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Sample records for supervised automated algorithm

  1. Validation of Supervised Automated Algorithm for Fast Quantitative Evaluation of Organ Motion on Magnetic Resonance Imaging

    SciTech Connect

    Prakash, Varuna; Stainsby, Jeffrey A.; Satkunasingham, Janakan; Craig, Tim; Catton, Charles; Chan, Philip; Dawson, Laura; Hensel, Jennifer; Jaffray, David; Milosevic, Michael; Nichol, Alan; Sussman, Marshall S.; Lockwood, Gina; Menard, Cynthia

    2008-07-15

    Purpose: To validate a correlation coefficient template-matching algorithm applied to the supervised automated quantification of abdominal-pelvic organ motion captured on time-resolved magnetic resonance imaging. Methods and Materials: Magnetic resonance images of 21 patients across four anatomic sites were analyzed. Representative anatomic points of interest were chosen as surrogates for organ motion. The point of interest displacements across each image frame relative to baseline were quantified manually and through the use of a template-matching software tool, termed 'Motiontrack.' Automated and manually acquired displacement measures, as well as the standard deviation of intrafraction motion, were compared for each image frame and for each patient. Results: Discrepancies between the automated and manual displacements of {>=}2 mm were uncommon, ranging in frequency of 0-9.7% (liver and prostate, respectively). The standard deviations of intrafraction motion measured with each method correlated highly (r = 0.99). Considerable interpatient variability in organ motion was demonstrated by a wide range of standard deviations in the liver (1.4-7.5 mm), uterus (1.1-8.4 mm), and prostate gland (0.8-2.7 mm). The automated algorithm performed successfully in all patients but 1 and substantially improved efficiency compared with manual quantification techniques (5 min vs. 60-90 min). Conclusion: Supervised automated quantification of organ motion captured on magnetic resonance imaging using a correlation coefficient template-matching algorithm was efficient, accurate, and may play an important role in off-line adaptive approaches to intrafraction motion management.

  2. Automated Classification and Correlation of Drill Cores using High-Resolution Hyperspectral Images and Supervised Pattern Classification Algorithms. Applications to Paleoseismology

    NASA Astrophysics Data System (ADS)

    Ragona, D. E.; Minster, B.; Rockwell, T.; Jasso, H.

    2006-12-01

    The standard methodology to describe, classify and correlate geologic materials in the field or lab rely on physical inspection of samples, sometimes with the assistance of conventional analytical techniques (e. g. XRD, microscopy, particle size analysis). This is commonly both time-consuming and inherently subjective. Many geological materials share identical visible properties (e.g. fine grained materials, alteration minerals) and therefore cannot be mapped using the human eye alone. Recent investigations have shown that ground- based hyperspectral imaging provides an effective method to study and digitally store stratigraphic and structural data from cores or field exposures. Neural networks and Naive Bayesian classifiers supply a variety of well-established techniques towards pattern recognition, especially for data examples with high- dimensionality input-outputs. In this poster, we present a new methodology for automatic mapping of sedimentary stratigraphy in the lab (drill cores, samples) or the field (outcrops, exposures) using short wave infrared (SWIR) hyperspectral images and these two supervised classification algorithms. High-spatial/spectral resolution data from large sediment samples (drill cores) from a paleoseismic excavation site were collected using a portable hyperspectral scanner with 245 continuous channels measured across the 960 to 2404 nm spectral range. The data were corrected for geometric and radiometric distortions and pre-processed to obtain reflectance at each pixel of the images. We built an example set using hundreds of reflectance spectra collected from the sediment core images. The examples were grouped into eight classes corresponding to materials found in the samples. We constructed two additional example sets by computing the 2-norm normalization, the derivative of the smoothed original reflectance examples. Each example set was divided into four subsets: training, training test, verification and validation. A multi

  3. Random forest automated supervised classification of Hipparcos periodic variable stars

    NASA Astrophysics Data System (ADS)

    Dubath, P.; Rimoldini, L.; Süveges, M.; Blomme, J.; López, M.; Sarro, L. M.; De Ridder, J.; Cuypers, J.; Guy, L.; Lecoeur, I.; Nienartowicz, K.; Jan, A.; Beck, M.; Mowlavi, N.; De Cat, P.; Lebzelter, T.; Eyer, L.

    2011-07-01

    We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V-I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency. Random forests and a multi-stage scheme involving Bayesian network and Gaussian mixture methods lead to statistically equivalent results. In standard 10-fold cross-validation (CV) experiments, the rate of correct classification is between 90 and 100 per cent, depending on the variability type. The main mis-classification cases, up to a rate of about 10 per cent, arise due to confusion between SPB and ACV blue variables and between eclipsing binaries, ellipsoidal variables and other variability types. Our training set and the predicted types for the other Hipparcos periodic stars are available online.

  4. Algorithms Could Automate Cancer Diagnosis

    NASA Technical Reports Server (NTRS)

    Baky, A. A.; Winkler, D. G.

    1982-01-01

    Five new algorithms are a complete statistical procedure for quantifying cell abnormalities from digitized images. Procedure could be basis for automated detection and diagnosis of cancer. Objective of procedure is to assign each cell an atypia status index (ASI), which quantifies level of abnormality. It is possible that ASI values will be accurate and economical enough to allow diagnoses to be made quickly and accurately by computer processing of laboratory specimens extracted from patients.

  5. Algorithms for automated DNA assembly

    PubMed Central

    Densmore, Douglas; Hsiau, Timothy H.-C.; Kittleson, Joshua T.; DeLoache, Will; Batten, Christopher; Anderson, J. Christopher

    2010-01-01

    Generating a defined set of genetic constructs within a large combinatorial space provides a powerful method for engineering novel biological functions. However, the process of assembling more than a few specific DNA sequences can be costly, time consuming and error prone. Even if a correct theoretical construction scheme is developed manually, it is likely to be suboptimal by any number of cost metrics. Modular, robust and formal approaches are needed for exploring these vast design spaces. By automating the design of DNA fabrication schemes using computational algorithms, we can eliminate human error while reducing redundant operations, thus minimizing the time and cost required for conducting biological engineering experiments. Here, we provide algorithms that optimize the simultaneous assembly of a collection of related DNA sequences. We compare our algorithms to an exhaustive search on a small synthetic dataset and our results show that our algorithms can quickly find an optimal solution. Comparison with random search approaches on two real-world datasets show that our algorithms can also quickly find lower-cost solutions for large datasets. PMID:20335162

  6. POSE Algorithms for Automated Docking

    NASA Technical Reports Server (NTRS)

    Heaton, Andrew F.; Howard, Richard T.

    2011-01-01

    POSE (relative position and attitude) can be computed in many different ways. Given a sensor that measures bearing to a finite number of spots corresponding to known features (such as a target) of a spacecraft, a number of different algorithms can be used to compute the POSE. NASA has sponsored the development of a flash LIDAR proximity sensor called the Vision Navigation Sensor (VNS) for use by the Orion capsule in future docking missions. This sensor generates data that can be used by a variety of algorithms to compute POSE solutions inside of 15 meters, including at the critical docking range of approximately 1-2 meters. Previously NASA participated in a DARPA program called Orbital Express that achieved the first automated docking for the American space program. During this mission a large set of high quality mated sensor data was obtained at what is essentially the docking distance. This data set is perhaps the most accurate truth data in existence for docking proximity sensors in orbit. In this paper, the flight data from Orbital Express is used to test POSE algorithms at 1.22 meters range. Two different POSE algorithms are tested for two different Fields-of-View (FOVs) and two different pixel noise levels. The results of the analysis are used to predict future performance of the POSE algorithms with VNS data.

  7. Automated training for algorithms that learn from genomic data.

    PubMed

    Cilingir, Gokcen; Broschat, Shira L

    2015-01-01

    Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable. PMID:25695053

  8. Benchmarking protein classification algorithms via supervised cross-validation.

    PubMed

    Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

    2008-04-24

    Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and

  9. Supervised and unsupervised discretization methods for evolutionary algorithms

    SciTech Connect

    Cantu-Paz, E

    2001-01-24

    This paper introduces simple model-building evolutionary algorithms (EAs) that operate on continuous domains. The algorithms are based on supervised and unsupervised discretization methods that have been used as preprocessing steps in machine learning. The basic idea is to discretize the continuous variables and use the discretization as a simple model of the solutions under consideration. The model is then used to generate new solutions directly, instead of using the usual operators based on sexual recombination and mutation. The algorithms presented here have fewer parameters than traditional and other model-building EAs. They expect that the proposed algorithms that use multivariate models scale up better to the dimensionality of the problem than existing EAs.

  10. Automated supervised classification of variable stars. I. Methodology

    NASA Astrophysics Data System (ADS)

    Debosscher, J.; Sarro, L. M.; Aerts, C.; Cuypers, J.; Vandenbussche, B.; Garrido, R.; Solano, E.

    2007-12-01

    Context: The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips. Aims: Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automatedsupervised classification” of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes. Methods: For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers. Results: We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions. Conclusions: The derived classifiers' performances are so good in terms of success rate and computational speed that we will evaluate them in practice from the application of our methodology to a large subset of variable stars in the OGLE database and from comparison of the results with published OGLE variable star classifications based on human intervention. These

  11. Biologically supervised hierarchical clustering algorithms for gene expression data.

    PubMed

    Boratyn, Grzegorz M; Datta, Susmita; Datta, Somnath

    2006-01-01

    Cluster analysis has become a standard part of gene expression analysis. In this paper, we propose a novel semi-supervised approach that offers the same flexibility as that of a hierarchical clustering. Yet it utilizes, along with the experimental gene expression data, common biological information about different genes that is being complied at various public, Web accessible databases. We argue that such an approach is inherently superior than the standard unsupervised approach of grouping genes based on expression data alone. It is shown that our biologically supervised methods produce better clustering results than the corresponding unsupervised methods as judged by the distance from the model temporal profiles. R-codes of the clustering algorithm are available from the authors upon request. PMID:17947147

  12. ALFA: Automated Line Fitting Algorithm

    NASA Astrophysics Data System (ADS)

    Wesson, R.

    2015-12-01

    ALFA fits emission line spectra of arbitrary wavelength coverage and resolution, fully automatically. It uses a catalog of lines which may be present to construct synthetic spectra, the parameters of which are then optimized by means of a genetic algorithm. Uncertainties are estimated using the noise structure of the residuals. An emission line spectrum containing several hundred lines can be fitted in a few seconds using a single processor of a typical contemporary desktop or laptop PC. Data cubes in FITS format can be analysed using multiple processors, and an analysis of tens of thousands of deep spectra obtained with instruments such as MUSE will take a few hours.

  13. Algorithms to Automate LCLS Undulator Tuning

    SciTech Connect

    Wolf, Zachary

    2010-12-03

    Automation of the LCLS undulator tuning offers many advantages to the project. Automation can make a substantial reduction in the amount of time the tuning takes. Undulator tuning is fairly complex and automation can make the final tuning less dependent on the skill of the operator. Also, algorithms are fixed and can be scrutinized and reviewed, as opposed to an individual doing the tuning by hand. This note presents algorithms implemented in a computer program written for LCLS undulator tuning. The LCLS undulators must meet the following specifications. The maximum trajectory walkoff must be less than 5 {micro}m over 10 m. The first field integral must be below 40 x 10{sup -6} Tm. The second field integral must be below 50 x 10{sup -6} Tm{sup 2}. The phase error between the electron motion and the radiation field must be less than 10 degrees in an undulator. The K parameter must have the value of 3.5000 {+-} 0.0005. The phase matching from the break regions into the undulator must be accurate to better than 10 degrees. A phase change of 113 x 2{pi} must take place over a distance of 3.656 m centered on the undulator. Achieving these requirements is the goal of the tuning process. Most of the tuning is done with Hall probe measurements. The field integrals are checked using long coil measurements. An analysis program written in Matlab takes the Hall probe measurements and computes the trajectories, phase errors, K value, etc. The analysis program and its calculation techniques were described in a previous note. In this note, a second Matlab program containing tuning algorithms is described. The algorithms to determine the required number and placement of the shims are discussed in detail. This note describes the operation of a computer program which was written to automate LCLS undulator tuning. The algorithms used to compute the shim sizes and locations are discussed.

  14. Experiments on Supervised Learning Algorithms for Text Categorization

    NASA Technical Reports Server (NTRS)

    Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

    2005-01-01

    Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

  15. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    PubMed Central

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  16. THE QUASIPERIODIC AUTOMATED TRANSIT SEARCH ALGORITHM

    SciTech Connect

    Carter, Joshua A.; Agol, Eric

    2013-03-10

    We present a new algorithm for detecting transiting extrasolar planets in time-series photometry. The Quasiperiodic Automated Transit Search (QATS) algorithm relaxes the usual assumption of strictly periodic transits by permitting a variable, but bounded, interval between successive transits. We show that this method is capable of detecting transiting planets with significant transit timing variations without any loss of significance-{sup s}mearing{sup -}as would be incurred with traditional algorithms; however, this is at the cost of a slightly increased stochastic background. The approximate times of transit are standard products of the QATS search. Despite the increased flexibility, we show that QATS has a run-time complexity that is comparable to traditional search codes and is comparably easy to implement. QATS is applicable to data having a nearly uninterrupted, uniform cadence and is therefore well suited to the modern class of space-based transit searches (e.g., Kepler, CoRoT). Applications of QATS include transiting planets in dynamically active multi-planet systems and transiting planets in stellar binary systems.

  17. Automated Antenna Design with Evolutionary Algorithms

    NASA Technical Reports Server (NTRS)

    Linden, Derek; Hornby, Greg; Lohn, Jason; Globus, Al; Krishunkumor, K.

    2006-01-01

    Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to

  18. Automated DNA Base Pair Calling Algorithm

    Energy Science and Technology Software Center (ESTSC)

    1999-07-07

    The procedure solves the problem of calling the DNA base pair sequence from two channel electropherogram separations in an automated fashion. The core of the program involves a peak picking algorithm based upon first, second, and third derivative spectra for each electropherogram channel, signal levels as a function of time, peak spacing, base pair signal to noise sequence patterns, frequency vs ratio of the two channel histograms, and confidence levels generated during the run. Themore » ratios of the two channels at peak centers can be used to accurately and reproducibly determine the base pair sequence. A further enhancement is a novel Gaussian deconvolution used to determine the peak heights used in generating the ratio.« less

  19. ALFA: an automated line fitting algorithm

    NASA Astrophysics Data System (ADS)

    Wesson, R.

    2016-03-01

    I present the automated line fitting algorithm, ALFA, a new code which can fit emission line spectra of arbitrary wavelength coverage and resolution, fully automatically. In contrast to traditional emission line fitting methods which require the identification of spectral features suspected to be emission lines, ALFA instead uses a list of lines which are expected to be present to construct a synthetic spectrum. The parameters used to construct the synthetic spectrum are optimized by means of a genetic algorithm. Uncertainties are estimated using the noise structure of the residuals. An emission line spectrum containing several hundred lines can be fitted in a few seconds using a single processor of a typical contemporary desktop or laptop PC. I show that the results are in excellent agreement with those measured manually for a number of spectra. Where discrepancies exist, the manually measured fluxes are found to be less accurate than those returned by ALFA. Together with the code NEAT, ALFA provides a powerful way to rapidly extract physical information from observations, an increasingly vital function in the era of highly multiplexed spectroscopy. The two codes can deliver a reliable and comprehensive analysis of very large data sets in a few hours with little or no user interaction.

  20. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

    NASA Astrophysics Data System (ADS)

    Luo, Wei-Ping; Li, Hong-Qi; Shi, Ning

    2016-06-01

    At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

  1. Automated segment matching algorithm-theory, test, and evaluation

    NASA Technical Reports Server (NTRS)

    Kalcic, M. T. (Principal Investigator)

    1982-01-01

    Results to automate the U.S. Department of Agriculture's process of segment shifting and obtain results within one-half pixel accuracy are presented. Given an initial registration, the digitized segment is shifted until a more precise fit to the LANDSAT data is found. The algorithm automates the shifting process and performs certain tests for matching and accepting the computed shift numbers. Results indicate the algorithm can obtain results within one-half pixel accuracy.

  2. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

    PubMed Central

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

  3. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    PubMed

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

  4. An Effective Intrusion Detection Algorithm Based on Improved Semi-supervised Fuzzy Clustering

    NASA Astrophysics Data System (ADS)

    Li, Xueyong; Zhang, Baojian; Sun, Jiaxia; Yan, Shitao

    An algorithm for intrusion detection based on improved evolutionary semi- supervised fuzzy clustering is proposed which is suited for situation that gaining labeled data is more difficulty than unlabeled data in intrusion detection systems. The algorithm requires a small number of labeled data only and a large number of unlabeled data and class labels information provided by labeled data is used to guide the evolution process of each fuzzy partition on unlabeled data, which plays the role of chromosome. This algorithm can deal with fuzzy label, uneasily plunges locally optima and is suited to implement on parallel architecture. Experiments show that the algorithm can improve classification accuracy and has high detection efficiency.

  5. Ordering and finding the best of K > 2 supervised learning algorithms.

    PubMed

    Yildiz, Olcay Taner; Alpaydin, Ethem

    2006-03-01

    Given a data set and a number of supervised learning algorithms, we would like to find the algorithm with the smallest expected error. Existing pairwise tests allow a comparison of two algorithms only; range tests and ANOVA check whether multiple algorithms have the same expected error and cannot be used for finding the smallest. We propose a methodology, the MultiTest algorithm, whereby we order supervised learning algorithms taking into account 1) the result of pairwise statistical tests on expected error (what the data tells us), and 2) our prior preferences, e.g., due to complexity. We define the problem in graph-theoretic terms and propose an algorithm to find the "best" learning algorithm in terms of these two criteria, or in the more general case, order learning algorithms in terms of their "goodness." Simulation results using five classification algorithms on 30 data sets indicate the utility of the method. Our proposed method can be generalized to regression and other loss functions by using a suitable pairwise test. PMID:16526425

  6. Novel maximum-margin training algorithms for supervised neural networks.

    PubMed

    Ludwig, Oswaldo; Nunes, Urbano

    2010-06-01

    This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

  7. Automating parallel implementation of neural learning algorithms.

    PubMed

    Rana, O F

    2000-06-01

    Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries

  8. Automated Vectorization of Decision-Based Algorithms

    NASA Technical Reports Server (NTRS)

    James, Mark

    2006-01-01

    Virtually all existing vectorization algorithms are designed to only analyze the numeric properties of an algorithm and distribute those elements across multiple processors. This advances the state of the practice because it is the only known system, at the time of this reporting, that takes high-level statements and analyzes them for their decision properties and converts them to a form that allows them to automatically be executed in parallel. The software takes a high-level source program that describes a complex decision- based condition and rewrites it as a disjunctive set of component Boolean relations that can then be executed in parallel. This is important because parallel architectures are becoming more commonplace in conventional systems and they have always been present in NASA flight systems. This technology allows one to take existing condition-based code and automatically vectorize it so it naturally decomposes across parallel architectures.

  9. An automated personalised intervention algorithm for remote patient monitoring.

    PubMed

    Fursse, Joanna; Clarke, Malcolm; Jones, Russell; Khemka, Sneh; Findlay, Genevieve

    2008-01-01

    An automated personalised intervention algorithm was developed to determine when and if patients with chronic disease in a remote monitoring programme required intervention for management of their condition. The effectiveness of the algorithm has so far been evaluated on 29 patients. It was found to be particularly effective in monitoring newly diagnosed patients, patients requiring a change in medication as well as highlighting those that were not conforming to their medication. Our approach indicates that RPM used with the intervention algorithm and a clinical protocol can be effective in a primary care setting for targeting those patients that would most benefit from monitoring. PMID:18487728

  10. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

    SciTech Connect

    Adal, Kedir M.; Sidebe, Desire; Ali, Sharib; Chaum, Edward; Karnowski, Thomas Paul; Meriaudeau, Fabrice

    2014-01-07

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  11. Comparative Study of Algorithms for Automated Generalization of Linear Objects

    NASA Astrophysics Data System (ADS)

    Azimjon, S.; Gupta, P. K.; Sukhmani, R. S. G. S.

    2014-11-01

    Automated generalization, rooted from conventional cartography, has become an increasing concern in both geographic information system (GIS) and mapping fields. All geographic phenomenon and the processes are bound to the scale, as it is impossible for human being to observe the Earth and the processes in it without decreasing its scale. To get optimal results, cartographers and map-making agencies develop set of rules and constraints, however these rules are under consideration and topic for many researches up until recent days. Reducing map generating time and giving objectivity is possible by developing automated map generalization algorithms (McMaster and Shea, 1988). Modification of the scale traditionally is a manual process, which requires knowledge of the expert cartographer, and it depends on the experience of the user, which makes the process very subjective as every user may generate different map with same requirements. However, automating generalization based on the cartographic rules and constrains can give consistent result. Also, developing automated system for map generation is the demand of this rapid changing world. The research that we have conveyed considers only generalization of the roads, as it is one of the indispensable parts of a map. Dehradun city, Uttarakhand state of India was selected as a study area. The study carried out comparative study of the generalization software sets, operations and algorithms available currently, also considers advantages and drawbacks of the existing software used worldwide. Research concludes with the development of road network generalization tool and with the final generalized road map of the study area, which explores the use of open source python programming language and attempts to compare different road network generalization algorithms. Thus, the paper discusses the alternative solutions for automated generalization of linear objects using GIS-technologies. Research made on automated of road network

  12. New Algorithms For Automated Symmetry Recognition

    NASA Astrophysics Data System (ADS)

    Paul, Jody; Kilgore, Tammy Elaine; Klinger, Allen

    1988-02-01

    In this paper we present new methods for computer-based symmetry identification that combine elements of group theory and pattern recognition. Detection of symmetry has diverse applications including: the reduction of image data to a manageable subset with minimal information loss, the interpretation of sensor data,1 such as the x-ray diffraction patterns which sparked the recent discovery of a new "quasicrystal" phase of solid matter,2 and music analysis and composition.3,4,5 Our algorithms are expressed as parallel operations on the data using the matrix representation and manipulation features of the APL programming language. We demonstrate the operation of programs that characterize symmetric and nearly-symmetric patterns by determining the degree of invariance with respect to candidate symmetry transformations. The results are completely general; they may be applied to pattern data of arbitrary dimension and from any source.

  13. An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

    PubMed

    Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

    2016-10-01

    Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. PMID:27480748

  14. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

    PubMed Central

    Gao, Fei; Mei, Jingyuan; Sun, Jinping; Wang, Jun; Yang, Erfu; Hussain, Amir

    2015-01-01

    For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment. PMID:26275294

  15. Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

    PubMed Central

    Liu, Li; Gao, Simon S.; Bailey, Steven T.; Huang, David; Li, Dengwang; Jia, Yali

    2015-01-01

    Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area. PMID:26417524

  16. The Marshall Automated Wind Algorithm for Geostationary Satellite Wind Applications

    NASA Technical Reports Server (NTRS)

    Jedlovec, Gary J.; Atkinson, Robert J.

    1998-01-01

    The Marshall Automated Wind (MAW) algorithm was developed over a decade ago in support of specialized studies of mesoscale meteorology. In recent years, the algorithm has been generalized to address global climate issues and other specific objectives related to NASA missions. The MAW algorithm uses a tracking scheme which minimizes image brightness temperature differences in a sequence of satellite images to determine feature displacement (winds). With the appropriate methodology accurate satellite derived winds can be obtained from visible, infrared, and water vapor imagery. Typical errors are less than 4 m/s but depend on the quality and control constraints used in post-processing. Key to this success is the judicious use of template size and search area used for tracking, image resolution and time sampling, and selection of appropriate statistical constraints which may vary with image type and desired application. The conference paper and subsequent poster will provide details of the technique and examples of its application.

  17. A recommendation algorithm for automating corollary order generation.

    PubMed

    Klann, Jeffrey; Schadow, Gunther; McCoy, J M

    2009-01-01

    Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards. PMID:20351875

  18. A model for testing centerfinding algorithms for automated optical navigation

    NASA Technical Reports Server (NTRS)

    Griffin, M. D.; Breckenridge, W. G.

    1979-01-01

    An efficient software simulation of the imaging process for optical navigation is presented, illustrating results using simple examples. The problems of image definition and optical system modeling, including ideal image containing features and realistic models of optical filtering performed by the entire camera system, are examined. A digital signal processing technique is applied to the problem of developing methods of automated optical navigation and the subsequent mathematical formulation is presented. Specific objectives such as an analysis of the effects of camera defocusing on centerfinding of planar targets, addition of noise filtering to the algorithm, and implementation of multiple frame capability were investigated.

  19. A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance.

    PubMed

    Ye, Cang; Yung, N C; Wang, Danwei

    2003-01-01

    Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system. PMID:18238153

  20. Algorithm of the automated choice of points of the acupuncture for EHF-therapy

    NASA Astrophysics Data System (ADS)

    Lyapina, E. P.; Chesnokov, I. A.; Anisimov, Ya. E.; Bushuev, N. A.; Murashov, E. P.; Eliseev, Yu. Yu.; Syuzanna, H.

    2007-05-01

    Offered algorithm of the automated choice of points of the acupuncture for EHF-therapy. The recipe formed by algorithm of an automated choice of points for acupunctural actions has a recommendational character. Clinical investigations showed that application of the developed algorithm in EHF-therapy allows to normalize energetic state of the meridians and to effectively solve many problems of an organism functioning.

  1. Automated eigensystem realisation algorithm for operational modal analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Guowen; Ma, Jinghua; Chen, Zhuo; Wang, Ruirong

    2014-07-01

    The eigensystem realisation algorithm (ERA) is one of the most popular methods in civil engineering applications for estimating modal parameters. Three issues have been addressed in the paper: spurious mode elimination, estimating the energy relationship between different modes, and automatic analysis of the stabilisation diagram. On spurious mode elimination, a new criterion, modal similarity index (MSI) is proposed to measure the reliability of the modes obtained by ERA. On estimating the energy relationship between different modes, the mode energy level (MEL) was introduced to measure the energy contribution of each mode, which can be used to indicate the dominant mode. On automatic analysis of the stabilisation diagram, an automation of the mode selection process based on a hierarchical clustering algorithm was developed. An experimental example of the parameter estimation for the Chaotianmen bridge model in Chongqing, China, is presented to demonstrate the efficacy of the proposed method.

  2. An automated intensity-weighted brachytherapy seed localization algorithm

    SciTech Connect

    Whitehead, Gregory; Chang Zheng; Ji, Jim

    2008-03-15

    Brachytherapy has proven to be an effective treatment for various forms of cancer, whereby radioactive material is inserted directly into the body to maximize dosage to malignant tumors while preserving healthy tissue. In order to validate the preoperative or intraoperative dosimetric model, a postimplant evaluation procedure is needed to ensure that the locations of the implanted seeds are consistent with the planning stage. Moreover, development of an automated algorithm for seed detection and localization is necessary to expedite the postimplant evaluation process and reduce human error. Most previously reported algorithms have performed binary transforms on images before attempting to localize seeds. Furthermore, traditional approaches based upon three-dimensional seed shape parameterization and matching require high resolution imaging. The authors propose a new computationally efficient algorithm for automatic seed localization for full three-dimensional, low-resolution data sets that directly applies voxel intensity to the estimation of both seed centroid location and angular seed orientation. Computer simulations, phantom studies, and in vivo computed tomography prostate seed imaging results show that the proposed algorithm can produce reliable results even for low-resolution images.

  3. An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies

    PubMed Central

    Abdi, Asad; Idris, Norisma; Alguliyev, Rasim M.; Aliguliyev, Ramiz M.

    2016-01-01

    Background Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively. Design/Results This paper aims to propose an algorithm based on the combination of semantic relations between words and their syntactic composition to identify summarizing strategies employed by students in summary writing. An innovative aspect of our algorithm lies in its ability to identify summarizing strategies at the syntactic and semantic levels. The efficiency of the algorithm is measured in terms of Precision, Recall and F-measure. We then implemented the algorithm for the automated summarization assessment system that can be used to identify the summarizing strategies used by students in summary writing. PMID:26735139

  4. Geostationary Fire Detection with the Wildfire Automated Biomass Burning Algorithm

    NASA Astrophysics Data System (ADS)

    Hoffman, J.; Schmidt, C. C.; Brunner, J. C.; Prins, E. M.

    2010-12-01

    The Wild Fire Automated Biomass Burning Algorithm (WF_ABBA), developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS), has a long legacy of operational wildfire detection and characterization. In recent years, applications of geostationary fire detection and characterization data have been expanding. Fires are detected with a contextual algorithm and when the fires meet certain conditions the instantaneous fire size, temperature, and radiative power are calculated and provided in user products. The WF_ABBA has been applied to data from Geostationary Operational Environmental Satellite (GOES)-8 through 15, Meteosat-8/-9, and Multifunction Transport Satellite (MTSAT)-1R/-2. WF_ABBA is also being developed for the upcoming platforms like GOES-R Advanced Baseline Imager (ABI) and other geostationary satellites. Development of the WF_ABBA for GOES-R ABI has focused on adapting the legacy algorithm to the new satellite system, enhancing its capabilities to take advantage of the improvements available from ABI, and addressing user needs. By its nature as a subpixel feature, observation of fire is extraordinarily sensitive to the characteristics of the sensor and this has been a fundamental part of the GOES-R WF_ABBA development work.

  5. Integrating GIS and genetic algorithms for automating land partitioning

    NASA Astrophysics Data System (ADS)

    Demetriou, Demetris; See, Linda; Stillwell, John

    2014-08-01

    Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.

  6. A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs

    NASA Astrophysics Data System (ADS)

    de Lannoy, G.; de Decker, A.; Verleysen, M.

    The wavelet transform is a widely used pre-filtering step for subsequent R spike detection by thresholding of the coefficients. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptativity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record's characteristics. The selected scales are then used for the decomposition of the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and average positive predictivity rate of 99.7%.

  7. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Acquaviva, Viviana

    2016-02-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3, respectively. We find that ensemble methods, such as random forests of trees and extremely randomized trees and support vector machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which |Ztrue - Zpred| > 0.2 dex) is 2.2 and 3.9 per cent, respectively and the RMSE decreases to 0.068 and 0.069 if those objects are excluded. Because of the ability of these algorithms to capture complex relationships between data and target, our technique performs better than previously proposed methods that sought to fit metallicity using an analytic fitting formula, and has 3× more constraining power than SED fitting-based methods. Additionally, this method is extremely forgiving of contamination in the training set, and can be used with very satisfactory results for sample sizes of a few hundred objects. We distribute all the routines to reproduce our results and apply them to other data sets.

  8. Automated classification of female facial beauty by image analysis and supervised learning

    NASA Astrophysics Data System (ADS)

    Gunes, Hatice; Piccardi, Massimo; Jan, Tony

    2004-01-01

    The fact that perception of facial beauty may be a universal concept has long been debated amongst psychologists and anthropologists. In this paper, we performed experiments to evaluate the extent of beauty universality by asking a number of diverse human referees to grade a same collection of female facial images. Results obtained show that the different individuals gave similar votes, thus well supporting the concept of beauty universality. We then trained an automated classifier using the human votes as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry and plastic surgery.

  9. Visualizing Global Wildfire Automated Biomass Burning Algorithm Data

    NASA Astrophysics Data System (ADS)

    Schmidt, C. C.; Hoffman, J.; Prins, E. M.

    2013-12-01

    The Wildfire Automated Biomass Burning Algorithm (WFABBA) produces fire detection and characterization from a global constellation of geostationary satellites on a realtime basis. Presentation of this data in a timely and meaningful way has been a challenge, but as hardware and software have advanced and web tools have evolved, new options have rapidly arisen. The WFABBA team at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the Space Science Engineering Center (SSEC) have begun implementation of a web-based framework that allows a user to visualize current and archived fire data from NOAA's Geostationary Operational Environmental Satellite (GOES), EUMETSAT's Meteosat Second Generation (MSG), JMA's Multifunction Transport Satellite (MTSAT), and KMA's COMS series of satellites. User group needs vary from simple examination of the most recent data to multi-hour composites to animations, as well as saving datasets for further review. In order to maximize the usefulness of the data, a user-friendly and scaleable interface has been under development that will, when complete, allow access to approximately 18 years of WFABBA data, as well as the data produced in real-time. Implemented, planned, and potential additional features will be examined.

  10. An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

    2012-01-01

    An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  12. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    NASA Technical Reports Server (NTRS)

    Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris

    2000-01-01

    Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency

  13. The GOES Wildfire Automated Biomass Burning Algorithm Processing System

    NASA Astrophysics Data System (ADS)

    Schmidt, C. C.; Prins, E. M.; Feltz, J.

    2002-05-01

    The need to systematically generate reliable diurnal information on biomass burning in near real-time led to the development of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) processing system at the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS). This presentation will include an overview of the WF_ABBA processing system and applications in various biomes of the Western Hemisphere. The WF_ABBA produces fire products from the Geostationary Operational Environmental Satellites (GOES) on a half-hourly basis for all land surfaces in view of GOES-8 and North America for GOES-10. Generally available within one hour of the nominal image time, the WF_ABBA provides information on fire location, fire classification flags, and estimates of fire sizes and temperatures. Fire products including data files and composite imagery are made available to the user community via anonymous ftp and the World Wide Web. WF_ABBA composite images are generated using a modified alpha-blending technique that merges GOES visible and infrared observations of cloud cover with the WF_ABBA fire product and a land cover characterization database derived from 1-km Advanced Very High Resolution Radiometer (AVHRR) data. Animations of the resulting product enable users to monitor diurnal changes in fire activity along with information describing the land characteristics and variations in cloud cover. The high temporal resolution, large areal coverage, ability to process archived GOES data, and high reliability available from the WF_ABBA processing system allow for fire monitoring and dissemination of data products to the user community on an unprecedented scale. Users include climate change research scientists, the aerosol and trace gas transport modeling community, government agencies, resource managers, fire managers, international policy and decision makers, and the general public.

  14. Evaluation of an automated single-channel sleep staging algorithm

    PubMed Central

    Wang, Ying; Loparo, Kenneth A; Kelly, Monica R; Kaplan, Richard F

    2015-01-01

    Background We previously published the performance evaluation of an automated electroencephalography (EEG)-based single-channel sleep–wake detection algorithm called Z-ALG used by the Zmachine® sleep monitoring system. The objective of this paper is to evaluate the performance of a new algorithm called Z-PLUS, which further differentiates sleep as detected by Z-ALG into Light Sleep, Deep Sleep, and Rapid Eye Movement (REM) Sleep, against laboratory polysomnography (PSG) using a consensus of expert visual scorers. Methods Single night, in-lab PSG recordings from 99 subjects (52F/47M, 18–60 years, median age 32.7 years), including both normal sleepers and those reporting a variety of sleep complaints consistent with chronic insomnia, sleep apnea, and restless leg syndrome, as well as those taking selective serotonin reuptake inhibitor/serotonin–norepinephrine reuptake inhibitor antidepressant medications, previously evaluated using Z-ALG were re-examined using Z-PLUS. EEG data collected from electrodes placed at the differential-mastoids (A1–A2) were processed by Z-ALG to determine wake and sleep, then those epochs detected as sleep were further processed by Z-PLUS to differentiate into Light Sleep, Deep Sleep, and REM. EEG data were visually scored by multiple certified polysomnographic technologists according to the Rechtschaffen and Kales criterion, and then combined using a majority-voting rule to create a PSG Consensus score file for each of the 99 subjects. Z-PLUS output was compared to the PSG Consensus score files for both epoch-by-epoch (eg, sensitivity, specificity, and kappa) and sleep stage-related statistics (eg, Latency to Deep Sleep, Latency to REM, Total Deep Sleep, and Total REM). Results Sensitivities of Z-PLUS compared to the PSG Consensus were 0.84 for Light Sleep, 0.74 for Deep Sleep, and 0.72 for REM. Similarly, positive predictive values were 0.85 for Light Sleep, 0.78 for Deep Sleep, and 0.73 for REM. Overall, kappa agreement of 0

  15. An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

    2003-01-01

    An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

  16. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    NASA Astrophysics Data System (ADS)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  17. Automated supervised classification of variable stars. II. Application to the OGLE database

    NASA Astrophysics Data System (ADS)

    Sarro, L. M.; Debosscher, J.; López, M.; Aerts, C.

    2009-02-01

    Context: Scientific exploitation of large variability databases can only be fully optimized if these archives contain, besides the actual observations, annotations about the variability class of the objects they contain. Supervised classification of observations produces these tags, and makes it possible to generate refined candidate lists and catalogues suitable for further investigation. Aims: We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable objects of unknown class. The results are compared to published classification results based on the so-called extractor methods. Methods: Two complementary analyses are carried out in parallel. In both cases, the original time series of OGLE observations of the Galactic bulge and Magellanic Clouds are processed in order to identify and characterize the frequency components. In the first approach, the classifiers are applied to the data and the results analyzed in terms of systematic errors and differences between the definition samples in the training set and in the extractor rules. In the second approach, the original classifiers are extended with colour information and, again, applied to OGLE light curves. Results: We have constructed a classification system that can process huge amounts of time series in negligible time and provide reliable samples of the main variability classes. We have evaluated its strengths and weaknesses and provide potential users of the classifier with a detailed description of its characteristics to aid in the interpretation of classification results. Finally, we apply the classifiers to obtain object samples of classes not previously studied in the OGLE database and analyse the results. We pay specific attention to the B-stars in the samples, as their pulsations are strongly dependent on metallicity. Variability

  18. Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data.

    PubMed

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

    2015-01-01

    The National Surgical Quality Improvement Project (NSQIP) is widely recognized as "the best in the nation" surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP's wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors' burden. PMID:26262143

  19. Effects of automation and task load on task switching during human supervision of multiple semi-autonomous robots in a dynamic environment.

    PubMed

    Squire, P N; Parasuraman, R

    2010-08-01

    The present study assessed the impact of task load and level of automation (LOA) on task switching in participants supervising a team of four or eight semi-autonomous robots in a simulated 'capture the flag' game. Participants were faster to perform the same task than when they chose to switch between different task actions. They also took longer to switch between different tasks when supervising the robots at a high compared to a low LOA. Task load, as manipulated by the number of robots to be supervised, did not influence switch costs. The results suggest that the design of future unmanned vehicle (UV) systems should take into account not simply how many UVs an operator can supervise, but also the impact of LOA and task operations on task switching during supervision of multiple UVs. The findings of this study are relevant for the ergonomics practice of UV systems. This research extends the cognitive theory of task switching to inform the design of UV systems and results show that switching between UVs is an important factor to consider. PMID:20658389

  20. Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

    ERIC Educational Resources Information Center

    Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

    2009-01-01

    Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

  1. Advanced Algorithms and Automation Tools for Discrete Ordinates Methods in Parallel Environments

    SciTech Connect

    Alireza Haghighat

    2003-05-07

    This final report discusses major accomplishments of a 3-year project under the DOE's NEER Program. The project has developed innovative and automated algorithms, codes, and tools for solving the discrete ordinates particle transport method efficiently in parallel environments. Using a number of benchmark and real-life problems, the performance and accuracy of the new algorithms have been measured and analyzed.

  2. Automated maneuver planning using a fuzzy logic algorithm

    NASA Technical Reports Server (NTRS)

    Conway, D.; Sperling, R.; Folta, D.; Richon, K.; Defazio, R.

    1994-01-01

    Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.

  3. Double regions growing algorithm for automated satellite image mosaicking

    NASA Astrophysics Data System (ADS)

    Tan, Yihua; Chen, Chen; Tian, Jinwen

    2011-12-01

    Feathering is a most widely used method in seamless satellite image mosaicking. A simple but effective algorithm - double regions growing (DRG) algorithm, which utilizes the shape content of images' valid regions, is proposed for generating robust feathering-line before feathering. It works without any human intervention, and experiment on real satellite images shows the advantages of the proposed method.

  4. Comparison of Automated Treponemal and Nontreponemal Test Algorithms as First-Line Syphilis Screening Assays

    PubMed Central

    Chung, Jae-Woo; Park, Seong Yeon; Chae, Seok Lae

    2016-01-01

    Background Automated Mediace Treponema pallidum latex agglutination (TPLA) and Mediace rapid plasma reagin (RPR) assays are used by many laboratories for syphilis diagnosis. This study compared the results of the traditional syphilis screening algorithm and a reverse algorithm using automated Mediace RPR or Mediace TPLA as first-line screening assays in subjects undergoing a health checkup. Methods Samples from 24,681 persons were included in this study. We routinely performed Mediace RPR and Mediace TPLA simultaneously. Results were analyzed according to both the traditional algorithm and reverse algorithm. Samples with discordant results on the reverse algorithm (e.g., positive Mediace TPLA, negative Mediace RPR) were tested with Treponema pallidum particle agglutination (TPPA). Results Among the 24,681 samples, 30 (0.1%) were found positive by traditional screening, and 190 (0.8%) by reverse screening. The identified syphilis rate and overall false-positive rate according to the traditional algorithm were lower than those according to the reverse algorithm (0.07% and 0.05% vs. 0.64% and 0.13%, respectively). A total of 173 discordant samples were tested with TPPA by using the reverse algorithm, of which 140 (80.9%) were TPPA positive. Conclusions Despite the increased false-positive results in populations with a low prevalence of syphilis, the reverse algorithm detected 140 samples with treponemal antibody that went undetected by the traditional algorithm. The reverse algorithm using Mediace TPLA as a screening test is more sensitive for the detection of syphilis. PMID:26522755

  5. Characterizing interplanetary shocks for development and optimization of an automated solar wind shock detection algorithm

    NASA Astrophysics Data System (ADS)

    Cash, M. D.; Wrobel, J. S.; Cosentino, K. C.; Reinard, A. A.

    2014-06-01

    Human evaluation of solar wind data for interplanetary (IP) shock identification relies on both heuristics and pattern recognition, with the former lending itself to algorithmic representation and automation. Such detection algorithms can potentially alert forecasters of approaching shocks, providing increased warning of subsequent geomagnetic storms. However, capturing shocks with an algorithmic treatment alone is challenging, as past and present work demonstrates. We present a statistical analysis of 209 IP shocks observed at L1, and we use this information to optimize a set of shock identification criteria for use with an automated solar wind shock detection algorithm. In order to specify ranges for the threshold values used in our algorithm, we quantify discontinuities in the solar wind density, velocity, temperature, and magnetic field magnitude by analyzing 8 years of IP shocks detected by the SWEPAM and MAG instruments aboard the ACE spacecraft. Although automatic shock detection algorithms have previously been developed, in this paper we conduct a methodical optimization to refine shock identification criteria and present the optimal performance of this and similar approaches. We compute forecast skill scores for over 10,000 permutations of our shock detection criteria in order to identify the set of threshold values that yield optimal forecast skill scores. We then compare our results to previous automatic shock detection algorithms using a standard data set, and our optimized algorithm shows improvements in the reliability of automated shock detection.

  6. Predicting pupylation sites in prokaryotic proteins using semi-supervised self-training support vector machine algorithm.

    PubMed

    Ju, Zhe; Gu, Hong

    2016-08-15

    As one important post-translational modification of prokaryotic proteins, pupylation plays a key role in regulating various biological processes. The accurate identification of pupylation sites is crucial for understanding the underlying mechanisms of pupylation. Although several computational methods have been developed for the identification of pupylation sites, the prediction accuracy of them is still unsatisfactory. Here, a novel bioinformatics tool named IMP-PUP is proposed to improve the prediction of pupylation sites. IMP-PUP is constructed on the composition of k-spaced amino acid pairs and trained with a modified semi-supervised self-training support vector machine (SVM) algorithm. The proposed algorithm iteratively trains a series of support vector machine classifiers on both annotated and non-annotated pupylated proteins. Computational results show that IMP-PUP achieves the area under receiver operating characteristic curves of 0.91, 0.73, and 0.75 on our training set, Tung's testing set, and our testing set, respectively, which are better than those of the different error costs SVM algorithm and the original self-training SVM algorithm. Independent tests also show that IMP-PUP significantly outperforms three other existing pupylation site predictors: GPS-PUP, iPUP, and pbPUP. Therefore, IMP-PUP can be a useful tool for accurate prediction of pupylation sites. A MATLAB software package for IMP-PUP is available at https://juzhe1120.github.io/. PMID:27197054

  7. Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm

    NASA Astrophysics Data System (ADS)

    Gilat-Schmidt, Taly; Wang, Adam; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

    2016-03-01

    The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

  8. SSME Automated Engine Calibrating System (AECS) alternative algorithm

    NASA Astrophysics Data System (ADS)

    Greene, William D.

    1993-06-01

    An algorithm is derived for the real-time calibration of the engine mixture ratio during SSME ground testing. Because currently used calibration methods are post-test operations, there exists no fail-safe way of predicting at what mixture ratio a planned test will run. It is proposed that the algorithm developed here be used as part of an AECS which could ensure that nearly all SSME tests are run at the proper mixture ratio. In this way, AECS has the potential of increasing the efficiency of the SSME ground test program. This algorithm is an alternative to that presented in a previous paper. In addition to the derivation of the algorithm, an overview of this calibration system is presented along with a discussion of a possible single coefficient calibration system and the list of test stand facility instrumentation necessary for AECS implementation.

  9. Normalized Cut Algorithm for Automated Assignment of Protein Domains

    NASA Technical Reports Server (NTRS)

    Samanta, M. P.; Liang, S.; Zha, H.; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    We present a novel computational method for automatic assignment of protein domains from structural data. At the core of our algorithm lies a recently proposed clustering technique that has been very successful for image-partitioning applications. This grap.,l-theory based clustering method uses the notion of a normalized cut to partition. an undirected graph into its strongly-connected components. Computer implementation of our method tested on the standard comparison set of proteins from the literature shows a high success rate (84%), better than most existing alternative In addition, several other features of our algorithm, such as reliance on few adjustable parameters, linear run-time with respect to the size of the protein and reduced complexity compared to other graph-theory based algorithms, would make it an attractive tool for structural biologists.

  10. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    NASA Technical Reports Server (NTRS)

    Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

    2000-01-01

    We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

  11. Algorithm for Automated Detection of Edges of Clouds

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.

    2006-01-01

    An algorithm processes cloud-physics data gathered in situ by an aircraft, along with reflectivity data gathered by ground-based radar, to determine whether the aircraft is inside or outside a cloud at a given time. A cloud edge is deemed to be detected when the in/out state changes, subject to a hysteresis constraint. Such determinations are important in continuing research on relationships among lightning, electric charges in clouds, and decay of electric fields with distance from cloud edges.

  12. An algorithm for automated layout of process description maps drawn in SBGN

    PubMed Central

    Genc, Begum; Dogrusoz, Ugur

    2016-01-01

    Motivation: Evolving technology has increased the focus on genomics. The combination of today’s advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. Results: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. Availability and implementation: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). Contact: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26363029

  13. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    PubMed

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies. PMID:26872778

  14. Design principles and algorithms for automated air traffic management

    NASA Technical Reports Server (NTRS)

    Erzberger, Heinz

    1995-01-01

    This paper presents design principles and algorithm for building a real time scheduler. The primary objective of the scheduler is to assign arrival aircraft to a favorable landing runway and schedule them to land at times that minimize delays. A further objective of the scheduler is to allocate delays between high altitude airspace far from the airport and low altitude airspace near the airport. A method of delay allocation is described that minimizes the average operating cost in the presence of errors in controlling aircraft to a specified landing time.

  15. Automated mineral identification algorithm using optical properties of crystals

    NASA Astrophysics Data System (ADS)

    Aligholi, Saeed; Khajavi, Reza; Razmara, Morteza

    2015-12-01

    A method has been developed to automatically characterize the type of mineral phases by means of digital image analysis using optical properties of crystals. The method relies on microscope automation, digital image acquisition, image processing and analysis. Two hundred series of digital images were taken from 45 standard thin sections using a digital camera mounted on a conventional microscope and then transmitted to a computer. CIELab color space is selected for the processing, in order to effectively employ its well-defined color difference metric for introducing appropriate color-based feature. Seven basic optical properties of minerals (A. color; B. pleochroism; C. interference color; D. birefringence; E. opacity; F. isotropy; G. extinction angle) are redefined. The Local Binary Pattern (LBP) operator and modeling texture is integrated in the Mineral Identification (MI) scheme to identify homogeneous regions in microscopic images of minerals. The accuracy of mineral identification using the method was %99, %98, %96 and %95 for biotite, hornblende, quartz and calcite minerals, respectively. The method is applicable to other minerals and phases for which individual optical properties of crystals do not provide enough discrimination between the relevant phases. On the basis of this research, it can be concluded that if the CIELab color space and the local binary pattern (LBP) are applied, it is possible to recognize the mineral samples with the accuracy of more than 98%.

  16. Automated Photogrammetric Image Matching with Sift Algorithm and Delaunay Triangulation

    NASA Astrophysics Data System (ADS)

    Karagiannis, Georgios; Antón Castro, Francesc; Mioc, Darka

    2016-06-01

    An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detected are invariant to image rotations, translations, scaling and also to changes in illumination, brightness and 3-dimensional viewpoint. Afterwards, each feature of the reference image is matched with one in the sensed image if, and only if, the distance between them multiplied by a threshold is shorter than the distances between the point and all the other points in the sensed image. Then, the matched features are used to compute the parameters of the homography that transforms the coordinate system of the sensed image to the coordinate system of the reference image. The Delaunay triangulations of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches.

  17. An automated algorithm for determining photometric redshifts of quasars

    NASA Astrophysics Data System (ADS)

    Wang, Dan; Zhang, Yanxia; Zhao, Yongheng

    2010-07-01

    We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within ▵z < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ≠ 1) for the given sample.

  18. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

    PubMed Central

    Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

    2014-01-01

    Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

  20. An automated cell-counting algorithm for fluorescently-stained cells in migration assays

    PubMed Central

    2011-01-01

    A cell-counting algorithm, developed in Matlab®, was created to efficiently count migrated fluorescently-stained cells on membranes from migration assays. At each concentration of cells used (10,000, and 100,000 cells), images were acquired at 2.5 ×, 5 ×, and 10 × objective magnifications. Automated cell counts strongly correlated to manual counts (r2 = 0.99, P < 0.0001 for a total of 47 images), with no difference in the measurements between methods under all conditions. We conclude that our automated method is accurate, more efficient, and void of variability and potential observer bias normally associated with manual counting. PMID:22011343

  1. Automated Target Planning for FUSE Using the SOVA Algorithm

    NASA Technical Reports Server (NTRS)

    Heatwole, Scott; Lanzi, R. James; Civeit, Thomas; Calvani, Humberto; Kruk, Jeffrey W.; Suchkov, Anatoly

    2007-01-01

    The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two

  2. Automated Algorithm for Extraction of Wetlands from IRS Resourcesat Liss III Data

    NASA Astrophysics Data System (ADS)

    Subramaniam, S.; Saxena, M.

    2011-09-01

    Wetlands play significant role in maintaining the ecological balance of both biotic and abiotic life in coastal and inland environments. Hence, understanding of their occurrence, spatial extent of change in wetland environment is very important and can be monitored using satellite remote sensing technique. The extraction of wetland features using remote sensing has so far been carried out using visual/ hybrid digital analysis techniques, which is time consuming. To monitor the wetland and their features at National/ State level, there is a need for the development of automated technique for the extraction of wetland features. A knowledge based algorithm has been developed using hierarchical decision tree approach for automated extraction of wetland features such as surface water spread, wet area, turbidity and wet vegetation including aquatic for pre and post monsoon period. The results obtained for Chhattisgarh, India using the automated technique has been found to be satisfactory, when compared with hybrid digital/visual analysis technique.

  3. Supervised feature ranking using a genetic algorithm optimized artificial neural network.

    PubMed

    Lin, Thy-Hou; Chiu, Shih-Hau; Tsai, Keng-Chang

    2006-01-01

    A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85x24 and 62x25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking. PMID:16859292

  4. Automated Escape Guidance Algorithms for An Escape Vehicle

    NASA Technical Reports Server (NTRS)

    Flanary, Ronald; Hammen, David; Ito, Daigoro; Rabalais, Bruce; Rishikof, Brian; Siebold, Karl

    2002-01-01

    An escape vehicle was designed to provide an emergency evacuation for crew members living on a space station. For maximum escape capability, the escape vehicle needs to have the ability to safely evacuate a station in a contingency scenario such as an uncontrolled (e.g., tumbling) station. This emergency escape sequence will typically be divided into three events: The fust separation event (SEP1), the navigation reconstruction event, and the second separation event (SEP2). SEP1 is responsible for taking the spacecraft from its docking port to a distance greater than the maximum radius of the rotating station. The navigation reconstruction event takes place prior to the SEP2 event and establishes the orbital state to within the tolerance limits necessary for SEP2. The SEP2 event calculates and performs an avoidance burn to prevent station recontact during the next several orbits. This paper presents the tools and results for the whole separation sequence with an emphasis on the two separation events. The fust challenge includes collision avoidance during the escape sequence while the station is in an uncontrolled rotational state, with rotation rates of up to 2 degrees per second. The task of avoiding a collision may require the use of the Vehicle's de-orbit propulsion system for maximum thrust and minimum dwell time within the vicinity of the station vicinity. The thrust of the propulsion system is in a single direction, and can be controlled only by the attitude of the spacecraft. Escape algorithms based on a look-up table or analytical guidance can be implemented since the rotation rate and the angular momentum vector can be sensed onboard and a-priori knowledge of the position and relative orientation are available. In addition, crew intervention has been provided for in the event of unforeseen obstacles in the escape path. The purpose of the SEP2 burn is to avoid re-contact with the station over an extended period of time. Performing this maneuver properly

  5. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    PubMed Central

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-01-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672

  6. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians.

    PubMed

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J; Adatia, Ian

    2016-01-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672

  7. Astronomical algorithms for automated analysis of tissue protein expression in breast cancer

    PubMed Central

    Ali, H R; Irwin, M; Morris, L; Dawson, S-J; Blows, F M; Provenzano, E; Mahler-Araujo, B; Pharoah, P D; Walton, N A; Brenton, J D; Caldas, C

    2013-01-01

    Background: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. Methods: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. Results: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to ‘positive' or ‘negative' categories with agreement rates of up to 96%. Conclusion: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology. PMID:23329232

  8. The GOES-R ABI Wild Fire Automated Biomass Burning Algorithm

    NASA Astrophysics Data System (ADS)

    Hoffman, J.; Schmidt, C. C.; Prins, E. M.; Brunner, J. C.

    2011-12-01

    The global Wild Fire Automated Biomass Burning Algorithm (WF_ABBA) at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) provides fire detection and characterization using data from a global constellation of geostationary satellites, currently including GOES, MTSAT, and Meteosat. CIMSS continues to enhance the legacy of the WF_ABBA by adapting the algorithm to utilize the advanced spatial, spectral, and temporal capabilities of GOES-R ABI. A wide range of simulated ABI data cases have been generated and processed with the GOES-R fire detection and characterization algorithm. Simulated cases included MODIS derived projections as well as model derived simulations that span a variety of satellite zenith angles and ecosystems. The GOES-R ABI fire product development focuses on active fire detection and sub-pixel characterization, including fire radiative power (FRP) and instantaneous fire size and temperature. With the algorithm delivered to the system contractor, the focus has moved to developing innovative new validation techniques.

  9. Automated decomposition algorithm for Raman spectra based on a Voigt line profile model.

    PubMed

    Chen, Yunliang; Dai, Liankui

    2016-05-20

    Raman spectra measured by spectrometers usually suffer from band overlap and random noise. In this paper, an automated decomposition algorithm based on a Voigt line profile model for Raman spectra is proposed to solve this problem. To decompose a measured Raman spectrum, a Voigt line profile model is introduced to parameterize the measured spectrum, and a Gaussian function is used as the instrumental broadening function. Hence, the issue of spectral decomposition is transformed into a multiparameter optimization problem of the Voigt line profile model parameters. The algorithm can eliminate instrumental broadening, obtain a recovered Raman spectrum, resolve overlapping bands, and suppress random noise simultaneously. Moreover, the recovered spectrum can be decomposed to a group of Lorentzian functions. Experimental results on simulated Raman spectra show that the performance of this algorithm is much better than a commonly used blind deconvolution method. The algorithm has also been tested on the industrial Raman spectra of ortho-xylene and proved to be effective. PMID:27411136

  10. Algorithm for automated analysis of surface vibrations using time-averaged digital speckle pattern interferometry.

    PubMed

    Krzemien, Leszek; Lukomski, Michal

    2012-07-20

    A fully automated algorithm was developed for the recording and analysis of vibrating objects with the help of digital speckle pattern interferometry utilizing continuous-wave laser light. A series of measurements were performed with increasing force inducing vibration to allow the spatial distribution of vibration amplitude to be reconstructed on the object's surface. The developed algorithm uses Hilbert transformation for an independent, quantitative evaluation of the Bessel function at every point of the investigated surface. The procedure does not require phase modulation, and thus can be implemented within any, even the simplest, DSPI apparatus. The proposed deformation analysis is fast and computationally inexpensive. PMID:22858957

  11. Improved automated monitoring and new analysis algorithm for circadian phototaxis rhythms in Chlamydomonas

    PubMed Central

    Gaskill, Christa; Forbes-Stovall, Jennifer; Kessler, Bruce; Young, Mike; Rinehart, Claire A.; Jacobshagen, Sigrid

    2010-01-01

    Automated monitoring of circadian rhythms is an efficient way of gaining insight into oscillation parameters like period and phase for the underlying pacemaker of the circadian clock. Measurement of the circadian rhythm of phototaxis (swimming towards light) exhibited by the green alga Chlamydomonas reinhardtii has been automated by directing a narrow and dim light beam through a culture at regular intervals and determining the decrease in light transmittance due to the accumulation of cells in the beam. In this study, the monitoring process was optimized by constructing a new computer-controlled measuring machine that limits the test beam to wavelengths reported to be specific for phototaxis and by choosing an algal strain, which does not need background illumination between test light cycles for proper expression of the rhythm. As a result, period and phase of the rhythm are now unaffected by the time a culture is placed into the machine. Analysis of the rhythm data was also optimized through a new algorithm, whose robustness was demonstrated using virtual rhythms with various noises. The algorithm differs in particular from other reported algorithms by maximizing the fit of the data to a sinusoidal curve that dampens exponentially. The algorithm was also used to confirm the reproducibility of rhythm monitoring by the machine. Machine and algorithm can now be used for a multitude of circadian clock studies that require unambiguous period and phase determinations such as light pulse experiments to identify the photoreceptor(s) that reset the circadian clock in C. reinhardtii. PMID:20116270

  12. Taboo search algorithm for item assignment in synchronized zone automated order picking system

    NASA Astrophysics Data System (ADS)

    Wu, Yingying; Wu, Yaohua

    2014-07-01

    The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

  13. Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

    SciTech Connect

    Chen, Hsin-Chen; Tan, Jun; Dolly, Steven; Kavanaugh, James; Harold Li, H.; Altman, Michael; Gay, Hiram; Thorstad, Wade L.; Mutic, Sasa; Li, Hua; Anastasio, Mark A.; Low, Daniel A.

    2015-02-15

    Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets

  14. Automated Development of Accurate Algorithms and Efficient Codes for Computational Aeroacoustics

    NASA Technical Reports Server (NTRS)

    Goodrich, John W.; Dyson, Rodger W.

    1999-01-01

    The simulation of sound generation and propagation in three space dimensions with realistic aircraft components is a very large time dependent computation with fine details. Simulations in open domains with embedded objects require accurate and robust algorithms for propagation, for artificial inflow and outflow boundaries, and for the definition of geometrically complex objects. The development, implementation, and validation of methods for solving these demanding problems is being done to support the NASA pillar goals for reducing aircraft noise levels. Our goal is to provide algorithms which are sufficiently accurate and efficient to produce usable results rapidly enough to allow design engineers to study the effects on sound levels of design changes in propulsion systems, and in the integration of propulsion systems with airframes. There is a lack of design tools for these purposes at this time. Our technical approach to this problem combines the development of new, algorithms with the use of Mathematica and Unix utilities to automate the algorithm development, code implementation, and validation. We use explicit methods to ensure effective implementation by domain decomposition for SPMD parallel computing. There are several orders of magnitude difference in the computational efficiencies of the algorithms which we have considered. We currently have new artificial inflow and outflow boundary conditions that are stable, accurate, and unobtrusive, with implementations that match the accuracy and efficiency of the propagation methods. The artificial numerical boundary treatments have been proven to have solutions which converge to the full open domain problems, so that the error from the boundary treatments can be driven as low as is required. The purpose of this paper is to briefly present a method for developing highly accurate algorithms for computational aeroacoustics, the use of computer automation in this process, and a brief survey of the algorithms that

  15. Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA.

    PubMed

    Gandola, Emanuele; Antonioli, Manuela; Traficante, Alessio; Franceschini, Simone; Scardi, Michele; Congestri, Roberta

    2016-09-01

    The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA ("ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning" (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements. PMID:27500194

  16. Algorithm design for automated transportation photo enforcement camera image and video quality diagnostic check modules

    NASA Astrophysics Data System (ADS)

    Raghavan, Ajay; Saha, Bhaskar

    2013-03-01

    Photo enforcement devices for traffic rules such as red lights, toll, stops, and speed limits are increasingly being deployed in cities and counties around the world to ensure smooth traffic flow and public safety. These are typically unattended fielded systems, and so it is important to periodically check them for potential image/video quality problems that might interfere with their intended functionality. There is interest in automating such checks to reduce the operational overhead and human error involved in manually checking large camera device fleets. Examples of problems affecting such camera devices include exposure issues, focus drifts, obstructions, misalignment, download errors, and motion blur. Furthermore, in some cases, in addition to the sub-algorithms for individual problems, one also has to carefully design the overall algorithm and logic to check for and accurately classifying these individual problems. Some of these issues can occur in tandem or have the potential to be confused for each other by automated algorithms. Examples include camera misalignment that can cause some scene elements to go out of focus for wide-area scenes or download errors that can be misinterpreted as an obstruction. Therefore, the sequence in which the sub-algorithms are utilized is also important. This paper presents an overview of these problems along with no-reference and reduced reference image and video quality solutions to detect and classify such faults.

  17. A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters

    PubMed Central

    Madany Mamlouk, Amir; Schicktanz, Simone; Kruse, Charli

    2011-01-01

    Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high

  18. A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters.

    PubMed

    Rapoport, Daniel H; Becker, Tim; Madany Mamlouk, Amir; Schicktanz, Simone; Kruse, Charli

    2011-01-01

    Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high

  19. Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning.

    PubMed

    Shi, Peng; Huang, Yue; Hong, Jinsheng

    2014-05-01

    A dendritic spine is a small membranous protrusion from a neuron's dendrite that typically receives input from a single synapse of an axon. Recent research shows that the morphological changes of dendritic spines have a close relationship with some specific diseases. The distribution of different dendritic spine phenotypes is a key indicator of such changes. Therefore, it is necessary to classify detected spines with different phenotypes online. Since the dendritic spines have complex three dimensional (3D) structures, current neuron morphological analysis approaches cannot classify the dendritic spines accurately with limited features. In this paper, we propose a novel semi-supervised learning approach in order to perform the online morphological classification of dendritic spines. Spines are detected by a new approach based on wavelet transform in the 3D space. A small training data set is chosen from the detected spines, which has the spines labeled by the neurobiologists. The remaining spines are then classified online by the semi-supervised learning (SSL) approach. Experimental results show that our method can quickly and accurately analyze neuron images with modest human intervention. PMID:24877014

  20. Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Chitchian, Shahab; Vincent, Kathleen L.; Vargas, Gracie; Motamedi, Massoud

    2012-11-01

    We have explored the use of optical coherence tomography (OCT) as a noninvasive tool for assessing the toxicity of topical microbicides, products used to prevent HIV, by monitoring the integrity of the vaginal epithelium. A novel feature-based segmentation algorithm using a nearest-neighbor classifier was developed to monitor changes in the morphology of vaginal epithelium. The two-step automated algorithm yielded OCT images with a clearly defined epithelial layer, enabling differentiation of normal and damaged tissue. The algorithm was robust in that it was able to discriminate the epithelial layer from underlying stroma as well as residual microbicide product on the surface. This segmentation technique for OCT images has the potential to be readily adaptable to the clinical setting for noninvasively defining the boundaries of the epithelium, enabling quantifiable assessment of microbicide-induced damage in vaginal tissue.

  1. Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

    PubMed Central

    2015-01-01

    Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

  2. Design of an automated algorithm for labeling cardiac blood pool in gated SPECT images of radiolabeled red blood cells

    SciTech Connect

    Hebert, T.J. |; Moore, W.H.; Dhekne, R.D.; Ford, P.V.; Wendt, J.A.; Murphy, P.H.; Ting, Y.

    1996-08-01

    The design of an automated computer algorithm for labeling the cardiac blood pool within gated 3-D reconstructions of the radiolabeled red blood cells is investigated. Due to patient functional abnormalities, limited resolution, and noise, certain spatial and temporal features of the cardiac blood pool that one would anticipate finding in every study are not present in certain frames or with certain patients. The labeling of the cardiac blood pool requires an algorithm that only relies upon features present in all patients. The authors investigate the design of a fully-automated region growing algorithm for this purpose.

  3. Fast automated yeast cell counting algorithm using bright-field and fluorescence microscopic images

    PubMed Central

    2013-01-01

    Background The faithful determination of the concentration and viability of yeast cells is important for biological research as well as industry. To this end, it is important to develop an automated cell counting algorithm that can provide not only fast but also accurate and precise measurement of yeast cells. Results With the proposed method, we measured the precision of yeast cell measurements by using 0%, 25%, 50%, 75% and 100% viability samples. As a result, the actual viability measured with the proposed yeast cell counting algorithm is significantly correlated to the theoretical viability (R2 = 0.9991). Furthermore, we evaluated the performance of our algorithm in various computing platforms. The results showed that the proposed algorithm could be feasible to use with low-end computing platforms without loss of its performance. Conclusions Our yeast cell counting algorithm can rapidly provide the total number and the viability of yeast cells with exceptional accuracy and precision. Therefore, we believe that our method can become beneficial for a wide variety of academic field and industries such as biotechnology, pharmaceutical and alcohol production. PMID:24215650

  4. Automated coronary artery calcium scoring from non-contrast CT using a patient-specific algorithm

    NASA Astrophysics Data System (ADS)

    Ding, Xiaowei; Slomka, Piotr J.; Diaz-Zamudio, Mariana; Germano, Guido; Berman, Daniel S.; Terzopoulos, Demetri; Dey, Damini

    2015-03-01

    Non-contrast cardiac CT is used worldwide to assess coronary artery calcium (CAC), a subclinical marker of coronary atherosclerosis. Manual quantification of regional CAC scores includes identifying candidate regions, followed by thresholding and connected component labeling. We aimed to develop and validate a fully-automated, algorithm for both overall and regional measurement of CAC scores from non-contrast CT using a hybrid multi-atlas registration, active contours and knowledge-based region separation algorithm. A co-registered segmented CT atlas was created from manually segmented non-contrast CT data from 10 patients (5 men, 5 women) and stored offline. For each patient scan, the heart region, left ventricle, right ventricle, ascending aorta and aortic root are located by multi-atlas registration followed by active contours refinement. Regional coronary artery territories (left anterior descending artery, left circumflex artery and right coronary artery) are separated using a knowledge-based region separation algorithm. Calcifications from these coronary artery territories are detected by region growing at each lesion. Global and regional Agatston scores and volume scores were calculated in 50 patients. Agatston scores and volume scores calculated by the algorithm and the expert showed excellent correlation (Agatston score: r = 0.97, p < 0.0001, volume score: r = 0.97, p < 0.0001) with no significant differences by comparison of individual data points (Agatston score: p = 0.30, volume score: p = 0.33). The total time was <60 sec on a standard computer. Our results show that fast accurate and automated quantification of CAC scores from non-contrast CT is feasible.

  5. Thermal depth profiling of vascular lesions: automated regularization of reconstruction algorithms

    PubMed Central

    Verkruysse, Wim; Choi, Bernard; Zhang, Jenny R; Kim, Jeehyun; Nelson, J Stuart

    2008-01-01

    Pulsed photo-thermal radiometry (PPTR) is a non-invasive, non-contact diagnostic technique used to locate cutaneous chromophores such as melanin (epidermis) and hemoglobin (vascular structures). Clinical utility of PPTR is limited because it typically requires trained user intervention to regularize the inversion solution. Herein, the feasibility of automated regularization was studied. A second objective of this study was to depart from modeling port wine stain PWS, a vascular skin lesion frequently studied with PPTR, as strictly layered structures since this may influence conclusions regarding PPTR reconstruction quality. Average blood vessel depths, diameters and densities derived from histology of 30 PWS patients were used to generate 15 randomized lesion geometries for which we simulated PPTR signals. Reconstruction accuracy for subjective regularization was compared with that for automated regularization methods. The objective regularization approach performed better. However, the average difference was much smaller than the variation between the 15 simulated profiles. Reconstruction quality depended more on the actual profile to be reconstructed than on the reconstruction algorithm or regularization method. Similar, or better, accuracy reconstructions can be achieved with an automated regularization procedure which enhances prospects for user friendly implementation of PPTR to optimize laser therapy on an individual patient basis. PMID:18296773

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  7. Identifying waking time in 24-h accelerometry data in adults using an automated algorithm.

    PubMed

    van der Berg, Julianne D; Willems, Paul J B; van der Velde, Jeroen H P M; Savelberg, Hans H C M; Schaper, Nicolaas C; Schram, Miranda T; Sep, Simone J S; Dagnelie, Pieter C; Bosma, Hans; Stehouwer, Coen D A; Koster, Annemarie

    2016-10-01

    As accelerometers are commonly used for 24-h measurements of daily activity, methods for separating waking from sleeping time are necessary for correct estimations of total daily activity levels accumulated during the waking period. Therefore, an algorithm to determine wake and bed times in 24-h accelerometry data was developed and the agreement of this algorithm with self-report was examined. One hundred seventy-seven participants (aged 40-75 years) of The Maastricht Study who completed a diary and who wore the activPAL3™ 24 h/day, on average 6 consecutive days were included. Intraclass correlation coefficient (ICC) was calculated and the Bland-Altman method was used to examine associations between the self-reported and algorithm-calculated waking hours. Mean self-reported waking hours was 15.8 h/day, which was significantly correlated with the algorithm-calculated waking hours (15.8 h/day, ICC = 0.79, P = < 0.001). The Bland-Altman plot indicated good agreement in waking hours as the mean difference was 0.02 h (95% limits of agreement (LoA) = -1.1 to 1.2 h). The median of the absolute difference was 15.6 min (Q1-Q3 = 7.6-33.2 min), and 71% of absolute differences was less than 30 min. The newly developed automated algorithm to determine wake and bed times was highly associated with self-reported times, and can therefore be used to identify waking time in 24-h accelerometry data in large-scale epidemiological studies. PMID:26837855

  8. Classification of audiograms by sequential testing: reliability and validity of an automated behavioral hearing screening algorithm.

    PubMed

    Eilers, R E; Ozdamar, O; Steffens, M L

    1993-05-01

    In 1990, CAST (classification of audiograms by sequential testing) was proposed and developed as an automated, innovative approach to screening infant hearing using a modified Bayesian method. The method generated a four-frequency audiogram in a minimal number of test trials using VRA (visual reinforcement audiometry) techniques. Computer simulations were used to explore the properties (efficiency and accuracy) of the paradigm. The current work is designed to further test the utility of the paradigm with human infants and young children. Accordingly, infants and children between 6 months and 2 years of age were screened for hearing loss. The algorithm's efficacy was studied with respect to validity and reliability. Validity was evaluated by comparing CAST results with tympanometric data and outcomes of staircase-based testing. Test-retest reliability was also assessed. Results indicate that CAST is a valid, efficient, reliable, and potentially cost-effective screening method. PMID:8318708

  9. Automated decision algorithm applied to a field experiment with multiple research objectives: The DC3 campaign

    NASA Astrophysics Data System (ADS)

    Hanlon, C. J.; Small, A.; Bose, S.; Young, G. S.; Verlinde, J.

    2013-12-01

    In airborne field campaigns, investigators confront complex decision challenges concerning when and where to deploy aircraft to meet scientific objectives within constraints of time and budgeted flight hours. An automated flight decision recommendation system was developed to assist investigators leading the Deep Convective Clouds and Chemistry (DC3) campaign in spring--summer 2012. In making flight decisions, DC3 investigators needed to integrate two distinct, potentially competing objectives: to maximize the total harvest of data collected, and also to maintain an approximate balance of data collected from each of three geographic study regions. Choices needed to satisfy several constraint conditions including, most prominently, a limit on the total number of flight hours, and a bound on the number of calendar days in the field. An automated recommendation system was developed by translating these objectives and bounds into a formal problem of constrained optimization. In this formalization, a key step involved the mathematical representation of investigators' scientific preferences over the set of possible data collection outcomes. Competing objectives were integrated into a single metric by means of a utility function, which served to quantify the value of alternative data portfolios. Flight recommendations were generated to maximize the expected utility of each daily decision, conditioned on that day's forecast. A calibrated forecast probability of flight success in each study region was generated according to a forecasting system trained on numerical weather prediction model output, as well as expected climatological probability of flight success on future days. System performance was evaluated by comparing the data yielded by the actual DC3 campaign, compared with the yield that would have been realized had the algorithmic recommendations been followed. It was found that the algorithmic system would have achieved 19%--59% greater utility than the decisions

  10. Application of supervised range-constrained thresholding to extract lung pleura for automated detection of pleural thickenings from thoracic CT images

    NASA Astrophysics Data System (ADS)

    Chaisaowong, K.; Knepper, A.; Kraus, T.; Aach, T.

    2007-03-01

    We develop an image analysis system to automatically detect pleural thickenings and assess their characteristic values from patients' thoracic spiral CT images. Algorithms are described to carry out the segmentation of pleural contours and to find the pleural thickenings. The method of thresholding was selected as the technique to separate lung's tissue from other. Instead thresholding based only on empirical considerations, the so-called "supervised range-constrained thresholding" is applied. The automatic detection of pleural thickenings is carried out based on the examination of its concavity and on the characteristic Hounsfield unit of tumorous tissue. After detection of pleural thickenings, in order to assess their growth rate, a spline-based interpolation technique is used to create a model of healthy pleura. Based on this healthy model, the size of the pleural thickenings is calculated. In conjunction with the spatio-temporal matching of CT images acquired at different times, the oncopathological assessment of morbidity can be documented. A graphical user interface is provided which is also equipped with 3D visualization of the pleura. Our overall aim is to develop an image analysis system for an efficient and reliable diagnosis of early stage pleural mesothelioma in order to ease the consequences of the expected peak of malignant pleural mesothelioma caused by asbestos exposure.

  11. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

    PubMed

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly A

    2013-02-15

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

  12. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

    PubMed Central

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

    2013-01-01

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

  13. Algorithms for the automated analysis of cellular dynamics within living fungal colonies.

    PubMed

    Angarita-Jaimes, N C; Roca, M G M; Towers, C E; Read, N D; Towers, D P

    2009-09-01

    We present robust and efficient algorithms to automate the measurement of nuclear movement and germ tube extension rates in living fungal networks. The aim is to facilitate the understanding of the dynamics and regulation of nuclear migration in growing fungal colonies. The proposed methodology combines a cascade correlation filter to identify nuclear centers from which 2D nuclear velocities are determined and a level set algorithm for centerline extraction to monitor spore (conidial) germling growth. We show how the proposed cascaded filter improves spatial resolution in the presence of noise and is robust when fluorescently labeled nuclei with different intensities are in close proximity to each other. The performance of the filter is evaluated by simulation in comparison to the well known Rayleigh and Sparrow criteria, and experimental evidence is given from clusters of nuclei and nuclei undergoing mitotic division. The capabilities developed have enabled the robust and objective analysis of 10's of Gigabytes of image data that is being exploited by biological scientists. PMID:19504570

  14. Reliability and accuracy of an automated tracking algorithm to measure controlled passive and active muscle fascicle length changes from ultrasound.

    PubMed

    Gillett, Jarred G; Barrett, Rod S; Lichtwark, Glen A

    2013-01-01

    Manual tracking of muscle fascicle length changes from ultrasound images is a subjective and time-consuming process. The purpose of this study was to assess the repeatability and accuracy of an automated algorithm for tracking fascicle length changes in the medial gastrocnemius (MG) muscle during passive length changes and active contractions (isometric, concentric and eccentric) performed on a dynamometer. The freely available, automated tracking algorithm was based on the Lucas-Kanade optical flow algorithm with an affine optic flow extension, which accounts for image translation, dilation, rotation and shear between consecutive frames of an image sequence. Automated tracking was performed by three experienced assessors, and within- and between-examiner repeatability was computed using the coefficient of multiple determination (CMD). Fascicle tracking data were also compared with manual digitisation of the same image sequences, and the level of agreement between the two methods was calculated using the coefficient of multiple correlation (CMC). The CMDs across all test conditions ranged from 0.50 to 0.93 and were all above 0.98 when recomputed after the systematic error due to the estimate of the initial fascicle length on the first ultrasound frame was removed from the individual fascicle length waveforms. The automated and manual tracking approaches produced similar fascicle length waveforms, with an overall CMC of 0.88, which improved to 0.94 when the initial length offset was removed. Overall results indicate that the automated fascicle tracking algorithm was a repeatable, accurate and time-efficient method for estimating fascicle length changes of the MG muscle in controlled passive and active conditions. PMID:22235878

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

    NASA Astrophysics Data System (ADS)

    Rainieri, Carlo; Fabbrocino, Giovanni

    2015-08-01

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

  16. Automated beam placement for breast radiotherapy using a support vector machine based algorithm

    SciTech Connect

    Zhao Xuan; Kong, Dewen; Jozsef, Gabor; Chang, Jenghwa; Wong, Edward K.; Formenti, Silvia C.; Wang Yao

    2012-05-15

    Purpose: To develop an automated beam placement technique for whole breast radiotherapy using tangential beams. We seek to find optimal parameters for tangential beams to cover the whole ipsilateral breast (WB) and minimize the dose to the organs at risk (OARs). Methods: A support vector machine (SVM) based method is proposed to determine the optimal posterior plane of the tangential beams. Relative significances of including/avoiding the volumes of interests are incorporated into the cost function of the SVM. After finding the optimal 3-D plane that separates the whole breast (WB) and the included clinical target volumes (CTVs) from the OARs, the gantry angle, collimator angle, and posterior jaw size of the tangential beams are derived from the separating plane equation. Dosimetric measures of the treatment plans determined by the automated method are compared with those obtained by applying manual beam placement by the physicians. The method can be further extended to use multileaf collimator (MLC) blocking by optimizing posterior MLC positions. Results: The plans for 36 patients (23 prone- and 13 supine-treated) with left breast cancer were analyzed. Our algorithm reduced the volume of the heart that receives >500 cGy dose (V5) from 2.7 to 1.7 cm{sup 3} (p = 0.058) on average and the volume of the ipsilateral lung that receives >1000 cGy dose (V10) from 55.2 to 40.7 cm{sup 3} (p = 0.0013). The dose coverage as measured by volume receiving >95% of the prescription dose (V95%) of the WB without a 5 mm superficial layer decreases by only 0.74% (p = 0.0002) and the V95% for the tumor bed with 1.5 cm margin remains unchanged. Conclusions: This study has demonstrated the feasibility of using a SVM-based algorithm to determine optimal beam placement without a physician's intervention. The proposed method reduced the dose to OARs, especially for supine treated patients, without any relevant degradation of dose homogeneity and coverage in general.

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

    PubMed Central

    Kaplan, Richard F; Wang, Ying; Loparo, Kenneth A; Kelly, Monica R; Bootzin, Richard R

    2014-01-01

    Background A need exists, from both a clinical and a research standpoint, for objective sleep measurement systems that are both easy to use and can accurately assess sleep and wake. This study evaluates the output of an automated sleep–wake detection algorithm (Z-ALG) used in the Zmachine (a portable, single-channel, electroencephalographic [EEG] acquisition and analysis system) against laboratory polysomnography (PSG) using a consensus of expert visual scorers. Methods Overnight laboratory PSG studies from 99 subjects (52 females/47 males, 18–60 years, median age 32.7 years), including both normal sleepers and those with a variety of sleep disorders, were assessed. PSG data obtained from the differential mastoids (A1–A2) were assessed by Z-ALG, which determines sleep versus wake every 30 seconds using low-frequency, intermediate-frequency, and high-frequency and time domain EEG features. PSG data were independently scored by two to four certified PSG technologists, using standard Rechtschaffen and Kales guidelines, and these score files were combined on an epoch-by-epoch basis, using a majority voting rule, to generate a single score file per subject to compare against the Z-ALG output. Both epoch-by-epoch and standard sleep indices (eg, total sleep time, sleep efficiency, latency to persistent sleep, and wake after sleep onset) were compared between the Z-ALG output and the technologist consensus score files. Results Overall, the sensitivity and specificity for detecting sleep using the Z-ALG as compared to the technologist consensus are 95.5% and 92.5%, respectively, across all subjects, and the positive predictive value and the negative predictive value for detecting sleep are 98.0% and 84.2%, respectively. Overall κ agreement is 0.85 (approaching the level of agreement observed among sleep technologists). These results persist when the sleep disorder subgroups are analyzed separately. Conclusion This study demonstrates that the Z-ALG automated sleep

  18. Automated Algorithms for Quantum-Level Accuracy in Atomistic Simulations: LDRD Final Report.

    SciTech Connect

    Thompson, Aidan P.; Schultz, Peter A.; Crozier, Paul; Moore, Stan Gerald; Swiler, Laura Painton; Stephens, John Adam; Trott, Christian Robert; Foiles, Stephen M.; Tucker, Garritt J.

    2014-09-01

    This report summarizes the result of LDRD project 12-0395, titled %22Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations.%22 During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel

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

    NASA Technical Reports Server (NTRS)

    Gay, Robert S.

    2011-01-01

    Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of

  20. ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

    PubMed

    Gandola, Emanuele; Antonioli, Manuela; Traficante, Alessio; Franceschini, Simone; Scardi, Michele; Congestri, Roberta

    2016-05-01

    Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments. PMID:27012737

  1. A segmentation algorithm for automated tracking of fast swimming unlabelled cells in three dimensions.

    PubMed

    Pimentel, J A; Carneiro, J; Darszon, A; Corkidi, G

    2012-01-01

    Recent advances in microscopy and cytolabelling methods enable the real time imaging of cells as they move and interact in their real physiological environment. Scenarios in which multiple cells move autonomously in all directions are not uncommon in biology. A remarkable example is the swimming of marine spermatozoa in search of the conspecific oocyte. Imaging cells in these scenarios, particularly when they move fast and are poorly labelled or even unlabelled requires very fast three-dimensional time-lapse (3D+t) imaging. This 3D+t imaging poses challenges not only to the acquisition systems but also to the image analysis algorithms. It is in this context that this work describes an original automated multiparticle segmentation method to analyse motile translucent cells in 3D microscopical volumes. The proposed segmentation technique takes advantage of the way the cell appearance changes with the distance to the focal plane position. The cells translucent properties and their interaction with light produce a specific pattern: when the cell is within or close to the focal plane, its two-dimensional (2D) appearance matches a bright spot surrounded by a dark ring, whereas when it is farther from the focal plane the cell contrast is inverted looking like a dark spot surrounded by a bright ring. The proposed method analyses the acquired video sequence frame-by-frame taking advantage of 2D image segmentation algorithms to identify and select candidate cellular sections. The crux of the method is in the sequential filtering of the candidate sections, first by template matching of the in-focus and out-of-focus templates and second by considering adjacent candidates sections in 3D. These sequential filters effectively narrow down the number of segmented candidate sections making the automatic tracking of cells in three dimensions a straightforward operation. PMID:21999166

  2. Supervised Learning

    NASA Astrophysics Data System (ADS)

    Rokach, Lior; Maimon, Oded

    This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

  3. "Supervision School"

    ERIC Educational Resources Information Center

    Kiddle, Henry; Schem, Alexander

    1976-01-01

    Proper supervision has been a concern of those responsible for education for many years. The 100-year-old definition used here may help us gain some insights regarding our sophistication and/or needs in the important area of supervision. (Editor/RK)

  4. Supervised Autonomy

    ERIC Educational Resources Information Center

    Sexton, Patrick; Levy, Linda S.; Willeford, K. Sean; Barnum, Mary G.; Gardner, Greg; Guyer, M. Susan; Fincher, A. Louise

    2009-01-01

    Objective: The primary objective of this paper is to present the evolution, purpose, and definition of direct supervision in the athletic training clinical education. The secondary objective is to briefly present the factors that may negatively affect the quality of direct supervision to allow remediation and provide higher quality clinical…

  5. Automated Transient Recovery Algorithm using Discrete Zernike Polynomials on Image-Subtracted Data

    NASA Astrophysics Data System (ADS)

    Ackley, Kendall; Eikenberry, Stephen S.; Klimenko, Sergey

    2016-01-01

    We present an unsupervised algorithm for the automated identification of astrophysical transients recovered through image subtraction techniques. We use a set of discrete Zernike polynomials to decompose and characterize residual energy discovered in the final subtracted image, identifying candidate sources which appear point-like in nature. This work is motivated for use in collaboration with Advanced gravitational wave (GW) interferometers, such as Advanced LIGO and Virgo, where multiwavelength electromagnetic (EM) emission is expected in parallel with gravitational radiation from compact binary object mergers of neutron stars (NS-NS) and stellar-mass black holes (NS-BH). Imaging an EM counterpart coincident with a GW trigger will help to constrain the multi-dimensional GW parameter space as well as aid in the resolution of long-standing astrophysical mysteries, such as the true nature of the progenitor relationship between short-duration GRBs and massive compact binary mergers. We are working on making our method an open-source package optimized for low-latency response for community use during the upcoming era of GW astronomy.

  6. Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

    USGS Publications Warehouse

    Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

    2013-01-01

    Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

  7. System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

    NASA Technical Reports Server (NTRS)

    Swieringa, Kurt A.; Wilson, Sara R.; Baxley, Brian T.

    2016-01-01

    The National Aeronautics and Space Administration's (NASA's) first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature ATM technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise time-based scheduling in the Terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools to enable precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind another aircraft. Recent simulations and IM algorithm development at NASA have focused on trajectory-based IM operations where aircraft equipped with IM avionics are expected to achieve a spacing goal, assigned by air traffic controllers, at the final approach fix. The recently published IM Minimum Operational Performance Standards describe five types of IM operations. This paper discusses the results and conclusions of a human-in-the-loop simulation that investigated three of those IM operations. The results presented in this paper focus on system performance and integration metrics. Overall, the IM operations conducted in this simulation integrated well with ground-based decisions support tools and certain types of IM operational were able to provide improved spacing precision at the final approach fix; however, some issues were identified that should be addressed prior to implementing IM procedures into real-world operations.

  8. Contour Photography Of The Ocular Fundus: Evaluation Of An Automated Image Processing Algorithm

    NASA Astrophysics Data System (ADS)

    Shapiro, Jerrold M.; Bush, Karen S.

    1986-07-01

    A new technique for making a three dimensional map of the optic nerve head is expected to have a major impact on the way in which glaucoma is diagnosed and treated. The new technique, contour photography, allows the health of the optic nerve head to be objectively evaluated every six months during the patient's routine office visit. In contour photography, a set of parallel lines of light are projected into the patient's eye and the back of the eye is photographed using a standard camera that is available in almost all ophthalmologist's offices. The three dimensional information is encoded in the positions of the photographed lines, and is decoded by treating each stripe as the intersection of a plane of light with the fundus. At present, a trained human observer identifies the edges of the stripes. In order to decrease the data extraction time, several automated edge detection algorithms were examined for their suitability in the analysis of contour photographs, and the best was extensively evaluated using Monte Carlo simulation. The accuracy and reproducibility of the edge position estimate in images with various amounts of film grain noise were measured for many values of the edge detector parameter and of the signal parameter, its modulation, m. When normalized by the amount of film grain noise, the relationship between reproducibility and l/m was found to be linear over the range of parameters likely to be encountered in contour photography. The accuracy was found to be independent of the amount of film grain noise, and linearly related to 1/m. By estimating m for each edge, the accuracy could be treated as a correctable systematic error of the edge detection process. A sample calculation which used parameter values that are likely to be found in contour photography showed that the automated edge detection process would be expected to produce a random variation in the measurement of the depth of the optic nerve head surface whose standard deviation is 0

  9. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

    PubMed

    Rudyanto, Rina D; Kerkstra, Sjoerd; van Rikxoort, Eva M; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, Ilkay; Ünay, Devrim; Kadipaşaoğlu, Kamuran; Estépar, Raúl San José; Ross, James C; Washko, George R; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C; Fabijanska, Anna; Smistad, Erik; Elster, Anne C; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G H; Campo, Arantza; Prokop, Mathias; de Jong, Pim A; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

    2014-10-01

    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases. PMID:25113321

  10. An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

    NASA Technical Reports Server (NTRS)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

  11. Automated Detection of Selective Logging in Amazon Forests Using Airborne Lidar Data and Pattern Recognition Algorithms

    NASA Astrophysics Data System (ADS)

    Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.

    2012-12-01

    Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ○S, 68.281 ○W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.

  12. SequenceL: Automated Parallel Algorithms Derived from CSP-NT Computational Laws

    NASA Technical Reports Server (NTRS)

    Cooke, Daniel; Rushton, Nelson

    2013-01-01

    With the introduction of new parallel architectures like the cell and multicore chips from IBM, Intel, AMD, and ARM, as well as the petascale processing available for highend computing, a larger number of programmers will need to write parallel codes. Adding the parallel control structure to the sequence, selection, and iterative control constructs increases the complexity of code development, which often results in increased development costs and decreased reliability. SequenceL is a high-level programming language that is, a programming language that is closer to a human s way of thinking than to a machine s. Historically, high-level languages have resulted in decreased development costs and increased reliability, at the expense of performance. In recent applications at JSC and in industry, SequenceL has demonstrated the usual advantages of high-level programming in terms of low cost and high reliability. SequenceL programs, however, have run at speeds typically comparable with, and in many cases faster than, their counterparts written in C and C++ when run on single-core processors. Moreover, SequenceL is able to generate parallel executables automatically for multicore hardware, gaining parallel speedups without any extra effort from the programmer beyond what is required to write the sequen tial/singlecore code. A SequenceL-to-C++ translator has been developed that automatically renders readable multithreaded C++ from a combination of a SequenceL program and sample data input. The SequenceL language is based on two fundamental computational laws, Consume-Simplify- Produce (CSP) and Normalize-Trans - pose (NT), which enable it to automate the creation of parallel algorithms from high-level code that has no annotations of parallelism whatsoever. In our anecdotal experience, SequenceL development has been in every case less costly than development of the same algorithm in sequential (that is, single-core, single process) C or C++, and an order of magnitude less

  13. Development of Automated Scoring Algorithms for Complex Performance Assessments: A Comparison of Two Approaches.

    ERIC Educational Resources Information Center

    Clauser, Brian E.; Margolis, Melissa J.; Clyman, Stephen G.; Ross, Linette P.

    1997-01-01

    Research on automated scoring is extended by comparing alternative automated systems for scoring a computer simulation of physicians' patient management skills. A regression-based system is more highly correlated with experts' evaluations than a system that uses complex rules to map performances into score levels, but both approaches are feasible.…

  14. Design and demonstration of automated data analysis algorithms for ultrasonic inspection of complex composite panels with bonds

    NASA Astrophysics Data System (ADS)

    Aldrin, John C.; Forsyth, David S.; Welter, John T.

    2016-02-01

    To address the data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the detection criteria and minimize false calls. The original design followed standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. However, certain complex panels with varying shape, ply drops and the presence of bonds can complicate this interpretation process. In this paper, enhancements to the automated data analysis algorithms are introduced to address these challenges. To estimate the thickness of the part and presence of bonds without prior information, an algorithm tracks potential backwall or bond-line signals, and evaluates a combination of spatial, amplitude, and time-of-flight metrics to identify bonded sections. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images, for evaluation of both first layer through thickness and layers under bonds. ADA processing results are presented for a variety of complex test specimens with inserted materials and other test discontinuities. Lastly, enhancements to the ADA software interface are presented, which improve the software usability for final data review by the inspectors and support the certification process.

  15. Automated infrasound signal detection algorithms implemented in MatSeis - Infra Tool.

    SciTech Connect

    Hart, Darren

    2004-07-01

    MatSeis's infrasound analysis tool, Infra Tool, uses frequency slowness processing to deconstruct the array data into three outputs per processing step: correlation, azimuth and slowness. Until now, an experienced analyst trained to recognize a pattern observed in outputs from signal processing manually accomplished infrasound signal detection. Our goal was to automate the process of infrasound signal detection. The critical aspect of infrasound signal detection is to identify consecutive processing steps where the azimuth is constant (flat) while the time-lag correlation of the windowed waveform is above background value. These two statements describe the arrival of a correlated set of wavefronts at an array. The Hough Transform and Inverse Slope methods are used to determine the representative slope for a specified number of azimuth data points. The representative slope is then used in conjunction with associated correlation value and azimuth data variance to determine if and when an infrasound signal was detected. A format for an infrasound signal detection output file is also proposed. The detection output file will list the processed array element names, followed by detection characteristics for each method. Each detection is supplied with a listing of frequency slowness processing characteristics: human time (YYYY/MM/DD HH:MM:SS.SSS), epochal time, correlation, fstat, azimuth (deg) and trace velocity (km/s). As an example, a ground truth event was processed using the four-element DLIAR infrasound array located in New Mexico. The event is known as the Watusi chemical explosion, which occurred on 2002/09/28 at 21:25:17 with an explosive yield of 38,000 lb TNT equivalent. Knowing the source and array location, the array-to-event distance was computed to be approximately 890 km. This test determined the station-to-event azimuth (281.8 and 282.1 degrees) to within 1.6 and 1.4 degrees for the Inverse Slope and Hough Transform detection algorithms, respectively, and

  16. Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by Eg5 Inhibition.

    PubMed

    Bigley, Alison L; Klein, Stephanie K; Davies, Barry; Williams, Leigh; Rudmann, Daniel G

    2016-07-01

    Modulation of the cell cycle may underlie the toxicologic or pharmacologic responses of a potential therapeutic agent and contributes to decisions on its preclinical and clinical safety and efficacy. The descriptive and quantitative assessment of normal, aberrant, and degenerate mitotic figures in tissue sections is an important end point characterizing the effect of xenobiotics on the cell cycle. Historically, pathologists used manual counting and special staining visualization techniques such as immunohistochemistry for quantification of normal, aberrant, and degenerate mitotic figures. We designed an automated image analysis algorithm for measuring these mitotic figures in hematoxylin and eosin (H&E)-stained sections. Algorithm validation methods used data generated from a subcutaneous human transitional cell carcinoma xenograft model in nude rats treated with the cell cycle inhibitor Eg5. In these studies, we scanned and digitized H&E-stained xenografts and applied a complex ruleset of sequential mathematical filters and shape discriminators for classification of cell populations demonstrating normal, aberrant, or degenerate mitotic figures. The resultant classification system enabled the representations of three identifiable degrees of morphological change associated with tumor differentiation and compound effects. The numbers of mitotic figure variants and mitotic indices data generated corresponded to a manual assessment by a pathologist and supported automated algorithm verification and application for both efficacy and toxicity studies. PMID:26936079

  17. Weakly supervised glasses removal

    NASA Astrophysics Data System (ADS)

    Wang, Zhicheng; Zhou, Yisu; Wen, Lijie

    2015-03-01

    Glasses removal is an important task on face recognition, in this paper, we provide a weakly supervised method to remove eyeglasses from an input face image automatically. We choose sparse coding as face reconstruction method, and optical flow to find exact shape of glasses. We combine the two processes iteratively to remove glasses more accurately. The experimental results reveal that our method works much better than these algorithms alone, and it can remove various glasses to obtain natural looking glassless facial images.

  18. An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms

    PubMed Central

    Pierce, Larry A.; Byrd, Darrin W.; Elston, Brian F.; Karp, Joel S.; Sunderland, John J.; Kinahan, Paul E.

    2016-01-01

    Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for inter-user variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plugin to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows runtimes between 3 and 4 minutes, and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and max values on the order of 0.5% and 2% respectively over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC. PMID:26894356

  19. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

    USGS Publications Warehouse

    Thenkabail, Prasad S.; Wu, Zhuoting

    2012-01-01

    The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (~80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland

  20. An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei

    NASA Astrophysics Data System (ADS)

    Yeo, Hangu; Sheinin, Vadim; Sheinin, Yuri

    2009-02-01

    As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for automated image processing and analysis technique. Most medical imaging methods require human visual inspection and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an automated image segmentation and classification method that identifies tumor cell nuclei in medical images and classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell carcinoma), and the automated results are compared with the hand-counted results by a pathologist.

  1. Quantitative mapping of hemodynamics in the lung, brain, and dorsal window chamber-grown tumors using a novel, automated algorithm

    PubMed Central

    Fontanella, Andrew N.; Schroeder, Thies; Hochman, Daryl W.; Chen, Raymond E.; Hanna, Gabi; Haglund, Michael M.; Secomb, Timothy W.; Palmer, Gregory M.; Dewhirst, Mark W.

    2013-01-01

    Hemodynamic properties of vascular beds are of great interest in a variety of clinical and laboratory settings. However, there presently exists no automated, accurate, technically simple method for generating blood velocity maps of complex microvessel networks. Here we present a novel algorithm that addresses this problem by applying pixel-by-pixel cross-correlation to video data. Temporal signals at every spatial coordinate are compared with signals at neighboring points, generating a series of correlation maps from which speed and direction are calculated. User assisted definition of vessel geometries is not required, and sequential data are analyzed automatically, without user bias. Velocity measurements are validated against the dual-slit method and against capillary flow with known velocities. The algorithm is tested in three different biological models. Along with simultaneously acquired hemoglobin saturation and vascular geometry information, the hemodynamic maps presented here demonstrate an accurate, quantitative method of analyzing dynamic vascular systems. PMID:23781901

  2. SU-E-T-497: Semi-Automated in Vivo Radiochromic Film Dosimetry Using a Novel Image Processing Algorithm

    SciTech Connect

    Reyhan, M; Yue, N

    2014-06-01

    Purpose: To validate an automated image processing algorithm designed to detect the center of radiochromic film used for in vivo film dosimetry against the current gold standard of manual selection. Methods: An image processing algorithm was developed to automatically select the region of interest (ROI) in *.tiff images that contain multiple pieces of radiochromic film (0.5x1.3cm{sup 2}). After a user has linked a calibration file to the processing algorithm and selected a *.tiff file for processing, an ROI is automatically detected for all films by a combination of thresholding and erosion, which removes edges and any additional markings for orientation. Calibration is applied to the mean pixel values from the ROIs and a *.tiff image is output displaying the original image with an overlay of the ROIs and the measured doses. Validation of the algorithm was determined by comparing in vivo dose determined using the current gold standard (manually drawn ROIs) versus automated ROIs for n=420 scanned films. Bland-Altman analysis, paired t-test, and linear regression were performed to demonstrate agreement between the processes. Results: The measured doses ranged from 0.2-886.6cGy. Bland-Altman analysis of the two techniques (automatic minus manual) revealed a bias of -0.28cGy and a 95% confidence interval of (5.5cGy,-6.1cGy). These values demonstrate excellent agreement between the two techniques. Paired t-test results showed no statistical differences between the two techniques, p=0.98. Linear regression with a forced zero intercept demonstrated that Automatic=0.997*Manual, with a Pearson correlation coefficient of 0.999. The minimal differences between the two techniques may be explained by the fact that the hand drawn ROIs were not identical to the automatically selected ones. The average processing time was 6.7seconds in Matlab on an IntelCore2Duo processor. Conclusion: An automated image processing algorithm has been developed and validated, which will help

  3. A hierarchical, automated target recognition algorithm for a parallel analog processor

    NASA Technical Reports Server (NTRS)

    Woodward, Gail; Padgett, Curtis

    1997-01-01

    A hierarchical approach is described for an automated target recognition (ATR) system, VIGILANTE, that uses a massively parallel, analog processor (3DANN). The 3DANN processor is capable of performing 64 concurrent inner products of size 1x4096 every 250 nanoseconds.

  4. Automating "Word of Mouth" to Recommend Classes to Students: An Application of Social Information Filtering Algorithms

    ERIC Educational Resources Information Center

    Booker, Queen Esther

    2009-01-01

    An approach used to tackle the problem of helping online students find the classes they want and need is a filtering technique called "social information filtering," a general approach to personalized information filtering. Social information filtering essentially automates the process of "word-of-mouth" recommendations: items are recommended to a…

  5. Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA)

    PubMed Central

    Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A.; Hammel, Naama; Yang, Zhiyong; Weinreb, Robert N.; Zangwill, Linda M.

    2016-01-01

    Purpose We determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images. Methods We followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions. Results Mean visual field mean deviation at baseline of the progressing glaucoma group was −7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit–intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm Conclusions Bruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility. PMID:26906156

  6. Image processing algorithm for automated monitoring of metal transfer in double-electrode GMAW

    NASA Astrophysics Data System (ADS)

    Wang, Zhen Zhou; Zhang, Yu Ming

    2007-07-01

    Controlled metal transfer in gas metal arc welding (GMAW) implies controllable weld quality. To understand, analyse and control the metal transfer process, the droplet should be monitored and tracked. To process the metal transfer images in double-electrode GMAW (DE-GMAW), a novel modification of GMAW, a brightness-based algorithm is proposed to locate the droplet and compute the droplet size automatically. Although this algorithm can locate the droplet with adequate accuracy, its accuracy in droplet size computation needs improvements. To this end, the correlation among adjacent images due to the droplet development is taken advantage of to improve the algorithm. Experimental results verified that the improved algorithm can automatically locate the droplets and compute the droplet size with an adequate accuracy.

  7. A statistical-based scheduling algorithm in automated data path synthesis

    NASA Technical Reports Server (NTRS)

    Jeon, Byung Wook; Lursinsap, Chidchanok

    1992-01-01

    In this paper, we propose a new heuristic scheduling algorithm based on the statistical analysis of the cumulative frequency distribution of operations among control steps. It has a tendency of escaping from local minima and therefore reaching a globally optimal solution. The presented algorithm considers the real world constraints such as chained operations, multicycle operations, and pipelined data paths. The result of the experiment shows that it gives optimal solutions, even though it is greedy in nature.

  8. Modeling pilot interaction with automated digital avionics systems: Guidance and control algorithms for contour and nap-of-the-Earth flight

    NASA Technical Reports Server (NTRS)

    Hess, Ronald A.

    1990-01-01

    A collection of technical papers are presented that cover modeling pilot interaction with automated digital avionics systems and guidance and control algorithms for contour and nap-of-the-earth flight. The titles of the papers presented are as follows: (1) Automation effects in a multiloop manual control system; (2) A qualitative model of human interaction with complex dynamic systems; (3) Generalized predictive control of dynamic systems; (4) An application of generalized predictive control to rotorcraft terrain-following flight; (5) Self-tuning generalized predictive control applied to terrain-following flight; and (6) Precise flight path control using a predictive algorithm.

  9. A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol

    NASA Astrophysics Data System (ADS)

    Carnes, Greg; Gaede, Stewart; Yu, Edward; Van Dyk, Jake; Battista, Jerry; Lee, Ting-Yim

    2009-04-01

    Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.

  10. Automated decision algorithm applied to a field experiment with multiple research objectives: The DC3 campaign

    NASA Astrophysics Data System (ADS)

    Hanlon, Christopher J.; Small, Arthur A.; Bose, Satyajit; Young, George S.; Verlinde, Johannes

    2014-10-01

    Automated decision systems have shown the potential to increase data yields from field experiments in atmospheric science. The present paper describes the construction and performance of a flight decision system designed for a case in which investigators pursued multiple, potentially competing objectives. The Deep Convective Clouds and Chemistry (DC3) campaign in 2012 sought in situ airborne measurements of isolated deep convection in three study regions: northeast Colorado, north Alabama, and a larger region extending from central Oklahoma through northwest Texas. As they confronted daily flight launch decisions, campaign investigators sought to achieve two mission objectives that stood in potential tension to each other: to maximize the total amount of data collected while also collecting approximately equal amounts of data from each of the three study regions. Creating an automated decision system involved understanding how investigators would themselves negotiate the trade-offs between these potentially competing goals, and representing those preferences formally using a utility function that served to rank-order the perceived value of alternative data portfolios. The decision system incorporated a custom-built method for generating probabilistic forecasts of isolated deep convection and estimated climatologies calibrated to historical observations. Monte Carlo simulations of alternative future conditions were used to generate flight decision recommendations dynamically consistent with the expected future progress of the campaign. Results show that a strict adherence to the recommendations generated by the automated system would have boosted the data yield of the campaign by between 10 and 57%, depending on the metrics used to score success, while improving portfolio balance.

  11. Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department

    PubMed Central

    Deleger, Louise; Brodzinski, Holly; Zhai, Haijun; Li, Qi; Lingren, Todd; Kirkendall, Eric S; Alessandrini, Evaline; Solti, Imre

    2013-01-01

    Objective To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR). Methods We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision. Results The system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit. Conclusions Automated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care. PMID:24130231

  12. An automated algorithm for extracting road edges from terrestrial mobile LiDAR data

    NASA Astrophysics Data System (ADS)

    Kumar, Pankaj; McElhinney, Conor P.; Lewis, Paul; McCarthy, Timothy

    2013-11-01

    Terrestrial mobile laser scanning systems provide rapid and cost effective 3D point cloud data which can be used for extracting features such as the road edge along a route corridor. This information can assist road authorities in carrying out safety risk assessment studies along road networks. The knowledge of the road edge is also a prerequisite for the automatic estimation of most other road features. In this paper, we present an algorithm which has been developed for extracting left and right road edges from terrestrial mobile LiDAR data. The algorithm is based on a novel combination of two modified versions of the parametric active contour or snake model. The parameters involved in the algorithm are selected empirically and are fixed for all the road sections. We have developed a novel way of initialising the snake model based on the navigation information obtained from the mobile mapping vehicle. We tested our algorithm on different types of road sections representing rural, urban and national primary road sections. The successful extraction of road edges from these multiple road section environments validates our algorithm. These findings and knowledge provide valuable insights as well as a prototype road edge extraction tool-set, for both national road authorities and survey companies.

  13. Algorithm development for automated outlier detection and background noise reduction during NIR spectroscopic data processing

    NASA Astrophysics Data System (ADS)

    Abookasis, David; Workman, Jerome J.

    2011-09-01

    This study describes a hybrid processing algorithm for use during calibration/validation of near-infrared spectroscopic signals based on a spectra cross-correlation and filtering process, combined with a partial-least square regression (PLS) analysis. In the first step of the algorithm, exceptional signals (outliers) are detected and remove based on spectra correlation criteria we have developed. Then, signal filtering based on direct orthogonal signal correction (DOSC) was applied, before being used in the PLS model, to filter out background variance. After outlier screening and DOSC treatment, a PLS calibration model matrix is formed. Once this matrix has been built, it is used to predict the concentration of the unknown samples. Common statistics such as standard error of cross-validation, mean relative error, coefficient of determination, etc. were computed to assess the fitting ability of the algorithm Algorithm performance was tested on several hundred blood samples prepared at different hematocrit and glucose levels using blood materials from thirteen healthy human volunteers. During measurements, these samples were subjected to variations in temperature, flow rate, and sample pathlength. Experimental results highlight the potential, applicability, and effectiveness of the proposed algorithm in terms of low error of prediction, high sensitivity and specificity, and low false negative (Type II error) samples.

  14. A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road Condition Monitoring System

    SciTech Connect

    Ndoye, Mandoye; Barker, Alan M; Krogmeier, James; Bullock, Darcy

    2011-01-01

    A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

  15. Microprocessor-based integration of microfluidic control for the implementation of automated sensor monitoring and multithreaded optimization algorithms.

    PubMed

    Ezra, Elishai; Maor, Idan; Bavli, Danny; Shalom, Itai; Levy, Gahl; Prill, Sebastian; Jaeger, Magnus S; Nahmias, Yaakov

    2015-08-01

    Microfluidic applications range from combinatorial synthesis to high throughput screening, with platforms integrating analog perfusion components, digitally controlled micro-valves and a range of sensors that demand a variety of communication protocols. Currently, discrete control units are used to regulate and monitor each component, resulting in scattered control interfaces that limit data integration and synchronization. Here, we present a microprocessor-based control unit, utilizing the MS Gadgeteer open framework that integrates all aspects of microfluidics through a high-current electronic circuit that supports and synchronizes digital and analog signals for perfusion components, pressure elements, and arbitrary sensor communication protocols using a plug-and-play interface. The control unit supports an integrated touch screen and TCP/IP interface that provides local and remote control of flow and data acquisition. To establish the ability of our control unit to integrate and synchronize complex microfluidic circuits we developed an equi-pressure combinatorial mixer. We demonstrate the generation of complex perfusion sequences, allowing the automated sampling, washing, and calibrating of an electrochemical lactate sensor continuously monitoring hepatocyte viability following exposure to the pesticide rotenone. Importantly, integration of an optical sensor allowed us to implement automated optimization protocols that require different computational challenges including: prioritized data structures in a genetic algorithm, distributed computational efforts in multiple-hill climbing searches and real-time realization of probabilistic models in simulated annealing. Our system offers a comprehensive solution for establishing optimization protocols and perfusion sequences in complex microfluidic circuits. PMID:26227212

  16. Terminal-Area Guidance Algorithms for Automated Air-Traffic Control

    NASA Technical Reports Server (NTRS)

    Erzberger, H.; Lee, H. Q.

    1972-01-01

    Terminal-area guidance problems are solved in the form of computer-oriented algorithms. A flyable, three-dimensional trajectory is constructed that begins at the current aircraft position, heading, speed, and altitude, and that terminates at a prescribed position, heading, speed, altitude, and time. The terminal position is a waypoint and the terminal time is the assigned landing slot. The algorithms developed are applicable to all possible combinations of initial and final conditiions, and thus can be used in a closed-loop feedback law.

  17. Integration of symbolic and algorithmic hardware and software for the automation of space station subsystems

    NASA Technical Reports Server (NTRS)

    Gregg, Hugh; Healey, Kathleen; Hack, Edmund; Wong, Carla

    1987-01-01

    Expert systems that require access to data bases, complex simulations and real time instrumentation have both symbolic as well as algorithmic computing needs. These needs could both be met using a general computing workstation running both symbolic and algorithmic code, or separate, specialized computers networked together. The later approach was chosen to implement TEXSYS, the thermal expert system, developed to demonstrate the ability of an expert system to autonomously control the thermal control system of the space station. TEXSYS has been implemented on a Symbolics workstation, and will be linked to a microVAX computer that will control a thermal test bed. Integration options are explored and several possible solutions are presented.

  18. Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm

    PubMed Central

    Saberkari, Hamidreza; Bahrami, Sheyda; Shamsi, Mousa; Amoshahy, Mohammad Javad; Ghavifekr, Habib Badri; Sedaaghi, Mohammad Hossein

    2015-01-01

    DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively. PMID:26284175

  19. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

    PubMed Central

    2014-01-01

    Background Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. Results We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. Conclusions The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of

  20. Towards an intercomparison of automated registration algorithms for multiple source remote sensing data

    NASA Technical Reports Server (NTRS)

    LeMoigne, Jacqueline; Xia, Wei; Chettri, Samir; El-Ghazawi, Tarek; Kaymaz, Emre; Lerner, Bao-Ting; Mareboyana, Manohar; Netanyahu, Nathan; Pierce, John; Raghavan, Srini; Tilton, James C.; Campbell, William J.; Cromp, Robert F.

    1997-01-01

    The first step in the integration of multiple data is registration, either relative image-to-image registration or absolute geo-registration, to a map or a fixed coordinate system. As the need for automating registration techniques is recognized, we feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. In this paper we will describe: 1) the operational toolbox which we are developing and which will consist in some of the most important registration techniques; and 2) the quantitative intercomparison of the different methods, which will allow a user to select the desired registration technique based on this evaluation and the visualization of the registration results.

  1. Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images.

    PubMed

    Wu, Jun; Wang, Yuanyuan; Yu, Jinhua; Shi, Xinling; Zhang, Junhua; Chen, Yue; Pang, Yun

    2015-02-01

    A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency. PMID:26366596

  2. An algorithm for automating the registration of USDA segment ground data to LANDSAT MSS data

    NASA Technical Reports Server (NTRS)

    Graham, M. H. (Principal Investigator)

    1981-01-01

    The algorithm is referred to as the Automatic Segment Matching Algorithm (ASMA). The ASMA uses control points or the annotation record of a P-format LANDSAT compter compatible tape as the initial registration to relate latitude and longitude to LANDSAT rows and columns. It searches a given area of LANDSAT data with a 2x2 sliding window and computes gradient values for bands 5 and 7 to match the segment boundaries. The gradient values are held in memory during the shifting (or matching) process. The reconstructed segment array, containing ones (1's) for boundaries and zeros elsewhere are computer compared to the LANDSAT array and the best match computed. Initial testing of the ASMA indicates that it has good potential for replacing the manual technique.

  3. Integration of symbolic and algorithmic hardware and software for the automation of space station subsystems

    NASA Technical Reports Server (NTRS)

    Gregg, Hugh; Healey, Kathleen; Hack, Edmund; Wong, Carla

    1988-01-01

    Expert systems that require access to data bases, complex simulations and real time instrumentation have both symbolic and algorithmic needs. Both of these needs could be met using a general purpose workstation running both symbolic and algorithmic codes, or separate, specialized computers networked together. The later approach was chosen to implement TEXSYS, the thermal expert system, developed by the NASA Ames Research Center in conjunction with the Johnson Space Center to demonstrate the ability of an expert system to autonomously monitor the thermal control system of the space station. TEXSYS has been implemented on a Symbolics workstation, and will be linked to a microVAX computer that will control a thermal test bed. The integration options and several possible solutions are presented.

  4. Integration of symbolic and algorithmic hardware and software for the automation of space station subsystems

    NASA Technical Reports Server (NTRS)

    Gregg, Hugh; Healey, Kathleen; Hack, Edmund; Wong, Carla

    1987-01-01

    Traditional expert systems, such as diagnostic and training systems, interact with users only through a keyboard and screen, and are usually symbolic in nature. Expert systems that require access to data bases, complex simulations and real-time instrumentation have both symbolic as well as algorithmic computing needs. These needs could both be met using a general purpose workstation running both symbolic and algorithmic code, or separate, specialized computers networked together. The latter approach was chosen to implement TEXSYS, the thermal expert system, developed by NASA Ames Research Center in conjunction with Johnson Space Center to demonstrate the ability of an expert system to autonomously monitor the thermal control system of the space station. TEXSYS has been implemented on a Symbolics workstation, and will be linked to a microVAX computer that will control a thermal test bed. This paper will explore the integration options, and present several possible solutions.

  5. Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data.

    PubMed

    Prieto, Sandra P; Lai, Keith K; Laryea, Jonathan A; Mizell, Jason S; Muldoon, Timothy J

    2016-04-01

    Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia. PMID:27335893

  6. Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning.

    PubMed

    Martínez-Martínez, Francisco; Kybic, Jan; Lambert, Lukáš; Mecková, Zuzana

    2016-04-01

    This paper presents a fully automated method for the identification of bone marrow infiltration in femurs in low-dose CT of patients with multiple myeloma. We automatically find the femurs and the bone marrow within them. In the next step, we create a probabilistic, spatially dependent density model of normal tissue. At test time, we detect unexpectedly high density voxels which may be related to bone marrow infiltration, as outliers to this model. Based on a set of global, aggregated features representing all detections from one femur, we classify the subjects as being either healthy or not. This method was validated on a dataset of 127 subjects with ground truth created from a consensus of two expert radiologists, obtaining an AUC of 0.996 for the task of distinguishing healthy controls and patients with bone marrow infiltration. To the best of our knowledge, no other automatic image-based method for this task has been published before. PMID:26894595

  7. Rationale for Clinical Supervision

    ERIC Educational Resources Information Center

    Cogan, Morris L.

    1976-01-01

    The author, one of the originators and developers of the clinical supervision process, offered a cogent rationale for clinical supervision. He defined clinical supervision and discussed the psychological-sociological basis for its practice. (Editor)

  8. Automated estimation of mass eruption rate of volcanic eruption on satellite imagery using a cloud pattern recognition algorithm

    NASA Astrophysics Data System (ADS)

    Pouget, Solene; Jansons, Emile; Bursik, Marcus; Tupper, Andrew; Patra, Abani; Pitman, Bruce; Carn, Simon

    2014-05-01

    The need to detect and track the position of ash in the atmosphere has been highlighted in the past few years following the eruption Eyjafjallajokull. As a result, Volcanic Ash Advisory Centers (VAACs) are using Volcanic Ash Transport and Dispersion models (VATD) to estimate and predict the whereabouts of the ash in the atmosphere. However, these models require inputs of eruption source parameters, such as the mass eruption rate (MER), and wind fields, which are vital to properly model the ash movements. These inputs might change with time as the eruption enters different phases. This implies tracking the ash movement as conditions change, and new satellite imagery comes in. Thus, ultimately, the eruption must be detectable, regardless of changing eruption source and meteorological conditions. Volcanic cloud recognition can be particularly challenging, especially when meteorological clouds are present, which is typically the case in the tropics. Given the fact that a large fraction of the eruptions in the world happen in a tropical environment, we have based an automated volcanic cloud recognition algorithm on the fact that meteorological clouds and volcanic clouds behave differently. As a result, the pattern definition algorithm detects and defines volcanic clouds as different object types from meteorological clouds on satellite imagery. Following detection and definition, the algorithm then estimates the area covered by the ash. The area is then analyzed with respect to a plume growth rate methodology to get estimation of the volumetric and mass growth with time. This way, we were able to get an estimation of the MER with time, as plume growth is dependent on MER. To test our approach, we used the examples of two eruptions of different source strength, in two different climatic regimes, and for which therefore the weather during the eruption was quite different: Manam (Papua New Guinea) January 27 2005, which produced a stratospheric umbrella cloud and was

  9. Prediction-based registration: an automated multi-INT registration algorithm

    NASA Astrophysics Data System (ADS)

    Purman, Benjamin; Spencer, James; Conk, Jennifer M.

    2004-09-01

    This paper presents an algorithm for the automatic georegistration of electro-optical (EO) and synthetic aperture radar (SAR) imagery intelligence (IMINT). The algorithm uses a scene reference model in a global coordinate frame to register the incoming IMINT, or mission image. Auxiliary data from the mission image and this model predict a synthetic reference image of a scene at the same collection geometry as the mission image. This synthetic image provides a traceback structure relating the synthetic reference image to the scene model. A correlation matching technique is used to register the mission image to the synthetic reference image. Once the matching has been completed, mission image pixels can be transformed into the corresponding synthetic reference image. Using the traceback structure associated with the synthetic reference image, these pixels can then be transformed into the scene model space. Since the scene model space exists in a global coordinate frame, the mission image has been georegistered. This algorithm is called Prediction-Based Registration (PBR). There are a number of advantages to the PBR approach. First, the transformation from image space to scene model space is computed as a 3D to 2D transformation. This avoids solving the ill-posed problem of directly transforming a 2D image into 3D space. The generation of a synthetic reference simplifies the image matching process by creating the synthetic reference at the same geometry as the mission image. Further, dissimilar sensor phenomenologies are accounted for by using the appropriate sensor model. This allows sensor platform and image formation errors to be accounted for in their own domain when multiple sensors are being registered.

  10. Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search.

    PubMed

    Hou, T; Wang, J; Chen, L; Xu, X

    1999-08-01

    A genetic algorithm (GA) combined with a tabu search (TA) has been applied as a minimization method to rake the appropriate associated sites for some biomolecular systems. In our docking procedure, surface complementarity and energetic complementarity of a ligand with its receptor have been considered separately in a two-stage docking method. The first stage was to find a set of potential associated sites mainly based on surface complementarity using a genetic algorithm combined with a tabu search. This step corresponds with the process of finding the potential binding sites where pharmacophores will bind. In the second stage, several hundreds of GA minimization steps were performed for each associated site derived from the first stage mainly based on the energetic complementarity. After calculations for both of the two stages, we can offer several solutions of associated sites for every complex. In this paper, seven biomolecular systems, including five bound complexes and two unbound complexes, were chosen from the Protein Data Bank (PDB) to test our method. The calculated results were very encouraging-the hybrid minimization algorithm successfully reaches the correct solutions near the best binded modes for these protein complexes. The docking results not only predict the bound complexes very well, but also get a relatively accurate complexed conformation for unbound systems. For the five bound complexes, the results show that surface complementarity is enough to find the precise binding modes, the top solution from the tabu list generally corresponds to the correct binding mode. For the two unbound complexes, due to the conformational changes upon binding, it seems more difficult to get their correct binding conformations. The predicted results show that the correct binding mode also corresponds to a relatively large surface complementarity score. In these two test cases, the correct solution can be found in the top several solutions from the tabu list. For

  11. Image processing algorithms for automated analysis of GMR data from inspection of multilayer structures

    NASA Astrophysics Data System (ADS)

    Karpenko, Oleksii; Safdernejad, Seyed; Dib, Gerges; Udpa, Lalita; Udpa, Satish; Tamburrino, Antonello

    2015-03-01

    Eddy current probes (EC) with Giant Magnetoresistive (GMR) sensors have recently emerged as a promising tool for rapid scanning of multilayer aircraft panels that helps detect cracks under fastener heads. However, analysis of GMR data is challenging due to the complexity of sensed magnetic fields. Further, probes that induce unidirectional currents are insensitive to cracks parallel to the current flow. In this paper, signal processing algorithms are developed for mixing data from two orthogonal EC-GMR scans in order to generate pseudo-rotating electromagnetic field images of fasteners with bottom layer cracks. Finite element simulations demonstrate that the normal component of numerically computed rotating field has uniform sensitivity to cracks emanating in all radial directions. The concept of pseudo-rotating field imaging is experimentally validated with the help of MAUS bilateral GMR array (Big-MR) designed by Boeing.

  12. Development of a Genetic Algorithm to Automate Clustering of a Dependency Structure Matrix

    NASA Technical Reports Server (NTRS)

    Rogers, James L.; Korte, John J.; Bilardo, Vincent J.

    2006-01-01

    Much technology assessment and organization design data exists in Microsoft Excel spreadsheets. Tools are needed to put this data into a form that can be used by design managers to make design decisions. One need is to cluster data that is highly coupled. Tools such as the Dependency Structure Matrix (DSM) and a Genetic Algorithm (GA) can be of great benefit. However, no tool currently combines the DSM and a GA to solve the clustering problem. This paper describes a new software tool that interfaces a GA written as an Excel macro with a DSM in spreadsheet format. The results of several test cases are included to demonstrate how well this new tool works.

  13. Unsupervised parameter optimization for automated retention time alignment of severely shifted gas chromatographic data using the piecework alignment algorithm.

    SciTech Connect

    Pierce, Karisa M.; Wright, Bob W.; Synovec, Robert E.

    2007-02-02

    First, simulated chromatographic separations with declining retention time precision were used to study the performance of the piecewise retention time alignment algorithm and to demonstrate an unsupervised parameter optimization method. The average correlation coefficient between the first chromatogram and every other chromatogram in the data set was used to optimize the alignment parameters. This correlation method does not require a training set, so it is unsupervised and automated. This frees the user from needing to provide class information and makes the alignment algorithm more generally applicable to classifying completely unknown data sets. For a data set of simulated chromatograms where the average chromatographic peak was shifted past two neighboring peaks between runs, the average correlation coefficient of the raw data was 0.46 ± 0.25. After automated, optimized piecewise alignment, the average correlation coefficient was 0.93 ± 0.02. Additionally, a relative shift metric and principal component analysis (PCA) were used to independently quantify and categorize the alignment performance, respectively. The relative shift metric was defined as four times the standard deviation of a given peak’s retention time in all of the chromatograms, divided by the peak-width-at-base. The raw simulated data sets that were studied contained peaks with average relative shifts ranging between 0.3 and 3.0. Second, a “real” data set of gasoline separations was gathered using three different GC methods to induce severe retention time shifting. In these gasoline separations, retention time precision improved ~8 fold following alignment. Finally, piecewise alignment and the unsupervised correlation optimization method were applied to severely shifted GC separations of reformate distillation fractions. The effect of piecewise alignment on peak heights and peak areas is also reported. Piecewise alignment either did not change the peak height, or caused it to slightly

  14. Automated Conflict Resolution For Air Traffic Control

    NASA Technical Reports Server (NTRS)

    Erzberger, Heinz

    2005-01-01

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

  15. An Automated Algorithm for Producing Land Cover Information from Landsat Surface Reflectance Data Acquired Between 1984 and Present

    NASA Astrophysics Data System (ADS)

    Rover, J.; Goldhaber, M. B.; Holen, C.; Dittmeier, R.; Wika, S.; Steinwand, D.; Dahal, D.; Tolk, B.; Quenzer, R.; Nelson, K.; Wylie, B. K.; Coan, M.

    2015-12-01

    Multi-year land cover mapping from remotely sensed data poses challenges. Producing land cover products at spatial and temporal scales required for assessing longer-term trends in land cover change are typically a resource-limited process. A recently developed approach utilizes open source software libraries to automatically generate datasets, decision tree classifications, and data products while requiring minimal user interaction. Users are only required to supply coordinates for an area of interest, land cover from an existing source such as National Land Cover Database and percent slope from a digital terrain model for the same area of interest, two target acquisition year-day windows, and the years of interest between 1984 and present. The algorithm queries the Landsat archive for Landsat data intersecting the area and dates of interest. Cloud-free pixels meeting the user's criteria are mosaicked to create composite images for training the classifiers and applying the classifiers. Stratification of training data is determined by the user and redefined during an iterative process of reviewing classifiers and resulting predictions. The algorithm outputs include yearly land cover raster format data, graphics, and supporting databases for further analysis. Additional analytical tools are also incorporated into the automated land cover system and enable statistical analysis after data are generated. Applications tested include the impact of land cover change and water permanence. For example, land cover conversions in areas where shrubland and grassland were replaced by shale oil pads during hydrofracking of the Bakken Formation were quantified. Analytical analysis of spatial and temporal changes in surface water included identifying wetlands in the Prairie Pothole Region of North Dakota with potential connectivity to ground water, indicating subsurface permeability and geochemistry.

  16. Validation of an Automated Cough Detection Algorithm for Tracking Recovery of Pulmonary Tuberculosis Patients

    PubMed Central

    Larson, Sandra; Comina, Germán; Gilman, Robert H.; Tracey, Brian H.; Bravard, Marjory; López, José W.

    2012-01-01

    Background A laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool. Methodology/Principal Findings Cough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of cough epochs. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment. PMID:23071550

  17. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

    PubMed

    Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

    2015-07-01

    Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. PMID:25841182

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

    PubMed

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

    2014-09-01

    Local Ca(2+) transients such as puffs and sparks form the building blocks of cellular Ca(2+) signaling in numerous cell types. They have traditionally been studied by linescan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) cameras now enable rapid (>500 frames s(-1)) imaging of subcellular Ca(2+) signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1 Gb min(-1)) than linescan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca(2+) events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca(2+) release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events. PMID:25047761

  19. Unsupervised parameter optimization for automated retention time alignment of severely shifted gas chromatographic data using the piecewise alignment algorithm.

    PubMed

    Pierce, Karisa M; Wright, Bob W; Synovec, Robert E

    2007-02-01

    Simulated chromatographic separations were used to study the performance of piecewise retention time alignment and to demonstrate automated unsupervised (without a training set) parameter optimization. The average correlation coefficient between the target chromatogram and all remaining chromatograms in the data set was used to optimize the alignment parameters. This approach frees the user from providing class information and makes the alignment algorithm applicable to classifying completely unknown data sets. The average peak in the raw simulated data set was shifted up to two peak-widths-at-base (average relative shift=2.0) and after alignment the average relative shift was improved to 0.3. Piecewise alignment was applied to severely shifted GC separations of gasolines and reformate distillation fraction samples. The average relative shifts in the raw gasolines and reformates data were 4.7 and 1.5, respectively, but after alignment improved to 0.5 and 0.4, respectively. The effect of piecewise alignment on peak heights and peak areas is also reported. The average relative difference in peak height was -0.20%. The average absolute relative difference in area was 0.15%. PMID:17174960

  20. Security system signal supervision

    SciTech Connect

    Chritton, M.R. ); Matter, J.C. )

    1991-09-01

    This purpose of this NUREG is to present technical information that should be useful to NRC licensees for understanding and applying line supervision techniques to security communication links. A review of security communication links is followed by detailed discussions of link physical protection and DC/AC static supervision and dynamic supervision techniques. Material is also presented on security for atmospheric transmission and video line supervision. A glossary of security communication line supervision terms is appended. 16 figs.

  1. Recent processing string and fusion algorithm improvements for automated sea mine classification in shallow water

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Fernandez, Manuel F.; Dobeck, Gerald J.

    2003-09-01

    A novel sea mine computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The overall CAD/CAC processing string consists of pre-processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, feature orthogonalization, optimal subset feature selection, classification and fusion processing blocks. The range-dimension ACF is matched both to average highlight and shadow information, while also adaptively suppressing background clutter. For each detected object, features are extracted and processed through an orthogonalization transformation, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule, in the orthogonal feature space domain. The classified objects of 4 distinct processing strings are fused using the classification confidence values as features and logic-based, "M-out-of-N", or LLRT-based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new shallow water high-resolution sonar imagery data. The processing string detection and classification parameters were tuned and the string classification performance was optimized, by appropriately selecting a subset of the original feature set. A significant improvement was made to the CAD/CAC processing string by utilizing a repeated application of the subset feature selection / LLRT classification blocks. It was shown that LLRT-based fusion algorithms outperform the logic based and the "M-out-of-N" ones. The LLRT-based fusion of the CAD/CAC processing strings resulted in up to a nine-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results, while maintaining a constant correct mine classification probability.

  2. SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

    SciTech Connect

    Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu; Pradier, Olivier; Cheze Le Rest, Catherine

    2015-10-15

    Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.

  3. Automated Means of Identifying Landslide Deposits using LiDAR Data using the Contour Connection Method Algorithm

    NASA Astrophysics Data System (ADS)

    Olsen, M. J.; Leshchinsky, B. A.; Tanyu, B. F.

    2014-12-01

    Landslides are a global natural hazard, resulting in severe economic, environmental and social impacts every year. Often, landslides occur in areas of repeated slope instability, but despite these trends, significant residential developments and critical infrastructure are built in the shadow of past landslide deposits and marginally stable slopes. These hazards, despite their sometimes enormous scale and regional propensity, however, are difficult to detect on the ground, often due to vegetative cover. However, new developments in remote sensing technology, specifically Light Detection and Ranging mapping (LiDAR) are providing a new means of viewing our landscape. Airborne LiDAR, combined with a level of post-processing, enable the creation of spatial data representative of the earth beneath the vegetation, highlighting the scars of unstable slopes of the past. This tool presents a revolutionary technique to mapping landslide deposits and their associated regions of risk; yet, their inventorying is often done manually, an approach that can be tedious, time-consuming and subjective. However, the associated LiDAR bare earth data present the opportunity to use this remote sensing technology and typical landslide geometry to create an automated algorithm that can detect and inventory deposits on a landscape scale. This algorithm, called the Contour Connection Method (CCM), functions by first detecting steep gradients, often associated with the headscarp of a failed hillslope, and initiating a search, highlighting deposits downslope of the failure. Based on input of search gradients, CCM can assist in highlighting regions identified as landslides consistently on a landscape scale, capable of mapping more than 14,000 hectares rapidly (<30 minutes). CCM has shown preliminary agreement with manual landslide inventorying in Oregon's Coast Range, realizing almost 90% agreement with inventorying performed by a trained geologist. The global threat of landslides necessitates

  4. Automated guidance algorithms for a space station-based crew escape vehicle.

    PubMed

    Flanary, R; Hammen, D G; Ito, D; Rabalais, B W; Rishikof, B H; Siebold, K H

    2003-04-01

    An escape vehicle was designed to provide an emergency evacuation for crew members living on a space station. For maximum escape capability, the escape vehicle needs to have the ability to safely evacuate a station in a contingency scenario such as an uncontrolled (e.g., tumbling) station. This emergency escape sequence will typically be divided into three events: The first separation event (SEP1), the navigation reconstruction event, and the second separation event (SEP2). SEP1 is responsible for taking the spacecraft from its docking port to a distance greater than the maximum radius of the rotating station. The navigation reconstruction event takes place prior to the SEP2 event and establishes the orbital state to within the tolerance limits necessary for SEP2. The SEP2 event calculates and performs an avoidance burn to prevent station recontact during the next several orbits. This paper presents the tools and results for the whole separation sequence with an emphasis on the two separation events. The first challenge includes collision avoidance during the escape sequence while the station is in an uncontrolled rotational state, with rotation rates of up to 2 degrees per second. The task of avoiding a collision may require the use of the Vehicle's de-orbit propulsion system for maximum thrust and minimum dwell time within the vicinity of the station vicinity. The thrust of the propulsion system is in a single direction, and can be controlled only by the attitude of the spacecraft. Escape algorithms based on a look-up table or analytical guidance can be implemented since the rotation rate and the angular momentum vector can be sensed onboard and a-priori knowledge of the position and relative orientation are available. In addition, crew intervention has been provided for in the event of unforeseen obstacles in the escape path. The purpose of the SEP2 burn is to avoid re-contact with the station over an extended period of time. Performing this maneuver requires

  5. Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

    NASA Astrophysics Data System (ADS)

    Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

    2009-05-01

    bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

  6. Nonlinear analysis of the heartbeats in public patient ECGs using an automated PD2i algorithm for risk stratification of arrhythmic death

    PubMed Central

    Skinner, James E; Anchin, Jerry M; Weiss, Daniel N

    2008-01-01

    Heart rate variability (HRV) reflects both cardiac autonomic function and risk of arrhythmic death (AD). Reduced indices of HRV based on linear stochastic models are independent risk factors for AD in post-myocardial infarct cohorts. Indices based on nonlinear deterministic models have a significantly higher sensitivity and specificity for predicting AD in retrospective data. A need exists for nonlinear analytic software easily used by a medical technician. In the current study, an automated nonlinear algorithm, the time-dependent point correlation dimension (PD2i), was evaluated. The electrocardiogram (ECG) data were provided through an National Institutes of Health-sponsored internet archive (PhysioBank) and consisted of all 22 malignant arrhythmia ECG files (VF/VT) and 22 randomly selected arrhythmia files as the controls. The results were blindly calculated by automated software (Vicor 2.0, Vicor Technologies, Inc., Boca Raton, FL) and showed all analyzable VF/VT files had PD2i < 1.4 and all analyzable controls had PD2i > 1.4. Five VF/VT and six controls were excluded because surrogate testing showed the RR-intervals to contain noise, possibly resulting from the low digitization rate of the ECGs. The sensitivity was 100%, specificity 85%, relative risk > 100; p < 0.01, power > 90%. Thus, automated heartbeat analysis by the time-dependent nonlinear PD2i-algorithm can accurately stratify risk of AD in public data made available for competitive testing of algorithms. PMID:18728829

  7. Analysis of new bone, cartilage, and fibrosis tissue in healing murine allografts using whole slide imaging and a new automated histomorphometric algorithm

    PubMed Central

    Zhang, Longze; Chang, Martin; Beck, Christopher A; Schwarz, Edward M; Boyce, Brendan F

    2016-01-01

    Histomorphometric analysis of histologic sections of normal and diseased bone samples, such as healing allografts and fractures, is widely used in bone research. However, the utility of traditional semi-automated methods is limited because they are labor-intensive and can have high interobserver variability depending upon the parameters being assessed, and primary data cannot be re-analyzed automatically. Automated histomorphometry has long been recognized as a solution for these issues, and recently has become more feasible with the development of digital whole slide imaging and computerized image analysis systems that can interact with digital slides. Here, we describe the development and validation of an automated application (algorithm) using Visiopharm’s image analysis system to quantify newly formed bone, cartilage, and fibrous tissue in healing murine femoral allografts in high-quality digital images of H&E/alcian blue-stained decalcified histologic sections. To validate this algorithm, we compared the results obtained independently using OsteoMeasureTM and Visiopharm image analysis systems. The intraclass correlation coefficient between Visiopharm and OsteoMeasure was very close to one for all tissue elements tested, indicating nearly perfect reproducibility across methods. This new algorithm represents an accurate and labor-efficient method to quantify bone, cartilage, and fibrous tissue in healing mouse allografts. PMID:26816658

  8. Classifying Force Spectroscopy of DNA Pulling Measurements Using Supervised and Unsupervised Machine Learning Methods.

    PubMed

    Karatay, Durmus U; Zhang, Jie; Harrison, Jeffrey S; Ginger, David S

    2016-04-25

    Dynamic force spectroscopy (DFS) measurements on biomolecules typically require classifying thousands of repeated force spectra prior to data analysis. Here, we study classification of atomic force microscope-based DFS measurements using machine-learning algorithms in order to automate selection of successful force curves. Notably, we collect a data set that has a testable positive signal using photoswitch-modified DNA before and after illumination with UV (365 nm) light. We generate a feature set consisting of six properties of force-distance curves to train supervised models and use principal component analysis (PCA) for an unsupervised model. For supervised classification, we train random forest models for binary and multiclass classification of force-distance curves. Random forest models predict successful pulls with an accuracy of 94% and classify them into five classes with an accuracy of 90%. The unsupervised method using Gaussian mixture models (GMM) reaches an accuracy of approximately 80% for binary classification. PMID:27010122

  9. Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

    NASA Astrophysics Data System (ADS)

    Barnard, L.; Scott, C. J.; Owens, M.; Lockwood, M.; Crothers, S. R.; Davies, J. A.; Harrison, R. A.

    2015-10-01

    Observations from the Heliospheric Imager (HI) instruments aboard the twin STEREO spacecraft have enabled the compilation of several catalogues of coronal mass ejections (CMEs), each characterizing the propagation of CMEs through the inner heliosphere. Three such catalogues are the Rutherford Appleton Laboratory (RAL)-HI event list, the Solar Stormwatch CME catalogue, and, presented here, the J-tracker catalogue. Each catalogue uses a different method to characterize the location of CME fronts in the HI images: manual identification by an expert, the statistical reduction of the manual identifications of many citizen scientists, and an automated algorithm. We provide a quantitative comparison of the differences between these catalogues and techniques, using 51 CMEs common to each catalogue. The time-elongation profiles of these CME fronts are compared, as are the estimates of the CME kinematics derived from application of three widely used single-spacecraft-fitting techniques. The J-tracker and RAL-HI profiles are most similar, while the Solar Stormwatch profiles display a small systematic offset. Evidence is presented that these differences arise because the RAL-HI and J-tracker profiles follow the sunward edge of CME density enhancements, while Solar Stormwatch profiles track closer to the antisunward (leading) edge. We demonstrate that the method used to produce the time-elongation profile typically introduces more variability into the kinematic estimates than differences between the various single-spacecraft-fitting techniques. This has implications for the repeatability and robustness of these types of analyses, arguably especially so in the context of space weather forecasting, where it could make the results strongly dependent on the methods used by the forecaster.

  10. A Supervision of Solidarity

    ERIC Educational Resources Information Center

    Reynolds, Vikki

    2010-01-01

    This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

  11. Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

    PubMed Central

    Ekberg, Peter; Su, Rong; Chang, Ernest W.; Yun, Seok Hyun; Mattsson, Lars

    2014-01-01

    Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 µm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness. PMID:24562018

  12. Clinical Supervision: A Conceptual Framework.

    ERIC Educational Resources Information Center

    Krajewski, Robert J.

    1982-01-01

    Various views of clinical supervision are analyzed and examined. The "process" definition of clinical supervision emphasizes an eight-step cycle of supervision. Clinical supervision as "concept" is also considered and seven conceptual elements are examined. (JN)

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

    PubMed

    Herbert, Alex D; Carr, Antony M; Hoffmann, Eva

    2014-01-01

    Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ. PMID:25478967

  14. Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

    SciTech Connect

    Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H; Brumby, Steven P; Macdonald, Brian; Balick, Lee; Oyer, Alden

    2009-01-01

    Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.

  15. Rx for Supervision

    ERIC Educational Resources Information Center

    Draves, David D.

    1972-01-01

    The secondary student teaching program at the University of New Hampshire incorporates resident supervisors'', teachers who supervise student teachers responsible for four of the supervisor's classes. (SP)

  16. Semi-supervised Learning for Phenotyping Tasks

    PubMed Central

    Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K.

    2015-01-01

    Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review. PMID:26958183

  17. Adaptation to an automated platform of algorithmic combinations of advantageous mutations in genes generated using amino acid scanning mutational strategy.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Recent mutational strategies for generating and screening of genes for optimized traits, including directed evolution, domain shuffling, random mutagenesis, and site-directed mutagenesis, have been adapted for automated platforms. Here we discuss the amino acid scanning mutational strategy and its ...

  18. Definition and Analysis of a System for the Automated Comparison of Curriculum Sequencing Algorithms in Adaptive Distance Learning

    ERIC Educational Resources Information Center

    Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia

    2011-01-01

    LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…

  19. Partially supervised speaker clustering.

    PubMed

    Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

    2012-05-01

    model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance. PMID:21844626

  20. Networks of Professional Supervision

    ERIC Educational Resources Information Center

    Annan, Jean; Ryba, Ken

    2013-01-01

    An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

  1. On Defining Supervision.

    ERIC Educational Resources Information Center

    Bolin, Frances S.

    1987-01-01

    The shift of focus from teachers to curriculum has left supervision as a field struggling to find its place within its own professional organization. This article searches for a proper definition of supervision, highlighting scientific, developmental, and democratic approaches and exploring problems of perspective and common ground. Includes 48…

  2. Experiments in Virtual Supervision.

    ERIC Educational Resources Information Center

    Walker, Rob

    This paper examines the use of First Class conferencing software to create a virtual culture among research students and as a vehicle for supervision and advising. Topics discussed include: computer-mediated communication and research; entry to cyberculture, i.e., research students' induction into the research community; supervision and the…

  3. Evaluating Effective Supervision.

    ERIC Educational Resources Information Center

    Worthen, Vaughn E.; Dougher, M. Kirk

    This paper outlines the purposes, professional obligations, and key components to consider when providing effective evaluation in psychotherapy supervision. An overview of various methods for gathering supervision data for evaluation purposes is provided including self-reporting; process notes; video and audiotaping; live observation; co-therapy;…

  4. Supervision in Libraries.

    ERIC Educational Resources Information Center

    Bailey, Martha J.

    Although the literature of library administration draws extensively on that of business management, it is difficult to compare library supervision to business or industrial supervision. Library supervisors often do not have managerial training and may consider their management role as secondary. The educational level of the staff they supervise…

  5. Improving Field Supervision through Collaborative Supervision Institutes

    ERIC Educational Resources Information Center

    Harvey, Virginia Smith; Amador, Andria; Finer, Diana; Gotthelf, David; Hintze, John; Kruger, Lou; Li, Chieh; Lichtenstein, Bob; Rogers, Laura; Struzziero, Joan; Wandle, Caroline

    2010-01-01

    Adequate and appropriate supervision of interns is frequently identified as a significant problem by training programs while, on their part, field placement sites often indicate that training programs generate expectations for interns that are not always "in synch" with district expectations of school psychologists. As a result of an increasing…

  6. A multi-stage heuristic algorithm for matching problem in the modified miniload automated storage and retrieval system of e-commerce

    NASA Astrophysics Data System (ADS)

    Wang, Wenrui; Wu, Yaohua; Wu, Yingying

    2016-05-01

    E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

  7. A multi-stage heuristic algorithm for matching problem in the modified miniload automated storage and retrieval system of e-commerce

    NASA Astrophysics Data System (ADS)

    Wang, Wenrui; Wu, Yaohua; Wu, Yingying

    2016-04-01

    E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

  8. An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters

    PubMed Central

    Rempe, Michael J; Clegern, William C; Wisor, Jonathan P

    2015-01-01

    Introduction Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2–10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations. Methods We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration. Results More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state

  9. A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naïve Bayes algorithms.

    PubMed

    Marucci-Wellman, Helen R; Lehto, Mark R; Corns, Helen L

    2015-11-01

    Public health surveillance programs in the U.S. are undergoing landmark changes with the availability of electronic health records and advancements in information technology. Injury narratives gathered from hospital records, workers compensation claims or national surveys can be very useful for identifying antecedents to injury or emerging risks. However, classifying narratives manually can become prohibitive for large datasets. The purpose of this study was to develop a human-machine system that could be relatively easily tailored to routinely and accurately classify injury narratives from large administrative databases such as workers compensation. We used a semi-automated approach based on two Naïve Bayesian algorithms to classify 15,000 workers compensation narratives into two-digit Bureau of Labor Statistics (BLS) event (leading to injury) codes. Narratives were filtered out for manual review if the algorithms disagreed or made weak predictions. This approach resulted in an overall accuracy of 87%, with consistently high positive predictive values across all two-digit BLS event categories including the very small categories (e.g., exposure to noise, needle sticks). The Naïve Bayes algorithms were able to identify and accurately machine code most narratives leaving only 32% (4853) for manual review. This strategy substantially reduces the need for resources compared with manual review alone. PMID:26412196

  10. Optimal Installation Locations for Automated External Defibrillators in Taipei 7-Eleven Stores: Using GIS and a Genetic Algorithm with a New Stirring Operator

    PubMed Central

    Wen, Tzai-Hung

    2014-01-01

    Immediate treatment with an automated external defibrillator (AED) increases out-of-hospital cardiac arrest (OHCA) patient survival potential. While considerable attention has been given to determining optimal public AED locations, spatial and temporal factors such as time of day and distance from emergency medical services (EMSs) are understudied. Here we describe a geocomputational genetic algorithm with a new stirring operator (GANSO) that considers spatial and temporal cardiac arrest occurrence factors when assessing the feasibility of using Taipei 7-Eleven stores as installation locations for AEDs. Our model is based on two AED conveyance modes, walking/running and driving, involving service distances of 100 and 300 meters, respectively. Our results suggest different AED allocation strategies involving convenience stores in urban settings. In commercial areas, such installations can compensate for temporal gaps in EMS locations when responding to nighttime OHCA incidents. In residential areas, store installations can compensate for long distances from fire stations, where AEDs are currently held in Taipei. PMID:25045396

  11. Validity of automated x-ray photoelectron spectroscopy algorithm to determine the amount of substance and the depth distribution of atoms

    SciTech Connect

    Tougaard, Sven

    2013-05-15

    The author reports a systematic study of the range of validity of a previously developed algorithm for automated x-ray photoelectron spectroscopy analysis, which takes into account the variation in both peak intensity and the intensity in the background of inelastically scattered electrons. This test was done by first simulating spectra for the Au4d peak with gold atoms distributed in the form of a wide range of nanostructures, which includes overlayers with varying thickness, a 5 A layer of atoms buried at varying depths and a substrate covered with an overlayer of varying thickness. Next, the algorithm was applied to analyze these spectra. The algorithm determines the number of atoms within the outermost 3 {lambda} of the surface. This amount of substance is denoted AOS{sub 3{lambda}} (where {lambda} is the electron inelastic mean free path). In general the determined AOS{sub 3{lambda}} is found to be accurate to within {approx}10-20% depending on the depth distribution of the atoms. The algorithm also determines a characteristic length L, which was found to give unambiguous information on the depth distribution of the atoms for practically all studied cases. A set of rules for this parameter, which relates the value of L to the depths where the atoms are distributed, was tested, and these rules were found to be generally valid with only a few exceptions. The results were found to be rather independent of the spectral energy range (from 20 to 40 eV below the peak energy) used in the analysis.

  12. A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

    NASA Astrophysics Data System (ADS)

    Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

    2014-12-01

    Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

  13. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    PubMed

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  14. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    PubMed Central

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  15. Automated real-time search and analysis algorithms for a non-contact 3D profiling system

    NASA Astrophysics Data System (ADS)

    Haynes, Mark; Wu, Chih-Hang John; Beck, B. Terry; Peterman, Robert J.

    2013-04-01

    The purpose of this research is to develop a new means of identifying and extracting geometrical feature statistics from a non-contact precision-measurement 3D profilometer. Autonomous algorithms have been developed to search through large-scale Cartesian point clouds to identify and extract geometrical features. These algorithms are developed with the intent of providing real-time production quality control of cold-rolled steel wires. The steel wires in question are prestressing steel reinforcement wires for concrete members. The geometry of the wire is critical in the performance of the overall concrete structure. For this research a custom 3D non-contact profilometry system has been developed that utilizes laser displacement sensors for submicron resolution surface profiling. Optimizations in the control and sensory system allow for data points to be collected at up to an approximate 400,000 points per second. In order to achieve geometrical feature extraction and tolerancing with this large volume of data, the algorithms employed are optimized for parsing large data quantities. The methods used provide a unique means of maintaining high resolution data of the surface profiles while keeping algorithm running times within practical bounds for industrial application. By a combination of regional sampling, iterative search, spatial filtering, frequency filtering, spatial clustering, and template matching a robust feature identification method has been developed. These algorithms provide an autonomous means of verifying tolerances in geometrical features. The key method of identifying the features is through a combination of downhill simplex and geometrical feature templates. By performing downhill simplex through several procedural programming layers of different search and filtering techniques, very specific geometrical features can be identified within the point cloud and analyzed for proper tolerancing. Being able to perform this quality control in real time

  16. Counselor Supervision: A Consumer's Guide.

    ERIC Educational Resources Information Center

    Yager, Geoffrey G.; Littrell, John M.

    This guide attempts to solve problems caused when a certain designated "brand" of supervision is forced on the counselor trainee with neither choice nor checklist of important criteria. As a tentative start on a guide to supervision the paper offers the following: a definition of supervision; a summary of the various types of supervision; a…

  17. Two Approaches to Clinical Supervision.

    ERIC Educational Resources Information Center

    Anderson, Eugene M.

    Criteria are established for a definition of "clinical supervision" and the effectiveness of such supervisory programs in a student teaching context are considered. Two differing genres of clinical supervision are constructed: "supervision by pattern analysis" is contrasted with "supervision by performance objectives." An outline of procedural…

  18. Semi-supervised and unsupervised extreme learning machines.

    PubMed

    Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

    2014-12-01

    Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency. PMID:25415946

  19. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.

    PubMed

    Naim, Iftekhar; Datta, Suprakash; Rebhahn, Jonathan; Cavenaugh, James S; Mosmann, Tim R; Sharma, Gaurav

    2014-05-01

    We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis is conducted in the multidimensional space retaining the semantics of the data, (b) an iterative weighted sampling procedure is utilized to maintain modest computational complexity and to retain discrimination of extremely small subpopulations (hundreds of cells from datasets containing tens of millions), and (c) a splitting and merging procedure is incorporated in the algorithm to preserve distinguishability between biologically distinct populations, while still providing a significant compaction relative to the original data. This article presents a detailed algorithmic description of SWIFT, outlining the application-driven motivations for the different design choices, a discussion of computational complexity of the different steps, and results obtained with SWIFT for synthetic data and relatively simple experimental data that allow validation of the desirable attributes. A companion paper (Part 2) highlights the use of SWIFT, in combination with additional computational tools, for more challenging biological problems. PMID:24677621

  20. A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

    NASA Astrophysics Data System (ADS)

    Qarib, Hossein; Adeli, Hojjat

    2015-12-01

    In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

  1. The implementation of an automated tracking algorithm for the track detection of migratory anticyclones affecting the Mediterranean

    NASA Astrophysics Data System (ADS)

    Hatzaki, Maria; Flocas, Elena A.; Simmonds, Ian; Kouroutzoglou, John; Keay, Kevin; Rudeva, Irina

    2013-04-01

    Migratory cyclones and anticyclones mainly account for the short-term weather variations in extra-tropical regions. By contrast to cyclones that have drawn major scientific attention due to their direct link to active weather and precipitation, climatological studies on anticyclones are limited, even though they also are associated with extreme weather phenomena and play an important role in global and regional climate. This is especially true for the Mediterranean, a region particularly vulnerable to climate change, and the little research which has been done is essentially confined to the manual analysis of synoptic charts. For the construction of a comprehensive climatology of migratory anticyclonic systems in the Mediterranean using an objective methodology, the Melbourne University automatic tracking algorithm is applied, based to the ERA-Interim reanalysis mean sea level pressure database. The algorithm's reliability in accurately capturing the weather patterns and synoptic climatology of the transient activity has been widely proven. This algorithm has been extensively applied for cyclone studies worldwide and it has been also successfully applied for the Mediterranean, though its use for anticyclone tracking is limited to the Southern Hemisphere. In this study the performance of the tracking algorithm under different data resolutions and different choices of parameter settings in the scheme is examined. Our focus is on the appropriate modification of the algorithm in order to efficiently capture the individual characteristics of the anticyclonic tracks in the Mediterranean, a closed basin with complex topography. We show that the number of the detected anticyclonic centers and the resulting tracks largely depend upon the data resolution and the search radius. We also find that different scale anticyclones and secondary centers that lie within larger anticyclone structures can be adequately represented; this is important, since the extensions of major

  2. Supervised classification of solar features using prior information

    NASA Astrophysics Data System (ADS)

    De Visscher, Ruben; Delouille, Véronique; Dupont, Pierre; Deledalle, Charles-Alban

    2015-10-01

    Context: The Sun as seen by Extreme Ultraviolet (EUV) telescopes exhibits a variety of large-scale structures. Of particular interest for space-weather applications is the extraction of active regions (AR) and coronal holes (CH). The next generation of GOES-R satellites will provide continuous monitoring of the solar corona in six EUV bandpasses that are similar to the ones provided by the SDO-AIA EUV telescope since May 2010. Supervised segmentations of EUV images that are consistent with manual segmentations by for example space-weather forecasters help in extracting useful information from the raw data. Aims: We present a supervised segmentation method that is based on the Maximum A Posteriori rule. Our method allows integrating both manually segmented images as well as other type of information. It is applied on SDO-AIA images to segment them into AR, CH, and the remaining Quiet Sun (QS) part. Methods: A Bayesian classifier is applied on training masks provided by the user. The noise structure in EUV images is non-trivial, and this suggests the use of a non-parametric kernel density estimator to fit the intensity distribution within each class. Under the Naive Bayes assumption we can add information such as latitude distribution and total coverage of each class in a consistent manner. Those information can be prescribed by an expert or estimated with an Expectation-Maximization algorithm. Results: The segmentation masks are in line with the training masks given as input and show consistency over time. Introduction of additional information besides pixel intensity improves upon the quality of the final segmentation. Conclusions: Such a tool can aid in building automated segmentations that are consistent with some ground truth' defined by the users.

  3. Automated Detection of Health Websites' HONcode Conformity: Can N-gram Tokenization Replace Stemming?

    PubMed

    Boyer, Célia; Dolamic, Ljiljana; Grabar, Natalia

    2015-01-01

    Authors evaluated supervised automatic classification algorithms for determination of health related web-page compliance with individual HONcode criteria of conduct using varying length character n-gram vectors to represent healthcare web page documents. The training/testing collection comprised web page fragments extracted by HONcode experts during the manual certification process. The authors compared automated classification performance of n-gram tokenization to the automated classification performance of document words and Porter-stemmed document words using a Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics. The study attempted to determine whether the automated, language-independent approach might safely replace word-based classification. Using 5-grams as document features, authors also compared the baseline DF reduction function to Chi-square and Z-score dimensionality reductions. Overall study results indicate that n-gram tokenization provided a potentially viable alternative to document word stemming. PMID:26262363

  4. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation

    NASA Astrophysics Data System (ADS)

    Carles, Montserrat; Fechter, Tobias; Nemer, Ursula; Nanko, Norbert; Mix, Michael; Nestle, Ursula; Schaefer, Andrea

    2015-12-01

    PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Δ φ =0.3+/- 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC  =  0.66+/- 0.04 ), Positive Predictive Value (PPV  =  0.81+/- 0.06 ) and Sensitivity (Sen.  =  0.49+/- 0.05 ). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol)  =  40+/- 30 , DSC  =  0.71+/- 0.07 and PPV  =  0.90+/- 0.13 ). High accuracy in target tracking position (Δ ME) was obtained for experimental and clinical data (Δ ME{{}\\text{exp}}=0+/- 3 mm; Δ ME{{}\\text{clin}}=0.3+/- 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume

  5. Multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility

    NASA Astrophysics Data System (ADS)

    Jourdan, Damien B.; de Weck, Olivier L.

    2004-09-01

    This paper examines the optimal placement of nodes for a Wireless Sensor Network (WSN) designed to monitor a critical facility in a hostile region. The sensors are dropped from an aircraft, and they must be connected (directly or via hops) to a High Energy Communication Node (HECN), which serves as a relay from the ground to a satellite or a high-altitude aircraft. The sensors are assumed to have fixed communication and sensing ranges. The facility is modeled as circular and served by two roads. This simple model is used to benchmark the performance of the optimizer (a Multi-Objective Genetic Algorithm, or MOGA) in creating WSN designs that provide clear assessments of movements in and out of the facility, while minimizing both the likelihood of sensors being discovered and the number of sensors to be dropped. The algorithm is also tested on two other scenarios; in the first one the WSN must detect movements in and out of a circular area, and in the second one it must cover uniformly a square region. The MOGA is shown again to perform well on those scenarios, which shows its flexibility and possible application to more complex mission scenarios with multiple and diverse targets of observation.

  6. Datamining the NOAO NVO Portal: Automated Image Classification

    NASA Astrophysics Data System (ADS)

    Vaswani, Pooja; Miller, C. J.; Barg, I.; Smith, R. C.

    2006-12-01

    Image metadata describes the properties of an image and can be used for classification, e.g., galactic, extra-galactic, solar system, standard star, among others. We are developing a data mining application to automate such a classification process based on supervised learning using decision trees. We are applying this application to the NOAO NVO Portal (www.nvo.noao.edu). The core concepts of Quinlan's C4.5 decision tree induction algorithm are used to train, build a decision tree, and generate classification rules. These rules are then used to classify previously unseen image metadata. We utilize a collection of decision trees instead of a single classifier and average the classification probabilities. The concept of ``Bagging'' was used to create the collection of classifiers. The classification algorithm also facilitates the addition of weights to the probability estimate of the classes when prior knowledge of the class distribution is known.

  7. A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning.

    PubMed

    Macedo, Maysa M G; Guimarães, Welingson V N; Galon, Micheli Z; Takimura, Celso K; Lemos, Pedro A; Gutierrez, Marco Antonio

    2015-12-01

    Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 μm. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features. PMID:26433615

  8. Distributed semi-supervised support vector machines.

    PubMed

    Scardapane, Simone; Fierimonte, Roberto; Di Lorenzo, Paolo; Panella, Massimo; Uncini, Aurelio

    2016-08-01

    The semi-supervised support vector machine (S(3)VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S(3)VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over networks, where communication is restricted only to neighboring agents and no coordinating authority is present. Using a standard relaxation of the original S(3)VM, we formulate the training problem as the distributed minimization of a non-convex social cost function. To find a (stationary) solution in a distributed manner, we employ two different strategies: (i) a distributed gradient descent algorithm; (ii) a recently developed framework for In-Network Nonconvex Optimization (NEXT), which is based on successive convexifications of the original problem, interleaved by state diffusion steps. Our experimental results show that the proposed distributed algorithms have comparable performance with respect to a centralized implementation, while highlighting the pros and cons of the proposed solutions. To the date, this is the first work that paves the way toward the broad field of distributed semi-supervised learning over networks. PMID:27179615

  9. Automated classification of seismic sources in large database using random forest algorithm: First results at Piton de la Fournaise volcano (La Réunion).

    NASA Astrophysics Data System (ADS)

    Hibert, Clément; Provost, Floriane; Malet, Jean-Philippe; Stumpf, André; Maggi, Alessia; Ferrazzini, Valérie

    2016-04-01

    In the past decades the increasing quality of seismic sensors and capability to transfer remotely large quantity of data led to a fast densification of local, regional and global seismic networks for near real-time monitoring. This technological advance permits the use of seismology to document geological and natural/anthropogenic processes (volcanoes, ice-calving, landslides, snow and rock avalanches, geothermal fields), but also led to an ever-growing quantity of seismic data. This wealth of seismic data makes the construction of complete seismicity catalogs, that include earthquakes but also other sources of seismic waves, more challenging and very time-consuming as this critical pre-processing stage is classically done by human operators. To overcome this issue, the development of automatic methods for the processing of continuous seismic data appears to be a necessity. The classification algorithm should satisfy the need of a method that is robust, precise and versatile enough to be deployed to monitor the seismicity in very different contexts. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forests classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied

  10. Supervising the Reading Clinician.

    ERIC Educational Resources Information Center

    Ridout, Susan Ramp; Bailey, Kevin Sue

    Designed for graduate students supervising undergraduate work in a reading clinic, this practicum manual provides guidelines and materials needed for the graduate section of the Reading Practicum (Methods of Teaching Reading II) at Indiana University Southeast. In addition to the syllabus, which includes course description and objectives, course…

  11. Handbook of Administrative Supervision.

    ERIC Educational Resources Information Center

    Falvey, Janet Elizabeth

    This handbook is one of four handbooks developed for preservice and inservice counselor preparation and professional development. It was developed as a practical guide which will provide resources for supervisors to use in the field for enhancing their own skills or for use with other counselors under their supervision. It is also appropriate for…

  12. Supervision as Cultural Inquiry.

    ERIC Educational Resources Information Center

    Flinders, David J.

    1991-01-01

    Describes a framework for "culturally responsive supervision." An understanding of analogic or iconic metaphors reveals the power of language to shape what are regarded as matters of fact. Kinesics, proxemics, and prosody bring into focus channels of nonverbal communication. The concept of "framing" calls attention to the metamessages of verbal…

  13. Supervising Graduate Assistants

    ERIC Educational Resources Information Center

    White, Jessica; Nonnamaker, John

    2011-01-01

    Discussions of personnel management in student affairs literature and at national conferences often focus on supervising new or midlevel professionals and the myriad challenges and possibilities these relationships entail (Carpenter, 2001; Winston and Creamer, 1997). Graduate students as employees and the often-complicated and ill-structured…

  14. Instructional Supervision: Dollars and Sense

    ERIC Educational Resources Information Center

    Krajewski, Robert J.

    1977-01-01

    Definitions of supervision and instructional supervision are provided, along with financial and educational rationale for better quality-control of public education through the development and maintenance of an effective instructional improvement program. A suggested supervisory model is described. (MJB)

  15. Optimization of automated segmentation of monkeypox virus-induced lung lesions from normal lung CT images using hard C-means algorithm

    NASA Astrophysics Data System (ADS)

    Castro, Marcelo A.; Thomasson, David; Avila, Nilo A.; Hufton, Jennifer; Senseney, Justin; Johnson, Reed F.; Dyall, Julie

    2013-03-01

    Monkeypox virus is an emerging zoonotic pathogen that results in up to 10% mortality in humans. Knowledge of clinical manifestations and temporal progression of monkeypox disease is limited to data collected from rare outbreaks in remote regions of Central and West Africa. Clinical observations show that monkeypox infection resembles variola infection. Given the limited capability to study monkeypox disease in humans, characterization of the disease in animal models is required. A previous work focused on the identification of inflammatory patterns using PET/CT image modality in two non-human primates previously inoculated with the virus. In this work we extended techniques used in computer-aided detection of lung tumors to identify inflammatory lesions from monkeypox virus infection and their progression using CT images. Accurate estimation of partial volumes of lung lesions via segmentation is difficult because of poor discrimination between blood vessels, diseased regions, and outer structures. We used hard C-means algorithm in conjunction with landmark based registration to estimate the extent of monkeypox virus induced disease before inoculation and after disease progression. Automated estimation is in close agreement with manual segmentation.

  16. Novice Supervisors' Understandings of Supervision.

    ERIC Educational Resources Information Center

    Waite, Duncan

    Findings of a study that examined novice supervisors' understandings of supervision are presented in this paper. Data were collected from 110 graduate-level students enrolled in an introductory supervision class. Four themes emerged from students' definitions of supervision-domains, relationships, traits, and tasks. The most surprising finding was…

  17. Outcome Evaluation in Supervision Research.

    ERIC Educational Resources Information Center

    Holloway, Elizabeth L.

    1984-01-01

    Presents a framework of different sources of outcome data in supervision, and classifies research according to this framework. Challenges assumptions of previous research in supervision. Emphasizes influences of trainee and client in supervision and counseling contexts, respectively. Supports broader definition and expanded view of outcome in…

  18. Supervision: In Retrospect and Prospect.

    ERIC Educational Resources Information Center

    Irvine, Freeman R., Jr.

    This literature review presents a concise chronology of supervision as it evolved on the American scene. It is emphasized that supervision has a unique definition for each user. Supervision in America evolved out of a basic school pattern inherited from the European school system. The early definition and concept of this position was vague. The…

  19. Automated Factor Slice Sampling.

    PubMed

    Tibbits, Matthew M; Groendyke, Chris; Haran, Murali; Liechty, John C

    2014-01-01

    Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution, can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate distribution. We describe a two-pronged approach for constructing efficient, automated MCMC algorithms: (1) we propose the "factor slice sampler", a generalization of the univariate slice sampler where we treat the selection of a coordinate basis (factors) as an additional tuning parameter, and (2) we develop an approach for automatically selecting tuning parameters in order to construct an efficient factor slice sampler. In addition to automating the factor slice sampler, our tuning approach also applies to the standard univariate slice samplers. We demonstrate the efficiency and general applicability of our automated MCMC algorithm with a number of illustrative examples. PMID:24955002

  20. Automated Factor Slice Sampling

    PubMed Central

    Tibbits, Matthew M.; Groendyke, Chris; Haran, Murali; Liechty, John C.

    2013-01-01

    Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution, can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate distribution. We describe a two-pronged approach for constructing efficient, automated MCMC algorithms: (1) we propose the “factor slice sampler”, a generalization of the univariate slice sampler where we treat the selection of a coordinate basis (factors) as an additional tuning parameter, and (2) we develop an approach for automatically selecting tuning parameters in order to construct an efficient factor slice sampler. In addition to automating the factor slice sampler, our tuning approach also applies to the standard univariate slice samplers. We demonstrate the efficiency and general applicability of our automated MCMC algorithm with a number of illustrative examples. PMID:24955002

  1. Semi-Supervised Kernel Mean Shift Clustering.

    PubMed

    Anand, Saket; Mittal, Sushil; Tuzel, Oncel; Meer, Peter

    2014-06-01

    Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms. PMID:26353281

  2. On Training Targets for Supervised Speech Separation

    PubMed Central

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation. PMID:25599083

  3. SU-E-I-89: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Pediatric Anthropomorphic and ACR Phantoms

    SciTech Connect

    Mahmood, U; Erdi, Y; Wang, W

    2014-06-01

    Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.

  4. Abdominal adipose tissue quantification on water-suppressed and non-water-suppressed MRI at 3T using semi-automated FCM clustering algorithm

    NASA Astrophysics Data System (ADS)

    Valaparla, Sunil K.; Peng, Qi; Gao, Feng; Clarke, Geoffrey D.

    2014-03-01

    Accurate measurements of human body fat distribution are desirable because excessive body fat is associated with impaired insulin sensitivity, type 2 diabetes mellitus (T2DM) and cardiovascular disease. In this study, we hypothesized that the performance of water suppressed (WS) MRI is superior to non-water suppressed (NWS) MRI for volumetric assessment of abdominal subcutaneous (SAT), intramuscular (IMAT), visceral (VAT), and total (TAT) adipose tissues. We acquired T1-weighted images on a 3T MRI system (TIM Trio, Siemens), which was analyzed using semi-automated segmentation software that employs a fuzzy c-means (FCM) clustering algorithm. Sixteen contiguous axial slices, centered at the L4-L5 level of the abdomen, were acquired in eight T2DM subjects with water suppression (WS) and without (NWS). Histograms from WS images show improved separation of non-fatty tissue pixels from fatty tissue pixels, compared to NWS images. Paired t-tests of WS versus NWS showed a statistically significant lower volume of lipid in the WS images for VAT (145.3 cc less, p=0.006) and IMAT (305 cc less, p<0.001), but not SAT (14.1 cc more, NS). WS measurements of TAT also resulted in lower fat volumes (436.1 cc less, p=0.002). There is strong correlation between WS and NWS quantification methods for SAT measurements (r=0.999), but poorer correlation for VAT studies (r=0.845). These results suggest that NWS pulse sequences may overestimate adipose tissue volumes and that WS pulse sequences are more desirable due to the higher contrast generated between fatty and non-fatty tissues.

  5. MAGIC: an automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS² approach.

    PubMed

    Lynn, Ke-Shiuan; Chen, Chen-Chun; Lih, T Mamie; Cheng, Cheng-Wei; Su, Wan-Chih; Chang, Chun-Hao; Cheng, Chia-Ying; Hsu, Wen-Lian; Chen, Yu-Ju; Sung, Ting-Yi

    2015-02-17

    Glycosylation is a highly complex modification influencing the functions and activities of proteins. Interpretation of intact glycopeptide spectra is crucial but challenging. In this paper, we present a mass spectrometry-based automated glycopeptide identification platform (MAGIC) to identify peptide sequences and glycan compositions directly from intact N-linked glycopeptide collision-induced-dissociation spectra. The identification of the Y1 (peptideY0 + GlcNAc) ion is critical for the correct analysis of unknown glycoproteins, especially without prior knowledge of the proteins and glycans present in the sample. To ensure accurate Y1-ion assignment, we propose a novel algorithm called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0-NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. To facilitate the subsequent peptide sequence identification by common database search engines, MAGIC generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. Finally, MAGIC computes the glycan compositions and ranks them. For the model glycoprotein horseradish peroxidase (HRP) and a 5-glycoprotein mixture, a 2- to 31-fold increase in the relative intensities of the peptide fragments was achieved, which led to the identification of 7 tryptic glycopeptides from HRP and 16 glycopeptides from the mixture via Mascot. In the HeLa cell proteome data set, MAGIC processed over a thousand MS(2) spectra in 3 min on a PC and reported 36 glycopeptides from 26 glycoproteins. Finally, a remarkable false discovery rate of 0 was achieved on the N-glycosylation-free Escherichia coli data set. MAGIC is available at http://ms.iis.sinica.edu.tw/COmics/Software_MAGIC.html . PMID:25629585

  6. Active link selection for efficient semi-supervised community detection.

    PubMed

    Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

    2015-01-01

    Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

  7. Active link selection for efficient semi-supervised community detection

    PubMed Central

    Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

    2015-01-01

    Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

  8. Dynamic hierarchical algorithm for accelerated microfossil identification

    NASA Astrophysics Data System (ADS)

    Wong, Cindy M.; Joseph, Dileepan

    2015-02-01

    Marine microfossils provide a useful record of the Earth's resources and prehistory via biostratigraphy. To study Hydrocarbon reservoirs and prehistoric climate, geoscientists visually identify the species of microfossils found in core samples. Because microfossil identification is labour intensive, automation has been investigated since the 1980s. With the initial rule-based systems, users still had to examine each specimen under a microscope. While artificial neural network systems showed more promise for reducing expert labour, they also did not displace manual identification for a variety of reasons, which we aim to overcome. In our human-based computation approach, the most difficult step, namely taxon identification is outsourced via a frontend website to human volunteers. A backend algorithm, called dynamic hierarchical identification, uses unsupervised, supervised, and dynamic learning to accelerate microfossil identification. Unsupervised learning clusters specimens so that volunteers need not identify every specimen during supervised learning. Dynamic learning means interim computation outputs prioritize subsequent human inputs. Using a dataset of microfossils identified by an expert, we evaluated correct and incorrect genus and species rates versus simulated time, where each specimen identification defines a moment. The proposed algorithm accelerated microfossil identification effectively, especially compared to benchmark results obtained using a k-nearest neighbour method.

  9. Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

    PubMed

    Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe; Frouin, Frederique; Garreau, Mireille

    2015-01-01

    This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

  10. Weakly Supervised Human Fixations Prediction.

    PubMed

    Zhang, Luming; Li, Xuelong; Nie, Liqiang; Yang, Yi; Xia, Yingjie

    2016-01-01

    Automatically predicting human eye fixations is a useful technique that can facilitate many multimedia applications, e.g., image retrieval, action recognition, and photo retargeting. Conventional approaches are frustrated by two drawbacks. First, psychophysical experiments show that an object-level interpretation of scenes influences eye movements significantly. Most of the existing saliency models rely on object detectors, and therefore, only a few prespecified categories can be discovered. Second, the relative displacement of objects influences their saliency remarkably, but current models cannot describe them explicitly. To solve these problems, this paper proposes weakly supervised fixations prediction, which leverages image labels to improve accuracy of human fixations prediction. The proposed model hierarchically discovers objects as well as their spatial configurations. Starting from the raw image pixels, we sample superpixels in an image, thereby seamless object descriptors termed object-level graphlets (oGLs) are generated by random walking on the superpixel mosaic. Then, a manifold embedding algorithm is proposed to encode image labels into oGLs, and the response map of each prespecified object is computed accordingly. On the basis of the object-level response map, we propose spatial-level graphlets (sGLs) to model the relative positions among objects. Afterward, eye tracking data is employed to integrate these sGLs for predicting human eye fixations. Thorough experiment results demonstrate the advantage of the proposed method over the state-of-the-art. PMID:26168451