobiliary surgeries and ERCP by direct visualization of the flukes and stone analysis. Elevated liver enzymes, white blood cells count and eosinophilia were notable laboratory indices. The dilated CBD, gallstones, liver cysts and abscess were found common in radiological images. Fascioliasis diagnosed during conventional surgical CBD exploration and choledochodoudenostomy, open cholecystectomy, surgical drainage of liver abscess, ERCP and during gallstone analysis. Conclusion Fascioliasis is indeed an emerging disease in our locality, but it is often underestimated and ignored. We recommend the differential diagnosis of patients suffering from Rt. Hypochondrial pain, fever and eosinophilia. The watercress ingestion was a common factor in patient’s history. PMID:23259859

  1. Sporadic incidence of Fascioliasis detected during Hepatobiliary procedures: A study of 18 patients from Sulaimaniyah governorate

    Directory of Open Access Journals (Sweden)

    Hawramy Tahir Abdullah Hussein

    2012-12-01

    Full Text Available Abstract Background Fascioliasis is an often-neglected zoonotic disease and currently is an emerging infection in Iraq. Fascioliasis has two distinct phases, an acute phase, exhibiting the hepatic migratory stage of the fluke’s life cycle, and a chronic biliary phase manifested with the presence of the parasite in the bile ducts through hepatic tissue. The incidence of Fascioliasis in Sulaimaniyah governorate was unexpected observation. We believe that shedding light on this disease in our locality will increase our physician awareness and experience in early detection, treatment in order to avoid unnecessary surgeries. Findings We retrospectively evaluated this disease in terms of the demographic features, clinical presentations, and managements by reviewing the medical records of 18 patients, who were admitted to the Sulaimani Teaching Hospital and Kurdistan Centre for Gastroenterology and Hepatology. Patients were complained from hepatobiliary and/or upper gastrointestinal symptoms and diagnosed accidentally with Fascioliasis during hepatobiliary surgeries and ERCP by direct visualization of the flukes and stone analysis. Elevated liver enzymes, white blood cells count and eosinophilia were notable laboratory indices. The dilated CBD, gallstones, liver cysts and abscess were found common in radiological images. Fascioliasis diagnosed during conventional surgical CBD exploration and choledochodoudenostomy, open cholecystectomy, surgical drainage of liver abscess, ERCP and during gallstone analysis. Conclusion Fascioliasis is indeed an emerging disease in our locality, but it is often underestimated and ignored. We recommend the differential diagnosis of patients suffering from Rt. Hypochondrial pain, fever and eosinophilia. The watercress ingestion was a common factor in patient’s history.

  2. Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

    DEFF Research Database (Denmark)

    Helle, K.B.; Müller, T.O.; Astrup, Poul

    2014-01-01

    of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64......Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often...... source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given...

  3. An automatic eye detection and tracking technique for stereo video sequences

    Science.gov (United States)

    Paduru, Anirudh; Charalampidis, Dimitrios; Fouts, Brandon; Jovanovich, Kim

    2009-05-01

    Human-computer interfacing (HCI) describes a system or process with which two information processors, namely a human and a computer, attempt to exchange information. Computer-to-human (CtH) information transfer has been relatively effective through visual displays and sound devices. On the other hand, the human-tocomputer (HtC) interfacing avenue has yet to reach its full potential. For instance, the most common HtC communication means are the keyboard and mouse, which are already becoming a bottleneck in the effective transfer of information. The solution to the problem is the development of algorithms that allow the computer to understand human intentions based on their facial expressions, head motion patterns, and speech. In this work, we are investigating the feasibility of a stereo system to effectively determine the head position, including the head rotation angles, based on the detection of eye pupils.

  4. Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares.

    Science.gov (United States)

    Van De Gucht, Tim; Van Weyenberg, Stephanie; Van Nuffel, Annelies; Lauwers, Ludwig; Vangeyte, Jürgen; Saeys, Wouter

    2017-10-08

    Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was €2.57 per % less missed lame cows, €1.65 per % less false alerts, and €12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system's potential adoption rate.

  5. Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows

    NARCIS (Netherlands)

    Schlageter-Tello, Andrés; Hertem, Van Tom; Bokkers, Eddie A.M.; Viazzi, Stefano; Bahr, Claudia; Lokhorst, Kees

    2018-01-01

    The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data

  6. Automatic method detection of artifacts for control of tomographic uniformity on SPECT; Metodo automatico de dteccion de artefactos para el control de la uniformidad tomografica en SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Reynes Llompart, G.; Puchal, R.

    2013-07-01

    The objective of this work is the find an automatic method for the detection and classification of artifacts produced in tomographic uniformity, extracting the characteristics necessary to apply a classification algorithm using pattern recognition techniques. The method has been trained and validated with synthetic images and tested with real images. (Author)

  7. Automatic detection of potentially illegal online sales of elephant ivory via data mining

    Directory of Open Access Journals (Sweden)

    Julio Hernandez-Castro

    2015-07-01

    Full Text Available In this work, we developed an automated system to detect potentially illegal elephant ivory items for sale on eBay. Two law enforcement experts, with specific knowledge of elephant ivory identification, manually classified items on sale in the Antiques section of eBay UK over an 8 week period. This set the “Gold Standard” that we aim to emulate using data-mining. We achieved close to 93% accuracy with less data than the experts, as we relied entirely on metadata, but did not employ item descriptions or associated images, thus proving the potential and generality of our approach. The reported accuracy may be improved with the addition of text mining techniques for the analysis of the item description, and by applying image classification for the detection of Schreger lines, indicative of elephant ivory. However, any solution relying on images or text description could not be employed on other wildlife illegal markets where pictures can be missing or misleading and text absent (e.g., Instagram. In our setting, we gave human experts all available information while only using minimal information for our analysis. Despite this, we succeeded at achieving a very high accuracy. This work is an important first step in speeding up the laborious, tedious and expensive task of expert discovery of illegal trade over the internet. It will also allow for faster reporting to law enforcement and better accountability. We hope this will also contribute to reducing poaching, by making this illegal trade harder and riskier for those involved.

  8. Fault Detection Coverage Quantification of Automatic Test Functions of Digital I and C System in NPPs

    International Nuclear Information System (INIS)

    Choi, Jong Gyun; Lee, Seung Jun; Hur, Seop; Lee, Young Jun; Jang, Seung Cheol

    2011-01-01

    Recently, analog instrument and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital systems for safer and more efficient operations. Digital I and C systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions in spite of the presence of faults. Each fault-tolerant technique has a different inspection period from real-time monitoring to monthly testing. The range covered by each fault-tolerant technique is also different. The digital I and C system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects have not been properly considered yet in most PSA models. Therefore, it is necessary to develop an evaluation method that can describe these features of a digital I and C system. Several issues must be considered in the fault coverage estimation of a digital I and C system, and two of them were handled in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, fault injection experiment was used to obtain the exact relations between faults and multiple barriers of fault-tolerant techniques. This experiment was applied to a bistable processor (BP) of a reactor protection system

  9. A burst-mode photon counting receiver with automatic channel estimation and bit rate detection

    Science.gov (United States)

    Rao, Hemonth G.; DeVoe, Catherine E.; Fletcher, Andrew S.; Gaschits, Igor D.; Hakimi, Farhad; Hamilton, Scott A.; Hardy, Nicholas D.; Ingwersen, John G.; Kaminsky, Richard D.; Moores, John D.; Scheinbart, Marvin S.; Yarnall, Timothy M.

    2016-04-01

    We demonstrate a multi-rate burst-mode photon-counting receiver for undersea communication at data rates up to 10.416 Mb/s over a 30-foot water channel. To the best of our knowledge, this is the first demonstration of burst-mode photon-counting communication. With added attenuation, the maximum link loss is 97.1 dB at λ=517 nm. In clear ocean water, this equates to link distances up to 148 meters. For λ=470 nm, the achievable link distance in clear ocean water is 450 meters. The receiver incorporates soft-decision forward error correction (FEC) based on a product code of an inner LDPC code and an outer BCH code. The FEC supports multiple code rates to achieve error-free performance. We have selected a burst-mode receiver architecture to provide robust performance with respect to unpredictable channel obstructions. The receiver is capable of on-the-fly data rate detection and adapts to changing levels of signal and background light. The receiver updates its phase alignment and channel estimates every 1.6 ms, allowing for rapid changes in water quality as well as motion between transmitter and receiver. We demonstrate on-the-fly rate detection, channel BER within 0.2 dB of theory across all data rates, and error-free performance within 1.82 dB of soft-decision capacity across all tested code rates. All signal processing is done in FPGAs and runs continuously in real time.

  10. A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

    Directory of Open Access Journals (Sweden)

    Marco Fagiani

    2016-08-01

    Full Text Available In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM, Hidden Markov Model (HMM and One-Class Support Vector Machine (OC-SVM, are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds, and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC, reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.

  11. LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons

    Directory of Open Access Journals (Sweden)

    Steinbiss Sascha

    2012-11-01

    Full Text Available Abstract Background Long terminal repeat (LTR retrotransposons are a class of eukaryotic mobile elements characterized by a distinctive sequence similarity-based structure. Hence they are well suited for computational identification. Current software allows for a comprehensive genome-wide de novo detection of such elements. The obvious next step is the classification of newly detected candidates resulting in (super-families. Such a de novo classification approach based on sequence-based clustering of transposon features has been proposed before, resulting in a preliminary assignment of candidates to families as a basis for subsequent manual refinement. However, such a classification workflow is typically split across a heterogeneous set of glue scripts and generic software (for example, spreadsheets, making it tedious for a human expert to inspect, curate and export the putative families produced by the workflow. Results We have developed LTRsift, an interactive graphical software tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations. Its user-friendly interface offers customizable filtering and classification functionality, displaying the putative candidate groups, their members and their internal structure in a hierarchical fashion. To ease manual work, it also supports graphical user interface-driven reassignment, splitting and further annotation of candidates. Export of grouped candidate sets in standard formats is possible. In two case studies, we demonstrate how LTRsift can be employed in the context of a genome-wide LTR retrotransposon survey effort. Conclusions LTRsift is a useful and convenient tool for semi-automated classification of newly detected LTR retrotransposons based on their internal features. Its efficient implementation allows for convenient and seamless filtering and classification in an integrated environment. Developed for life scientists, it is helpful in postprocessing and refining

  12. Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

    Science.gov (United States)

    Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

    2016-05-20

    In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

  13. LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons.

    Science.gov (United States)

    Steinbiss, Sascha; Kastens, Sascha; Kurtz, Stefan

    2012-11-07

    Long terminal repeat (LTR) retrotransposons are a class of eukaryotic mobile elements characterized by a distinctive sequence similarity-based structure. Hence they are well suited for computational identification. Current software allows for a comprehensive genome-wide de novo detection of such elements. The obvious next step is the classification of newly detected candidates resulting in (super-)families. Such a de novo classification approach based on sequence-based clustering of transposon features has been proposed before, resulting in a preliminary assignment of candidates to families as a basis for subsequent manual refinement. However, such a classification workflow is typically split across a heterogeneous set of glue scripts and generic software (for example, spreadsheets), making it tedious for a human expert to inspect, curate and export the putative families produced by the workflow. We have developed LTRsift, an interactive graphical software tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations. Its user-friendly interface offers customizable filtering and classification functionality, displaying the putative candidate groups, their members and their internal structure in a hierarchical fashion. To ease manual work, it also supports graphical user interface-driven reassignment, splitting and further annotation of candidates. Export of grouped candidate sets in standard formats is possible. In two case studies, we demonstrate how LTRsift can be employed in the context of a genome-wide LTR retrotransposon survey effort. LTRsift is a useful and convenient tool for semi-automated classification of newly detected LTR retrotransposons based on their internal features. Its efficient implementation allows for convenient and seamless filtering and classification in an integrated environment. Developed for life scientists, it is helpful in postprocessing and refining the output of software for predicting LTR

  14. Automatic analysis of online image data for law enforcement agencies by concept detection and instance search

    Science.gov (United States)

    de Boer, Maaike H. T.; Bouma, Henri; Kruithof, Maarten C.; ter Haar, Frank B.; Fischer, Noëlle M.; Hagendoorn, Laurens K.; Joosten, Bart; Raaijmakers, Stephan

    2017-10-01

    The information available on-line and off-line, from open as well as from private sources, is growing at an exponential rate and places an increasing demand on the limited resources of Law Enforcement Agencies (LEAs). The absence of appropriate tools and techniques to collect, process, and analyze the volumes of complex and heterogeneous data has created a severe information overload. If a solution is not found, the impact on law enforcement will be dramatic, e.g. because important evidence is missed or the investigation time is too long. Furthermore, there is an uneven level of capabilities to deal with the large volumes of complex and heterogeneous data that come from multiple open and private sources at national level across the EU, which hinders cooperation and information sharing. Consequently, there is a pertinent need to develop tools, systems and processes which expedite online investigations. In this paper, we describe a suite of analysis tools to identify and localize generic concepts, instances of objects and logos in images, which constitutes a significant portion of everyday law enforcement data. We describe how incremental learning based on only a few examples and large-scale indexing are addressed in both concept detection and instance search. Our search technology allows querying of the database by visual examples and by keywords. Our tools are packaged in a Docker container to guarantee easy deployment on a system and our tools exploit possibilities provided by open source toolboxes, contributing to the technical autonomy of LEAs.

  15. Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

    Science.gov (United States)

    Fergus, Paul; Hignett, David; Hussain, Abir; Al-Jumeily, Dhiya; Abdel-Aziz, Khaled

    2015-01-01

    The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

  16. The MicroActive project: automatic detection of disease-related molecular cell activity

    Science.gov (United States)

    Furuberg, Liv; Mielnik, Michal; Johansen, Ib-Rune; Voitel, Jörg; Gulliksen, Anja; Solli, Lars; Karlsen, Frank; Bayer, Tobias; Schönfeld, Friedhelm; Drese, Klaus; Keegan, Helen; Martin, Cara; O'Leary, John; Riegger, Lutz; Koltay, Peter

    2007-05-01

    The aim of the MicroActive project is to develop an instrument for molecular diagnostics. The instrument will first be tested for patient screening for a group of viruses causing cervical cancer. Two disposable polymer chips with reagents stored on-chip will be inserted into the instrument for each patient sample. The first chip performs sample preparation of the epithelial cervical cells while mRNA amplification and fluorescent detection takes place in the second chip. More than 10 different virus markers will be analysed in one chip. We report results on sub-functions of the amplification chip. The sample is split into smaller droplets, and the droplets move in parallel channels containing different dried reagents for the different analyses. We report experimental results on parallel droplet movement control using one external pump only, combined with hydrophobic valves. Valve burst pressures are controlled by geometry. We show droplet control using valves with burst pressures between 800 and 4500 Pa. We also monitored the re-hydration times for two necessary dried reagents. After sample insertion, uniform concentration of the reagents in the droplet was reached after respectively 60 s and 10 min. These times are acceptable for successful amplification. Finally we have shown positive amplification of HPV type 16 using dried enzymes stored in micro chambers.

  17. Machine learning approach to automatic exudate detection in retinal images from diabetic patients

    Science.gov (United States)

    Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

    2010-01-01

    Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform