Autofocusing and Polar Body Detection in Automated Cell Manipulation.

PubMed

Wang, Zenan; Feng, Chen; Ang, Wei Tech; Tan, Steven Yih Min; Latt, Win Tun

2017-05-01

Autofocusing and feature detection are two essential processes for performing automated biological cell manipulation tasks. In this paper, we have introduced a technique capable of focusing on a holding pipette and a mammalian cell under a bright-field microscope automatically, and a technique that can detect and track the presence and orientation of the polar body of an oocyte that is rotated at the tip of a micropipette. Both algorithms were evaluated by using mouse oocytes. Experimental results show that both algorithms achieve very high success rates: 100% and 96%. As robust and accurate image processing methods, they can be widely applied to perform various automated biological cell manipulations.

Automated Detection of Craters in Martian Satellite Imagery Using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Norman, C. J.; Paxman, J.; Benedix, G. K.; Tan, T.; Bland, P. A.; Towner, M.

2018-04-01

Crater counting is used in determining surface age of planets. We propose improvements to martian Crater Detection Algorithms by implementing an end-to-end detection approach with the possibility of scaling the algorithm planet-wide.

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy

PubMed Central

Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-01-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum. The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. PMID:27707942

Screening for Human Immunodeficiency Virus, Hepatitis B Virus, Hepatitis C Virus, and Treponema pallidum by Blood Testing Using a Bio-Flash Technology-Based Algorithm before Gastrointestinal Endoscopy.

PubMed

Jun, Zhou; Zhen, Chen; QuiuLi, Zhang; YuanQi, An; Casado, Verónica Vocero; Fan, Yuan

2016-12-01

Currently, conventional enzyme immunoassays which use manual gold immunoassays and colloidal tests (GICTs) are used as screening tools to detect Treponema pallidum (syphilis), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus type 1 (HIV-1), and HIV-2 in patients undergoing surgery. The present observational, cross-sectional study compared the sensitivity, specificity, and work flow characteristics of the conventional algorithm with manual GICTs with those of a newly proposed algorithm that uses the automated Bio-Flash technology as a screening tool in patients undergoing gastrointestinal (GI) endoscopy. A total of 956 patients were examined for the presence of serological markers of infection with HIV-1/2, HCV, HBV, and T. pallidum The proposed algorithm with the Bio-Flash technology was superior for the detection of all markers (100.0% sensitivity and specificity for detection of anti-HIV and anti-HCV antibodies, HBV surface antigen [HBsAg], and T. pallidum) compared with the conventional algorithm based on the manual method (80.0% sensitivity and 98.6% specificity for the detection of anti-HIV, 75.0% sensitivity for the detection of anti-HCV, 94.7% sensitivity for the detection of HBsAg, and 100% specificity for the detection of anti-HCV and HBsAg) in these patients. The automated Bio-Flash technology-based screening algorithm also reduced the operation time by 85.0% (205 min) per day, saving up to 24 h/week. In conclusion, the use of the newly proposed screening algorithm based on the automated Bio-Flash technology can provide an advantage over the use of conventional algorithms based on manual methods for screening for HIV, HBV, HCV, and syphilis before GI endoscopy. Copyright © 2016 Jun et al.

Automated detection and characterization of harmonic tremor in continuous seismic data

NASA Astrophysics Data System (ADS)

Roman, Diana C.

2017-06-01

Harmonic tremor is a common feature of volcanic, hydrothermal, and ice sheet seismicity and is thus an important proxy for monitoring changes in these systems. However, no automated methods for detecting harmonic tremor currently exist. Because harmonic tremor shares characteristics with speech and music, digital signal processing techniques for analyzing these signals can be adapted. I develop a novel pitch-detection-based algorithm to automatically identify occurrences of harmonic tremor and characterize their frequency content. The algorithm is applied to seismic data from Popocatepetl Volcano, Mexico, and benchmarked against a monthlong manually detected catalog of harmonic tremor events. During a period of heightened eruptive activity from December 2014 to May 2015, the algorithm detects 1465 min of harmonic tremor, which generally precede periods of heightened explosive activity. These results demonstrate the algorithm's ability to accurately characterize harmonic tremor while highlighting the need for additional work to understand its causes and implications at restless volcanoes.

Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing

PubMed Central

Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

2018-01-01

Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, feature extraction algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system. PMID:29462855

Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing.

PubMed

Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

2018-02-15

Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED light target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, direction location algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography.

PubMed

Huang, Alex S; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M; Weinreb, Robert N

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

collected data. These statistical techniques are under the area of descriptive statistics, which is a methodology to condense the data in quantitative ...ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...report when it is no longer needed. Do not return it to the originator. ARL-TR-8270 ● JAN 2017 US Army Research Laboratory An

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

PubMed

Geertsema, Evelien E; Thijs, Roland D; Gutter, Therese; Vledder, Ben; Arends, Johan B; Leijten, Frans S; Visser, Gerhard H; Kalitzin, Stiliyan N

2018-06-01

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance. © 2018 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Application of Novel Software Algorithms to Spectral-Domain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy.

PubMed

Adhi, Mehreen; Semy, Salim K; Stein, David W; Potter, Daniel M; Kuklinski, Walter S; Sleeper, Harry A; Duker, Jay S; Waheed, Nadia K

2016-05-01

To present novel software algorithms applied to spectral-domain optical coherence tomography (SD-OCT) for automated detection of diabetic retinopathy (DR). Thirty-one diabetic patients (44 eyes) and 18 healthy, nondiabetic controls (20 eyes) who underwent volumetric SD-OCT imaging and fundus photography were retrospectively identified. A retina specialist independently graded DR stage. Trained automated software generated a retinal thickness score signifying macular edema and a cluster score signifying microaneurysms and/or hard exudates for each volumetric SD-OCT. Of 44 diabetic eyes, 38 had DR and six eyes did not have DR. Leave-one-out cross-validation using a linear discriminant at missed detection/false alarm ratio of 3.00 computed software sensitivity and specificity of 92% and 69%, respectively, for DR detection when compared to clinical assessment. Novel software algorithms applied to commercially available SD-OCT can successfully detect DR and may have potential as a viable screening tool for DR in future. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.]. Copyright 2016, SLACK Incorporated.

Automated detection of sperm whale sounds as a function of abrupt changes in sound intensity

NASA Astrophysics Data System (ADS)

Walker, Christopher D.; Rayborn, Grayson H.; Brack, Benjamin A.; Kuczaj, Stan A.; Paulos, Robin L.

2003-04-01

An algorithm designed to detect abrupt changes in sound intensity was developed and used to identify and count sperm whale vocalizations and to measure boat noise. The algorithm is a MATLAB routine that counts the number of occurrences for which the change in intensity level exceeds a threshold. The algorithm also permits the setting of a ``dead time'' interval to prevent the counting of multiple pulses within a single sperm whale click. This algorithm was used to analyze digitally sampled recordings of ambient noise obtained from the Gulf of Mexico using near bottom mounted EARS buoys deployed as part of the Littoral Acoustic Demonstration Center experiment. Because the background in these data varied slowly, the result of the application of the algorithm was automated detection of sperm whale clicks and creaks with results that agreed well with those obtained by trained human listeners. [Research supported by ONR.

Automated recognition of microcalcification clusters in mammograms

NASA Astrophysics Data System (ADS)

Bankman, Isaac N.; Christens-Barry, William A.; Kim, Dong W.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

1993-07-01

The widespread and increasing use of mammographic screening for early breast cancer detection is placing a significant strain on clinical radiologists. Large numbers of radiographic films have to be visually interpreted in fine detail to determine the subtle hallmarks of cancer that may be present. We developed an algorithm for detecting microcalcification clusters, the most common and useful signs of early, potentially curable breast cancer. We describe this algorithm, which utilizes contour map representations of digitized mammographic films, and discuss its benefits in overcoming difficulties often encountered in algorithmic approaches to radiographic image processing. We present experimental analyses of mammographic films employing this contour-based algorithm and discuss practical issues relevant to its use in an automated film interpretation instrument.

Automated attribution of remotely-sensed ecological disturbances using spatial and temporal characteristics of common disturbance classes.

NASA Astrophysics Data System (ADS)

Cooper, L. A.; Ballantyne, A.

2017-12-01

Forest disturbances are critical components of ecosystems. Knowledge of their prevalence and impacts is necessary to accurately describe forest health and ecosystem services through time. While there are currently several methods available to identify and describe forest disturbances, especially those which occur in North America, the process remains inefficient and inaccessible in many parts of the world. Here, we introduce a preliminary approach to streamline and automate both the detection and attribution of forest disturbances. We use a combination of the Breaks for Additive Season and Trend (BFAST) detection algorithm to detect disturbances in combination with supervised and unsupervised classification algorithms to attribute the detections to disturbance classes. Both spatial and temporal disturbance characteristics are derived and utilized for the goal of automating the disturbance attribution process. The resulting preliminary algorithm is applied to up-scaled (100m) Landsat data for several different ecosystems in North America, with varying success. Our results indicate that supervised classification is more reliable than unsupervised classification, but that limited training data are required for a region. Future work will improve the algorithm through refining and validating at sites within North America before applying this approach globally.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

Improved automated lumen contour detection by novel multifrequency processing algorithm with current intravascular ultrasound system.

PubMed

Kume, Teruyoshi; Kim, Byeong-Keuk; Waseda, Katsuhisa; Sathyanarayana, Shashidhar; Li, Wenguang; Teo, Tat-Jin; Yock, Paul G; Fitzgerald, Peter J; Honda, Yasuhiro

2013-02-01

The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry. Copyright © 2011 Wiley Periodicals, Inc.

An Automated Energy Detection Algorithm Based on Consecutive Mean Excision

DTIC Science & Technology

2018-01-01

present in the RF spectrum. 15. SUBJECT TERMS RF spectrum, detection threshold algorithm, consecutive mean excision, rank order filter , statistical...Median 4 3.1.9 Rank Order Filter (ROF) 4 3.1.10 Crest Factor (CF) 5 3.2 Statistical Summary 6 4. Algorithm 7 5. Conclusion 8 6. References 9...energy detection algorithm based on morphological filter processing with a semi- disk structure. Adelphi (MD): Army Research Laboratory (US); 2018 Jan

Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.

PubMed

Karnowski, T P; Aykac, D; Giancardo, L; Li, Y; Nichols, T; Tobin, K W; Chaum, E

2011-01-01

The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

NASA Astrophysics Data System (ADS)

Amrute, Junedh M.; Athanasiou, Lambros S.; Rikhtegar, Farhad; de la Torre Hernández, José M.; Camarero, Tamara García; Edelman, Elazer R.

2018-03-01

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

Clinical evaluation of the vector algorithm for neonatal hearing screening using automated auditory brainstem response.

PubMed

Keohane, Bernie M; Mason, Steve M; Baguley, David M

2004-02-01

A novel auditory brainstem response (ABR) detection and scoring algorithm, entitled the Vector algorithm is described. An independent clinical evaluation of the algorithm using 464 tests (120 non-stimulated and 344 stimulated tests) on 60 infants, with a mean age of approximately 6.5 weeks, estimated test sensitivity greater than 0.99 and test specificity at 0.87 for one test. Specificity was estimated to be greater than 0.95 for a two stage screen. Test times were of the order of 1.5 minutes per ear for detection of an ABR and 4.5 minutes per ear in the absence of a clear response. The Vector algorithm is commercially available for both automated screening and threshold estimation in hearing screening devices.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

USGS Publications Warehouse

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

PubMed Central

Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.

2009-01-01

BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728

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

PubMed Central

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

2016-01-01

Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H

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-supervisedmore » 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.« less

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

PubMed

Kayasandik, Cihan Bilge; Labate, Demetrio

2016-12-01

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community. Copyright © 2016 Elsevier B.V. All rights reserved.

A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos; Hazle, John D; Kagadis, George C

2015-07-01

Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.

Early detection of glaucoma using fully automated disparity analysis of the optic nerve head (ONH) from stereo fundus images

NASA Astrophysics Data System (ADS)

Sharma, Archie; Corona, Enrique; Mitra, Sunanda; Nutter, Brian S.

2006-03-01

Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. Traditional early detection techniques involve observing changes in the ONH through an ophthalmoscope. Stereo fundus photography is also routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the Heidelberg Retina Tomograph (HRT) has not been found to be sufficiently sensitive for early detection. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. A 3-D visualization of the disc and cup is achieved assuming camera geometry. High correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study involving 159 stereo fundus image pairs has already been demonstrated. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this paper, we present a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.

High precision automated face localization in thermal images: oral cancer dataset as test case

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Raman, S. K.; Mukhopadhyay, S.; Patsa, S.; Anjum, N.; Ray, J. G.

2017-02-01

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

PubMed

Gregg, Richard E; Zhou, Sophia H; Dubin, Anne M

2016-01-01

With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence. Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n=772) consisted of VPE ECGs (n=37), normal ECGs (n=492) and a high concentration of conduction defects, RBBB (n=232) and LBBB (n=11). The testing database was a random sample (n=763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database. The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%. This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population. Copyright © 2016 Elsevier Inc. All rights reserved.

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

PubMed

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

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

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 monitoring purposes. They are documented in the last sections of the paper.

Automated detection of hospital outbreaks: A systematic review of methods.

PubMed

Leclère, Brice; Buckeridge, David L; Boëlle, Pierre-Yves; Astagneau, Pascal; Lepelletier, Didier

2017-01-01

Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.

Automated segmentation of oral mucosa from wide-field OCT images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Goldan, Ryan N.; Lee, Anthony M. D.; Cahill, Lucas; Liu, Kelly; MacAulay, Calum; Poh, Catherine F.; Lane, Pierre

2016-03-01

Optical Coherence Tomography (OCT) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness. We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo, rapid, wide-field (up to 90 x 2.5mm2) imaging in the oral cavity. Due to the size and complexity of these OCT data sets, rapid automated image processing software that immediately displays important tissue features is required to facilitate prompt bed-side clinical decisions. We present an automated segmentation algorithm capable of detecting the epithelial surface and basement membrane in 3D OCT images of the oral cavity. The algorithm was trained using volumetric OCT data acquired in vivo from a variety of tissue types and histology-confirmed pathologies spanning normal through cancer (8 sites, 21 patients). The algorithm was validated using a second dataset of similar size and tissue diversity. We demonstrate application of the algorithm to an entire OCT volume to map epithelial thickness, and detection of the basement membrane, over the tissue surface. These maps may be clinically useful for delineating pre-surgical tumor margins, or for biopsy site guidance.

Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMA) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably separate touching cells in hematoxylin stained breast TMA specimens which have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach which utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and tissue microarrays containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) which resulted in significant speed-up over the C/C++ implementation. PMID:22167559

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zappala, D.; Tavner, P.; Crabtree, C.

2013-01-01

Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data representmore » one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linguraru, Marius George; Panjwani, Neil; Fletcher, Joel G.

2011-12-15

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

Long-Term Pavement Performance Automated Faulting Measurement

DOT National Transportation Integrated Search

2015-02-01

This study focused on identifying transverse joint locations on jointed plain concrete pavements using an automated joint detection algorithm and computing faulting at these locations using Long-Term Pavement Performance (LTPP) Program profile data c...

Evaluation of an Automated Swallow-Detection Algorithm Using Visual Biofeedback in Healthy Adults and Head and Neck Cancer Survivors.

PubMed

Constantinescu, Gabriela; Kuffel, Kristina; Aalto, Daniel; Hodgetts, William; Rieger, Jana

2018-06-01

Mobile health (mHealth) technologies may offer an opportunity to address longstanding clinical challenges, such as access and adherence to swallowing therapy. Mobili-T ® is an mHealth device that uses surface electromyography (sEMG) to provide biofeedback on submental muscles activity during exercise. An automated swallow-detection algorithm was developed for Mobili-T ® . This study evaluated the performance of the swallow-detection algorithm. Ten healthy participants and 10 head and neck cancer (HNC) patients were fitted with the device. Signal was acquired during regular, effortful, and Mendelsohn maneuver saliva swallows, as well as lip presses, tongue, and head movements. Signals of interest were tagged during data acquisition and used to evaluate algorithm performance. Sensitivity and positive predictive values (PPV) were calculated for each participant. Saliva swallows were compared between HNC and controls in the four sEMG-based parameters used in the algorithm: duration, peak amplitude ratio, median frequency, and 15th percentile of the power spectrum density. In healthy participants, sensitivity and PPV were 92.3 and 83.9%, respectively. In HNC patients, sensitivity was 92.7% and PPV was 72.2%. In saliva swallows, HNC patients had longer event durations (U = 1925.5, p < 0.001), lower median frequency (U = 2674.0, p < 0.001), and lower 15th percentile of the power spectrum density [t(176.9) = 2.07, p < 0.001] than healthy participants. The automated swallow-detection algorithm performed well with healthy participants and retained a high sensitivity, but had lowered PPV with HNC patients. With respect to Mobili-T ® , the algorithm will next be evaluated using the mHealth system.

Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts

NASA Astrophysics Data System (ADS)

Lorsakul, Auranuch; Andersson, Emilia; Vega Harring, Suzana; Sade, Hadassah; Grimm, Oliver; Bredno, Joerg

2017-03-01

Multiplex-brightfield immunohistochemistry (IHC) staining and quantitative measurement of multiple biomarkers can support therapeutic targeting of carcinoma-associated fibroblasts (CAF). This paper presents an automated digitalpathology solution to simultaneously analyze multiple biomarker expressions within a single tissue section stained with an IHC duplex assay. Our method was verified against ground truth provided by expert pathologists. In the first stage, the automated method quantified epithelial-carcinoma cells expressing cytokeratin (CK) using robust nucleus detection and supervised cell-by-cell classification algorithms with a combination of nucleus and contextual features. Using fibroblast activation protein (FAP) as biomarker for CAFs, the algorithm was trained, based on ground truth obtained from pathologists, to automatically identify tumor-associated stroma using a supervised-generation rule. The algorithm reported distance to nearest neighbor in the populations of tumor cells and activated-stromal fibroblasts as a wholeslide measure of spatial relationships. A total of 45 slides from six indications (breast, pancreatic, colorectal, lung, ovarian, and head-and-neck cancers) were included for training and verification. CK-positive cells detected by the algorithm were verified by a pathologist with good agreement (R2=0.98) to ground-truth count. For the area occupied by FAP-positive cells, the inter-observer agreement between two sets of ground-truth measurements was R2=0.93 whereas the algorithm reproduced the pathologists' areas with R2=0.96. The proposed methodology enables automated image analysis to measure spatial relationships of cells stained in an IHC-multiplex assay. Our proof-of-concept results show an automated algorithm can be trained to reproduce the expert assessment and provide quantitative readouts that potentially support a cutoff determination in hypothesis testing related to CAF-targeting-therapy decisions.

Automated detection of hospital outbreaks: A systematic review of methods

PubMed Central

Buckeridge, David L.; Lepelletier, Didier

2017-01-01

Objectives Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. Methods We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Results Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Conclusion Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results. PMID:28441422

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

PubMed

Despins, Laurel A

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

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

PubMed Central

Srinivasan, Pratul P.; Kim, Leo A.; Mettu, Priyatham S.; Cousins, Scott W.; Comer, Grant M.; Izatt, Joseph A.; Farsiu, Sina

2014-01-01

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases. PMID:25360373

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.

Anomaly detection using temporal data mining in a smart home environment.

PubMed

Jakkula, V; Cook, D J

2008-01-01

To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

PubMed Central

Pour, Elias Khalili; Pourreza, Hamidreza; Zamani, Kambiz Ameli; Mahmoudi, Alireza; Sadeghi, Arash Mir Mohammad; Shadravan, Mahla; Karkhaneh, Reza; Pour, Ramak Rouhi

2017-01-01

Purpose To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. Methods Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. Results Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively. Conclusions The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field. PMID:29022295

Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

2016-02-01

Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

Linear feature detection algorithm for astronomical surveys - I. Algorithm description

NASA Astrophysics Data System (ADS)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

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

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

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

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

PubMed Central

Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna

2015-01-01

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution. PMID:26230717

Processing Images of Craters for Spacecraft Navigation

NASA Technical Reports Server (NTRS)

Cheng, Yang; Johnson, Andrew E.; Matthies, Larry H.

2009-01-01

A crater-detection algorithm has been conceived to enable automation of what, heretofore, have been manual processes for utilizing images of craters on a celestial body as landmarks for navigating a spacecraft flying near or landing on that body. The images are acquired by an electronic camera aboard the spacecraft, then digitized, then processed by the algorithm, which consists mainly of the following steps: 1. Edges in an image detected and placed in a database. 2. Crater rim edges are selected from the edge database. 3. Edges that belong to the same crater are grouped together. 4. An ellipse is fitted to each group of crater edges. 5. Ellipses are refined directly in the image domain to reduce errors introduced in the detection of edges and fitting of ellipses. 6. The quality of each detected crater is evaluated. It is planned to utilize this algorithm as the basis of a computer program for automated, real-time, onboard processing of crater-image data. Experimental studies have led to the conclusion that this algorithm is capable of a detection rate >93 percent, a false-alarm rate <5 percent, a geometric error <0.5 pixel, and a position error <0.3 pixel.

Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction

NASA Astrophysics Data System (ADS)

Narayan, Nikhil S.; Sivanandan, Srinivasan; Kudavelly, Srinivas; Patwardhan, Kedar A.; Ramaraju, G. A.

2018-02-01

Follicle quantification refers to the computation of the number and size of follicles in 3D ultrasound volumes of the ovary. This is one of the key factors in determining hormonal dosage during female infertility treatments. In this paper, we propose an automated algorithm to detect and segment follicles in 3D ultrasound volumes of the ovary for quantification. In a first of its kind attempt, we employ noise-robust phase symmetry feature maps as likelihood function to perform mean-shift based follicle center detection. Max-flow algorithm is used for segmentation and gray weighted distance transform is employed for post-processing the results. We have obtained state-of-the-art results with a true positive detection rate of >90% on 26 3D volumes with 323 follicles.

Automated system for analyzing the activity of individual neurons

NASA Technical Reports Server (NTRS)

Bankman, Isaac N.; Johnson, Kenneth O.; Menkes, Alex M.; Diamond, Steve D.; Oshaughnessy, David M.

1993-01-01

This paper presents a signal processing system that: (1) provides an efficient and reliable instrument for investigating the activity of neuronal assemblies in the brain; and (2) demonstrates the feasibility of generating the command signals of prostheses using the activity of relevant neurons in disabled subjects. The system operates online, in a fully automated manner and can recognize the transient waveforms of several neurons in extracellular neurophysiological recordings. Optimal algorithms for detection, classification, and resolution of overlapping waveforms are developed and evaluated. Full automation is made possible by an algorithm that can set appropriate decision thresholds and an algorithm that can generate templates on-line. The system is implemented with a fast IBM PC compatible processor board that allows on-line operation.

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

Automated peroperative assessment of stents apposition from OCT pullbacks.

PubMed

Dubuisson, Florian; Péry, Emilie; Ouchchane, Lemlih; Combaret, Nicolas; Kauffmann, Claude; Souteyrand, Géraud; Motreff, Pascal; Sarry, Laurent

2015-04-01

This study's aim was to control the stents apposition by automatically analyzing endovascular optical coherence tomography (OCT) sequences. Lumen is detected using threshold, morphological and gradient operators to run a Dijkstra algorithm. Wrong detection tagged by the user and caused by bifurcation, struts'presence, thrombotic lesions or dissections can be corrected using a morphing algorithm. Struts are also segmented by computing symmetrical and morphological operators. Euclidian distance between detected struts and wall artery initializes a stent's complete distance map and missing data are interpolated with thin-plate spline functions. Rejection of detected outliers, regularization of parameters by generalized cross-validation and using the one-side cyclic property of the map also optimize accuracy. Several indices computed from the map provide quantitative values of malapposition. Algorithm was run on four in-vivo OCT sequences including different incomplete stent apposition's cases. Comparison with manual expert measurements validates the segmentation׳s accuracy and shows an almost perfect concordance of automated results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

PubMed

Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

2015-10-01

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

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

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.

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

PubMed

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

2017-02-15

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

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.

PubMed

Rovira, Ericka; Parasuraman, Raja

2010-06-01

This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation. Research has shown that the type of automation imperfection leads to differential human performance costs. Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence. ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance. The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.

Kepler Planet Detection Metrics: Automatic Detection of Background Objects Using the Centroid Robovetter

NASA Technical Reports Server (NTRS)

Mullally, Fergal

2017-01-01

We present an automated method of identifying background eclipsing binaries masquerading as planet candidates in the Kepler planet candidate catalogs. We codify the manual vetting process for Kepler Objects of Interest (KOIs) described in Bryson et al. (2013) with a series of measurements and tests that can be performed algorithmically. We compare our automated results with a sample of manually vetted KOIs from the catalog of Burke et al. (2014) and find excellent agreement. We test the performance on a set of simulated transits and find our algorithm correctly identifies simulated false positives approximately 50 of the time, and correctly identifies 99 of simulated planet candidates.

AWARE - The Automated EUV Wave Analysis and REduction algorithm

NASA Astrophysics Data System (ADS)

Ireland, J.; Inglis; A. R.; Shih, A. Y.; Christe, S.; Mumford, S.; Hayes, L. A.; Thompson, B. J.

2016-10-01

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. Since their discovery over two hundred papers discussing their properties, causes and physics have been published. However, their fundamental nature and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, and their relation to other solar phenomena, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm for the detection of EUV waves over the full Sun. The AWARE algorithm is based on a novel image processing approach to isolating the bright wavefront of the EUV as it propagates across the corona. AWARE detects the presence of a wavefront, and measures the distance, velocity and acceleration of that wavefront across the Sun. Results from AWARE are compared to results from other algorithms for some well known EUV wave events. Suggestions are also give for further refinements to the basic algorithm presented here.

Using laser altimetry-based segmentation to refine automated tree identification in managed forests of the Black Hills, South Dakota

Treesearch

Eric Rowell; Carl Selelstad; Lee Vierling; Lloyd Queen; Wayne Sheppard

2006-01-01

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy...

An Automated Road Roughness Detection from Mobile Laser Scanning Data

NASA Astrophysics Data System (ADS)

Kumar, P.; Angelats, E.

2017-05-01

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

Automated fall detection on privacy-enhanced video.

PubMed

Edgcomb, Alex; Vahid, Frank

2012-01-01

A privacy-enhanced video obscures the appearance of a person in the video. We consider four privacy enhancements: blurring of the person, silhouetting of the person, covering the person with a graphical box, and covering the person with a graphical oval. We demonstrate that an automated video-based fall detection algorithm can be as accurate on privacy-enhanced video as on raw video. The algorithm operated on video from a stationary in-home camera, using a foreground-background segmentation algorithm to extract a minimum bounding rectangle (MBR) around the motion in the video, and using time series shapelet analysis on the height and width of the rectangle to detect falls. We report accuracy applying fall detection on 23 scenarios depicted as raw video and privacy-enhanced videos involving a sole actor portraying normal activities and various falls. We found that fall detection on privacy-enhanced video, except for the common approach of blurring of the person, was competitive with raw video, and in particular that the graphical oval privacy enhancement yielded the same accuracy as raw video, namely 0.91 sensitivity and 0.92 specificity.

Image processing improvement for optical observations of space debris with the TAROT telescopes

NASA Astrophysics Data System (ADS)

Thiebaut, C.; Theron, S.; Richard, P.; Blanchet, G.; Klotz, A.; Boër, M.

2016-07-01

CNES is involved in the Inter-Agency Space Debris Coordination Committee (IADC) and is observing space debris with two robotic ground based fully automated telescopes called TAROT and operated by the CNRS. An image processing algorithm devoted to debris detection in geostationary orbit is implemented in the standard pipeline. Nevertheless, this algorithm is unable to deal with debris tracking mode images, this mode being the preferred one for debris detectability. We present an algorithm improvement for this mode and give results in terms of false detection rate.

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

PubMed Central

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Revie, Crawford W.; Sanchez, Javier

2013-01-01

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel. PMID:23576782

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

PubMed

Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Revie, Crawford W; Sanchez, Javier

2013-06-06

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

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

NASA Astrophysics Data System (ADS)

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

2016-08-01

Automated source extraction and parametrization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper, we present a new algorithm, called CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parametrization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, also including different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the Evolutionary Map of the Universe (EMU) survey at the Australian Square Kilometre Array Pathfinder (ASKAP). The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography.

PubMed

Zang, Pengxiao; Gao, Simon S; Hwang, Thomas S; Flaxel, Christina J; Wilson, David J; Morrison, John C; Huang, David; Li, Dengwang; Jia, Yali

2017-03-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

PubMed Central

Zang, Pengxiao; Gao, Simon S.; Hwang, Thomas S.; Flaxel, Christina J.; Wilson, David J.; Morrison, John C.; Huang, David; Li, Dengwang; Jia, Yali

2017-01-01

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch’s membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm). PMID:28663830

Progress on automated data analysis algorithms for ultrasonic inspection of composites

NASA Astrophysics Data System (ADS)

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

2015-03-01

Progress is presented on the development and demonstration of automated data analysis (ADA) software to address the burden in interpreting ultrasonic inspection data for large composite structures. The automated data analysis algorithm is presented in detail, which follows standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. New algorithms have been implemented to reliably identify indications in time-of-flight images near the front and back walls of composite panels. Adaptive call criteria have also been applied to address sensitivity to variation in backwall signal level, panel thickness variation, and internal signal noise. ADA processing results are presented for a variety of test specimens that include inserted materials and discontinuities produced under poor manufacturing conditions. Software tools have been developed to support both ADA algorithm design and certification, producing a statistical evaluation of indication results and false calls using a matching process with predefined truth tables. Parametric studies were performed to evaluate detection and false call results with respect to varying algorithm settings.

Explosive Detection in Aviation Applications Using CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martz, H E; Crawford, C R

2011-02-15

CT scanners are deployed world-wide to detect explosives in checked and carry-on baggage. Though very similar to single- and dual-energy multi-slice CT scanners used today in medical imaging, some recently developed explosives detection scanners employ multiple sources and detector arrays to eliminate mechanical rotation of a gantry, photon counting detectors for spectral imaging, and limited number of views to reduce cost. For each bag scanned, the resulting reconstructed images are first processed by automated threat recognition algorithms to screen for explosives and other threats. Human operators review the images only when these automated algorithms report the presence of possible threats.more » The US Department of Homeland Security (DHS) has requirements for future scanners that include dealing with a larger number of threats, higher probability of detection, lower false alarm rates and lower operating costs. One tactic that DHS is pursuing to achieve these requirements is to augment the capabilities of the established security vendors with third-party algorithm developers. A third-party in this context refers to academics and companies other than the established vendors. DHS is particularly interested in exploring the model that has been used very successfully by the medical imaging industry, in which university researchers develop algorithms that are eventually deployed in commercial medical imaging equipment. The purpose of this paper is to discuss opportunities for third-parties to develop advanced reconstruction and threat detection algorithms.« less

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

PubMed

Abràmoff, Michael David; Lou, Yiyue; Erginay, Ali; Clarida, Warren; Amelon, Ryan; Folk, James C; Niemeijer, Meindert

2016-10-01

To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.

PubMed

Ulrich, Ethan J; Sunderland, John J; Smith, Brian J; Mohiuddin, Imran; Parkhurst, Jessica; Plichta, Kristin A; Buatti, John M; Beichel, Reinhard R

2018-01-01

Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R 2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ≪ 0.001) compared to manual analysis (mean: 247.8 s). The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts. © 2017 American Association of Physicists in Medicine.

Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens

NASA Astrophysics Data System (ADS)

Litjens, G.; Ehteshami Bejnordi, B.; Timofeeva, N.; Swadi, G.; Kovacs, I.; Hulsbergen-van de Kaa, C.; van der Laak, J.

2015-03-01

Automated detection of prostate cancer in digitized H and E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H and E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity.

External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.

PubMed

McKown, Andrew C; Brown, Ryan M; Ware, Lorraine B; Wanderer, Jonathan P

2017-01-01

Automated electronic sniffers may be useful for early detection of acute respiratory distress syndrome (ARDS) for institution of treatment or clinical trial screening. In a prospective cohort of 2929 critically ill patients, we retrospectively applied published sniffer algorithms for automated detection of acute lung injury to assess their utility in diagnosis of ARDS in the first 4 ICU days. Radiographic full-text reports were searched for "edema" OR ("bilateral" AND "infiltrate") and a more detailed algorithm for descriptions consistent with ARDS. Patients were flagged as possible ARDS if a radiograph met search criteria and had a PaO 2 /FiO 2 or SpO 2 /FiO 2 of 300 or 315, respectively. Test characteristics of the electronic sniffers and clinical suspicion of ARDS were compared to a gold standard of 2-physician adjudicated ARDS. Thirty percent of 2841 patients included in the analysis had gold standard diagnosis of ARDS. The simpler algorithm had sensitivity for ARDS of 78.9%, specificity of 52%, positive predictive value (PPV) of 41%, and negative predictive value (NPV) of 85.3% over the 4-day study period. The more detailed algorithm had sensitivity of 88.2%, specificity of 55.4%, PPV of 45.6%, and NPV of 91.7%. Both algorithms were more sensitive but less specific than clinician suspicion, which had sensitivity of 40.7%, specificity of 94.8%, PPV of 78.2%, and NPV of 77.7%. Published electronic sniffer algorithms for ARDS may be useful automated screening tools for ARDS and improve on clinical recognition, but they are limited to screening rather than diagnosis because their specificity is poor.

Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices.

PubMed

Gradl, Stefan; Kugler, Patrick; Lohmuller, Clemens; Eskofier, Bjoern

2012-01-01

We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices.

Semi-automated based ground-truthing GUI for airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Lydic, Rich; Moore, Tim; Trang, Anh; Agarwal, Sanjeev; Tiwari, Spandan

2005-06-01

Over the past several years, an enormous amount of airborne imagery consisting of various formats has been collected and will continue into the future to support airborne mine/minefield detection processes, improve algorithm development, and aid in imaging sensor development. The ground-truthing of imagery is a very essential part of the algorithm development process to help validate the detection performance of the sensor and improving algorithm techniques. The GUI (Graphical User Interface) called SemiTruth was developed using Matlab software incorporating signal processing, image processing, and statistics toolboxes to aid in ground-truthing imagery. The semi-automated ground-truthing GUI is made possible with the current data collection method, that is including UTM/GPS (Universal Transverse Mercator/Global Positioning System) coordinate measurements for the mine target and fiducial locations on the given minefield layout to support in identification of the targets on the raw imagery. This semi-automated ground-truthing effort has developed by the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division, Airborne Application Branch with some support by the University of Missouri-Rolla.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Adaptive algorithm of selecting optimal variant of errors detection system for digital means of automation facility of oil and gas complex

NASA Astrophysics Data System (ADS)

Poluyan, A. Y.; Fugarov, D. D.; Purchina, O. A.; Nesterchuk, V. V.; Smirnova, O. V.; Petrenkova, S. B.

2018-05-01

To date, the problems associated with the detection of errors in digital equipment (DE) systems for the automation of explosive objects of the oil and gas complex are extremely actual. Especially this problem is actual for facilities where a violation of the accuracy of the DE will inevitably lead to man-made disasters and essential material damage, at such facilities, the diagnostics of the accuracy of the DE operation is one of the main elements of the industrial safety management system. In the work, the solution of the problem of selecting the optimal variant of the errors detection system of errors detection by a validation criterion. Known methods for solving these problems have an exponential valuation of labor intensity. Thus, with a view to reduce time for solving the problem, a validation criterion is compiled as an adaptive bionic algorithm. Bionic algorithms (BA) have proven effective in solving optimization problems. The advantages of bionic search include adaptability, learning ability, parallelism, the ability to build hybrid systems based on combining. [1].

Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques.

PubMed

Aquino, Arturo; Gegundez-Arias, Manuel Emilio; Marin, Diego

2010-11-01

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

NASA Astrophysics Data System (ADS)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data

Treesearch

Liya Thomas; R. Edward Thomas

2011-01-01

We have developed an automated defect detection system and a state-of-the-art Graphic User Interface (GUI) for hardwood logs. The algorithm identifies defects at least 0.5 inch high and at least 3 inches in diameter on barked hardwood log and stem surfaces. To summarize defect features and to build a knowledge base, hundreds of defects were measured, photographed, and...

Automated power management and control

NASA Technical Reports Server (NTRS)

Dolce, James L.

1991-01-01

A comprehensive automation design is being developed for Space Station Freedom's electric power system. A joint effort between NASA's Office of Aeronautics and Exploration Technology and NASA's Office of Space Station Freedom, it strives to increase station productivity by applying expert systems and conventional algorithms to automate power system operation. The initial station operation will use ground-based dispatches to perform the necessary command and control tasks. These tasks constitute planning and decision-making activities that strive to eliminate unplanned outages. We perceive an opportunity to help these dispatchers make fast and consistent on-line decisions by automating three key tasks: failure detection and diagnosis, resource scheduling, and security analysis. Expert systems will be used for the diagnostics and for the security analysis; conventional algorithms will be used for the resource scheduling.

Improving single molecule force spectroscopy through automated real-time data collection and quantification of experimental conditions

PubMed Central

Scholl, Zackary N.; Marszalek, Piotr E.

2013-01-01

The benefits of single molecule force spectroscopy (SMFS) clearly outweigh the challenges which include small sample sizes, tedious data collection and introduction of human bias during the subjective data selection. These difficulties can be partially eliminated through automation of the experimental data collection process for atomic force microscopy (AFM). Automation can be accomplished using an algorithm that triages usable force-extension recordings quickly with positive and negative selection. We implemented an algorithm based on the windowed fast Fourier transform of force-extension traces that identifies peaks using force-extension regimes to correctly identify usable recordings from proteins composed of repeated domains. This algorithm excels as a real-time diagnostic because it involves <30 ms computational time, has high sensitivity and specificity, and efficiently detects weak unfolding events. We used the statistics provided by the automated procedure to clearly demonstrate the properties of molecular adhesion and how these properties change with differences in the cantilever tip and protein functional groups and protein age. PMID:24001740

Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter L.

2012-02-01

Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 +/- 0.33 mm and 3.10 +/- 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

CHARACTERIZATION OF THE COMPLETE FIBER NETWORK TOPOLOGY OF PLANAR FIBROUS TISSUES AND SCAFFOLDS

PubMed Central

D'Amore, Antonio; Stella, John A.; Wagner, William R.; Sacks, Michael S.

2010-01-01

Understanding how engineered tissue scaffold architecture affects cell morphology, metabolism, phenotypic expression, as well as predicting material mechanical behavior have recently received increased attention. In the present study, an image-based analysis approach that provides an automated tool to characterize engineered tissue fiber network topology is presented. Micro-architectural features that fully defined fiber network topology were detected and quantified, which include fiber orientation, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using scanning electron microscopy (SEM) images of electrospun poly(ester urethane)urea (ES-PEUU) scaffolds. SEM images of rabbit mesenchymal stem cell (MSC) seeded collagen gel scaffolds and decellularized rat carotid arteries were also analyzed to further evaluate the ability of the algorithm to capture fiber network morphology regardless of scaffold type and the evaluated size scale. The image analysis procedure was validated qualitatively and quantitatively, comparing fiber network topology manually detected by human operators (n=5) with that automatically detected by the algorithm. Correlation values between manual detected and algorithm detected results for the fiber angle distribution and for the fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersections and fiber diameter values were comparable (within the mean ± standard deviation) with those detected by human operators. This automated approach identifies and quantifies fiber network morphology as demonstrated for three relevant scaffold types and provides a means to: (1) guarantee objectivity, (2) significantly reduce analysis time, and (3) potentiate broader analysis of scaffold architecture effects on cell behavior and tissue development both in vitro and in vivo. PMID:20398930

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

Automated Detection of Optic Disc in Fundus Images

NASA Astrophysics Data System (ADS)

Burman, R.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.

Optic disc (OD) localization is an important preprocessing step in the automated image detection of fundus image infected with glaucoma. An Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution (DE) is applied to determine the location of the OD in the right of left eye retinal fundus image. The algorithm, when applied to 460 fundus images from the MESSIDOR dataset, shows a success rate of 99.07 % for 217 normal images and 95.47 % for 243 pathological images. The mean computational time is 1.709 s for normal images and 1.753 s for pathological images. These results are important for automated detection of glaucoma and for telemedicine purposes.

STREAMFINDER - I. A new algorithm for detecting stellar streams

NASA Astrophysics Data System (ADS)

Malhan, Khyati; Ibata, Rodrigo A.

2018-07-01

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution, we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams, and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the European Space Agency/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia data set.

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

PubMed

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

Pilot study on real-time motion detection in UAS video data by human observer and image exploitation algorithm

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Voit, Michael; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2017-05-01

Real-time motion video analysis is a challenging and exhausting task for the human observer, particularly in safety and security critical domains. Hence, customized video analysis systems providing functions for the analysis of subtasks like motion detection or target tracking are welcome. While such automated algorithms relieve the human operators from performing basic subtasks, they impose additional interaction duties on them. Prior work shows that, e.g., for interaction with target tracking algorithms, a gaze-enhanced user interface is beneficial. In this contribution, we present an investigation on interaction with an independent motion detection (IDM) algorithm. Besides identifying an appropriate interaction technique for the user interface - again, we compare gaze-based and traditional mouse-based interaction - we focus on the benefit an IDM algorithm might provide for an UAS video analyst. In a pilot study, we exposed ten subjects to the task of moving target detection in UAS video data twice, once performing with automatic support, once performing without it. We compare the two conditions considering performance in terms of effectiveness (correct target selections). Additionally, we report perceived workload (measured using the NASA-TLX questionnaire) and user satisfaction (measured using the ISO 9241-411 questionnaire). The results show that a combination of gaze input and automated IDM algorithm provides valuable support for the human observer, increasing the number of correct target selections up to 62% and reducing workload at the same time.

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.

PubMed

Hurnanen, Tero; Lehtonen, Eero; Tadi, Mojtaba Jafari; Kuusela, Tom; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pankaala, Mikko

2017-09-01

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Ice crystal characterization in cirrus clouds: a sun-tracking camera system and automated detection algorithm for halo displays

NASA Astrophysics Data System (ADS)

Forster, Linda; Seefeldner, Meinhard; Wiegner, Matthias; Mayer, Bernhard

2017-07-01

Halo displays in the sky contain valuable information about ice crystal shape and orientation: e.g., the 22° halo is produced by randomly oriented hexagonal prisms while parhelia (sundogs) indicate oriented plates. HaloCam, a novel sun-tracking camera system for the automated observation of halo displays is presented. An initial visual evaluation of the frequency of halo displays for the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign from October to mid-November 2014 showed that sundogs were observed more often than 22° halos. Thus, the majority of halo displays was produced by oriented ice crystals. During the campaign about 27 % of the cirrus clouds produced 22° halos, sundogs or upper tangent arcs. To evaluate the HaloCam observations collected from regular measurements in Munich between January 2014 and June 2016, an automated detection algorithm for 22° halos was developed, which can be extended to other halo types as well. This algorithm detected 22° halos about 2 % of the time for this dataset. The frequency of cirrus clouds during this time period was estimated by co-located ceilometer measurements using temperature thresholds of the cloud base. About 25 % of the detected cirrus clouds occurred together with a 22° halo, which implies that these clouds contained a certain fraction of smooth, hexagonal ice crystals. HaloCam observations complemented by radiative transfer simulations and measurements of aerosol and cirrus cloud optical thickness (AOT and COT) provide a possibility to retrieve more detailed information about ice crystal roughness. This paper demonstrates the feasibility of a completely automated method to collect and evaluate a long-term database of halo observations and shows the potential to characterize ice crystal properties.

Clustering analysis of moving target signatures

NASA Astrophysics Data System (ADS)

Martone, Anthony; Ranney, Kenneth; Innocenti, Roberto

2010-04-01

Previously, we developed a moving target indication (MTI) processing approach to detect and track slow-moving targets inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultra-wideband radar. Our MTI algorithms include change detection, automatic target detection (ATD), clustering, and tracking. The MTI algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the cluster algorithm is unknown and requires manual selection. A critical need exists to automate all aspects of the MTI processing formulation. In this paper, we investigate two techniques that automatically determine the number of clusters: the adaptive knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is based on a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both algorithms are used to analyze the false alarm and detection rates of three operational scenarios of personnel walking inside wood and cinderblock buildings.

Automated knot detection with visual post-processing of Douglas-fir veneer images

Treesearch

C.L. Todoroki; Eini C. Lowell; Dennis Dykstra

2010-01-01

Knots on digital images of 51 full veneer sheets, obtained from nine peeler blocks crosscut from two 35-foot (10.7 m) long logs and one 18-foot (5.5 m) log from a single Douglas-fir tree, were detected using a two-phase algorithm. The algorithm was developed using one image, the Development Sheet, refined on five other images, the Training Sheets, and then applied to...

[Algorithm of locally adaptive region growing based on multi-template matching applied to automated detection of hemorrhages].

PubMed

Gao, Wei-Wei; Shen, Jian-Xin; Wang, Yu-Liang; Liang, Chun; Zuo, Jing

2013-02-01

In order to automatically detect hemorrhages in fundus images, and develop an automated diabetic retinopathy screening system, a novel algorithm named locally adaptive region growing based on multi-template matching was established and studied. Firstly, spectral signature of major anatomical structures in fundus was studied, so that the right channel among RGB channels could be selected for different segmentation objects. Secondly, the fundus image was preprocessed by means of HSV brightness correction and contrast limited adaptive histogram equalization (CLAHE). Then, seeds of region growing were founded out by removing optic disc and vessel from the resulting image of normalized cross-correlation (NCC) template matching on the previous preprocessed image with several templates. Finally, locally adaptive region growing segmentation was used to find out the exact contours of hemorrhages, and the automated detection of the lesions was accomplished. The approach was tested on 90 different resolution fundus images with variable color, brightness and quality. Results suggest that the approach could fast and effectively detect hemorrhages in fundus images, and it is stable and robust. As a result, the approach can meet the clinical demands.

A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds

PubMed Central

Dorninger, Peter; Pfeifer, Norbert

2008-01-01

Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects. PMID:27873931

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

NASA Astrophysics Data System (ADS)

Khidir, Jarjees; Chen, Youhua; Anderson, Gary

2013-05-01

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

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

NASA Astrophysics Data System (ADS)

Yin, Xin; Liu, Aiping; Thornburg, Kent L.; Wang, Ruikang K.; Rugonyi, Sandra

2012-09-01

Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.

Detection of cardiac activity using a 5.8 GHz radio frequency sensor.

PubMed

Vasu, V; Fox, N; Brabetz, T; Wren, M; Heneghan, C; Sezer, S

2009-01-01

A 5.8-GHz ISM-Band radio-frequency sensor has been developed for non-contact measurement of respiration and heart rate from stationary and semi-stationary subjects at a distance of 0.5 to 1.5 meters. We report on the accuracy of the heart rate measurements obtained using two algorithmic approaches, as compared to a reference heart rate obtained using a pulse oximeter. Simultaneous Photoplethysmograph (PPG) and non-contact sensor recordings were recorded over fifteen minute periods for ten healthy subjects (8M/2F, ages 29.6 + or - 5.6 yrs) One algorithm is based on automated detection of individual peaks associated with each cardiac cycle; a second algorithm extracts a heart rate over a 60-second period using spectral analysis. Peaks were also extracted manually for comparison with the automated method. The peak-detection methods were less accurate than the spectral methods, but suggest the possibility of acquiring beat by beat data; the spectral algorithms measured heart rate to within + or -10% for the ten subjects chosen. Non-contact measurement of heart rate will be useful in chronic disease monitoring for conditions such as heart failure and cardiovascular disease.

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 intended to be detected: the lower this limit is, the higher the false detection rates are. A detailed evaluation is performed with breakdown results by crater dimension and image or surface type, permitting to realize that automated detections in large crater datasets in HiRISE imagery datasets with 25cm/pixel resolution can be successfully done (high correct and low false positive detections) until a crater dimension of about 8-10 m or 32-40 pixels. [1] Martins L, Pina P. Marques JS, Silveira M, 2009, Crater detection by a boosting approach. IEEE Geoscience and Remote Sensing Letters 6: 127-131. [2] Salamuniccar G, Loncaric S, Pina P. Bandeira L., Saraiva J, 2011, MA130301GT catalogue of Martian impact craters and advanced evaluation of crater detection algorithms using diverse topography and image datasets. Planetary and Space Science 59: 111-131. [3] Bandeira L, Ding W, Stepinski T, 2012, Detection of sub-kilometer craters in high resolution planetary images using shape and texture features. Advances in Space Research 49: 64-74.

Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

PubMed

Ai, Tomohiko; Tabe, Yoko; Takemura, Hiroyuki; Kimura, Konobu; Takahashi, Toshihiro; Yang, Haeun; Tsuchiya, Koji; Konishi, Aya; Uchihashi, Kinya; Horii, Takashi; Ohsaka, Akimichi

2018-01-01

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode "XN-BF gating algorithm" to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples.

Automated detection of new impact sites on Martian surface from HiRISE images

NASA Astrophysics Data System (ADS)

Xin, Xin; Di, Kaichang; Wang, Yexin; Wan, Wenhui; Yue, Zongyu

2017-10-01

In this study, an automated method for Martian new impact site detection from single images is presented. It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm. Experimental results using 100 HiRISE images show that the overall detection rate of proposed method is 84.5%, with a true positive rate of 86.9%. The detection rate and true positive rate in the flat regions are 93.0% and 91.5%, respectively.

Improved Cloud Detection Utilizing Defense Meteorological Satellite Program near Infrared Measurements

DTIC Science & Technology

1982-01-27

Visible 3. 3 Ea r th Location, Colocation, and Normalization 4. IMAGE ANALYSIS 4. 1 Interactive Capabilities 4.2 Examples 5. AUTOMATED CLOUD...computer Interactive Data Access System (McIDAS) before image analysis and algorithm development were done. Earth-location is an automated procedure to...the factor l / s in (SSE) toward the gain settings given in Table 5. 4. IMAGE ANALYSIS 4.1 Interactive Capabilities The development of automated

Algorithms and data structures for automated change detection and classification of sidescan sonar imagery

NASA Astrophysics Data System (ADS)

Gendron, Marlin Lee

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3--48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author's future research to develop additional algorithms and data structures for ACDC.

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array.

PubMed

Zu, Xingshui; Guo, Feng; Huang, Jingchang; Zhao, Qin; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2017-03-04

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively.

Targeted Acoustic Data Processing for Ocean Ecological Studies

NASA Astrophysics Data System (ADS)

Sidorovskaia, N.; Li, K.; Tiemann, C.; Ackleh, A. S.; Tang, T.; Ioup, G. E.; Ioup, J. W.

2015-12-01

The Gulf of Mexico is home to many species of deep diving marine mammals. In recent years several ecological studies have collected large volumes of Passive Acoustic Monitoring (PAM) data to investigate the effects of anthropogenic activities on protected and endangered marine mammal species. To utilize these data to their fullest potential for abundance estimates and habitat preference studies, automated detection and classification algorithms are needed to extract species acoustic encounters from a continuous stream of data. The species which phonate in overlapping frequency bands represent a particular challenge. This paper analyzes the performance of a newly developed automated detector for the classification of beaked whale clicks in the Northern Gulf of Mexico. Current used beaked whale classification algorithms rely heavily on experienced human operator involvement in manually associating potential events with a particular species of beaked whales. Our detection algorithm is two-stage: the detector is triggered when the species-representative phonation band energy exceeds the baseline detection threshold. Then multiple event attributes (temporal click duration, central frequency, frequency band, frequency sweep rate, Choi-Williams distribution shape indices) are measured. An attribute vector is then used to discriminate among different species of beaked whales present in the Gulf of Mexico and Risso's dolphins which were recognized to mask the detections of beaked whales in the case of widely used energy-band detectors. The detector is applied to the PAM data collected by the Littoral Acoustic Demonstration Center to estimate abundance trends of beaked whales in the vicinity of the 2010 oil spill before and after the disaster. This algorithm will allow automated processing with minimal operator involvement for new and archival PAM data. [The research is supported by a BP/GOMRI 2015-2017 consortium grant.

Automated Studies of Continuing Current in Lightning Flashes

NASA Astrophysics Data System (ADS)

Martinez-Claros, Jose

Continuing current (CC) is a continuous luminosity in the lightning channel that lasts longer than 10 ms following a lightning return stroke to ground. Lightning flashes following CC are associated with direct damage to power lines and are thought to be responsible for causing lightning-induced forest fires. The development of an algorithm that automates continuing current detection by combining NLDN (National Lightning Detection Network) and LEFA (Langmuir Electric Field Array) datasets for CG flashes will be discussed. The algorithm was applied to thousands of cloud-to-ground (CG) flashes within 40 km of Langmuir Lab, New Mexico measured during the 2013 monsoon season. It counts the number of flashes in a single minute of data and the number of return strokes of an individual lightning flash; records the time and location of each return stroke; performs peak analysis on E-field data, and uses the slope of interstroke interval (ISI) E-field data fits to recognize whether continuing current (CC) exists within the interval. Following CC detection, duration and magnitude are measured. The longest observed C in 5588 flashes was 631 ms. The performance of the algorithm (vs. human judgement) was checked on 100 flashes. At best, the reported algorithm is "correct" 80% of the time, where correct means that multiple stations agree with each other and with a human on both the presence and duration of CC. Of the 100 flashes that were validated against human judgement, 62% were hybrid. Automated analysis detects the first but misses the second return stroke in many cases where the second return stroke is followed by long CC. This problem is also present in human interpretation of field change records.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

PubMed

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

NASA Astrophysics Data System (ADS)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Segmentation of the whole breast from low-dose chest CT images

NASA Astrophysics Data System (ADS)

Liu, Shuang; Salvatore, Mary; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2015-03-01

The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

Properties of induced seismicity at the geothermal reservoir Insheim, Germany

NASA Astrophysics Data System (ADS)

Olbert, Kai; Küperkoch, Ludger; Thomas, Meier

2017-04-01

Within the framework of the German MAGS2 Project the processing of induced events at the geothermal power plant Insheim, Germany, has been reassessed and evaluated. The power plant is located close to the western rim of the Upper Rhine Graben in a region with a strongly heterogeneous subsurface. Therefore, the location of seismic events particularly the depth estimation is challenging. The seismic network consisting of up to 50 stations has an aperture of approximately 15 km around the power plant. Consequently, the manual processing is time consuming. Using a waveform similarity detection algorithm, the existing dataset from 2012 to 2016 has been reprocessed to complete the catalog of induced seismic events. Based on the waveform similarity clusters of similar events have been detected. Automated P- and S-arrival time determination using an improved multi-component autoregressive prediction algorithm yields approximately 14.000 P- and S-arrivals for 758 events. Applying a dataset of manual picks as reference the automated picking algorithm has been optimized resulting in a standard deviation of the residuals between automated and manual picks of about 0.02s. The automated locations show uncertainties comparable to locations of the manual reference dataset. 90 % of the automated relocations fall within the error ellipsoid of the manual locations. The remaining locations are either badly resolved due to low numbers of picks or so well resolved that the automatic location is outside the error ellipsoid although located close to the manual location. The developed automated processing scheme proved to be a useful tool to supplement real-time monitoring. The event clusters are located at small patches of faults known from reflection seismic studies. The clusters are observed close to both the injection as well as the production wells.

Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

NASA Astrophysics Data System (ADS)

Bonev, George

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily composite level. Results show that at the single overpass level for clear-sky regions, the developed multi-sensor algorithm performs with accuracy similar to that of the optical/infrared products, with the advantage of being able to also classify partially cloud-obscured regions with the help of passive microwave data. At the daily composite level, results show that the algorithm's performance with respect to total ice extent is in line with other daily products, with the novelty of being fully automated and having higher resolution.

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 simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.

PubMed

Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

2016-05-01

Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.

Comparison of probability statistics for automated ship detection in SAR imagery

NASA Astrophysics Data System (ADS)

Henschel, Michael D.; Rey, Maria T.; Campbell, J. W. M.; Petrovic, D.

1998-12-01

This paper discuses the initial results of a recent operational trial of the Ocean Monitoring Workstation's (OMW) ship detection algorithm which is essentially a Constant False Alarm Rate filter applied to Synthetic Aperture Radar data. The choice of probability distribution and methodologies for calculating scene specific statistics are discussed in some detail. An empirical basis for the choice of probability distribution used is discussed. We compare the results using a l-look, k-distribution function with various parameter choices and methods of estimation. As a special case of sea clutter statistics the application of a (chi) 2-distribution is also discussed. Comparisons are made with reference to RADARSAT data collected during the Maritime Command Operation Training exercise conducted in Atlantic Canadian Waters in June 1998. Reference is also made to previously collected statistics. The OMW is a commercial software suite that provides modules for automated vessel detection, oil spill monitoring, and environmental monitoring. This work has been undertaken to fine tune the OMW algorithm's, with special emphasis on the false alarm rate of each algorithm.

Automated detection of the retinal from OCT spectral domain images of healthy eyes

NASA Astrophysics Data System (ADS)

Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

2015-06-01

Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retinal. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

Automated detection of retinal layers from OCT spectral-domain images of healthy eyes

NASA Astrophysics Data System (ADS)

Giovinco, Gaspare; Savastano, Maria Cristina; Ventre, Salvatore; Tamburrino, Antonello

2015-12-01

Optical coherence tomography (OCT) has become one of the most relevant diagnostic tools for retinal diseases. Besides being a non-invasive technique, one distinguished feature is its unique capability of providing (in vivo) cross-sectional view of the retina. Specifically, OCT images show the retinal layers. From the clinical point of view, the identification of the retinal layers opens new perspectives to study the correlation between morphological and functional aspects of the retinal tissue. The main contribution of this paper is a new method/algorithm for the automated segmentation of cross-sectional images of the retina of healthy eyes, obtained by means of spectral-domain optical coherence tomography (SD-OCT). Specifically, the proposed segmentation algorithm provides the automated detection of different retinal layers. Tests on experimental SD-OCT scans performed by three different instruments/manufacturers have been successfully carried out and compared to a manual segmentation made by an independent ophthalmologist, showing the generality and the effectiveness of the proposed method.

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups.

PubMed

Saleh, George Michael; Wawrzynski, James; Caputo, Silvestro; Peto, Tunde; Al Turk, Lutfiah Ismail; Wang, Su; Hu, Yin; Da Cruz, Lyndon; Smith, Phil; Tang, Hongying Lilian

2016-01-01

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system's performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

PubMed

Gregoire, John M; Dale, Darren; van Dover, R Bruce

2011-01-01

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

Automated biosurveillance data from England and Wales, 1991-2011.

PubMed

Enki, Doyo G; Noufaily, Angela; Garthwaite, Paul H; Andrews, Nick J; Charlett, André; Lane, Chris; Farrington, C Paddy

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

Automated Biosurveillance Data from England and Wales, 1991–2011

PubMed Central

Enki, Doyo G.; Noufaily, Angela; Garthwaite, Paul H.; Andrews, Nick J.; Charlett, André; Lane, Chris

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity. PMID:23260848

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

PubMed

Suleimanov, Yury V; Green, William H

2015-09-08

We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

Automated measurement of stent strut coverage in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Ahn, Chi Young; Kim, Byeong-Keuk; Hong, Myeong-Ki; Jang, Yangsoo; Heo, Jung; Joo, Chulmin; Seo, Jin Keun

2015-02-01

Optical coherence tomography (OCT) is a non-invasive, cross-sectional imaging modality that has become a prominent imaging method in percutaneous intracoronary intervention. We present an automated detection algorithm for stent strut coordinates and coverage in OCT images. The algorithm for stent strut detection is composed of a coordinate transformation from the polar to the Cartesian domains and application of second derivative operators in the radial and the circumferential directions. Local region-based active contouring was employed to detect lumen boundaries. We applied the method to the OCT pullback images acquired from human patients in vivo to quantitatively measure stent strut coverage. The validation studies against manual expert assessments demonstrated high Pearson's coefficients ( R = 0.99) in terms of the stent strut coordinates, with no significant bias. An averaged Hausdorff distance of < 120 μm was obtained for vessel border detection. Quantitative comparison in stent strut to vessel wall distance found a bias of < 12.3 μm and a 95% confidence of < 110 μm.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

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

PubMed

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

2016-06-01

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

Multi-modal automatic montaging of adaptive optics retinal images

PubMed Central

Chen, Min; Cooper, Robert F.; Han, Grace K.; Gee, James; Brainard, David H.; Morgan, Jessica I. W.

2016-01-01

We present a fully automated adaptive optics (AO) retinal image montaging algorithm using classic scale invariant feature transform with random sample consensus for outlier removal. Our approach is capable of using information from multiple AO modalities (confocal, split detection, and dark field) and can accurately detect discontinuities in the montage. The algorithm output is compared to manual montaging by evaluating the similarity of the overlapping regions after montaging, and calculating the detection rate of discontinuities in the montage. Our results show that the proposed algorithm has high alignment accuracy and a discontinuity detection rate that is comparable (and often superior) to manual montaging. In addition, we analyze and show the benefits of using multiple modalities in the montaging process. We provide the algorithm presented in this paper as open-source and freely available to download. PMID:28018714

Automated determination of arterial input function for DCE-MRI of the prostate

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Chang, Ming-Ching; Gupta, Sandeep

2011-03-01

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.

DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

PubMed

Shuvaev, Sergey A; Lazutkin, Alexander A; Kedrov, Alexander V; Anokhin, Konstantin V; Enikolopov, Grigori N; Koulakov, Alexei A

2017-01-01

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Automated Detection of Stereotypical Motor Movements

ERIC Educational Resources Information Center

Goodwin, Matthew S.; Intille, Stephen S.; Albinali, Fahd; Velicer, Wayne F.

2011-01-01

To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average,…

Automated kidney morphology measurements from ultrasound images using texture and edge analysis

NASA Astrophysics Data System (ADS)

Ravishankar, Hariharan; Annangi, Pavan; Washburn, Michael; Lanning, Justin

2016-04-01

In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

Automatic detection of left and right ventricles from CTA enables efficient alignment of anatomy with myocardial perfusion data.

PubMed

Piccinelli, Marina; Faber, Tracy L; Arepalli, Chesnal D; Appia, Vikram; Vinten-Johansen, Jakob; Schmarkey, Susan L; Folks, Russell D; Garcia, Ernest V; Yezzi, Anthony

2014-02-01

Accurate alignment between cardiac CT angiographic studies (CTA) and nuclear perfusion images is crucial for improved diagnosis of coronary artery disease. This study evaluated in an animal model the accuracy of a CTA fully automated biventricular segmentation algorithm, a necessary step for automatic and thus efficient PET/CT alignment. Twelve pigs with acute infarcts were imaged using Rb-82 PET and 64-slice CTA. Post-mortem myocardium mass measurements were obtained. Endocardial and epicardial myocardial boundaries were manually and automatically detected on the CTA and both segmentations used to perform PET/CT alignment. To assess the segmentation performance, image-based myocardial masses were compared to experimental data; the hand-traced profiles were used as a reference standard to assess the global and slice-by-slice robustness of the automated algorithm in extracting myocardium, LV, and RV. Mean distances between the automated and the manual 3D segmented surfaces were computed. Finally, differences in rotations and translations between the manual and automatic surfaces were estimated post-PET/CT alignment. The largest, smallest, and median distances between interactive and automatic surfaces averaged 1.2 ± 2.1, 0.2 ± 1.6, and 0.7 ± 1.9 mm. The average angular and translational differences in CT/PET alignments were 0.4°, -0.6°, and -2.3° about x, y, and z axes, and 1.8, -2.1, and 2.0 mm in x, y, and z directions. Our automatic myocardial boundary detection algorithm creates surfaces from CTA that are similar in accuracy and provide similar alignments with PET as those obtained from interactive tracing. Specific difficulties in a reliable segmentation of the apex and base regions will require further improvements in the automated technique.

Algorithm improvement program nuclide identification algorithm scoring criteria and scoring application.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enghauser, Michael

2016-02-01

The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

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

2005-01-01

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.

PubMed

Salvi, Massimo; Molinari, Filippo

2018-06-20

Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues. The aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique. To the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer

PubMed Central

Tabe, Yoko; Takemura, Hiroyuki; Kimura, Konobu; Takahashi, Toshihiro; Yang, Haeun; Tsuchiya, Koji; Konishi, Aya; Uchihashi, Kinya; Horii, Takashi; Ohsaka, Akimichi

2018-01-01

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode “XN-BF gating algorithm” to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples. PMID:29425230

Counterflow Dielectrophoresis for Trypanosome Enrichment and Detection in Blood

NASA Astrophysics Data System (ADS)

Menachery, Anoop; Kremer, Clemens; Wong, Pui E.; Carlsson, Allan; Neale, Steven L.; Barrett, Michael P.; Cooper, Jonathan M.

2012-10-01

Human African trypanosomiasis or sleeping sickness is a deadly disease endemic in sub-Saharan Africa, caused by single-celled protozoan parasites. Although it has been targeted for elimination by 2020, this will only be realized if diagnosis can be improved to enable identification and treatment of afflicted patients. Existing techniques of detection are restricted by their limited field-applicability, sensitivity and capacity for automation. Microfluidic-based technologies offer the potential for highly sensitive automated devices that could achieve detection at the lowest levels of parasitemia and consequently help in the elimination programme. In this work we implement an electrokinetic technique for the separation of trypanosomes from both mouse and human blood. This technique utilises differences in polarisability between the blood cells and trypanosomes to achieve separation through opposed bi-directional movement (cell counterflow). We combine this enrichment technique with an automated image analysis detection algorithm, negating the need for a human operator.

Quantifying Standing Dead Tree Volume and Structural Loss with Voxelized Terrestrial Lidar Data

NASA Astrophysics Data System (ADS)

Popescu, S. C.; Putman, E.

2017-12-01

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The overall objective of this study was to provide automated aboveground volume estimates of SDTs and automated procedures to detect, quantify, and characterize structural losses over time with terrestrial lidar data. The specific objectives of this study were: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned in dense forests; 2) develop an automated change detection methodology to accurately detect and quantify SDT structural loss between subsequent terrestrial lidar observations; and 3) characterize the structural loss rates of pine and oak SDTs in southeastern Texas. A voxel-based volume estimation algorithm, "TreeVolX", was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models (RTMs). TreeVolX estimated large and small branch volume with an RMSE of 7.3% and 13.8%, respectively. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 - 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in this study.

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.

Automated parameterization of intermolecular pair potentials using global optimization techniques

NASA Astrophysics Data System (ADS)

Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk

2014-12-01

In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.

Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images.

PubMed

Rangel-Fonseca, Piero; Gómez-Vieyra, Armando; Malacara-Hernández, Daniel; Wilson, Mario C; Williams, David R; Rossi, Ethan A

2013-12-01

Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.

Fast and objective detection and analysis of structures in downhole images

NASA Astrophysics Data System (ADS)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

PubMed

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

PubMed Central

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

Automated Discovery of Long Intergenic RNAs Associated with Breast Cancer Progression

DTIC Science & Technology

2012-02-01

manuscript in preparation), (2) development and publication of an algorithm for detecting gene fusions in RNA-Seq data [1], and (3) discovery of outlier long...subjected to de novo assembly algorithms to discover novel transcripts representing either unannotated genes or novel somatic mutations such as gene...fusions. To this end the P.I. developed and published a novel algorithm called ChimeraScan to facilitate the discovery and validation of gene

Detect and Avoid (DAA) Automation Maneuver Study

DTIC Science & Technology

2017-02-01

88ABW-2017-2261. 14. ABSTRACT The study described herein was an operator–in–the–loop assessment supporting the development of a Sense and Avoid ( SAA ...display that enables effective teaming of an Unmanned Aerial Systems (UAS) operator with an advanced SAA maneuver algorithm to safely avoid proximal...air traffic. This study examined performance differences between candidate SAA display configurations and automation thresholds while UAS operators

Automated Breast Ultrasound for Ductal Pattern Reconstruction: Ground Truth File Generation and CADe Evaluation

NASA Astrophysics Data System (ADS)

Manousaki, D.; Panagiotopoulou, A.; Bizimi, V.; Haynes, M. S.; Love, S.; Kallergi, M.

2017-11-01

The purpose of this study was the generation of ground truth files (GTFs) of the breast ducts from 3D images of the Invenia™ Automated Breast Ultrasound System (ABUS) system (GE Healthcare, Little Chalfont, UK) and the application of these GTFs for the optimization of the imaging protocol and the evaluation of a computer aided detection (CADe) algorithm developed for automated duct detection. Six lactating, nursing volunteers were scanned with the ABUS before and right after breastfeeding their infants. An expert in breast ultrasound generated rough outlines of the milk-filled ducts in the transaxial slices of all image volumes and the final GTFs were created by using thresholding and smoothing tools in ImageJ. In addition, a CADe algorithm automatically segmented duct like areas and its results were compared to the expert’s GTFs by estimating true positive fraction (TPF) or % overlap. The CADe output differed significantly from the expert’s but both detected a smaller than expected volume of the ducts due to insufficient contrast (ducts were partially filled with milk), discontinuities, and artifacts. GTFs were used to modify the imaging protocol and improve the CADe method. In conclusion, electronic GTFs provide a valuable tool in the optimization of a tomographic imaging system, the imaging protocol, and the CADe algorithms. Their generation, however, is an extremely time consuming, strenuous process, particularly for multi-slice examinations, and alternatives based on phantoms or simulations are highly desirable.

Characterizing volcanic activity: Application of freely-available webcams

NASA Astrophysics Data System (ADS)

Dehn, J.; Harrild, M.; Webley, P. W.

2017-12-01

In recent years, freely-available web-based cameras, or webcams, have become more readily available allowing an increased level of monitoring at active volcanoes across the globe. While these cameras have been extensively used as qualitative tools, they provide a unique dataset to perform quantitative analyzes of the changing behavior of the particular volcano within the cameras field of view. We focus on the multitude of these freely-available webcams and present a new algorithm to detect changes in volcanic activity using nighttime webcam data. Our approach uses a quick, efficient, and fully automated algorithm to identify changes in webcam data in near real-time, including techniques such as edge detection, Gaussian mixture models, and temporal/spatial statistical tests, which are applied to each target image. Often the image metadata (exposure, gain settings, aperture, focal length, etc.) are unknown, meaning we developed our algorithm to identify the quantity of volcanically incandescent pixels as well as the number of specific algorithm tests needed to detect thermal activity, instead of directly correlating brightness in the webcam to eruption temperatures. We compared our algorithm results to a manual analysis of webcam data for several volcanoes and determined a false detection rate of less than 3% for the automated approach. In our presentation, we describe the different tests integrated into our algorithm, lessons learned, and how we applied our method to several volcanoes across the North Pacific during its development and implementation. We will finish with a discussion on the global applicability of our approach and how to build a 24/7, 365 day a year tool that can be used as an additional data source for real-time analysis of volcanic activity.

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

PubMed Central

Liu, Min-Yin; Huang, Adam; Huang, Norden E.

2017-01-01

Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737. PMID:28572762

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

PubMed

Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0.05. Despite some level of disagreement between human and AAPS-generated scores, the use of an automated system yields important advantages over currently available human-based alternatives. These results underscore the value of using the AAPS, that can be quickly deployed in the field and/or in outdoor settings with minimal set-up time. Finally, the AAPS can record multiple error types and their time course with extremely high temporal resolution. These features are not achievable by humans, who cannot keep track of multiple balance errors with such a high resolution. Together, these results suggest that computerized BESS calculation may provide more accurate and consistent measures of balance than those derived from human experts.

Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images

NASA Astrophysics Data System (ADS)

Lisitsa, Y. V.; Yatskou, M. M.; Apanasovich, V. V.; Apanasovich, T. V.

2015-09-01

We have developed an algorithm for segmentation of cancer cell nuclei in three-channel luminescent images of microbiological specimens. The algorithm is based on using a correlation between fluorescence signals in the detection channels for object segmentation, which permits complete automation of the data analysis procedure. We have carried out a comparative analysis of the proposed method and conventional algorithms implemented in the CellProfiler and ImageJ software packages. Our algorithm has an object localization uncertainty which is 2-3 times smaller than for the conventional algorithms, with comparable segmentation accuracy.

Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks

PubMed Central

Kreshuk, Anna; Koethe, Ullrich; Pax, Elizabeth; Bock, Davi D.; Hamprecht, Fred A.

2014-01-01

We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem). PMID:24516550

Algorithms for Autonomous Plume Detection on Outer Planet Satellites

NASA Astrophysics Data System (ADS)

Lin, Y.; Bunte, M. K.; Saripalli, S.; Greeley, R.

2011-12-01

We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to increase effectiveness. We have demonstrated an ability to detect transient events above the planetary limb and on the surface and to distinguish feature classes in spacecraft images.

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.

Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images.

PubMed

Lazareva, Anfisa; Liatsis, Panos; Rauscher, Franziska G

2016-01-01

Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting.

Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hughes, Michael J.; Hayes, Daniel J

2014-01-01

Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for cloudsmore » (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.« less

Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.

PubMed

Yi, Faliu; Yang, Lin; Wang, Shidan; Guo, Lei; Huang, Chenglong; Xie, Yang; Xiao, Guanghua

2018-02-27

Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis. In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes. This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

PubMed

Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca

2017-10-01

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5-6Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1Hz) with a power budget of 56mW. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated detection of open magnetic field regions in EUV images

NASA Astrophysics Data System (ADS)

Krista, Larisza Diana; Reinard, Alysha

2016-05-01

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

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

PubMed

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-09-01

Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

On the Agreement between Manual and Automated Methods for Single-Trial Detection and Estimation of Features from Event-Related Potentials

PubMed Central

Biurrun Manresa, José A.; Arguissain, Federico G.; Medina Redondo, David E.; Mørch, Carsten D.; Andersen, Ole K.

2015-01-01

The agreement between humans and algorithms on whether an event-related potential (ERP) is present or not and the level of variation in the estimated values of its relevant features are largely unknown. Thus, the aim of this study was to determine the categorical and quantitative agreement between manual and automated methods for single-trial detection and estimation of ERP features. To this end, ERPs were elicited in sixteen healthy volunteers using electrical stimulation at graded intensities below and above the nociceptive withdrawal reflex threshold. Presence/absence of an ERP peak (categorical outcome) and its amplitude and latency (quantitative outcome) in each single-trial were evaluated independently by two human observers and two automated algorithms taken from existing literature. Categorical agreement was assessed using percentage positive and negative agreement and Cohen’s κ, whereas quantitative agreement was evaluated using Bland-Altman analysis and the coefficient of variation. Typical values for the categorical agreement between manual and automated methods were derived, as well as reference values for the average and maximum differences that can be expected if one method is used instead of the others. Results showed that the human observers presented the highest categorical and quantitative agreement, and there were significantly large differences between detection and estimation of quantitative features among methods. In conclusion, substantial care should be taken in the selection of the detection/estimation approach, since factors like stimulation intensity and expected number of trials with/without response can play a significant role in the outcome of a study. PMID:26258532

Real-time heart rate measurement for multi-people using compressive tracking

NASA Astrophysics Data System (ADS)

Liu, Lingling; Zhao, Yuejin; Liu, Ming; Kong, Lingqin; Dong, Liquan; Ma, Feilong; Pang, Zongguang; Cai, Zhi; Zhang, Yachu; Hua, Peng; Yuan, Ruifeng

2017-09-01

The rise of aging population has created a demand for inexpensive, unobtrusive, automated health care solutions. Image PhotoPlethysmoGraphy(IPPG) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. However, the main deficiencies of the recent IPPG methods are non-automated, non-real-time and susceptible to motion artifacts(MA). In this paper, a real-time heart rate(HR) detection method for multiple subjects simultaneously was proposed and realized using the open computer vision(openCV) library, which consists of getting multiple subjects' facial video automatically through a Webcam, detecting the region of interest (ROI) in the video, reducing the false detection rate by our improved Adaboost algorithm, reducing the MA by our improved compress tracking(CT) algorithm, wavelet noise-suppression algorithm for denoising and multi-threads for higher detection speed. For comparison, HR was measured simultaneously using a medical pulse oximetry device for every subject during all sessions. Experimental results on a data set of 30 subjects show that the max average absolute error of heart rate estimation is less than 8 beats per minute (BPM), and the processing speed of every frame has almost reached real-time: the experiments with video recordings of ten subjects under the condition of the pixel resolution of 600× 800 pixels show that the average HR detection time of 10 subjects was about 17 frames per second (fps).

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

PubMed Central

Schmitz, Christoph; Eastwood, Brian S.; Tappan, Susan J.; Glaser, Jack R.; Peterson, Daniel A.; Hof, Patrick R.

2014-01-01

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. PMID:24847213

A Method for Automated Detection of Usability Problems from Client User Interface Events

PubMed Central

Saadawi, Gilan M.; Legowski, Elizabeth; Medvedeva, Olga; Chavan, Girish; Crowley, Rebecca S.

2005-01-01

Think-aloud usability analysis provides extremely useful data but is very time-consuming and expensive to perform because of the extensive manual video analysis that is required. We describe a simple method for automated detection of usability problems from client user interface events for a developing medical intelligent tutoring system. The method incorporates (1) an agent-based method for communication that funnels all interface events and system responses to a centralized database, (2) a simple schema for representing interface events and higher order subgoals, and (3) an algorithm that reproduces the criteria used for manual coding of usability problems. A correction factor was empirically determining to account for the slower task performance of users when thinking aloud. We tested the validity of the method by simultaneously identifying usability problems using TAU and manually computing them from stored interface event data using the proposed algorithm. All usability problems that did not rely on verbal utterances were detectable with the proposed method. PMID:16779121

Road detection and buried object detection in elevated EO/IR imagery

NASA Astrophysics Data System (ADS)

Kennedy, Levi; Kolba, Mark P.; Walters, Joshua R.

2012-06-01

To assist the warfighter in visually identifying potentially dangerous roadside objects, the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) has developed an elevated video sensor system testbed for data collection. This system provides color and mid-wave infrared (MWIR) imagery. Signal Innovations Group (SIG) has developed an automated processing capability that detects the road within the sensor field of view and identifies potentially threatening buried objects within the detected road. The road detection algorithm leverages system metadata to project the collected imagery onto a flat ground plane, allowing for more accurate detection of the road as well as the direct specification of realistic physical constraints in the shape of the detected road. Once the road has been detected in an image frame, a buried object detection algorithm is applied to search for threatening objects within the detected road space. The buried object detection algorithm leverages textural and pixel intensity-based features to detect potential anomalies and then classifies them as threatening or non-threatening objects. Both the road detection and the buried object detection algorithms have been developed to facilitate their implementation in real-time in the NVESD system.

Rotor Smoothing and Vibration Monitoring Results for the US Army VMEP

DTIC Science & Technology

2009-06-01

individual component CI detection thresholds, and development of models for diagnostics, prognostics , and anomaly detection . Figure 16 VMEP Server...and prognostics are of current interest. Development of those systems requires large amounts of data (collection, monitoring , manipulation) to capture...development of automated systems and for continuous updating of algorithms to improve detection , classification, and prognostic performance. A test

Algorithms of Crescent Structure Detection in Human Biological Fluid Facies

NASA Astrophysics Data System (ADS)

Krasheninnikov, V. R.; Malenova, O. E.; Yashina, A. S.

2017-05-01

One of the effective methods of early medical diagnosis is based on the image analysis of human biological fluids. In the process of fluid crystallization there appear characteristic patterns (markers) in the resulting layer (facies). Each marker is a highly probable sign of some pathology even at an early stage of a disease development. When mass health examination is carried out, it is necessary to analyze a large number of images. That is why, the problem of algorithm and software development for automated processing of images is rather urgent nowadays. This paper presents algorithms to detect a crescent structures in images of blood serum and cervical mucus facies. Such a marker indicates the symptoms of ischemic disease. The algorithm presented detects this marker with high probability when the probability of false alarm is low.

Algorithm Improvement Program Nuclide Identification Algorithm Scoring Criteria And Scoring Application - DNDO.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Enghauser, Michael

2015-02-01

The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.

Rapid Change Detection Algorithm for Disaster Management

NASA Astrophysics Data System (ADS)

Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

2012-07-01

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Studying fish near ocean energy devices using underwater video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Hull, Ryan E.; Harker-Klimes, Genevra EL

The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish ormore » not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.« less

Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin

NASA Astrophysics Data System (ADS)

Kurugol, Sila; Dy, Jennifer G.; Rajadhyaksha, Milind; Gossage, Kirk W.; Weissmann, Jesse; Brooks, Dana H.

2011-03-01

The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast, heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions) and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of around 4.7μm for dark skin and around 7-14μm for fair skin.

Sunglint Detection for Unmanned and Automated Platforms

PubMed Central

Garaba, Shungudzemwoyo Pascal; Schulz, Jan; Wernand, Marcel Robert; Zielinski, Oliver

2012-01-01

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

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

PubMed

Welikala, R A; Fraz, M M; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

2016-04-01

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyhan, M; Yue, N

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.more » 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 minimize user interaction and processing time of radiochromic film used for in vivo dosimetry.« less

Improving cervical region of interest by eliminating vaginal walls and cotton-swabs for automated image analysis

NASA Astrophysics Data System (ADS)

Venkataraman, Sankar; Li, Wenjing

2008-03-01

Image analysis for automated diagnosis of cervical cancer has attained high prominence in the last decade. Automated image analysis at all levels requires a basic segmentation of the region of interest (ROI) within a given image. The precision of the diagnosis is often reflected by the precision in detecting the initial region of interest, especially when some features outside the ROI mimic the ones within the same. Work described here discusses algorithms that are used to improve the cervical region of interest as a part of automated cervical image diagnosis. A vital visual aid in diagnosing cervical cancer is the aceto-whitening of the cervix after the application of acetic acid. Color and texture are used to segment acetowhite regions within the cervical ROI. Vaginal walls along with cottonswabs sometimes mimic these essential features leading to several false positives. Work presented here is focused towards detecting in-focus vaginal wall boundaries and then extrapolating them to exclude vaginal walls from the cervical ROI. In addition, discussed here is a marker-controlled watershed segmentation that is used to detect cottonswabs from the cervical ROI. A dataset comprising 50 high resolution images of the cervix acquired after 60 seconds of acetic acid application were used to test the algorithm. Out of the 50 images, 27 benefited from a new cervical ROI. Significant improvement in overall diagnosis was observed in these images as false positives caused by features outside the actual ROI mimicking acetowhite region were eliminated.

Automated object detection and tracking with a flash LiDAR system

NASA Astrophysics Data System (ADS)

Hammer, Marcus; Hebel, Marcus; Arens, Michael

2016-10-01

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

C-band Joint Active/Passive Dual Polarization Sea Ice Detection

NASA Astrophysics Data System (ADS)

Keller, M. R.; Gifford, C. M.; Winstead, N. S.; Walton, W. C.; Dietz, J. E.

2017-12-01

A technique for synergistically-combining high-resolution SAR returns with like-frequency passive microwave emissions to detect thin (<30 cm) ice under the difficult conditions of late melt and freeze-up is presented. As the Arctic sea ice cover thins and shrinks, the algorithm offers an approach to adapting existing sensors monitoring thicker ice to provide continuing coverage. Lower resolution (10-26 km) ice detections with spaceborne radiometers and scatterometers are challenged by rapidly changing thin ice. Synthetic Aperture Radar (SAR) is high resolution (5-100m) but because of cross section ambiguities automated algorithms have had difficulty separating thin ice types from water. The radiometric emissivity of thin ice versus water at microwave frequencies is generally unambiguous in the early stages of ice growth. The method, developed using RADARSAT-2 and AMSR-E data, uses higher-ordered statistics. For the SAR, the COV (coefficient of variation, ratio of standard deviation to mean) has fewer ambiguities between ice and water than cross sections, but breaking waves still produce ice-like signatures for both polarizations. For the radiometer, the PRIC (polarization ratio ice concentration) identifies areas that are unambiguously water. Applying cumulative statistics to co-located COV levels adaptively determines an ice/water threshold. Outcomes from extensive testing with Sentinel and AMSR-2 data are shown in the results. The detection algorithm was applied to the freeze-up in the Beaufort, Chukchi, Barents, and East Siberian Seas in 2015 and 2016, spanning mid-September to early November of both years. At the end of the melt, 6 GHz PRIC values are 5-10% greater than those reported by radiometric algorithms at 19 and 37 GHz. During freeze-up, COV separates grease ice (<5 cm thick) from water. As the ice thickens, the COV is less reliable, but adding a mask based on either the PRIC or the cross-pol/co-pol SAR ratio corrects for COV deficiencies. In general, the dual-sensor detection algorithm reports 10-15% higher total ice concentrations than operational scatterometer or radiometer algorithms, mostly from ice edge and coastal areas. In conclusion, the algorithm presented combines high-resolution SAR returns with passive microwave emissions for automated ice detection at SAR resolutions.

Near-Real-Time Detection and Monitoring of Intense Pyroconvection from Geostationary Satellites

NASA Astrophysics Data System (ADS)

Peterson, D. A.; Fromm, M. D.; Hyer, E. J.; Surratt, M. L.; Solbrig, J. E.; Campbell, J. R.

2016-12-01

Intense fire-triggered thunderstorms, known as pyrocumulonimbus (or pyroCb), can alter fire behavior, influence smoke plume trajectories, and hinder fire suppression efforts. PyroCb are also known for injecting a significant quantity of aerosol mass into the upper-troposphere and lower-stratosphere (UTLS). Near-real-time (NRT) detection and monitoring of pyroCb is highly desirable for a variety of forecasting and research applications. The Naval Research Laboratory (NRL) recently developed the first automated NRT pyroCb detection algorithm for geostationary satellite sensors. The algorithm uses multispectral infrared observations to isolate deep convective clouds with the distinct microphysical signal of pyroCb. Application of this algorithm to 88 intense wildfires observed during the 2013 fire season in western North America resulted in detection of individual intense events, pyroCb embedded within traditional convection, and multiple, short-lived pulses of activity. Comparisons with a community inventory indicate that this algorithm captures the majority of pyroCb. The primary limitation of the current system is that pyroCb anvils can be small relative to satellite pixel size, especially in in regions with large viewing angles. The algorithm is also sensitive to some false positives from traditional convection that either ingests smoke or exhibits extreme updraft velocities. This algorithm has been automated using the GeoIPS processing system developed at NRL, which produces a variety of imagery products and statistical output for rapid analysis of potential pyroCb events. NRT application of this algorithm has been extended to the majority of regions worldwide known to have a high frequency of pyroCb occurrence. This involves a constellation comprised of GOES-East, GOES-West, and Himawari-8. Imagery is posted immediately to an NRL-maintained web page. Alerts are generated by the system and disseminated via email. This detection system also has potential to serve as a data source for other NRT environmental monitoring systems. While the current geostationary constellation has several important limitations, the next-generation of geostationary sensors will offer significant advantages for achieving the goal of global NRT pyroCb detection.

Automated detection of geological landforms on Mars using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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

PubMed

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

2017-04-01

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

Automated reduction of sub-millimetre single-dish heterodyne data from the James Clerk Maxwell Telescope using ORAC-DR

NASA Astrophysics Data System (ADS)

Jenness, Tim; Currie, Malcolm J.; Tilanus, Remo P. J.; Cavanagh, Brad; Berry, David S.; Leech, Jamie; Rizzi, Luca

2015-10-01

With the advent of modern multidetector heterodyne instruments that can result in observations generating thousands of spectra per minute it is no longer feasible to reduce these data as individual spectra. We describe the automated data reduction procedure used to generate baselined data cubes from heterodyne data obtained at the James Clerk Maxwell Telescope (JCMT). The system can automatically detect baseline regions in spectra and automatically determine regridding parameters, all without input from a user. Additionally, it can detect and remove spectra suffering from transient interference effects or anomalous baselines. The pipeline is written as a set of recipes using the ORAC-DR pipeline environment with the algorithmic code using Starlink software packages and infrastructure. The algorithms presented here can be applied to other heterodyne array instruments and have been applied to data from historical JCMT heterodyne instrumentation.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

PubMed

Canan, Chelsea; Polinski, Jennifer M; Alexander, G Caleb; Kowal, Mary K; Brennan, Troyen A; Shrank, William H

2017-11-01

Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

[The study of medical supplies automation replenishment algorithm in hospital on medical supplies supplying chain].

PubMed

Sheng, Xi

2012-07-01

The thesis aims to study the automation replenishment algorithm in hospital on medical supplies supplying chain. The mathematical model and algorithm of medical supplies automation replenishment are designed through referring to practical data form hospital on the basis of applying inventory theory, greedy algorithm and partition algorithm. The automation replenishment algorithm is proved to realize automatic calculation of the medical supplies distribution amount and optimize medical supplies distribution scheme. A conclusion could be arrived that the model and algorithm of inventory theory, if applied in medical supplies circulation field, could provide theoretical and technological support for realizing medical supplies automation replenishment of hospital on medical supplies supplying chain.

Integration of a Self-Coherence Algorithm into DISAT for Forced Oscillation Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Follum, James D.; Tuffner, Francis K.; Amidan, Brett G.

2015-03-03

With the increasing number of phasor measurement units on the power system, behaviors typically not observable on the power system are becoming more apparent. Oscillatory behavior on the power system, notably forced oscillations, are one such behavior. However, the large amounts of data coming from the PMUs makes manually detecting and locating these oscillations difficult. To automate portions of the process, an oscillation detection routine was coded into the Data Integrity and Situational Awareness Tool (DISAT) framework. Integration into the DISAT framework allows forced oscillations to be detected and information about the event provided to operational engineers. The oscillation detectionmore » algorithm integrates with the data handling and atypical data detecting capabilities of DISAT, building off of a standard library of functions. This report details that integration with information on the algorithm, some implementation issues, and some sample results from the western United States’ power grid.« less

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

PubMed Central

Hamahashi, Shugo; Onami, Shuichi; Kitano, Hiroaki

2005-01-01

Background The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) Nomarski differential interference contrast (DIC) microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages. Results We developed a system that automates the detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. Local image entropy is used to produce regions of the images that have the image texture of the nucleus. From these regions, those that actually detect nuclei are manually selected at the first and last time points of the image set, and an object-tracking algorithm then selects regions that detect nuclei in between the first and last time points. The use of local image entropy makes the system applicable to multiple image sets without the need to change its parameter values. The use of an object-tracking algorithm enables the system to detect nuclei in the process of cell division. The system detected nuclei with high sensitivity and specificity from the one- to 24-cell stages. Conclusion A combination of local image entropy and an object-tracking algorithm enabled highly objective and productive detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. The system will facilitate genomic and computational analyses of C. elegans embryos. PMID:15910690

The Use of Meteorlogical Data to Improve Contrail Detection in Thermal Imagery over Ireland.

NASA Technical Reports Server (NTRS)

Whelan, Gillian M.; Cawkwell, Fiona; Mannstein, Hermann; Minnis, Patrick

2009-01-01

Aircraft induced contrails have been found to have a net warming influence on the climate system, with strong regional dependence. Persistent linear contrails are detectable in 1 Km thermal imagery and, using an automated Contrail Detection Algorithm (CDA), can be identified on the basis of their different properties at the 11 and 12 m w av.el enTgthshe algorithm s ability to distinguish contrails from other linear features depends on the sensitivity of its tuning parameters. In order to keep the number of false identifications low, the algorithm imposes strict limits on contrail size, linearity and intensity. This paper investigates whether including additional information (i.e. meteorological data) within the CDA may allow for these criteria to be less rigorous, thus increasing the contrail-detection rate, without increasing the false alarm rate.

Improved imaging algorithm for bridge crack detection

NASA Astrophysics Data System (ADS)

Lu, Jingxiao; Song, Pingli; Han, Kaihong

2012-04-01

This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

DOE PAGES

Venkatraman, S.; Doktycz, M. J.; Qi, H.; ...

2006-01-01

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

Unobtrusive Multi-Static Serial LiDAR Imager (UMSLI) First Generation Shape-Matching Based Classifier for 2D Contours

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Zheng; Ouyang, Bing; Principe, Jose

A multi-static serial LiDAR system prototype was developed under DE-EE0006787 to detect, classify, and record interactions of marine life with marine hydrokinetic generation equipment. This software implements a shape-matching based classifier algorithm for the underwater automated detection of marine life for that system. In addition to applying shape descriptors, the algorithm also adopts information theoretical learning based affine shape registration, improving point correspondences found by shape descriptors as well as the final similarity measure.

Automated asteroseismic peak detections

NASA Astrophysics Data System (ADS)

García Saravia Ortiz de Montellano, Andrés; Hekker, S.; Themeßl, N.

2018-05-01

Space observatories such as Kepler have provided data that can potentially revolutionize our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible in a power density spectrum. Identification of oscillation modes is usually done by visual inspection that is time-consuming and has a degree of subjectivity. Here, we present a peak-detection algorithm especially suited for the detection of solar-like oscillations. It reliably characterizes the solar-like oscillations in a power density spectrum and estimates their parameters without human intervention. Furthermore, we provide a metric to characterize the false positive and false negative rates to provide further information about the reliability of a detected oscillation mode or the significance of a lack of detected oscillation modes. The algorithm presented here opens the possibility for detailed and automated peak bagging of the thousands of solar-like oscillators observed by Kepler.

Architectures and algorithms for digital image processing; Proceedings of the Meeting, Cannes, France, December 5, 6, 1985

NASA Technical Reports Server (NTRS)

Duff, Michael J. B. (Editor); Siegel, Howard J. (Editor); Corbett, Francis J. (Editor)

1986-01-01

The conference presents papers on the architectures, algorithms, and applications of image processing. Particular attention is given to a very large scale integration system for image reconstruction from projections, a prebuffer algorithm for instant display of volume data, and an adaptive image sequence filtering scheme based on motion detection. Papers are also presented on a simple, direct practical method of sensing local motion and analyzing local optical flow, image matching techniques, and an automated biological dosimetry system.

Development of an Automatic Detection Program of Halo CMEs

NASA Astrophysics Data System (ADS)

Choi, K.; Park, M. Y.; Kim, J.

2017-12-01

The front-side halo CMEs are the major cause for large geomagnetic storms. Halo CMEs can result in damage to satellites, communication, electrical transmission lines and power systems. Thus automated techniques for detecting and analysing Halo CMEs from coronagraph data are of ever increasing importance for space weather monitoring and forecasting. In this study, we developed the algorithm that can automatically detect and do image processing the Halo CMEs in the images from the LASCO C3 coronagraph on board the SOHO spacecraft. With the detection algorithm, we derived the geometric and kinematical parameters of halo CMEs, such as source location, width, actual CME speed and arrival time at 21.5 solar radii.

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

PubMed

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

2016-04-01

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy.

PubMed

Rasta, Seyed Hossein; Nikfarjam, Shima; Javadzadeh, Alireza

2015-01-01

Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images. Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts. Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%.The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals. This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.

A semi-automated technique for labeling and counting of apoptosing retinal cells

PubMed Central

2014-01-01

Background Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. Results A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods. Conclusions The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly. PMID:24902592

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA; Lawrence Berkeley National Laboratory, One Cyclotron Road, Building 64R0121, Berkeley, CA 94720, USA; Department of Haematology, University of Cambridge, Cambridge CB2 0XY, England

The PHENIX AutoBuild Wizard is a highly automated tool for iterative model-building, structure refinement and density modification using RESOLVE or TEXTAL model-building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 {angstrom} tomore » 3.2 {angstrom}, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution.« less

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard.

PubMed

Terwilliger, Thomas C; Grosse-Kunstleve, Ralf W; Afonine, Pavel V; Moriarty, Nigel W; Zwart, Peter H; Hung, Li Wei; Read, Randy J; Adams, Paul D

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 A, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution.

Segmentation and detection of fluorescent 3D spots.

PubMed

Ram, Sundaresh; Rodríguez, Jeffrey J; Bosco, Giovanni

2012-03-01

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques. Copyright © 2012 International Society for Advancement of Cytometry.

Cloud detection algorithm comparison and validation for operational Landsat data products

USGS Publications Warehouse

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

PubMed Central

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-01-01

Purpose Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Methods Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. Results There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). Conclusions MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics. PMID:28873173

Adaptive Algorithms for Automated Processing of Document Images

DTIC Science & Technology

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

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

PubMed Central

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

2016-01-01

Abstract. 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

Digital tripwire: a small automated human detection system

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography.

PubMed

Lu, Yansha; Simonett, Joseph M; Wang, Jie; Zhang, Miao; Hwang, Thomas; Hagag, Ahmed M; Huang, David; Li, Dengwang; Jia, Yali

2018-05-01

To describe an automated algorithm to quantify the foveal avascular zone (FAZ), using optical coherence tomography angiography (OCTA), and to compare its performance for diagnosis of diabetic retinopathy (DR) and association with best-corrected visual acuity (BCVA) to that of extrafoveal avascular area (EAA). We obtained 3 × 3-mm macular OCTA scans in diabetic patients with various levels of DR and healthy controls. An algorithm based on a generalized gradient vector flow (GGVF) snake model detected the FAZ, and metrics assessing FAZ size and irregularity were calculated. We compared the automated FAZ segmentation to manual delineation and tested the within-visit repeatability of FAZ metrics. The correlations of two conventional FAZ metrics, two novel FAZ metrics, and EAA with DR severity and BCVA, as determined by Early Treatment Diabetic Retinopathy Study (ETDRS) charts, were assessed. Sixty-six eyes from 66 diabetic patients and 19 control eyes from 19 healthy participants were included. The agreement between manual and automated FAZ delineation had a Jaccard index > 0.82, and the repeatability of automated FAZ detection was excellent in eyes at all levels of DR severity. FAZ metrics that incorporated both FAZ size and shape irregularity had the strongest correlation with clinical DR grade and BCVA. Of all the tested OCTA metrics, EAA had the greatest sensitivity in differentiating diabetic eyes without clinical evidence of retinopathy, mild to moderate nonproliferative DR (NPDR), and severe NPDR to proliferative DR from healthy controls. The GGVF snake algorithm tested in this study can accurately and reliably detect the FAZ, using OCTA data at all DR severity grades, and may be used to obtain clinically useful information from OCTA data regarding macular ischemia in patients with diabetes. While FAZ metrics can provide clinically useful information regarding macular ischemia, and possibly visual acuity potential, EAA measurements may be a better biomarker for DR.

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

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

An automated fog monitoring system for the Indo-Gangetic Plains based on satellite measurements

NASA Astrophysics Data System (ADS)

Patil, Dinesh; Chourey, Reema; Rizvi, Sarwar; Singh, Manoj; Gautam, Ritesh

2016-05-01

Fog is a meteorological phenomenon that causes reduction in regional visibility and affects air quality, thus leading to various societal and economic implications, especially disrupting air and rail transportation. The persistent and widespread winter fog impacts the entire the Indo-Gangetic Plains (IGP), as frequently observed in satellite imagery. The IGP is a densely populated region in south Asia, inhabiting about 1/6th of the world's population, with a strong upward pollution trend. In this study, we have used multi-spectral radiances and aerosol/cloud retrievals from Terra/Aqua MODIS data for developing an automated web-based fog monitoring system over the IGP. Using our previous and existing methodologies, and ongoing algorithm development for the detection of fog and retrieval of associated microphysical properties (e.g. fog droplet effective radius), we characterize the widespread fog detection during both daytime and nighttime. Specifically, for the night time fog detection, the algorithm employs a satellite-based bi-spectral brightness temperature difference technique between two spectral channels: MODIS band-22 (3.9μm) and band-31 (10.75μm). Further, we are extending our algorithm development to geostationary satellites, for providing continuous monitoring of the spatial-temporal variation of fog. We anticipate that the ongoing and future development of a fog monitoring system would be of assistance to air, rail and vehicular transportation management, as well as for dissemination of fog information to government agencies and general public. The outputs of fog detection algorithm and related aerosol/cloud parameters are operationally disseminated via http://fogsouthasia.com/.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy.

PubMed

Hunyadi, Borbála; Siekierska, Aleksandra; Sourbron, Jo; Copmans, Daniëlle; de Witte, Peter A M

2017-08-01

Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively low cost, they are amenable to high-throughput screening. However, the assessment of seizure occurrences in zebrafish larvae remains a bottleneck, as visual analysis is subjective and time-consuming. For the first time, we present an automated algorithm to detect epileptic discharges in single-channel local field potential (LFP) recordings in zebrafish. First, candidate seizure segments are selected based on their energy and length. Afterwards, discriminative features are extracted from each segment. Using a labeled dataset, a support vector machine (SVM) classifier is trained to learn an optimal feature mapping. Finally, this SVM classifier is used to detect seizure segments in new signals. We tested the proposed algorithm both in a chemically-induced seizure model and a genetic epilepsy model. In both cases, the algorithm delivered similar results to visual analysis and found a significant difference in number of seizures between the epileptic and control group. Direct comparison with multichannel techniques or methods developed for different animal models is not feasible. Nevertheless, a literature review shows that our algorithm outperforms state-of-the-art techniques in terms of accuracy, precision and specificity, while maintaining a reasonable sensitivity. Our seizure detection system is a generic, time-saving and objective method to analyze zebrafish LPF, which can replace visual analysis and facilitate true high-throughput studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar

NASA Astrophysics Data System (ADS)

Maas, Christian; Schmalzl, Jörg

2013-08-01

Ground Penetrating Radar (GPR) is used for the localization of supply lines, land mines, pipes and many other buried objects. These objects can be recognized in the recorded data as reflection hyperbolas with a typical shape depending on depth and material of the object and the surrounding material. To obtain the parameters, the shape of the hyperbola has to be fitted. In the last years several methods were developed to automate this task during post-processing. In this paper we show another approach for the automated localization of reflection hyperbolas in GPR data by solving a pattern recognition problem in grayscale images. In contrast to other methods our detection program is also able to immediately mark potential objects in real-time. For this task we use a version of the Viola-Jones learning algorithm, which is part of the open source library "OpenCV". This algorithm was initially developed for face recognition, but can be adapted to any other simple shape. In our program it is used to narrow down the location of reflection hyperbolas to certain areas in the GPR data. In order to extract the exact location and the velocity of the hyperbolas we apply a simple Hough Transform for hyperbolas. Because the Viola-Jones Algorithm reduces the input for the computational expensive Hough Transform dramatically the detection system can also be implemented on normal field computers, so on-site application is possible. The developed detection system shows promising results and detection rates in unprocessed radargrams. In order to improve the detection results and apply the program to noisy radar images more data of different GPR systems as input for the learning algorithm is necessary.

An automated cross-correlation based event detection technique and its application to surface passive data set

USGS Publications Warehouse

Forghani-Arani, Farnoush; Behura, Jyoti; Haines, Seth S.; Batzle, Mike

2013-01-01

In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

Automated measurement of office, home and ambulatory blood pressure in atrial fibrillation.

PubMed

Kollias, Anastasios; Stergiou, George S

2014-01-01

1. Hypertension and atrial fibrillation (AF) often coexist and are strong risk factors for stroke. Current guidelines for blood pressure (BP) measurement in AF recommend repeated measurements using the auscultatory method, whereas the accuracy of the automated devices is regarded as questionable. This review presents the current evidence on the feasibility and accuracy of automated BP measurement in the presence of AF and the potential for automated detection of undiagnosed AF during such measurements. 2. Studies evaluating the use of automated BP monitors in AF are limited and have significant heterogeneity in methodology and protocols. Overall, the oscillometric method is feasible for static (office or home) and ambulatory use and appears to be more accurate for systolic than diastolic BP measurement. 3. Given that systolic hypertension is particularly common and important in the elderly, the automated BP measurement method may be acceptable for self-home and ambulatory monitoring, but not for professional office or clinic measurement. 4. An embedded algorithm for the detection of asymptomatic AF during routine automated BP measurement with high diagnostic accuracy has been developed and appears to be a useful screening tool for elderly hypertensives. © 2013 Wiley Publishing Asia Pty Ltd.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium.

PubMed

Holmström, Oscar; Linder, Nina; Ngasala, Billy; Mårtensson, Andreas; Linder, Ewert; Lundin, Mikael; Moilanen, Hannu; Suutala, Antti; Diwan, Vinod; Lundin, Johan

2017-06-01

Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3-100%) in the test set (n = 217) of manually labeled helminth eggs. In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

PubMed Central

Holmström, Oscar; Linder, Nina; Ngasala, Billy; Mårtensson, Andreas; Linder, Ewert; Lundin, Mikael; Moilanen, Hannu; Suutala, Antti; Diwan, Vinod; Lundin, Johan

2017-01-01

ABSTRACT Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs. Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images. PMID:28838305

Event detection for car park entries by video-surveillance

NASA Astrophysics Data System (ADS)

Coquin, Didier; Tailland, Johan; Cintract, Michel

2007-10-01

Intelligent surveillance has become an important research issue due to the high cost and low efficiency of human supervisors, and machine intelligence is required to provide a solution for automated event detection. In this paper we describe a real-time system that has been used for detecting car park entries, using an adaptive background learning algorithm and two indicators representing activity and identity to overcome the difficulty of tracking objects.

Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

PubMed

Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

2014-07-01

To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

Microaneurysms detection with the radon cliff operator in retinal fundus images

NASA Astrophysics Data System (ADS)

Giancardo, Luca; Mériaudeau, Fabrice; Karnowski, Thomas P.; Tobin, Kenneth W.; Li, Yaqin; Chaum, Edward

2010-03-01

Diabetic Retinopathy (DR) is one of the leading causes of blindness in the industrialized world. Early detection is the key in providing effective treatment. However, the current number of trained eye care specialists is inadequate to screen the increasing number of diabetic patients. In recent years, automated and semi-automated systems to detect DR with color fundus images have been developed with encouraging, but not fully satisfactory results. In this study we present the initial results of a new technique for the detection and localization of microaneurysms, an early sign of DR. The algorithm is based on three steps: candidates selection, the actual microaneurysms detection and a final probability evaluation. We introduce the new Radon Cliff operator which is our main contribution to the field. Making use of the Radon transform, the operator is able to detect single noisy Gaussian-like circular structures regardless of their size or strength. The advantages over existing microaneurysms detectors are manifold: the size of the lesions can be unknown, it automatically distinguishes lesions from the vasculature and it provides a fair approach to microaneurysm localization even without post-processing the candidates with machine learning techniques, facilitating the training phase. The algorithm is evaluated on a publicly available dataset from the Retinopathy Online Challenge.

Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection.

PubMed

Mathieson, Sean R; Livingstone, Vicki; Low, Evonne; Pressler, Ronit; Rennie, Janet M; Boylan, Geraldine B

2016-10-01

Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Post-phenobarbital seizures showed significantly lower amplitude (p<0.001) and involved fewer EEG channels at the peak of seizure (p<0.05). No other features or SDA detection rates showed a statistical difference. These findings show that phenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions

PubMed Central

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject’s face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject’s face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network. PMID:26859884

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.

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.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

PubMed

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography

NASA Astrophysics Data System (ADS)

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

2016-07-01

Quantification of choroidal neovascularization (CNV) as visualized by optical coherence tomography angiography (OCTA) may have importance clinically when diagnosing or tracking disease. Here, we present an automated algorithm to quantify the vessel skeleton of CNV as vessel length. Initial segmentation of the CNV on en face angiograms was achieved using saliency-based detection and thresholding. A level set method was then used to refine vessel edges. Finally, a skeleton algorithm was applied to identify vessel centerlines. The algorithm was tested on nine OCTA scans from participants with CNV and comparisons of the algorithm's output to manual delineation showed good agreement.

An Algorithm to Detect the Retinal Region of Interest

NASA Astrophysics Data System (ADS)

Şehirli, E.; Turan, M. K.; Demiral, E.

2017-11-01

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

[Algorithm for the automated processing of rheosignals].

PubMed

Odinets, G S

1988-01-01

Algorithm for rheosignals recognition for a microprocessing device with a representation apparatus and with automated and manual cursor control was examined. The algorithm permits to automate rheosignals registrating and processing taking into account their changeability.

Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

NASA Astrophysics Data System (ADS)

Ghaffarian, Saman; Ghaffarian, Salar

2014-11-01

This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

Prospective study of atrial fibrillation termination during ablation guided by automated detection of fractionated electrograms.

PubMed

Porter, Michael; Spear, William; Akar, Joseph G; Helms, Ray; Brysiewicz, Neil; Santucci, Peter; Wilber, David J

2008-06-01

Complex fractionated atrial electrograms (CFAE) may identify critical sites for perpetuation of atrial fibrillation (AF) and provide useful targets for ablation. Current assessment of CFAE is subjective; automated detection algorithms may improve reproducibility, but their utility in guiding ablation has not been tested. In 67 patients presenting for initial AF ablation (42 paroxysmal, 25 persistent), LA and CS mapping were performed during induced or spontaneous AF. CFAE were identified by an online automated computer algorithm and displayed on electroanatomical maps. A mean of 28 +/- 18 sites/patient were identified (20 +/- 13% of mapped sites), and were more frequent during persistent AF. CFAE occurred most commonly within the CS, on the atrial septum, and around the pulmonary veins. Ablation initially targeting CFAE terminated AF in 88% of paroxysmal AF, but only 20% of persistent AF (P < 0.001). Subsequently, additional ablation was performed in all patients (PV isolation for paroxysmal AF, PV isolation + mitral and roof lines for persistent AF). Minimum follow-up was 1 year. One-year freedom from recurrent atrial arrhythmias without antiarrhythmic drug therapy after a single procedure was 90% for paroxysmal AF, and 68% for persistent AF. Ablation guided by automated detection of CFAE proved feasible, and was associated with a high AF termination rate in paroxysmal, but not persistent AF. As an adjunct to conventional techniques, it was associated with excellent long-term single procedure outcomes in both groups. Criteria for identifying optimal CFAE sites for ablation, and selection of patients most likely to benefit, require additional study.

Automatic Whistler Detector and Analyzer system: Implementation of the analyzer algorithm

NASA Astrophysics Data System (ADS)

Lichtenberger, JáNos; Ferencz, Csaba; Hamar, Daniel; Steinbach, Peter; Rodger, Craig J.; Clilverd, Mark A.; Collier, Andrew B.

2010-12-01

The full potential of whistlers for monitoring plasmaspheric electron density variations has not yet been realized. The primary reason is the vast human effort required for the analysis of whistler traces. Recently, the first part of a complete whistler analysis procedure was successfully automated, i.e., the automatic detection of whistler traces from the raw broadband VLF signal was achieved. This study describes a new algorithm developed to determine plasmaspheric electron density measurements from whistler traces, based on a Virtual (Whistler) Trace Transformation, using a 2-D fast Fourier transform transformation. This algorithm can be automated and can thus form the final step to complete an Automatic Whistler Detector and Analyzer (AWDA) system. In this second AWDA paper, the practical implementation of the Automatic Whistler Analyzer (AWA) algorithm is discussed and a feasible solution is presented. The practical implementation of the algorithm is able to track the variations of plasmasphere in quasi real time on a PC cluster with 100 CPU cores. The electron densities obtained by the AWA method can be used in investigations such as plasmasphere dynamics, ionosphere-plasmasphere coupling, or in space weather models.

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images.

PubMed

Pachiyappan, Arulmozhivarman; Das, Undurti N; Murthy, Tatavarti Vsp; Tatavarti, Rao

2012-06-13

We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images

PubMed Central

2012-01-01

We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized. PMID:22695250

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Brown, A.; Brown, J.

2010-09-01

We develop and evaluate the performance of advanced algorithms which provide significantly improved capabilities for automated detection and tracking of ballistic and flying dim objects in the presence of highly structured intense clutter. Applications include ballistic missile early warning, midcourse tracking, trajectory prediction, and resident space object detection and tracking. The set of algorithms include, in particular, adaptive spatiotemporal clutter estimation-suppression and nonlinear filtering-based multiple-object track-before-detect. These algorithms are suitable for integration into geostationary, highly elliptical, or low earth orbit scanning or staring sensor suites, and are based on data-driven processing that adapts to real-world clutter backgrounds, including celestial, earth limb, or terrestrial clutter. In many scenarios of interest, e.g., for highly elliptic and, especially, low earth orbits, the resulting clutter is highly nonstationary, providing a significant challenge for clutter suppression to or below sensor noise levels, which is essential for dim object detection and tracking. We demonstrate the success of the developed algorithms using semi-synthetic and real data. In particular, our algorithms are shown to be capable of detecting and tracking point objects with signal-to-clutter levels down to 1/1000 and signal-to-noise levels down to 1/4.

Proof of Concept of Automated Collision Detection Technology in Rugby Sevens.

PubMed

Clarke, Anthea C; Anson, Judith M; Pyne, David B

2017-04-01

Clarke, AC, Anson, JM, and Pyne, DB. Proof of concept of automated collision detection technology in rugby sevens. J Strength Cond Res 31(4): 1116-1120, 2017-Developments in microsensor technology allow for automated detection of collisions in various codes of football, removing the need for time-consuming postprocessing of video footage. However, little research is available on the ability of microsensor technology to be used across various sports or genders. Game video footage was matched with microsensor-detected collisions (GPSports) in one men's (n = 12 players) and one women's (n = 12) rugby sevens match. True-positive, false-positive, and false-negative events between video and microsensor-detected collisions were used to calculate recall (ability to detect a collision) and precision (accurately identify a collision). The precision was similar between the men's and women's rugby sevens game (∼0.72; scale 0.00-1.00); however, the recall in the women's game (0.45) was less than that for the men's game (0.69). This resulted in 45% of collisions for men and 62% of collisions for women being incorrectly labeled. Currently, the automated collision detection system in GPSports microtechnology units has only modest utility in rugby sevens, and it seems that a rugby sevens-specific algorithm is needed. Differences in measures between the men's and women's game may be a result of physical size, and strength, and physicality, as well as technical and tactical factors.

The Chandra Source Catalog: Algorithms

NASA Astrophysics Data System (ADS)

McDowell, Jonathan; Evans, I. N.; Primini, F. A.; Glotfelty, K. J.; McCollough, M. L.; Houck, J. C.; Nowak, M. A.; Karovska, M.; Davis, J. E.; Rots, A. H.; Siemiginowska, A. L.; Hain, R.; Evans, J. D.; Anderson, C. S.; Bonaventura, N. R.; Chen, J. C.; Doe, S. M.; Fabbiano, G.; Galle, E. C.; Gibbs, D. G., II; Grier, J. D.; Hall, D. M.; Harbo, P. N.; He, X.; Lauer, J.; Miller, J. B.; Mitschang, A. W.; Morgan, D. L.; Nichols, J. S.; Plummer, D. A.; Refsdal, B. L.; Sundheim, B. A.; Tibbetts, M. S.; van Stone, D. W.; Winkelman, S. L.; Zografou, P.

2009-09-01

Creation of the Chandra Source Catalog (CSC) required adjustment of existing pipeline processing, adaptation of existing interactive analysis software for automated use, and development of entirely new algorithms. Data calibration was based on the existing pipeline, but more rigorous data cleaning was applied and the latest calibration data products were used. For source detection, a local background map was created including the effects of ACIS source readout streaks. The existing wavelet source detection algorithm was modified and a set of post-processing scripts used to correct the results. To analyse the source properties we ran the SAO Traceray trace code for each source to generate a model point spread function, allowing us to find encircled energy correction factors and estimate source extent. Further algorithms were developed to characterize the spectral, spatial and temporal properties of the sources and to estimate the confidence intervals on count rates and fluxes. Finally, sources detected in multiple observations were matched, and best estimates of their merged properties derived. In this paper we present an overview of the algorithms used, with more detailed treatment of some of the newly developed algorithms presented in companion papers.

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest

NASA Astrophysics Data System (ADS)

Miltiadou, Milto; Campbell, Neil D. F.; Gonzalez Aracil, Susana; Brown, Tony; Grant, Michael G.

2018-05-01

In Australia, many birds and arboreal animals use hollows for shelters, but studies predict shortage of hollows in near future. Aged dead trees are more likely to contain hollows and therefore automated detection of them plays a substantial role in preserving biodiversity and consequently maintaining a resilient ecosystem. For this purpose full-waveform LiDAR data were acquired from a native Eucalypt forest in Southern Australia. The structure of the forest significantly varies in terms of tree density, age and height. Additionally, Eucalyptus camaldulensis have multiple trunk splits making tree delineation very challenging. For that reason, this paper investigates automated detection of dead standing Eucalyptus camaldulensis without tree delineation. It also presents the new feature of the open source software DASOS, which extracts features for 3D object detection in voxelised FW LiDAR. A random forest classifier, a weighted-distance KNN algorithm and a seed growth algorithm are used to create a 2D probabilistic field and to then predict potential positions of dead trees. It is shown that tree health assessment is possible without tree delineation but since it is a new research directions there are many improvements to be made.

Automated Detection of Firearms and Knives in a CCTV Image

PubMed Central

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

2016-01-01

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

Automated Detection of Firearms and Knives in a CCTV Image.

PubMed

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

2016-01-01

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

TU-H-206-01: An Automated Approach for Identifying Geometric Distortions in Gamma Cameras

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mann, S; Nelson, J; Samei, E

2016-06-15

Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; lowmore » values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deployed to monitor data from all gamma cameras within our health system.« less

Pilot study analyzing automated ECG screening of hypertrophic cardiomyopathy.

PubMed

Campbell, Matthew J; Zhou, Xuefu; Han, Chia; Abrishami, Hedayat; Webster, Gregory; Miyake, Christina Y; Sower, Christopher T; Anderson, Jeffrey B; Knilans, Timothy K; Czosek, Richard J

2017-06-01

Hypertrophic cardiomyopathy (HCM) is one of the leading causes of sudden cardiac death in athletes. However, preparticipation ECG screening has often been criticized for failing to meet cost-effectiveness thresholds, in part because of high false-positive rates and the cost of ECG screening itself. The purpose of this study was to assess the testing characteristics of an automated ECG algorithm designed to screen for HCM in a multi-institutional pediatric cohort. ECGs from patients with HCM aged 12 to 20 years from 3 pediatric institutions were screened for ECG criteria for HCM using a previously described automated computer algorithm developed specifically for HCM ECG screening. The results were compared to a known healthy pediatric cohort. The studies then were read by trained electrophysiologists using standard ECG criteria and compared to the results of automated screening. One hundred twenty-eight ECGs from unique patients with phenotypic HCM were obtained and compared with 256 studies from healthy control patients matched in 2:1 fashion. When presented with the ECGs, the non-voltage-based algorithm resulted in 81.2% sensitivity and 90.7% specificity. A trained electrophysiologist read the same data according to the Seattle Criteria, with 71% sensitivity with 95.7% specificity. The sensitivity of screening as well as the components of the ECG screening itself varied by institution. This pilot study demonstrates a potential for automated ECG screening algorithms to detect HCM with testing characteristics similar to that of a trained electrophysiologist. In addition, there appear to be differences in ECG characteristics between patient populations, which may account for the difficulties in universal screening. Copyright © 2017 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

The detectability of supernovae against elliptical galactic disks.

NASA Astrophysics Data System (ADS)

Pearce, E. C.

A 75 cm telescope has been automated with a Prime 300 mini-computer to search approximately 250 galaxies per hour for young supernovae. The high-speed star-location and comparison algorithms used in the Digitized Astronomy Supernova Search (DASS) system is described.

ANGIOCARE: an automated system for fast three-dimensional coronary reconstruction by integrating angiographic and intracoronary ultrasound data.

PubMed

Bourantas, Christos V; Kalatzis, Fanis G; Papafaklis, Michail I; Fotiadis, Dimitrios I; Tweddel, Ann C; Kourtis, Iraklis C; Katsouras, Christos S; Michalis, Lampros K

2008-08-01

The development of an automated, user-friendly system (ANGIOCARE), for rapid three-dimensional (3D) coronary reconstruction, integrating angiographic and, intracoronary ultrasound (ICUS) data. Biplane angiographic and ICUS sequence images are imported into the system where a prevalidated method is used for coronary reconstruction. This incorporates extraction of the catheter path from two end-diastolic X-ray images and detection of regions of interest (lumen, outer vessel wall) in the ICUS sequence by an automated border detection algorithm. The detected borders are placed perpendicular to the catheter path and established algorithms used to estimate their absolute orientation. The resulting 3D object is imported into an advanced visualization module with which the operator can interact, examine plaque distribution (depicted as a color coded map) and assess plaque burden by virtual endoscopy. Data from 19 patients (27 vessels) undergoing biplane angiography and ICUS were examined. The reconstructed vessels were 21.3-80.2 mm long. The mean difference was 0.9 +/- 2.9% between the plaque volumes measured using linear 3D ICUS analysis and the volumes, estimated by taking into account the curvature of the vessel. The time required to reconstruct a luminal narrowing of 25 mm was approximately 10 min. The ANGIOCARE system provides rapid coronary reconstruction allowing the operator accurately to estimate the length of the lesion and determine plaque distribution and volume. (c) 2008 Wiley-Liss, Inc.

Automatic detection of erythemato-squamous diseases using k-means clustering.

PubMed

Ubeyli, Elif Derya; Doğdu, Erdoğan

2010-04-01

A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer.

PubMed

Lu, Hao; Papathomas, Thomas G; van Zessen, David; Palli, Ivo; de Krijger, Ronald R; van der Spek, Peter J; Dinjens, Winand N M; Stubbs, Andrew P

2014-11-25

In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI. We have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking. We have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the 'hottest' hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/Automated_Selection_of_Hotspots . The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216.

An algorithm for the detection and characterisation of volcanic plumes using thermal camera imagery

NASA Astrophysics Data System (ADS)

Bombrun, Maxime; Jessop, David; Harris, Andrew; Barra, Vincent

2018-02-01

Volcanic plumes are turbulent mixtures of particles and gas which are injected into the atmosphere during a volcanic eruption. Depending on the intensity of the eruption, plumes can rise from a few tens of metres up to many tens of kilometres above the vent and thus, present a major hazard for the surrounding population. Currently, however, few if any algorithms are available for automated plume tracking and assessment. Here, we present a new image processing algorithm for segmentation, tracking and parameters extraction of convective plume recorded with thermal cameras. We used thermal video of two volcanic eruptions and two plumes simulated in laboratory to develop and test an efficient technique for analysis of volcanic plumes. We validated our method by two different approaches. First, we compare our segmentation method to previously published algorithms. Next, we computed plume parameters, such as height, width and spreading angle at regular intervals of time. These parameters allowed us to calculate an entrainment coefficient and obtain information about the entrainment efficiency in Strombolian eruptions. Our proposed algorithm is rapid, automated while producing better visual outlines compared to the other segmentation algorithms, and provides output that is at least as accurate as manual measurements of plumes.

Automated measurement of estrogen receptor in breast cancer: a comparison of fluorescent and chromogenic methods of measurement

PubMed Central

Zarrella, Elizabeth; Coulter, Madeline; Welsh, Allison; Carvajal, Daniel; Schalper, Kurt; Harigopal, Malini; Rimm, David; Neumeister, Veronique

2016-01-01

While FDA approved methods of assessment of Estrogen Receptor (ER) are “fit for purpose”, they represent a 30-year-old technology. New quantitative methods, both chromogenic and fluorescent, have been developed and studies have shown that these methods increase the accuracy of assessment of ER. Here, we compare three methods of ER detection and assessment on two retrospective tissue microarray cohorts of breast cancer patients: estimates of percent nuclei positive by pathologists and by Aperio’s nuclear algorithm (standard chromogenic immunostaining), and immunofluorescence as quantified with the AQUA® method of quantitative immunofluorescence (QIF). Reproducibility was excellent (R2 > 0.95) between users for both automated analysis methods, and the Aperio and QIF scoring results were also highly correlated, despite the different detection systems. The subjective readings show lower levels of reproducibility and a discontinuous, bimodal distribution of scores not seen by either mechanized method. Kaplan-Meier analysis of 10-year disease-free survival was significant for each method (Pathologist, P=0.0019; Aperio, P=0.0053, AQUA, P=0.0026), but there were discrepancies in patient classification in 19 out of 233 cases analyzed. Out of these, 11 were visually positive by both chromogenic and fluorescent detection. In 10 cases, the Aperio nuclear algorithm labeled the nuclei as negative, in 1 case, the AQUA score was just under the cutoff for positivity (determined by an Index TMA). In contrast, 8 out of 19 discrepant cases had clear nuclear positivity by fluorescence that was unable to be visualized by chromogenic detection, perhaps due to low positivity masked by the hematoxylin counterstain. These results demonstrate that automated systems enable objective, precise quantification of ER. Furthermore immunofluorescence detection offers the additional advantage of a signal that cannot be masked by a counterstaining agent. These data support the usage of automated methods for measurement of this and other biomarkers that may be used in companion diagnostic tests. PMID:27348626

Enhancement of surface definition and gridding in the EAGLE code

NASA Technical Reports Server (NTRS)

Thompson, Joe F.

1991-01-01

Algorithms for smoothing of curves and surfaces for the EAGLE grid generation program are presented. The method uses an existing automated technique which detects undesirable geometric characteristics by using a local fairness criterion. The geometry entity is then smoothed by repeated removal and insertion of spline knots in the vicinity of the geometric irregularity. The smoothing algorithm is formulated for use with curves in Beta spline form and tensor product B-spline surfaces.

Detection of anomaly in human retina using Laplacian Eigenmaps and vectorized matched filtering

NASA Astrophysics Data System (ADS)

Yacoubou Djima, Karamatou A.; Simonelli, Lucia D.; Cunningham, Denise; Czaja, Wojciech

2015-03-01

We present a novel method for automated anomaly detection on auto fluorescent data provided by the National Institute of Health (NIH). This is motivated by the need for new tools to improve the capability of diagnosing macular degeneration in its early stages, track the progression over time, and test the effectiveness of new treatment methods. In previous work, macular anomalies have been detected automatically through multiscale analysis procedures such as wavelet analysis or dimensionality reduction algorithms followed by a classification algorithm, e.g., Support Vector Machine. The method that we propose is a Vectorized Matched Filtering (VMF) algorithm combined with Laplacian Eigenmaps (LE), a nonlinear dimensionality reduction algorithm with locality preserving properties. By applying LE, we are able to represent the data in the form of eigenimages, some of which accentuate the visibility of anomalies. We pick significant eigenimages and proceed with the VMF algorithm that classifies anomalies across all of these eigenimages simultaneously. To evaluate our performance, we compare our method to two other schemes: a matched filtering algorithm based on anomaly detection on single images and a combination of PCA and VMF. LE combined with VMF algorithm performs best, yielding a high rate of accurate anomaly detection. This shows the advantage of using a nonlinear approach to represent the data and the effectiveness of VMF, which operates on the images as a data cube rather than individual images.

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

PubMed Central

Howsmon, Daniel P.; Cameron, Faye; Baysal, Nihat; Ly, Trang T.; Forlenza, Gregory P.; Maahs, David M.; Buckingham, Bruce A.; Hahn, Juergen; Bequette, B. Wayne

2017-01-01

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis—a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios. PMID:28098839

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs).

PubMed

Howsmon, Daniel P; Cameron, Faye; Baysal, Nihat; Ly, Trang T; Forlenza, Gregory P; Maahs, David M; Buckingham, Bruce A; Hahn, Juergen; Bequette, B Wayne

2017-01-15

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis-a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

PubMed Central

De Cooman, Thomas; Gu, Ying; Cleeren, Evy; Claes, Kasper; Van Paesschen, Wim; Van Huffel, Sabine; Hunyadi, Borbála

2017-01-01

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG. PMID:29027928

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment.

PubMed

Vandecasteele, Kaat; De Cooman, Thomas; Gu, Ying; Cleeren, Evy; Claes, Kasper; Paesschen, Wim Van; Huffel, Sabine Van; Hunyadi, Borbála

2017-10-13

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

PubMed

Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng; Blevins, Tyler; Conrad, Erin C; Leyde, Kent; Cook, Mark J; Khambhati, Ankit N; Wagenaar, Joost B; Worrell, Gregory A; Litt, Brian

2017-06-01

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Actigraphy features for predicting mobility disability in older adults

USDA-ARS?s Scientific Manuscript database

Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mo...

"SmartMonitor"--an intelligent security system for the protection of individuals and small properties with the possibility of home automation.

PubMed

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

2014-06-05

"SmartMonitor" is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the "SmartMonitor" system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons.

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

PubMed Central

Murphy, Sean Patrick; Burkom, Howard

2008-01-01

Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings.

PubMed

Dutta, Sayon; Long, William J; Brown, David F M; Reisner, Andrew T

2013-08-01

As use of radiology studies increases, there is a concurrent increase in incidental findings (eg, lung nodules) for which the radiologist issues recommendations for additional imaging for follow-up. Busy emergency physicians may be challenged to carefully communicate recommendations for additional imaging not relevant to the patient's primary evaluation. The emergence of electronic health records and natural language processing algorithms may help address this quality gap. We seek to describe recommendations for additional imaging from our institution and develop and validate an automated natural language processing algorithm to reliably identify recommendations for additional imaging. We developed a natural language processing algorithm to detect recommendations for additional imaging, using 3 iterative cycles of training and validation. The third cycle used 3,235 radiology reports (1,600 for algorithm training and 1,635 for validation) of discharged emergency department (ED) patients from which we determined the incidence of discharge-relevant recommendations for additional imaging and the frequency of appropriate discharge documentation. The test characteristics of the 3 natural language processing algorithm iterations were compared, using blinded chart review as the criterion standard. Discharge-relevant recommendations for additional imaging were found in 4.5% (95% confidence interval [CI] 3.5% to 5.5%) of ED radiology reports, but 51% (95% CI 43% to 59%) of discharge instructions failed to note those findings. The final natural language processing algorithm had 89% (95% CI 82% to 94%) sensitivity and 98% (95% CI 97% to 98%) specificity for detecting recommendations for additional imaging. For discharge-relevant recommendations for additional imaging, sensitivity improved to 97% (95% CI 89% to 100%). Recommendations for additional imaging are common, and failure to document relevant recommendations for additional imaging in ED discharge instructions occurs frequently. The natural language processing algorithm's performance improved with each iteration and offers a promising error-prevention tool. Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

PubMed Central

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. PMID:26066315

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2003-09-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. This paper describes a method for training several multivariate Gaussian classifiers such that their And-ing dramatically reduces false alarms while maintaining a high probability of classification. This training approach is referred to as the Focused- Training method. This work extends our 2001-2002 work where the Focused-Training method was used with three other types of classifiers: the Attractor-based K-Nearest Neighbor Neural Network (a type of radial-basis, probabilistic neural network), the Optimal Discrimination Filter Classifier (based linear discrimination theory), and the Quadratic Penalty Function Support Vector Machine (QPFSVM). Although our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to a wide range of pattern recognition and automatic target recognition (ATR) problems.

Imaging Performance of a Handheld Ultrasound System With Real-Time Computer-Aided Detection of Lumbar Spine Anatomy: A Feasibility Study.

PubMed

Tiouririne, Mohamed; Dixon, Adam J; Mauldin, F William; Scalzo, David; Krishnaraj, Arun

2017-08-01

The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%-98.1%) and a specificity of 85.5% (95% CI, 81.7%-88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%-97.7%) and specificity of 91.3% (95% CI, 83.6%-96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.

Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer.

PubMed

Krecsák, László; Micsik, Tamás; Kiszler, Gábor; Krenács, Tibor; Szabó, Dániel; Jónás, Viktor; Császár, Gergely; Czuni, László; Gurzó, Péter; Ficsor, Levente; Molnár, Béla

2011-01-18

The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications. The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides. The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two scoring schemes. NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14 software application proved to be a reliable image analysis tool for pathologists testing ER and PR status in breast cancer.

Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Yonggang; Thomas, Maikael A.

We propose the development of proven deep learning algorithms to flag objects and events of interest in Next Generation Surveillance System (NGSS) surveillance to make IAEA image review more efficient. Video surveillance is one of the core monitoring technologies used by the IAEA Department of Safeguards when implementing safeguards at nuclear facilities worldwide. The current image review software GARS has limited automated functions, such as scene-change detection, black image detection and missing scene analysis, but struggles with highly cluttered backgrounds. A cutting-edge algorithm to be developed in this project will enable efficient and effective searches in images and video streamsmore » by identifying and tracking safeguards relevant objects and detect anomalies in their vicinity. In this project, we will develop the algorithm, test it with the IAEA surveillance cameras and data sets collected at simulated nuclear facilities at BNL and SNL, and implement it in a software program for potential integration into the IAEA’s IRAP (Integrated Review and Analysis Program).« less

Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurugol, Sila, E-mail: sila.kurugol@childrens.harvard.edu; Come, Carolyn E.; Diaz, Alejandro A.

Purpose: The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. Methods: The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearbymore » edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. Results: The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. Conclusions: The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.« less

Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions.

PubMed

Kurugol, Sila; Come, Carolyn E; Diaz, Alejandro A; Ross, James C; Kinney, Greg L; Black-Shinn, Jennifer L; Hokanson, John E; Budoff, Matthew J; Washko, George R; San Jose Estepar, Raul

2015-09-01

The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.

Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

PubMed Central

Kurugol, Sila; Come, Carolyn E.; Diaz, Alejandro A.; Ross, James C.; Kinney, Greg L.; Black-Shinn, Jennifer L.; Hokanson, John E.; Budoff, Matthew J.; Washko, George R.; San Jose Estepar, Raul

2015-01-01

Purpose: The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. Methods: The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. Results: The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. Conclusions: The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers. PMID:26328995

Semi-Automated Digital Image Analysis of Pick’s Disease and TDP-43 Proteinopathy

PubMed Central

Irwin, David J.; Byrne, Matthew D.; McMillan, Corey T.; Cooper, Felicia; Arnold, Steven E.; Lee, Edward B.; Van Deerlin, Vivianna M.; Xie, Sharon X.; Lee, Virginia M.-Y.; Grossman, Murray; Trojanowski, John Q.

2015-01-01

Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick’s disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes. PMID:26538548

Semi-Automated Digital Image Analysis of Pick's Disease and TDP-43 Proteinopathy.

PubMed

Irwin, David J; Byrne, Matthew D; McMillan, Corey T; Cooper, Felicia; Arnold, Steven E; Lee, Edward B; Van Deerlin, Vivianna M; Xie, Sharon X; Lee, Virginia M-Y; Grossman, Murray; Trojanowski, John Q

2016-01-01

Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick's disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes. © The Author(s) 2015.

Real-time portable system for fabric defect detection using an ARM processor

NASA Astrophysics Data System (ADS)

Fernandez-Gallego, J. A.; Yañez-Puentes, J. P.; Ortiz-Jaramillo, B.; Alvarez, J.; Orjuela-Vargas, S. A.; Philips, W.

2012-06-01

Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.

Collaborative real-time motion video analysis by human observer and image exploitation algorithms

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2015-05-01

Motion video analysis is a challenging task, especially in real-time applications. In most safety and security critical applications, a human observer is an obligatory part of the overall analysis system. Over the last years, substantial progress has been made in the development of automated image exploitation algorithms. Hence, we investigate how the benefits of automated video analysis can be integrated suitably into the current video exploitation systems. In this paper, a system design is introduced which strives to combine both the qualities of the human observer's perception and the automated algorithms, thus aiming to improve the overall performance of a real-time video analysis system. The system design builds on prior work where we showed the benefits for the human observer by means of a user interface which utilizes the human visual focus of attention revealed by the eye gaze direction for interaction with the image exploitation system; eye tracker-based interaction allows much faster, more convenient, and equally precise moving target acquisition in video images than traditional computer mouse selection. The system design also builds on prior work we did on automated target detection, segmentation, and tracking algorithms. Beside the system design, a first pilot study is presented, where we investigated how the participants (all non-experts in video analysis) performed in initializing an object tracking subsystem by selecting a target for tracking. Preliminary results show that the gaze + key press technique is an effective, efficient, and easy to use interaction technique when performing selection operations on moving targets in videos in order to initialize an object tracking function.

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

DOE PAGES

Grambow, Colin A.; Jamal, Adeel; Li, Yi -Pei; ...

2017-12-22

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The presentmore » joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Altogether, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).« less

The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy geens

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Kim, Moon S.; Chuang, Yung-Kun; Lee, Hoyoung

2013-05-01

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.

PubMed

Zolgharni, Massoud; Negoita, Madalina; Dhutia, Niti M; Mielewczik, Michael; Manoharan, Karikaran; Sohaib, S M Afzal; Finegold, Judith A; Sacchi, Stefania; Cole, Graham D; Francis, Darrel P

2017-07-01

Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%). An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame. © 2017, Wiley Periodicals, Inc.

Automated selected reaction monitoring software for accurate label-free protein quantification.

PubMed

Teleman, Johan; Karlsson, Christofer; Waldemarson, Sofia; Hansson, Karin; James, Peter; Malmström, Johan; Levander, Fredrik

2012-07-06

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard

PubMed Central

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Zwart, Peter H.; Hung, Li-Wei; Read, Randy J.; Adams, Paul D.

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution. PMID:18094468

SkinScan©: A PORTABLE LIBRARY FOR MELANOMA DETECTION ON HANDHELD DEVICES

PubMed Central

Wadhawan, Tarun; Situ, Ning; Lancaster, Keith; Yuan, Xiaojing; Zouridakis, George

2011-01-01

We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4. Our results demonstrate that the proposed library with the advanced image processing and analysis algorithms has excellent performance on handheld and desktop computers. Therefore, deployment of smartphones as screening devices for skin cancer and other skin diseases can have a significant impact on health care delivery in underserved and remote areas. PMID:21892382

Ambulatory REACT: real-time seizure detection with a DSP microprocessor.

PubMed

McEvoy, Robert P; Faul, Stephen; Marnane, William P

2010-01-01

REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis. Furthermore, the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages. This hardware profile is used to select a reduced, platform-aware feature set, in order to evaluate the seizure classification accuracy of a lower-complexity, lower-power REACT system.

Task-oriented situation recognition

NASA Astrophysics Data System (ADS)

Bauer, Alexander; Fischer, Yvonne

2010-04-01

From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building.

Using microwave Doppler radar in automated manufacturing applications

NASA Astrophysics Data System (ADS)

Smith, Gregory C.

Since the beginning of the Industrial Revolution, manufacturers worldwide have used automation to improve productivity, gain market share, and meet growing or changing consumer demand for manufactured products. To stimulate further industrial productivity, manufacturers need more advanced automation technologies: "smart" part handling systems, automated assembly machines, CNC machine tools, and industrial robots that use new sensor technologies, advanced control systems, and intelligent decision-making algorithms to "see," "hear," "feel," and "think" at the levels needed to handle complex manufacturing tasks without human intervention. The investigator's dissertation offers three methods that could help make "smart" CNC machine tools and industrial robots possible: (1) A method for detecting acoustic emission using a microwave Doppler radar detector, (2) A method for detecting tool wear on a CNC lathe using a Doppler radar detector, and (3) An online non-contact method for detecting industrial robot position errors using a microwave Doppler radar motion detector. The dissertation studies indicate that microwave Doppler radar could be quite useful in automated manufacturing applications. In particular, the methods developed may help solve two difficult problems that hinder further progress in automating manufacturing processes: (1) Automating metal-cutting operations on CNC machine tools by providing a reliable non-contact method for detecting tool wear, and (2) Fully automating robotic manufacturing tasks by providing a reliable low-cost non-contact method for detecting on-line position errors. In addition, the studies offer a general non-contact method for detecting acoustic emission that may be useful in many other manufacturing and non-manufacturing areas, as well (e.g., monitoring and nondestructively testing structures, materials, manufacturing processes, and devices). By advancing the state of the art in manufacturing automation, the studies may help stimulate future growth in industrial productivity, which also promises to fuel economic growth and promote economic stability. The study also benefits the Department of Industrial Technology at Iowa State University and the field of Industrial Technology by contributing to the ongoing "smart" machine research program within the Department of Industrial Technology and by stimulating research into new sensor technologies within the University and within the field of Industrial Technology.

Photoplethysmography as a single source for analysis of sleep-disordered breathing in patients with severe cardiovascular disease.

PubMed

Amir, Offer; Barak-Shinar, Deganit; Henry, Antonietta; Smart, Frank W

2012-02-01

Sleep-disordered breathing and Cheyne-Stokes breathing are often not diagnosed, especially in cardiovascular patients. An automated system based on photoplethysmographic signals might provide a convenient screening and diagnostic solution for patient evaluation at home or in an ambulatory setting. We compared event detection and classification obtained by full polysomnography (the 'gold standard') and by an automated new algorithm system in 74 subjects. Each subject underwent overnight polysomnography, 60 in a hospital cardiology department and 14 while being tested for suspected sleep-disordered breathing in a sleep laboratory. The sleep-disordered breathing and Cheyne-Stokes breathing parameters measured by a new automated algorithm system correlated very well with the corresponding results obtained by full polysomnography. The sensitivity of the Cheyne-Stokes breathing detected from the system compared to full polysomnography was 92% [95% confidence interval (CI): 78.6-98.3%] and specificity 94% (95% CI: 81.3-99.3%). Comparison of the Apnea Hyponea Index with a cutoff level of 15 shows a sensitivity of 98% (95% CI: 87.1-99.6%) and specificity of 96% (95% CI: 79.8-99.3%). The detection of respiratory events showed agreement of approximately 80%. Regression and Bland-Altman plots revealed good agreement between the two methods. Relative to gold-standard polysomnography, the simply used automated system in this study yielded an acceptable analysis of sleep- and/or cardiac-related breathing disorders. Accordingly, and given the convenience and simplicity of its application, this system can be considered as a suitable platform for home and ambulatory screening and diagnosis of sleep-disordered breathing in patients with cardiovascular disease. © 2011 European Sleep Research Society.

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Sanchez, Javier; Revie, Crawford W.

2013-01-01

Background Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes. PMID:24349216

Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.

PubMed

Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Sanchez, Javier; Revie, Crawford W

2013-01-01

Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.

BgCut: automatic ship detection from UAV images.

PubMed

Xu, Chao; Zhang, Dongping; Zhang, Zhengning; Feng, Zhiyong

2014-01-01

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

BgCut: Automatic Ship Detection from UAV Images

PubMed Central

Zhang, Zhengning; Feng, Zhiyong

2014-01-01

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches. PMID:24977182

Automated vehicle detection in forward-looking infrared imagery.

PubMed

Der, Sandor; Chan, Alex; Nasrabadi, Nasser; Kwon, Heesung

2004-01-10

We describe an algorithm for the detection and clutter rejection of military vehicles in forward-looking infrared (FLIR) imagery. The detection algorithm is designed to be a prescreener that selects regions for further analysis and uses a spatial anomaly approach that looks for target-sized regions of the image that differ in texture, brightness, edge strength, or other spatial characteristics. The features are linearly combined to form a confidence image that is thresholded to find likely target locations. The clutter rejection portion uses target-specific information extracted from training samples to reduce the false alarms of the detector. The outputs of the clutter rejecter and detector are combined by a higher-level evidence integrator to improve performance over simple concatenation of the detector and clutter rejecter. The algorithm has been applied to a large number of FLIR imagery sets, and some of these results are presented here.

Detection and severity classification of extracardiac interference in {sup 82}Rb PET myocardial perfusion imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orton, Elizabeth J., E-mail: eorton@physics.carleton.ca; Kemp, Robert A. de; Glenn Wells, R.

2014-10-15

Purpose: Myocardial perfusion imaging (MPI) is used for diagnosis and prognosis of coronary artery disease. When MPI studies are performed with positron emission tomography (PET) and the radioactive tracer rubidium-82 chloride ({sup 82}Rb), a small but non-negligible fraction of studies (∼10%) suffer from extracardiac interference: high levels of tracer uptake in structures adjacent to the heart which mask the true cardiac tracer uptake. At present, there are no clinically available options for automated detection or correction of this problem. This work presents an algorithm that detects and classifies the severity of extracardiac interference in {sup 82}Rb PET MPI images andmore » reports the accuracy and failure rate of the method. Methods: A set of 200 {sup 82}Rb PET MPI images were reviewed by a trained nuclear cardiologist and interference severity reported on a four-class scale, from absent to severe. An automated algorithm was developed that compares uptake at the external border of the myocardium to three thresholds, separating the four interference severity classes. A minimum area of interference was required, and the search region was limited to that facing the stomach wall and spleen. Maximizing concordance (Cohen’s Kappa) and minimizing failure rate for the set of 200 clinician-read images were used to find the optimal population-based constants defining search limit and minimum area parameters and the thresholds for the algorithm. Tenfold stratified cross-validation was used to find optimal thresholds and report accuracy measures (sensitivity, specificity, and Kappa). Results: The algorithm was capable of detecting interference with a mean [95% confidence interval] sensitivity/specificity/Kappa of 0.97 [0.94, 1.00]/0.82 [0.66, 0.98]/0.79 [0.65, 0.92], and a failure rate of 1.0% ± 0.2%. The four-class overall Kappa was 0.72 [0.64, 0.81]. Separation of mild versus moderate-or-greater interference was performed with good accuracy (sensitivity/specificity/Kappa = 0.92 [0.86, 0.99]/0.86 [0.71, 1.00]/0.78 [0.64, 0.92]), while separation of moderate versus severe interference severity classes showed reduced sensitivity/Kappa but little change in specificity (sensitivity/specificity/Kappa = 0.83 [0.77, 0.88]/0.82 [0.77, 0.88]/0.65 [0.60, 0.70]). Specificity was greater than sensitivity for all interference classes. Algorithm execution time was <1 min. Conclusions: The algorithm produced here has a low failure rate and high accuracy for detection of extracardiac interference in {sup 82}Rb PET MPI scans. It provides a fast, reliable, automated method for assessing severity of extracardiac interference.« less

Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery

NASA Astrophysics Data System (ADS)

Hardtke, L. A.; Blanco, P. D.; del Valle, H. F.; Metternicht, G. I.; Sione, W. F.

2015-04-01

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

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

PubMed

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

2010-01-01

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

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

PubMed

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

2016-10-01

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

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

PubMed Central

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

2016-01-01

Abstract. The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was −7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors. PMID:27921070

Person detection and tracking with a 360° lidar system

NASA Astrophysics Data System (ADS)

Hammer, Marcus; Hebel, Marcus; Arens, Michael

2017-10-01

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks. In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed. The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

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.

Semi-automated identification of cones in the human retina using circle Hough transform

PubMed Central

Bukowska, Danuta M.; Chew, Avenell L.; Huynh, Emily; Kashani, Irwin; Wan, Sue Ling; Wan, Pak Ming; Chen, Fred K

2015-01-01

A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately. PMID:26713186

Automated deep-phenotyping of the vertebrate brain

PubMed Central

Allalou, Amin; Wu, Yuelong; Ghannad-Rezaie, Mostafa; Eimon, Peter M; Yanik, Mehmet Fatih

2017-01-01

Here, we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex. DOI: http://dx.doi.org/10.7554/eLife.23379.001 PMID:28406399

Focused ultrasound: concept for automated transcutaneous control of hemorrhage in austere settings.

PubMed

Kucewicz, John C; Bailey, Michael R; Kaczkowski, Peter J; Carter, Stephen J

2009-04-01

High intensity focused ultrasound (HIFU) is being developed for a range of clinical applications. Of particular interest to NASA and the military is the use of HIFU for traumatic injuries because HIFU has the unique ability to transcutaneously stop bleeding. Automation of this technology would make possible its use in remote, austere settings by personnel not specialized in medical ultrasound. Here a system to automatically detect and target bleeding is tested and reported. The system uses Doppler ultrasound images from a clinical ultrasound scanner for bleeding detection and hardware for HIFU therapy. The system was tested using a moving string to simulate blood flow and targeting was visualized by Schlieren imaging to show the focusing of the HIFU acoustic waves. When instructed by the operator, a Doppler ultrasound image is acquired and processed to detect and localize the moving string, and the focus of the HIFU array is electronically adjusted to target the string. Precise and accurate targeting was verified in the Schlieren images. An automated system to detect and target simulated bleeding has been built and tested. The system could be combined with existing algorithms to detect, target, and treat clinical bleeding.

STEM Leader from the Roeper School: An Interview with Nuclear Engineer Clair J. Sullivan

ERIC Educational Resources Information Center

Ambrose, Don

2016-01-01

Clair J. Sullivan is an assistant professor in the Department of Nuclear, Plasma and Radiological Engineering at the University of Illinois at Urbana-Champaign (UIUC). Her research interests include radiation detection and measurements; gamma-ray spectroscopy; automated isotope identification algorithms; nuclear forensics; nuclear security;…

Inertial aided cycle slip detection and identification for integrated PPP GPS and INS.

PubMed

Du, Shuang; Gao, Yang

2012-10-25

The recently developed integrated Precise Point Positioning (PPP) GPS/INS system can be useful to many applications, such as UAV navigation systems, land vehicle/machine automation and mobile mapping systems. Since carrier phase measurements are the primary observables in PPP GPS, cycle slips, which often occur due to high dynamics, signal obstructions and low satellite elevation, must be detected and repaired in order to ensure the navigation performance. In this research, a new algorithm of cycle slip detection and identification has been developed. With the aiding from INS, the proposed method jointly uses WL and EWL phase combinations to uniquely determine cycle slips in the L1 and L2 frequencies. To verify the efficiency of the algorithm, both tactical-grade and consumer-grade IMUs are tested by using a real dataset collected from two field tests. The results indicate that the proposed algorithm can efficiently detect and identify the cycle slips and subsequently improve the navigation performance of the integrated system.

Automated High-Speed Video Detection of Small-Scale Explosives Testing

NASA Astrophysics Data System (ADS)

Ford, Robert; Guymon, Clint

2013-06-01

Small-scale explosives sensitivity test data is used to evaluate hazards of processing, handling, transportation, and storage of energetic materials. Accurate test data is critical to implementation of engineering and administrative controls for personnel safety and asset protection. Operator mischaracterization of reactions during testing contributes to either excessive or inadequate safety protocols. Use of equipment and associated algorithms to aid the operator in reaction determination can significantly reduce operator error. Safety Management Services, Inc. has developed an algorithm to evaluate high-speed video images of sparks from an ESD (Electrostatic Discharge) machine to automatically determine whether or not a reaction has taken place. The algorithm with the high-speed camera is termed GoDetect (patent pending). An operator assisted version for friction and impact testing has also been developed where software is used to quickly process and store video of sensitivity testing. We have used this method for sensitivity testing with multiple pieces of equipment. We present the fundamentals of GoDetect and compare it to other methods used for reaction detection.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

NASA Astrophysics Data System (ADS)

Phipps, Jennifer E.; Gorpas, Dimitris; Unger, Jakob; Darrow, Morgan; Bold, Richard J.; Marcu, Laura

2018-01-01

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N  =  20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System

PubMed Central

Wang, Ruiping; Jiang, Yonggen; Guo, Xiaoqin; Wu, Yiling; Zhao, Genming

2017-01-01

Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period. Results The optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD. Conclusions Selection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS. PMID:28728470

Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System.

PubMed

Wang, Ruiping; Jiang, Yonggen; Guo, Xiaoqin; Wu, Yiling; Zhao, Genming

2018-01-01

Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period. Results The optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD. Conclusions Selection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS.

Closing the Loop in ICU Decision Support: Physiologic Event Detection, Alerts, and Documentation

PubMed Central

Norris, Patrick R.; Dawant, Benoit M.

2002-01-01

Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users’ alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility.

Retina Image Analysis and Ocular Telehealth: The Oak Ridge National Laboratory-Hamilton Eye Institute Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karnowski, Thomas Paul; Giancardo, Luca; Li, Yaquin

2013-01-01

Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to howmore » validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.« less

Robust object tracking techniques for vision-based 3D motion analysis applications

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

2016-04-01

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

Global Bathymetry: Machine Learning for Data Editing

NASA Astrophysics Data System (ADS)

Sandwell, D. T.; Tea, B.; Freund, Y.

2017-12-01

The accuracy of global bathymetry depends primarily on the coverage and accuracy of the sounding data and secondarily on the depth predicted from gravity. A main focus of our research is to add newly-available data to the global compilation. Most data sources have 1-12% of erroneous soundings caused by a wide array of blunders and measurement errors. Over the years we have hand-edited this data using undergraduate employees at UCSD (440 million soundings at 500 m resolution). We are developing a machine learning approach to refine the flagging of the older soundings and provide automated editing of newly-acquired soundings. The approach has three main steps: 1) Combine the sounding data with additional information that may inform the machine learning algorithm. The additional parameters include: depth predicted from gravity; distance to the nearest sounding from other cruises; seafloor age; spreading rate; sediment thickness; and vertical gravity gradient. 2) Use available edit decisions as training data sets for a boosted tree algorithm with a binary logistic objective function and L2 regularization. Initial results with poor quality single beam soundings show that the automated algorithm matches the hand-edited data 89% of the time. The results show that most of the information for detecting outliers comes from predicted depth with secondary contributions from distance to the nearest sounding and longitude. A similar analysis using very high quality multibeam data shows that the automated algorithm matches the hand-edited data 93% of the time. Again, most of the information for detecting outliers comes from predicted depth secondary contributions from distance to the nearest sounding and longitude. 3) The third step in the process is to use the machine learning parameters, derived from the training data, to edit 12 million newly acquired single beam sounding data provided by the National Geospatial-Intelligence Agency. The output of the learning algorithm will be confidence ratedindicating which edits the algorithm is confident on and which it is not confident. We expect the majority ( 90%) of edits to be confident and not require human intervention. Human intervention will be required only on the 10% unconfident decisions, thus reducing the amount of human work by a factor of 10 or more.

Computational strategies for the automated design of RNA nanoscale structures from building blocks using NanoTiler.

PubMed

Bindewald, Eckart; Grunewald, Calvin; Boyle, Brett; O'Connor, Mary; Shapiro, Bruce A

2008-10-01

One approach to designing RNA nanoscale structures is to use known RNA structural motifs such as junctions, kissing loops or bulges and to construct a molecular model by connecting these building blocks with helical struts. We previously developed an algorithm for detecting internal loops, junctions and kissing loops in RNA structures. Here we present algorithms for automating or assisting many of the steps that are involved in creating RNA structures from building blocks: (1) assembling building blocks into nanostructures using either a combinatorial search or constraint satisfaction; (2) optimizing RNA 3D ring structures to improve ring closure; (3) sequence optimisation; (4) creating a unique non-degenerate RNA topology descriptor. This effectively creates a computational pipeline for generating molecular models of RNA nanostructures and more specifically RNA ring structures with optimized sequences from RNA building blocks. We show several examples of how the algorithms can be utilized to generate RNA tecto-shapes.

Computational strategies for the automated design of RNA nanoscale structures from building blocks using NanoTiler☆

PubMed Central

Bindewald, Eckart; Grunewald, Calvin; Boyle, Brett; O’Connor, Mary; Shapiro, Bruce A.

2013-01-01

One approach to designing RNA nanoscale structures is to use known RNA structural motifs such as junctions, kissing loops or bulges and to construct a molecular model by connecting these building blocks with helical struts. We previously developed an algorithm for detecting internal loops, junctions and kissing loops in RNA structures. Here we present algorithms for automating or assisting many of the steps that are involved in creating RNA structures from building blocks: (1) assembling building blocks into nanostructures using either a combinatorial search or constraint satisfaction; (2) optimizing RNA 3D ring structures to improve ring closure; (3) sequence optimisation; (4) creating a unique non-degenerate RNA topology descriptor. This effectively creates a computational pipeline for generating molecular models of RNA nanostructures and more specifically RNA ring structures with optimized sequences from RNA building blocks. We show several examples of how the algorithms can be utilized to generate RNA tecto-shapes. PMID:18838281

A novel tracing method for the segmentation of cell wall networks.

PubMed

De Vylder, Jonas; Rooms, Filip; Dhondt, Stijn; Inze, Dirk; Philips, Wilfried

2013-01-01

Cell wall networks are a common subject of research in biology, which are important for plant growth analysis, organ studies, etc. In order to automate the detection of individual cells in such cell wall networks, we propose a new segmentation algorithm. The proposed method is a network tracing algorithm, exploiting the prior knowledge of the network structure. The method is applicable on multiple microscopy modalities such as fluorescence, but also for images captured using non invasive microscopes such as differential interference contrast (DIC) microscopes.

Interdisciplinary Investigations in Support of Project DI-MOD

NASA Technical Reports Server (NTRS)

Starks, Scott A. (Principal Investigator)

1996-01-01

Various concepts from time series analysis are used as the basis for the development of algorithms to assist in the analysis and interpretation of remote sensed imagery. An approach to trend detection that is based upon the fractal analysis of power spectrum estimates is presented. Additionally, research was conducted toward the development of a software architecture to support processing tasks associated with databases housing a variety of data. An algorithmic approach which provides for the automation of the state monitoring process is presented.

Bio-ALIRT biosurveillance detection algorithm evaluation.

PubMed

Siegrist, David; Pavlin, J

2004-09-24

Early detection of disease outbreaks by a medical biosurveillance system relies on two major components: 1) the contribution of early and reliable data sources and 2) the sensitivity, specificity, and timeliness of biosurveillance detection algorithms. This paper describes an effort to assess leading detection algorithms by arranging a common challenge problem and providing a common data set. The objectives of this study were to determine whether automated detection algorithms can reliably and quickly identify the onset of natural disease outbreaks that are surrogates for possible terrorist pathogen releases, and do so at acceptable false-alert rates (e.g., once every 2-6 weeks). Historic de-identified data were obtained from five metropolitan areas over 23 months; these data included International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. An outbreak detection group identified and labeled two natural disease outbreaks in these data and provided them to analysts for training of detection algorithms. All outbreaks in the remaining test data were identified but not revealed to the detection groups until after their analyses. The algorithms established a probability of outbreak for each day's counts. The probability of outbreak was assessed as an "actual" alert for different false-alert rates. The best algorithms were able to detect all of the outbreaks at false-alert rates of one every 2-6 weeks. They were often able to detect for the same day human investigators had identified as the true start of the outbreak. Because minimal data exists for an actual biologic attack, determining how quickly an algorithm might detect such an attack is difficult. However, application of these algorithms in combination with other data-analysis methods to historic outbreak data indicates that biosurveillance techniques for analyzing syndrome counts can rapidly detect seasonal respiratory and gastrointestinal illness outbreaks. Further research is needed to assess the value of electronic data sources for predictive detection. In addition, simulations need to be developed and implemented to better characterize the size and type of biologic attack that can be detected by current methods by challenging them under different projected operational conditions.

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline.

PubMed

Gao, Shan; van 't Klooster, Ronald; Brandts, Anne; Roes, Stijntje D; Alizadeh Dehnavi, Reza; de Roos, Albert; Westenberg, Jos J M; van der Geest, Rob J

2017-01-01

To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images. 3T MRI data acquired with T 1 -weighted gradient-echo black-blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B-spline surface to the boundaries of lumen and outer wall. The tube-fitting was based on the edge detection performed on the signal intensity (SI) profile along the surface normal. To achieve a fully automated process, Hough Transform (HT) was developed to estimate the lumen centerline and radii for the target vessel. Using the outputs of HT, a tube model for lumen segmentation was initialized and deformed to fit the image data. Finally, lumen segmentation was dilated to initiate the adaptation procedure of outer wall tube. The algorithm was validated by determining: 1) its performance against manual tracing; 2) its interscan reproducibility in quantifying vessel wall thickness (VWT); 3) its capability of detecting VWT difference in hypertensive patients compared with healthy controls. Statistical analysis including Bland-Altman analysis, t-test, and sample size calculation were performed for the purpose of algorithm evaluation. The mean distance between the manual and automatically detected lumen/outer wall contours was 0.00 ± 0.23/0.09 ± 0.21 mm for CCA and 0.12 ± 0.24/0.14 ± 0.35 mm for DAO. No significant difference was observed between the interscan VWT assessment using automated segmentation for both CCA (P = 0.19) and DAO (P = 0.94). Both manual and automated segmentation detected significantly higher carotid (P = 0.016 and P = 0.005) and aortic (P < 0.001 and P = 0.021) wall thickness in the hypertensive patients. A reliable and reproducible pipeline for fully automatic vessel wall quantification was developed and validated on healthy volunteers as well as patients with increased vessel wall thickness. This method holds promise for helping in efficient image interpretation for large-scale cohort studies. 4 J. Magn. Reson. Imaging 2017;45:215-228. © 2016 International Society for Magnetic Resonance in Medicine.

A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite-Based Dust Storm Detection

NASA Astrophysics Data System (ADS)

Miller, Steven D.; Bankert, Richard L.; Solbrig, Jeremy E.; Forsythe, John M.; Noh, Yoo-Jeong; Grasso, Lewis D.

2017-12-01

This paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear-sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear-sky equivalent signal for selected infrared-based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.

Inclusion of temporal priors for automated neonatal EEG classification

NASA Astrophysics Data System (ADS)

Temko, Andriy; Stevenson, Nathan; Marnane, William; Boylan, Geraldine; Lightbody, Gordon

2012-08-01

The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%.

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

PubMed Central

2017-01-01

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects. PMID:28984823

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data.

PubMed

Falque, Raphael; Vidal-Calleja, Teresa; Miro, Jaime Valls

2017-10-06

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

An FPGA-Based People Detection System

NASA Astrophysics Data System (ADS)

Nair, Vinod; Laprise, Pierre-Olivier; Clark, James J.

2005-12-01

This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about[InlineEquation not available: see fulltext.] frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at[InlineEquation not available: see fulltext.], communicating with dedicated hardware over FSL links.

Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells

PubMed Central

Kim, Mary S.; Tsutsui, Kenta; Stern, Michael D.; Lakatta, Edward G.; Maltsev, Victor A.

2017-01-01

Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle cells and Ca2+ puffs and syntillas in neurons. PMID:28683095

Lightning Jump Algorithm Development for the GOES·R Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

Schultz. E.; Schultz. C.; Chronis, T.; Stough, S.; Carey, L.; Calhoun, K.; Ortega, K.; Stano, G.; Cecil, D.; Bateman, M.;

Automatic detection of typical dust devils from Mars landscape images

NASA Astrophysics Data System (ADS)

Ogohara, Kazunori; Watanabe, Takeru; Okumura, Susumu; Hatanaka, Yuji

2018-02-01

This paper presents an improved algorithm for automatic detection of Martian dust devils that successfully extracts tiny bright dust devils and obscured large dust devils from two subtracted landscape images. These dust devils are frequently observed using visible cameras onboard landers or rovers. Nevertheless, previous research on automated detection of dust devils has not focused on these common types of dust devils, but on dust devils that appear on images to be irregularly bright and large. In this study, we detect these common dust devils automatically using two kinds of parameter sets for thresholding when binarizing subtracted images. We automatically extract dust devils from 266 images taken by the Spirit rover to evaluate our algorithm. Taking dust devils detected by visual inspection to be ground truth, the precision, recall and F-measure values are 0.77, 0.86, and 0.81, respectively.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS

NASA Astrophysics Data System (ADS)

Bang, Eugene; Lee, Jiyun

2013-11-01

ionospheric anomalies occurring during severe ionospheric storms can pose integrity threats to Global Navigation Satellite System (GNSS) Ground-Based Augmentation Systems (GBAS). Ionospheric anomaly threat models for each region of operation need to be developed to analyze the potential impact of these anomalies on GBAS users and develop mitigation strategies. Along with the magnitude of ionospheric gradients, the speed of the ionosphere "fronts" in which these gradients are embedded is an important parameter for simulation-based GBAS integrity analysis. This paper presents a methodology for automated ionosphere front velocity estimation which will be used to analyze a vast amount of ionospheric data, build ionospheric anomaly threat models for different regions, and monitor ionospheric anomalies continuously going forward. This procedure automatically selects stations that show a similar trend of ionospheric delays, computes the orientation of detected fronts using a three-station-based trigonometric method, and estimates speeds for the front using a two-station-based method. It also includes fine-tuning methods to improve the estimation to be robust against faulty measurements and modeling errors. It demonstrates the performance of the algorithm by comparing the results of automated speed estimation to those manually computed previously. All speed estimates from the automated algorithm fall within error bars of ± 30% of the manually computed speeds. In addition, this algorithm is used to populate the current threat space with newly generated threat points. A larger number of velocity estimates helps us to better understand the behavior of ionospheric gradients under geomagnetic storm conditions.

Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry.

PubMed

Abbatiello, Susan E; Mani, D R; Keshishian, Hasmik; Carr, Steven A

2010-02-01

Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope-labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. The algorithm identified problematic transitions and achieved accuracies of 94%-100%, with a sensitivity and specificity of 83%-100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AuDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software.

Automated Detection of Inaccurate and Imprecise Transitions in Peptide Quantification by Multiple Reaction Monitoring Mass Spectrometry

PubMed Central

Abbatiello, Susan E.; Mani, D. R.; Keshishian, Hasmik; Carr, Steven A.

2010-01-01

BACKGROUND Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope–labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. METHODS The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. RESULTS The algorithm identified problematic transitions and achieved accuracies of 94%–100%, with a sensitivity and specificity of 83%–100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. CONCLUSIONS This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AuDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software. PMID:20022980

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

PubMed Central

Rezaee, Kh.; Azizi, E.; Haddadnia, J.

2016-01-01

Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trujillo, Christopher J.

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

3D reconstruction of synapses with deep learning based on EM Images

NASA Astrophysics Data System (ADS)

Xiao, Chi; Rao, Qiang; Zhang, Dandan; Chen, Xi; Han, Hua; Xie, Qiwei

2017-03-01

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

Developing an Automated Science Analysis System for Mars Surface Exploration for MSL and Beyond

NASA Technical Reports Server (NTRS)

Gulick, V. C.; Hart, S. D.; Shi, X.; Siegel, V. L.

2004-01-01

We are developing an automated science analysis system that could be utilized by robotic or human explorers on Mars (or even in remote locations on Earth) to improve the quality and quantity of science data returned. Three components of this system (our rock, layer, and horizon detectors) [1] have been incorporated into the JPL CLARITY system for possible use by MSL and future Mars robotic missions. Two other components include a multi-spectral image compression (SPEC) algorithm for pancam-type images with multiple filters and image fusion algorithms that identify the in focus regions of individual images in an image focal series [2]. Recently, we have been working to combine image and spectral data, and other knowledge to identify both rocks and minerals. Here we present our progress on developing an igneous rock detection system.

Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

ERIC Educational Resources Information Center

Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne

2014-01-01

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…

Automated Test Case Generator for Phishing Prevention Using Generative Grammars and Discriminative Methods

ERIC Educational Resources Information Center

Palka, Sean

2015-01-01

This research details a methodology designed for creating content in support of various phishing prevention tasks including live exercises and detection algorithm research. Our system uses probabilistic context-free grammars (PCFG) and variable interpolation as part of a multi-pass method to create diverse and consistent phishing email content on…

“SmartMonitor” — An Intelligent Security System for the Protection of Individuals and Small Properties with the Possibility of Home Automation

PubMed Central

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

2014-01-01

“SmartMonitor” is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the “SmartMonitor” system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons. PMID:24905854

Intact mass detection, interpretation, and visualization to automate Top-Down proteomics on a large scale

PubMed Central

Durbin, Kenneth R.; Tran, John C.; Zamdborg, Leonid; Sweet, Steve M. M.; Catherman, Adam D.; Lee, Ji Eun; Li, Mingxi; Kellie, John F.; Kelleher, Neil L.

2011-01-01

Applying high-throughput Top-Down MS to an entire proteome requires a yet-to-be-established model for data processing. Since Top-Down is becoming possible on a large scale, we report our latest software pipeline dedicated to capturing the full value of intact protein data in automated fashion. For intact mass detection, we combine algorithms for processing MS1 data from both isotopically resolved (FT) and charge-state resolved (ion trap) LC-MS data, which are then linked to their fragment ions for database searching using ProSight. Automated determination of human keratin and tubulin isoforms is one result. Optimized for the intricacies of whole proteins, new software modules visualize proteome-scale data based on the LC retention time and intensity of intact masses and enable selective detection of PTMs to automatically screen for acetylation, phosphorylation, and methylation. Software functionality was demonstrated using comparative LC-MS data from yeast strains in addition to human cells undergoing chemical stress. We further these advances as a key aspect of realizing Top-Down MS on a proteomic scale. PMID:20848673

Fully automated segmentation of callus by micro-CT compared to biomechanics.

PubMed

Bissinger, Oliver; Götz, Carolin; Wolff, Klaus-Dietrich; Hapfelmeier, Alexander; Prodinger, Peter Michael; Tischer, Thomas

2017-07-11

A high percentage of closed femur fractures have slight comminution. Using micro-CT (μCT), multiple fragment segmentation is much more difficult than segmentation of unfractured or osteotomied bone. Manual or semi-automated segmentation has been performed to date. However, such segmentation is extremely laborious, time-consuming and error-prone. Our aim was to therefore apply a fully automated segmentation algorithm to determine μCT parameters and examine their association with biomechanics. The femura of 64 rats taken after randomised inhibitory or neutral medication, in terms of the effect on fracture healing, and controls were closed fractured after a Kirschner wire was inserted. After 21 days, μCT and biomechanical parameters were determined by a fully automated method and correlated (Pearson's correlation). The fully automated segmentation algorithm automatically detected bone and simultaneously separated cortical bone from callus without requiring ROI selection for each single bony structure. We found an association of structural callus parameters obtained by μCT to the biomechanical properties. However, results were only explicable by additionally considering the callus location. A large number of slightly comminuted fractures in combination with therapies that influence the callus qualitatively and/or quantitatively considerably affects the association between μCT and biomechanics. In the future, contrast-enhanced μCT imaging of the callus cartilage might provide more information to improve the non-destructive and non-invasive prediction of callus mechanical properties. As studies evaluating such important drugs increase, fully automated segmentation appears to be clinically important.

Optical Detection of Degraded Therapeutic Proteins.

PubMed

Herrington, William F; Singh, Gajendra P; Wu, Di; Barone, Paul W; Hancock, William; Ram, Rajeev J

2018-03-23

The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms. Detection of the degraded protein differs from previous applications of machine-learning and classification to spectral analysis: only example spectra of genuine, high-quality drug products are used to construct the classifier. The algorithm is tested on Raman spectra acquired on protein dilutions typical of formulated drug product and at sample volumes of 25 µL, below the typical overfill (waste) volumes present in vials of injectable drug product. The algorithm is demonstrated to correctly classify anomalous recombinant human growth hormone (rhGH) with 92% sensitivity and 98% specificity even when the algorithm has only previously encountered high-quality drug product.

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

NASA Technical Reports Server (NTRS)

Smith, Phillip N.

1990-01-01

The automation of low-altitude rotorcraft flight depends on the ability to detect, locate, and navigate around obstacles lying in the rotorcraft's intended flightpath. Computer vision techniques provide a passive method of obstacle detection and range estimation, for obstacle avoidance. Several algorithms based on computer vision methods have been developed for this purpose using laboratory data; however, further development and validation of candidate algorithms require data collected from rotorcraft flight. A data base containing low-altitude imagery augmented with the rotorcraft and sensor parameters required for passive range estimation is not readily available. Here, the emphasis is on the methodology used to develop such a data base from flight-test data consisting of imagery, rotorcraft and sensor parameters, and ground-truth range measurements. As part of the data preparation, a technique for obtaining the sensor calibration parameters is described. The data base will enable the further development of algorithms for computer vision-based obstacle detection and passive range estimation, as well as provide a benchmark for verification of range estimates against ground-truth measurements.

Automated segmentation and feature extraction of product inspection items

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-03-01

X-ray film and linescan images of pistachio nuts on conveyor trays for product inspection are considered. The final objective is the categorization of pistachios into good, blemished and infested nuts. A crucial step before classification is the separation of touching products and the extraction of features essential for classification. This paper addresses new detection and segmentation algorithms to isolate touching or overlapping items. These algorithms employ a new filter, a new watershed algorithm, and morphological processing to produce nutmeat-only images. Tests on a large database of x-ray film and real-time x-ray linescan images of around 2900 small, medium and large nuts showed excellent segmentation results. A new technique to detect and segment dark regions in nutmeat images is also presented and tested on approximately 300 x-ray film and approximately 300 real-time linescan x-ray images with 95-97 percent detection and correct segmentation. New algorithms are described that determine nutmeat fill ratio and locate splits in nutmeat. The techniques formulated in this paper are of general use in many different product inspection and computer vision problems.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

PubMed

Hsu, Yu-Liang; Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-07-15

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

A Combined Approach to Cartographic Displacement for Buildings Based on Skeleton and Improved Elastic Beam Algorithm

PubMed Central

Liu, Yuangang; Guo, Qingsheng; Sun, Yageng; Ma, Xiaoya

2014-01-01

Scale reduction from source to target maps inevitably leads to conflicts of map symbols in cartography and geographic information systems (GIS). Displacement is one of the most important map generalization operators and it can be used to resolve the problems that arise from conflict among two or more map objects. In this paper, we propose a combined approach based on constraint Delaunay triangulation (CDT) skeleton and improved elastic beam algorithm for automated building displacement. In this approach, map data sets are first partitioned. Then the displacement operation is conducted in each partition as a cyclic and iterative process of conflict detection and resolution. In the iteration, the skeleton of the gap spaces is extracted using CDT. It then serves as an enhanced data model to detect conflicts and construct the proximity graph. Then, the proximity graph is adjusted using local grouping information. Under the action of forces derived from the detected conflicts, the proximity graph is deformed using the improved elastic beam algorithm. In this way, buildings are displaced to find an optimal compromise between related cartographic constraints. To validate this approach, two topographic map data sets (i.e., urban and suburban areas) were tested. The results were reasonable with respect to each constraint when the density of the map was not extremely high. In summary, the improvements include (1) an automated parameter-setting method for elastic beams, (2) explicit enforcement regarding the positional accuracy constraint, added by introducing drag forces, (3) preservation of local building groups through displacement over an adjusted proximity graph, and (4) an iterative strategy that is more likely to resolve the proximity conflicts than the one used in the existing elastic beam algorithm. PMID:25470727

Lost in Virtual Reality: Pathfinding Algorithms Detect Rock Fractures and Contacts in Point Clouds

NASA Astrophysics Data System (ADS)

Thiele, S.; Grose, L.; Micklethwaite, S.

2016-12-01

UAV-based photogrammetric and LiDAR techniques provide high resolution 3D point clouds and ortho-rectified photomontages that can capture surface geology in outstanding detail over wide areas. Automated and semi-automated methods are vital to extract full value from these data in practical time periods, though the nuances of geological structures and materials (natural variability in colour and geometry, soft and hard linkage, shadows and multiscale properties) make this a challenging task. We present a novel method for computer assisted trace detection in dense point clouds, using a lowest cost path solver to "follow" fracture traces and lithological contacts between user defined end points. This is achieved by defining a local neighbourhood network where each point in the cloud is linked to its neighbours, and then using a least-cost path algorithm to search this network and estimate the trace of the fracture or contact. A variety of different algorithms can then be applied to calculate the best fit plane, produce a fracture network, or map properties such as roughness, curvature and fracture intensity. Our prototype of this method (Fig. 1) suggests the technique is feasible and remarkably good at following traces under non-optimal conditions such as variable-shadow, partial occlusion and complex fracturing. Furthermore, if a fracture is initially mapped incorrectly, the user can easily provide further guidance by defining intermediate waypoints. Future development will include optimization of the algorithm to perform well on large point clouds and modifications that permit the detection of features such as step-overs. We also plan on implementing this approach in an interactive graphical user environment.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

PubMed Central

Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-01-01

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment. PMID:28714884

Near real-time, on-the-move software PED using VPEF

NASA Astrophysics Data System (ADS)

Green, Kevin; Geyer, Chris; Burnette, Chris; Agarwal, Sanjeev; Swett, Bruce; Phan, Chung; Deterline, Diane

2015-05-01

The scope of the Micro-Cloud for Operational, Vehicle-Based EO-IR Reconnaissance System (MOVERS) development effort, managed by the Night Vision and Electronic Sensors Directorate (NVESD), is to develop, integrate, and demonstrate new sensor technologies and algorithms that improve improvised device/mine detection using efficient and effective exploitation and fusion of sensor data and target cues from existing and future Route Clearance Package (RCP) sensor systems. Unfortunately, the majority of forward looking Full Motion Video (FMV) and computer vision processing, exploitation, and dissemination (PED) algorithms are often developed using proprietary, incompatible software. This makes the insertion of new algorithms difficult due to the lack of standardized processing chains. In order to overcome these limitations, EOIR developed the Government off-the-shelf (GOTS) Video Processing and Exploitation Framework (VPEF) to be able to provide standardized interfaces (e.g., input/output video formats, sensor metadata, and detected objects) for exploitation software and to rapidly integrate and test computer vision algorithms. EOIR developed a vehicle-based computing framework within the MOVERS and integrated it with VPEF. VPEF was further enhanced for automated processing, detection, and publishing of detections in near real-time, thus improving the efficiency and effectiveness of RCP sensor systems.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

PubMed

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Using medication list--problem list mismatches as markers of potential error.

PubMed Central

Carpenter, James D.; Gorman, Paul N.

2002-01-01

The goal of this project was to specify and develop an algorithm that will check for drug and problem list mismatches in an electronic medical record (EMR). The algorithm is based on the premise that a patient's problem list and medication list should agree, and a mismatch may indicate medication error. Successful development of this algorithm could mean detection of some errors, such as medication orders entered into a wrong patient record, or drug therapy omissions, that are not otherwise detected via automated means. Additionally, mismatches may identify opportunities to improve problem list integrity. To assess the concept's feasibility, this study compared medications listed in a pharmacy information system with findings in an online nursing adult admission assessment, serving as a proxy for the problem list. Where drug and problem list mismatches were discovered, examination of the patient record confirmed the mismatch, and identified any potential causes. Evaluation of the algorithm in diabetes treatment indicates that it successfully detects both potential medication error and opportunities to improve problem list completeness. This algorithm, once fully developed and deployed, could prove a valuable way to improve the patient problem list, and could decrease the risk of medication error. PMID:12463796

Automated flaw detection scheme for cast austenitic stainless steel weld specimens using Hilbert-Huang transform of ultrasonic phased array data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Tariq; Majumdar, Shantanu; Udpa, Lalita

2012-05-17

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

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts.

PubMed

Torney, Colin J; Dobson, Andrew P; Borner, Felix; Lloyd-Jones, David J; Moyer, David; Maliti, Honori T; Mwita, Machoke; Fredrick, Howard; Borner, Markus; Hopcraft, J Grant C

2016-01-01

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

NASA Astrophysics Data System (ADS)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with small mass. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest machine learning algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. The processing chain is implemented to work in a High Performance Computers centre which permits to explore years of continuous seismic data rapidly. We present here the preliminary results of the application of this processing chain for years of continuous seismic record by the Alaskan permanent seismic network and Hi-Climb trans-Himalayan seismic network. The processing chain we developed also opens the possibility for a near-real time seismic detection of landslides, in association with remote-sensing automated detection from Sentinel 2 images for example.

Automated detection of retinal whitening in malarial retinopathy

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Automated Speech Rate Measurement in Dysarthria.

PubMed

Martens, Heidi; Dekens, Tomas; Van Nuffelen, Gwen; Latacz, Lukas; Verhelst, Werner; De Bodt, Marc

2015-06-01

In this study, a new algorithm for automated determination of speech rate (SR) in dysarthric speech is evaluated. We investigated how reliably the algorithm calculates the SR of dysarthric speech samples when compared with calculation performed by speech-language pathologists. The new algorithm was trained and tested using Dutch speech samples of 36 speakers with no history of speech impairment and 40 speakers with mild to moderate dysarthria. We tested the algorithm under various conditions: according to speech task type (sentence reading, passage reading, and storytelling) and algorithm optimization method (speaker group optimization and individual speaker optimization). Correlations between automated and human SR determination were calculated for each condition. High correlations between automated and human SR determination were found in the various testing conditions. The new algorithm measures SR in a sufficiently reliable manner. It is currently being integrated in a clinical software tool for assessing and managing prosody in dysarthric speech. Further research is needed to fine-tune the algorithm to severely dysarthric speech, to make the algorithm less sensitive to background noise, and to evaluate how the algorithm deals with syllabic consonants.

Cubic spline anchored grid pattern algorithm for high-resolution detection of subsurface cavities by the IR-CAT method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kassab, A.J.; Pollard, J.E.

An algorithm is presented for the high-resolution detection of irregular-shaped subsurface cavities within irregular-shaped bodies by the IR-CAT method. The theoretical basis of the algorithm is rooted in the solution of an inverse geometric steady-state heat conduction problem. A Cauchy boundary condition is prescribed at the exposed surface, and the inverse geometric heat conduction problem is formulated by specifying the thermal condition at the inner cavities walls, whose unknown geometries are to be detected. The location of the inner cavities is initially estimated, and the domain boundaries are discretized. Linear boundary elements are used in conjunction with cubic splines formore » high resolution of the cavity walls. An anchored grid pattern (AGP) is established to constrain the cubic spline knots that control the inner cavity geometry to evolve along the AGP at each iterative step. A residual is defined measuring the difference between imposed and computed boundary conditions. A Newton-Raphson method with a Broyden update is used to automate the detection of inner cavity walls. During the iterative procedure, the movement of the inner cavity walls is restricted to physically realistic intermediate solutions. Numerical simulation demonstrates the superior resolution of the cubic spline AGP algorithm over the linear spline-based AGP in the detection of an irregular-shaped cavity. Numerical simulation is also used to test the sensitivity of the linear and cubic spline AGP algorithms by simulating bias and random error in measured surface temperature. The proposed AGP algorithm is shown to satisfactorily detect cavities with these simulated data.« less

High throughput light absorber discovery, Part 1: An algorithm for automated tauc analysis

DOE PAGES

Suram, Santosh K.; Newhouse, Paul F.; Gregoire, John M.

2016-09-23

High-throughput experimentation provides efficient mapping of composition-property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe 2O 3, Cu 2V 2O 7, and BiVOmore » 4. Here, the applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra.« less

Automated cortical bone segmentation for multirow-detector CT imaging with validation and application to human studies

PubMed Central

Li, Cheng; Jin, Dakai; Chen, Cheng; Letuchy, Elena M.; Janz, Kathleen F.; Burns, Trudy L.; Torner, James C; Levy, Steven M.; Saha, Punam K

2015-01-01

Purpose: Cortical bone supports and protects human skeletal functions and plays an important role in determining bone strength and fracture risk. Cortical bone segmentation at a peripheral site using multirow-detector CT (MD-CT) imaging is useful for in vivo assessment of bone strength and fracture risk. Major challenges for the task emerge from limited spatial resolution, low signal-to-noise ratio, presence of cortical pores, and structural complexity over the transition between trabecular and cortical bones. An automated algorithm for cortical bone segmentation at the distal tibia from in vivo MD-CT imaging is presented and its performance and application are examined. Methods: The algorithm is completed in two major steps—(1) bone filling, alignment, and region-of-interest computation and (2) segmentation of cortical bone. After the first step, the following sequence of tasks is performed to accomplish cortical bone segmentation—(1) detection of marrow space and possible pores, (2) computation of cortical bone thickness, detection of recession points, and confirmation and filling of true pores, and (3) detection of endosteal boundary and delineation of cortical bone. Effective generalizations of several digital topologic and geometric techniques are introduced and a fully automated algorithm is presented for cortical bone segmentation. Results: An accuracy of 95.1% in terms of volume of agreement with manual outlining of cortical bone was observed in human MD-CT scans, while an accuracy of 88.5% was achieved when compared with manual outlining on postregistered high resolution micro-CT imaging. An intraclass correlation coefficient of 0.98 was obtained in cadaveric repeat scans. A pilot study was conducted to describe gender differences in cortical bone properties. This study involved 51 female and 46 male participants (age: 19–20 yr) from the Iowa Bone Development Study. Results from this pilot study suggest that, on average after adjustment for height and weight differences, males have thicker cortex (mean difference 0.33 mm and effect size 0.92 at the anterior region) with lower bone mineral density (mean difference −28.73 mg/cm3 and effect size 1.35 at the posterior region) as compared to females. Conclusions: The algorithm presented is suitable for fully automated segmentation of cortical bone in MD-CT imaging of the distal tibia with high accuracy and reproducibility. Analysis of data from a pilot study demonstrated that the cortical bone indices allow quantification of gender differences in cortical bone from MD-CT imaging. Application to larger population groups, including those with compromised bone, is needed. PMID:26233184

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

PubMed Central

Navarro, Pedro J.; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-01-01

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%). PMID:28025565

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.

PubMed

Navarro, Pedro J; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-12-23

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

PubMed

Chan, Pak-Hei; Wong, Chun-Ka; Poh, Yukkee C; Pun, Louise; Leung, Wangie Wan-Chiu; Wong, Yu-Fai; Wong, Michelle Man-Ying; Poh, Ming-Zher; Chu, Daniel Wai-Sing; Siu, Chung-Wah

2016-07-21

Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]). The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines

NASA Astrophysics Data System (ADS)

Bazhin, V. Yu; Danilov, I. V.; Petrov, P. A.

2018-05-01

During the casting of light alloys and ligatures based on aluminum and magnesium, problems of the qualitative distribution of the metal and its crystallization in the mold arise. To monitor the defects of molds on the casting conveyor, a camera with a resolution of 780 x 580 pixels and a shooting rate of 75 frames per second was selected. Images of molds from casting machines were used as input data for neural network algorithm. On the preparation of a digital database and its analytical evaluation stage, the architecture of the convolutional neural network was chosen for the algorithm. The information flow from the local controller is transferred to the OPC server and then to the SCADA system of foundry. After the training, accuracy of neural network defect recognition was about 95.1% on a validation split. After the training, weight coefficients of the neural network were used on testing split and algorithm had identical accuracy with validation images. The proposed technical solutions make it possible to increase the efficiency of the automated process control system in the foundry by expanding the digital database.

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

PubMed

Uppal, Karan; Soltow, Quinlyn A; Strobel, Frederick H; Pittard, W Stephen; Gernert, Kim M; Yu, Tianwei; Jones, Dean P

2013-01-16

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

Automatic detection of blurred images in UAV image sets

NASA Astrophysics Data System (ADS)

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

2016-12-01

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values from the same dataset. The speed and reliability of the method was tested using a range of different UAV datasets. Two datasets will be presented in this paper to demonstrate the effectiveness of the algorithm. The algorithm proves to be fast and the returned values are optically correct, making the algorithm applicable for UAV datasets. Additionally, a close range dataset was processed to determine whether the method is also useful for close range applications. The results show that the method is also reliable for close range images, which significantly extends the field of application for the algorithm.

Algorithm for Identifying Erroneous Rain-Gauge Readings

NASA Technical Reports Server (NTRS)

Rickman, Doug

2005-01-01

An algorithm analyzes rain-gauge data to identify statistical outliers that could be deemed to be erroneous readings. Heretofore, analyses of this type have been performed in burdensome manual procedures that have involved subjective judgements. Sometimes, the analyses have included computational assistance for detecting values falling outside of arbitrary limits. The analyses have been performed without statistically valid knowledge of the spatial and temporal variations of precipitation within rain events. In contrast, the present algorithm makes it possible to automate such an analysis, makes the analysis objective, takes account of the spatial distribution of rain gauges in conjunction with the statistical nature of spatial variations in rainfall readings, and minimizes the use of arbitrary criteria. The algorithm implements an iterative process that involves nonparametric statistics.

Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images.

PubMed

Bergeles, Christos; Dubis, Adam M; Davidson, Benjamin; Kasilian, Melissa; Kalitzeos, Angelos; Carroll, Joseph; Dubra, Alfredo; Michaelides, Michel; Ourselin, Sebastien

2017-06-01

Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.

Automatic Fringe Detection for Oil Film Interferometry Measurement of Skin Friction

NASA Technical Reports Server (NTRS)

Naughton, Jonathan W.; Decker, Robert K.; Jafari, Farhad

2001-01-01

This report summarizes two years of work on investigating algorithms for automatically detecting fringe patterns in images acquired using oil-drop interferometry for the determination of skin friction. Several different analysis methods were tested, and a combination of a windowed Fourier transform followed by a correlation was found to be most effective. The implementation of this method is discussed and details of the process are described. The results indicate that this method shows promise for automating the fringe detection process, but further testing is required.

Automated diagnosis of fetal alcohol syndrome using 3D facial image analysis

PubMed Central

Fang, Shiaofen; McLaughlin, Jason; Fang, Jiandong; Huang, Jeffrey; Autti-Rämö, Ilona; Fagerlund, Åse; Jacobson, Sandra W.; Robinson, Luther K.; Hoyme, H. Eugene; Mattson, Sarah N.; Riley, Edward; Zhou, Feng; Ward, Richard; Moore, Elizabeth S.; Foroud, Tatiana

2012-01-01

Objectives Use three-dimensional (3D) facial laser scanned images from children with fetal alcohol syndrome (FAS) and controls to develop an automated diagnosis technique that can reliably and accurately identify individuals prenatally exposed to alcohol. Methods A detailed dysmorphology evaluation, history of prenatal alcohol exposure, and 3D facial laser scans were obtained from 149 individuals (86 FAS; 63 Control) recruited from two study sites (Cape Town, South Africa and Helsinki, Finland). Computer graphics, machine learning, and pattern recognition techniques were used to automatically identify a set of facial features that best discriminated individuals with FAS from controls in each sample. Results An automated feature detection and analysis technique was developed and applied to the two study populations. A unique set of facial regions and features were identified for each population that accurately discriminated FAS and control faces without any human intervention. Conclusion Our results demonstrate that computer algorithms can be used to automatically detect facial features that can discriminate FAS and control faces. PMID:18713153

High-speed autoverifying technology for printed wiring boards

NASA Astrophysics Data System (ADS)

Ando, Moritoshi; Oka, Hiroshi; Okada, Hideo; Sakashita, Yorihiro; Shibutani, Nobumi

1996-10-01

We have developed an automated pattern verification technique. The output of an automated optical inspection system contains many false alarms. Verification is needed to distinguish between minor irregularities and serious defects. In the past, this verification was usually done manually, which led to unsatisfactory product quality. The goal of our new automated verification system is to detect pattern features on surface mount technology boards. In our system, we employ a new illumination method, which uses multiple colors and multiple direction illumination. Images are captured with a CCD camera. We have developed a new algorithm that uses CAD data for both pattern matching and pattern structure determination. This helps to search for patterns around a defect and to examine defect definition rules. These are processed with a high speed workstation and a hard-wired circuits. The system can verify a defect within 1.5 seconds. The verification system was tested in a factory. It verified 1,500 defective samples and detected all significant defects with only a 0.1 percent of error rate (false alarm).

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

PubMed Central

2013-01-01

The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation.

PubMed

Hodneland, Erlend; Kögel, Tanja; Frei, Dominik Michael; Gerdes, Hans-Hermann; Lundervold, Arvid

2013-08-09

: The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

Experimental Verification of Bayesian Planet Detection Algorithms with a Shaped Pupil Coronagraph

NASA Astrophysics Data System (ADS)

Savransky, D.; Groff, T. D.; Kasdin, N. J.

2010-10-01

We evaluate the feasibility of applying Bayesian detection techniques to discovering exoplanets using high contrast laboratory data with simulated planetary signals. Background images are generated at the Princeton High Contrast Imaging Lab (HCIL), with a coronagraphic system utilizing a shaped pupil and two deformable mirrors (DMs) in series. Estimates of the electric field at the science camera are used to correct for quasi-static speckle and produce symmetric high contrast dark regions in the image plane. Planetary signals are added in software, or via a physical star-planet simulator which adds a second off-axis point source before the coronagraph with a beam recombiner, calibrated to a fixed contrast level relative to the source. We produce a variety of images, with varying integration times and simulated planetary brightness. We then apply automated detection algorithms such as matched filtering to attempt to extract the planetary signals. This allows us to evaluate the efficiency of these techniques in detecting planets in a high noise regime and eliminating false positives, as well as to test existing algorithms for calculating the required integration times for these techniques to be applicable.

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment

PubMed Central

Guevara, Rainer Dane; Rantz, Marilyn

2015-01-01

We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from in-home sensor data. A 1-D alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues. PMID:27170900

Closing the loop in ICU decision support: physiologic event detection, alerts, and documentation.

PubMed Central

Norris, P. R.; Dawant, B. M.

2001-01-01

Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility. PMID:11825238

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

The optimization of edge and line detectors for forest image analysis

Treesearch

Zhiling Long; Joseph Picone; Victor A. Rudis

2000-01-01

Automated image analysis for forestry applications is becoming increasingly important with the rapid evolution of satellite and land-based remote imaging industries. Features derived from line information play a very important role in analyses of such images. Many edge and line detection algorithms have been proposed but few, if any, comprehensive studies exist that...

Twelve automated thresholding methods for segmentation of PET images: a phantom study.

PubMed

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

2012-06-21

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Twelve automated thresholding methods for segmentation of PET images: a phantom study

NASA Astrophysics Data System (ADS)

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

2012-06-01

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Automated rice leaf disease detection using color image analysis

NASA Astrophysics Data System (ADS)

Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

2011-06-01

In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

SAND: an automated VLBI imaging and analysing pipeline - I. Stripping component trajectories

NASA Astrophysics Data System (ADS)

Zhang, M.; Collioud, A.; Charlot, P.

2018-02-01

We present our implementation of an automated very long baseline interferometry (VLBI) data-reduction pipeline that is dedicated to interferometric data imaging and analysis. The pipeline can handle massive VLBI data efficiently, which makes it an appropriate tool to investigate multi-epoch multiband VLBI data. Compared to traditional manual data reduction, our pipeline provides more objective results as less human interference is involved. The source extraction is carried out in the image plane, while deconvolution and model fitting are performed in both the image plane and the uv plane for parallel comparison. The output from the pipeline includes catalogues of CLEANed images and reconstructed models, polarization maps, proper motion estimates, core light curves and multiband spectra. We have developed a regression STRIP algorithm to automatically detect linear or non-linear patterns in the jet component trajectories. This algorithm offers an objective method to match jet components at different epochs and to determine their proper motions.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

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

PubMed

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

2016-01-08

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 interuser 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 plug-in 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 run-times between 3 and 4 min 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 maximum 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.

Fast internal marker tracking algorithm for onboard MV and kV imaging systems

PubMed Central

Mao, W.; Wiersma, R. D.; Xing, L.

2008-01-01

Intrafraction organ motion can limit the advantage of highly conformal dose techniques such as intensity modulated radiation therapy (IMRT) due to target position uncertainty. To ensure high accuracy in beam targeting, real-time knowledge of the target location is highly desired throughout the beam delivery process. This knowledge can be gained through imaging of internally implanted radio-opaque markers with fluoroscopic or electronic portal imaging devices (EPID). In the case of MV based images, marker detection can be problematic due to the significantly lower contrast between different materials in comparison to their kV-based counterparts. This work presents a fully automated algorithm capable of detecting implanted metallic markers in both kV and MV images with high consistency. Using prior CT information, the algorithm predefines the volumetric search space without manual region-of-interest (ROI) selection by the user. Depending on the template selected, both spherical and cylindrical markers can be detected. Multiple markers can be simultaneously tracked without indexing confusion. Phantom studies show detection success rates of 100% for both kV and MV image data. In addition, application of the algorithm to real patient image data results in successful detection of all implanted markers for MV images. Near real-time operational speeds of ∼10 frames∕sec for the detection of five markers in a 1024×768 image are accomplished using an ordinary PC workstation. PMID:18561670

A robust algorithm for automated target recognition using precomputed radar cross sections

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2004-09-01

Passive radar is an emerging technology that offers a number of unique benefits, including covert operation. Many such systems are already capable of detecting and tracking aircraft. The goal of this work is to develop a robust algorithm for adding automated target recognition (ATR) capabilities to existing passive radar systems. In previous papers, we proposed conducting ATR by comparing the precomputed RCS of known targets to that of detected targets. To make the precomputed RCS as accurate as possible, a coordinated flight model is used to estimate aircraft orientation. Once the aircraft's position and orientation are known, it is possible to determine the incident and observed angles on the aircraft, relative to the transmitter and receiver. This makes it possible to extract the appropriate radar cross section (RCS) from our simulated database. This RCS is then scaled to account for propagation losses and the receiver's antenna gain. A Rician likelihood model compares these expected signals from different targets to the received target profile. We have previously employed Monte Carlo runs to gauge the probability of error in the ATR algorithm; however, generation of a statistically significant set of Monte Carlo runs is computationally intensive. As an alternative to Monte Carlo runs, we derive the relative entropy (also known as Kullback-Liebler distance) between two Rician distributions. Since the probability of Type II error in our hypothesis testing problem can be expressed as a function of the relative entropy via Stein's Lemma, this provides us with a computationally efficient method for determining an upper bound on our algorithm's performance. It also provides great insight into the types of classification errors we can expect from our algorithm. This paper compares the numerically approximated probability of Type II error with the results obtained from a set of Monte Carlo runs.

Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

PubMed

Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

2015-12-01

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

PubMed

Dereymaeker, Anneleen; Pillay, Kirubin; Vervisch, Jan; Van Huffel, Sabine; Naulaers, Gunnar; Jansen, Katrien; De Vos, Maarten

2017-09-01

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

PubMed

Esses, Steven J; Lu, Xiaoguang; Zhao, Tiejun; Shanbhogue, Krishna; Dane, Bari; Bruno, Mary; Chandarana, Hersh

2018-03-01

To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 -weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728. © 2017 International Society for Magnetic Resonance in Medicine.

Automated and real-time segmentation of suspicious breast masses using convolutional neural network

PubMed Central

Gregory, Adriana; Denis, Max; Meixner, Duane D.; Bayat, Mahdi; Whaley, Dana H.; Fatemi, Mostafa; Alizad, Azra

2018-01-01

In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13–55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm. PMID:29768415

Iris Segmentation and Normalization Algorithm Based on Zigzag Collarette

NASA Astrophysics Data System (ADS)

Rizky Faundra, M.; Ratna Sulistyaningrum, Dwi

2017-01-01

In this paper, we proposed iris segmentation and normalization algorithm based on the zigzag collarette. First of all, iris images are processed by using Canny Edge Detection to detect pupil edge, then finding the center and the radius of the pupil with the Hough Transform Circle. Next, isolate important part in iris based zigzag collarette area. Finally, Daugman Rubber Sheet Model applied to get the fixed dimensions or normalization iris by transforming cartesian into polar format and thresholding technique to remove eyelid and eyelash. This experiment will be conducted with a grayscale eye image data taken from a database of iris-Chinese Academy of Sciences Institute of Automation (CASIA). Data iris taken is the data reliable and widely used to study the iris biometrics. The result show that specific threshold level is 0.3 have better accuracy than other, so the present algorithm can be used to segmentation and normalization zigzag collarette with accuracy is 98.88%

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Neural and Decision Theoretic Approaches for the Automated Segmentation of Radiodense Tissue in Digitized Mammograms

NASA Astrophysics Data System (ADS)

Eckert, R.; Neyhart, J. T.; Burd, L.; Polikar, R.; Mandayam, S. A.; Tseng, M.

2003-03-01

Mammography is the best method available as a non-invasive technique for the early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions due to fat and radiodense (light) regions due to connective and epithelial tissue. The amount of radiodense tissue can be used as a marker for predicting breast cancer risk. Previously, we have shown that the use of statistical models is a reliable technique for segmenting radiodense tissue. This paper presents improvements in the model that allow for further development of an automated system for segmentation of radiodense tissue. The segmentation algorithm employs a two-step process. In the first step, segmentation of tissue and non-tissue regions of a digitized X-ray mammogram image are identified using a radial basis function neural network. The second step uses a constrained Neyman-Pearson algorithm, developed especially for this research work, to determine the amount of radiodense tissue. Results obtained using the algorithm have been validated by comparing with estimates provided by a radiologist employing previously established methods.

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

NASA Astrophysics Data System (ADS)

Staley, T. D.; Anderson, G. E.

2015-11-01

In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of several component libraries which have been packaged separately for open-source release. The most scientifically significant of these is chimenea, which implements a telescope-agnostic algorithm for automated imaging of pre-calibrated multi-epoch radio-synthesis data, of the sort typically acquired for transient surveys or follow-up. The algorithm aims to improve upon standard imaging pipelines by utilizing iterative RMS-estimation and automated source-detection to avoid so called 'Clean-bias', and makes use of CASA subroutines for the underlying image-synthesis operations. At a lower level, AMIsurvey relies upon two libraries, drive-ami and drive-casa, built to allow use of mature radio-astronomy software packages from within Python scripts. While targeted at automated imaging, the drive-casa interface can also be used to automate interaction with any of the CASA subroutines from a generic Python process. Additionally, these packages may be of wider technical interest beyond radio-astronomy, since they demonstrate use of the Python library pexpect to emulate terminal interaction with an external process. This approach allows for rapid development of a Python interface to any legacy or externally-maintained pipeline which accepts command-line input, without requiring alterations to the original code.

Automated spot defect characterization in a field portable night vision goggle test set

NASA Astrophysics Data System (ADS)

Scopatz, Stephen; Ozten, Metehan; Aubry, Gilles; Arquetoux, Guillaume

2018-05-01

This paper discusses a new capability developed for and results from a field portable test set for Gen 2 and Gen 3 Image Intensifier (I2) tube-based Night Vision Goggles (NVG). A previous paper described the test set and the automated and semi-automated tests supported for NVGs including a Knife Edge MTF test to replace the operator's interpretation of the USAF 1951 resolution chart. The major improvement and innovation detailed in this paper is the use of image analysis algorithms to automate the characterization of spot defects of I² tubes with the same test set hardware previously presented. The original and still common Spot Defect Test requires the operator to look through the NVGs at target of concentric rings; compare the size of the defects to a chart and manually enter the results into a table based on the size and location of each defect; this is tedious and subjective. The prior semi-automated improvement captures and displays an image of the defects and the rings; allowing the operator determine the defects with less eyestrain; while electronically storing the image and the resulting table. The advanced Automated Spot Defect Test utilizes machine vision algorithms to determine the size and location of the defects, generates the result table automatically and then records the image and the results in a computer-generated report easily usable for verification. This is inherently a more repeatable process that ensures consistent spot detection independent of the operator. Results of across several NVGs will be presented.

2D Bayesian automated tilted-ring fitting of disc galaxies in large H I galaxy surveys: 2DBAT

NASA Astrophysics Data System (ADS)

Oh, Se-Heon; Staveley-Smith, Lister; Spekkens, Kristine; Kamphuis, Peter; Koribalski, Bärbel S.

2018-01-01

We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disc galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimization procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multimodal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (H I) surveys with the Square Kilometre Array and its pathfinders. The so-called 2D Bayesian Automated Tilted-ring fitter (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial H I data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array H I data from the Local Volume H I Survey. We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20° < i < 70°), complementing 3D techniques better suited to modelling inclined galaxies.

Comparison of Traditional and Reverse Syphilis Screening Algorithms in Medical Health Checkups.

PubMed

Nah, Eun Hee; Cho, Seon; Kim, Suyoung; Cho, Han Ik; Chai, Jong Yil

2017-11-01

The syphilis diagnostic algorithms applied in different countries vary significantly depending on the local syphilis epidemiology and other considerations, including the expected workload, the need for automation in the laboratory and budget factors. This study was performed to investigate the efficacy of traditional and reverse syphilis diagnostic algorithms during general health checkups. In total, 1,000 blood specimens were obtained from 908 men and 92 women during their regular health checkups. Traditional screening and reverse screening were applied to the same specimens using automatic rapid plasma regain (RPR) and Treponema pallidum latex agglutination (TPLA) tests, respectively. Specimens that were reverse algorithm (TPLA) reactive, were subjected to a second treponemal test performed by using the chemiluminescent microparticle immunoassay (CMIA). Of the 1,000 specimens tested, 68 (6.8%) were reactive by reverse screening (TPLA) compared with 11 (1.1%) by traditional screening (RPR). The traditional algorithm failed to detect 48 specimens [TPLA(+)/RPR(-)/CMIA(+)]. The median TPLA cutoff index (COI) was higher in CMIA-reactive cases than in CMIA-nonreactive cases (90.5 vs 12.5 U). The reverse screening algorithm could detect the subjects with possible latent syphilis who were not detected by the traditional algorithm. Those individuals could be provided with opportunities for evaluating syphilis during their health checkups. The COI values of the initial TPLA test may be helpful in excluding false-positive TPLA test results in the reverse algorithm. © The Korean Society for Laboratory Medicine

Automated detection and recognition of wildlife using thermal cameras.

PubMed

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

2014-07-30

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

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

PubMed

Seghier, Mohamed L; Kolanko, Magdalena A; Leff, Alexander P; Jäger, Hans R; Gregoire, Simone M; Werring, David J

2011-03-23

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

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

NASA Astrophysics Data System (ADS)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

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–wake detection algorithm, using the single A1–A2 EEG channel, has a level of accuracy that is similar to PSG technologists in the scoring of sleep and wake, thereby making it suitable for a variety of in-home monitoring applications, such as in conjunction with the Zmachine system. PMID:25342922

Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology.

PubMed

Niazi, Muhammad Khalid Khan; Abas, Fazly Salleh; Senaras, Caglar; Pennell, Michael; Sahiner, Berkman; Chen, Weijie; Opfer, John; Hasserjian, Robert; Louissaint, Abner; Shana'ah, Arwa; Lozanski, Gerard; Gurcan, Metin N

2018-01-01

Automatic and accurate detection of positive and negative nuclei from images of immunostained tissue biopsies is critical to the success of digital pathology. The evaluation of most nuclei detection algorithms relies on manually generated ground truth prepared by pathologists, which is unfortunately time-consuming and suffers from inter-pathologist variability. In this work, we developed a digital immunohistochemistry (IHC) phantom that can be used for evaluating computer algorithms for enumeration of IHC positive cells. Our phantom development consists of two main steps, 1) extraction of the individual as well as nuclei clumps of both positive and negative nuclei from real WSI images, and 2) systematic placement of the extracted nuclei clumps on an image canvas. The resulting images are visually similar to the original tissue images. We created a set of 42 images with different concentrations of positive and negative nuclei. These images were evaluated by four board certified pathologists in the task of estimating the ratio of positive to total number of nuclei. The resulting concordance correlation coefficients (CCC) between the pathologist and the true ratio range from 0.86 to 0.95 (point estimates). The same ratio was also computed by an automated computer algorithm, which yielded a CCC value of 0.99. Reading the phantom data with known ground truth, the human readers show substantial variability and lower average performance than the computer algorithm in terms of CCC. This shows the limitation of using a human reader panel to establish a reference standard for the evaluation of computer algorithms, thereby highlighting the usefulness of the phantom developed in this work. Using our phantom images, we further developed a function that can approximate the true ratio from the area of the positive and negative nuclei, hence avoiding the need to detect individual nuclei. The predicted ratios of 10 held-out images using the function (trained on 32 images) are within ±2.68% of the true ratio. Moreover, we also report the evaluation of a computerized image analysis method on the synthetic tissue dataset.

Automated detection of Pi 2 pulsations using wavelet analysis: 1. Method and an application for substorm monitoring

USGS Publications Warehouse

Nose, M.; Iyemori, T.; Takeda, M.; Kamei, T.; Milling, D.K.; Orr, D.; Singer, H.J.; Worthington, E.W.; Sumitomo, N.

1998-01-01

Wavelet analysis is suitable for investigating waves, such as Pi 2 pulsations, which are limited in both time and frequency. We have developed an algorithm to detect Pi 2 pulsations by wavelet analysis. We tested the algorithm and found that the results of Pi 2 detection are consistent with those obtained by visual inspection. The algorithm is applied in a project which aims at the nowcasting of substorm onsets. In this project we use real-time geomagnetic field data, with a sampling rate of 1 second, obtained at mid- and low-latitude stations (Mineyama in Japan, the York SAMNET station in the U.K., and Boulder in the U.S.). These stations are each separated by about 120??in longitude, so at least one station is on the nightside at all times. We plan to analyze the real-time data at each station using the Pi 2 detection algorithm, and to exchange the detection results among these stations via the Internet. Therefore we can obtain information about substorm onsets in real-time, even if we are on the dayside. We have constructed a system to detect Pi 2 pulsations automatically at Mineyama observatory. The detection results for the period of February to August 1996 showed that the rate of successful detection of Pi 2 pulsations was 83.4% for the nightside (18-06MLT) and 26.5% for the dayside (06-18MLT). The detection results near local midnight (20-02MLT) give the rate of successful detection of 93.2%.

Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering.

PubMed

Sansone, Mario; Zeni, Olga; Esposito, Giovanni

2012-05-01

Comet assay is one of the most popular tests for the detection of DNA damage at single cell level. In this study, an algorithm for comet assay analysis has been proposed, aiming to minimize user interaction and providing reproducible measurements. The algorithm comprises two-steps: (a) comet identification via Gaussian pre-filtering and morphological operators; (b) comet segmentation via fuzzy clustering. The algorithm has been evaluated using comet images from human leukocytes treated with a commonly used DNA damaging agent. A comparison of the proposed approach with a commercial system has been performed. Results show that fuzzy segmentation can increase overall sensitivity, giving benefits in bio-monitoring studies where weak genotoxic effects are expected.

Automated analysis of plethysmograms for functional studies of hemodynamics

NASA Astrophysics Data System (ADS)

Zatrudina, R. Sh.; Isupov, I. B.; Gribkov, V. Yu.

2018-04-01

The most promising method for the quantitative determination of cardiovascular tone indicators and of cerebral hemodynamics indicators is the method of impedance plethysmography. The accurate determination of these indicators requires the correct identification of the characteristic points in the thoracic impedance plethysmogram and the cranial impedance plethysmogram respectively. An algorithm for automatic analysis of these plethysmogram is presented. The algorithm is based on the hard temporal relationships between the phases of the cardiac cycle and the characteristic points of the plethysmogram. The proposed algorithm does not require estimation of initial data and selection of processing parameters. Use of the method on healthy subjects showed a very low detection error of characteristic points.

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.

Power of automated algorithms for combining time-line follow-back and urine drug screening test results in stimulant-abuse clinical trials.

PubMed

Oden, Neal L; VanVeldhuisen, Paul C; Wakim, Paul G; Trivedi, Madhukar H; Somoza, Eugene; Lewis, Daniel

2011-09-01

In clinical trials of treatment for stimulant abuse, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. To compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. We performed Monte Carlo simulations patterned in part on real data to model self-report reliability, UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use. Further investigation is needed to determine if simple, naïve procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials (NIDA. NIDA Monograph-57: Self-Report Methods of Estimating Drug Abuse: Meeting Current Challenges to Validity. NTIS PB 88248083. Bethesda, MD: National Institutes of Health, 1985; NIDA. NIDA Research Monograph 73: Urine Testing for Drugs of Abuse. NTIS PB 89151971. Bethesda, MD: National Institutes of Health, 1987; NIDA. NIDA Research Monograph 167: The Validity of Self-Reported Drug Use: Improving the Accuracy of Survey Estimates. NTIS PB 97175889. GPO 017-024-01607-1. Bethesda, MD: National Institutes of Health, 1997).

Automatic burst detection for the EEG of the preterm infant.

PubMed

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

2011-10-01

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

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

NASA Astrophysics Data System (ADS)

Hardtke, Leonardo A.; Blanco, Paula D.; Valle, Héctor F. del; Metternicht, Graciela I.; Sione, Walter F.

2015-06-01

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l'Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

PubMed

Liu, Shuang; Xie, Yiting; Reeves, Anthony P

2016-05-01

A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection. The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location. For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max [Formula: see text] 0.957, min [Formula: see text] 0.906 and standard deviation [Formula: see text] 0.011) using manual annotations as the ground truth. Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

An Automated Method of Scanning Probe Microscopy (SPM) Data Analysis and Reactive Site Tracking for Mineral-Water Interface Reactions Observed at the Nanometer Scale

NASA Astrophysics Data System (ADS)

Campbell, B. D.; Higgins, S. R.

2008-12-01

Developing a method for bridging the gap between macroscopic and microscopic measurements of reaction kinetics at the mineral-water interface has important implications in geological and chemical fields. Investigating these reactions on the nanometer scale with SPM is often limited by image analysis and data extraction due to the large quantity of data usually obtained in SPM experiments. Here we present a computer algorithm for automated analysis of mineral-water interface reactions. This algorithm automates the analysis of sequential SPM images by identifying the kinetically active surface sites (i.e., step edges), and by tracking the displacement of these sites from image to image. The step edge positions in each image are readily identified and tracked through time by a standard edge detection algorithm followed by statistical analysis on the Hough Transform of the edge-mapped image. By quantifying this displacement as a function of time, the rate of step edge displacement is determined. Furthermore, the total edge length, also determined from analysis of the Hough Transform, combined with the computed step speed, yields the surface area normalized rate of the reaction. The algorithm was applied to a study of the spiral growth of the calcite(104) surface from supersaturated solutions, yielding results almost 20 times faster than performing this analysis by hand, with results being statistically similar for both analysis methods. This advance in analysis of kinetic data from SPM images will facilitate the building of experimental databases on the microscopic kinetics of mineral-water interface reactions.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

USGS Publications Warehouse

Horstmann, Tobias; Harrington, Rebecca M.; Cochran, Elizabeth S.

2013-01-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low‒amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross‒correlation technique, followed by a Self‒Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being “semiautomated”. We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal‒to‒noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal‒to‒noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

NASA Astrophysics Data System (ADS)

Horstmann, T.; Harrington, R. M.; Cochran, E. S.

2013-09-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low-amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross-correlation technique, followed by a Self-Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being "semiautomated". We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal-to-noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal-to-noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy.

PubMed

Medyukhina, Anna; Meyer, Tobias; Schmitt, Michael; Romeike, Bernd F M; Dietzek, Benjamin; Popp, Jürgen

2012-11-01

Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mouse EEG spike detection based on the adapted continuous wavelet transform

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

2016-04-01

Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

Orbital Debris Detection and Tracking Strategies for the NASA/AFRL Meter Class Autonomous Telescope (MCAT)

NASA Technical Reports Server (NTRS)

Mulrooney, M.; Hickson, P.; Stansbery, Eugene G.

2010-01-01

MCAT (Meter-Class Autonomous Telescope) is a 1.3m f/4 Ritchey-Chr tien on a double horseshoe equatorial mount that will be deployed in early 2011 to the western pacific island of Legan in the Kwajalein Atoll to perform orbital debris observations. MCAT will be capable of tracking earth orbital objects at all inclinations and at altitudes from 200 km to geosynchronous. MCAT s primary objective is the detection of new orbital debris in both low-inclination low-earth orbits (LEO) and at geosynchronous earth orbit (GEO). MCAT was thus designed with a fast focal ratio and a large unvignetted image circle able to accommodate a detector sized to yield a large field of view. The selected primary detector is a close-cycle cooled 4Kx4K 15um pixel CCD camera that yields a 0.9 degree diagonal field. For orbital debris detection in widely spaced angular rate regimes, the camera must offer low read-noise performance over a wide range of framing rates. MCAT s 4-port camera operates from 100 kHz to 1.5 MHz per port at 2 e- and 10 e- read noise respectively. This enables low-noise multi-second exposures for GEO observations as well as rapid (several frames per second) exposures for LEO. GEO observations will be performed using a counter-sidereal time delay integration (TDI) technique which NASA has used successfully in the past. For MCAT the GEO survey, detection, and follow-up prediction algorithms will be automated. These algorithms will be detailed herein. For LEO observations two methods will be employed. The first, Orbit Survey Mode (OSM), will scan specific orbital inclination and altitude regimes, detect new orbital debris objects against trailed background stars, and adjust the telescope track to follow the detected object. The second, Stare and Chase Mode (SCM), will perform a stare, then detect and track objects that enter the field of view which satisfy specific rate and brightness criteria. As with GEO, the LEO operational modes will be fully automated and will be described herein. The automation of photometric and astrometric processing (thus streamlining data collection for environmental modeling) will also be discussed.

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

Topological leakage detection and freeze-and-grow propagation for improved CT-based airway segmentation

NASA Astrophysics Data System (ADS)

Nadeem, Syed Ahmed; Hoffman, Eric A.; Sieren, Jered P.; Saha, Punam K.

2018-03-01

Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning.

PubMed

Levitt, Joshua; Nitenson, Adam; Koyama, Suguru; Heijmans, Lonne; Curry, James; Ross, Jason T; Kamerling, Steven; Saab, Carl Y

2018-06-23

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. Copyright © 2018. Published by Elsevier B.V.

Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures.

PubMed

Radecki, Pierce; Ledda, Mirko; Aviran, Sharon

2018-06-14

High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA , an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA . We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA ’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements.

Retention time alignment of LC/MS data by a divide-and-conquer algorithm.

PubMed

Zhang, Zhongqi

2012-04-01

Liquid chromatography-mass spectrometry (LC/MS) has become the method of choice for characterizing complex mixtures. These analyses often involve quantitative comparison of components in multiple samples. To achieve automated sample comparison, the components of interest must be detected and identified, and their retention times aligned and peak areas calculated. This article describes a simple pairwise iterative retention time alignment algorithm, based on the divide-and-conquer approach, for alignment of ion features detected in LC/MS experiments. In this iterative algorithm, ion features in the sample run are first aligned with features in the reference run by applying a single constant shift of retention time. The sample chromatogram is then divided into two shorter chromatograms, which are aligned to the reference chromatogram the same way. Each shorter chromatogram is further divided into even shorter chromatograms. This process continues until each chromatogram is sufficiently narrow so that ion features within it have a similar retention time shift. In six pairwise LC/MS alignment examples containing a total of 6507 confirmed true corresponding feature pairs with retention time shifts up to five peak widths, the algorithm successfully aligned these features with an error rate of 0.2%. The alignment algorithm is demonstrated to be fast, robust, fully automatic, and superior to other algorithms. After alignment and gap-filling of detected ion features, their abundances can be tabulated for direct comparison between samples.

A Cough-Based Algorithm for Automatic Diagnosis of Pertussis.

PubMed

Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

2016-01-01

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.

A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

PubMed Central

Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

2016-01-01

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

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

PubMed Central

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

2012-01-01

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector. PMID:22615890

Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.

PubMed

Soto-Pedre, Enrique; Navea, Amparo; Millan, Saray; Hernaez-Ortega, Maria C; Morales, Jesús; Desco, Maria C; Pérez, Pablo

2015-02-01

To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme. Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded. A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9). The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes. © 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

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

PubMed

Holan, Scott H; Viator, John A

2008-06-21

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

The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

PubMed

Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

2013-01-01

This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

Image-Based 3D Face Modeling System

NASA Astrophysics Data System (ADS)

Park, In Kyu; Zhang, Hui; Vezhnevets, Vladimir

2005-12-01

This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2[InlineEquation not available: see fulltext.]3 minutes.

Automated intelligent video surveillance system for ships

NASA Astrophysics Data System (ADS)

Wei, Hai; Nguyen, Hieu; Ramu, Prakash; Raju, Chaitanya; Liu, Xiaoqing; Yadegar, Jacob

2009-05-01

To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions, while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships (AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting, classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in- the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

Automated Speech Rate Measurement in Dysarthria

ERIC Educational Resources Information Center

Martens, Heidi; Dekens, Tomas; Van Nuffelen, Gwen; Latacz, Lukas; Verhelst, Werner; De Bodt, Marc

2015-01-01

Purpose: In this study, a new algorithm for automated determination of speech rate (SR) in dysarthric speech is evaluated. We investigated how reliably the algorithm calculates the SR of dysarthric speech samples when compared with calculation performed by speech-language pathologists. Method: The new algorithm was trained and tested using Dutch…

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

PubMed

Ehteshami Bejnordi, Babak; Veta, Mitko; Johannes van Diest, Paul; van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A W M; Hermsen, Meyke; Manson, Quirine F; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; van Dijk, Marcory Crf; Bult, Peter; Beca, Francisco; Beck, Andrew H; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang-Jing; Heng, Pheng-Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee-Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa; Albarqouni, Shadi; Mungal, Bharti; George, Ami; Demirci, Stefanie; Navab, Nassir; Watanabe, Seiryo; Seno, Shigeto; Takenaka, Yoichi; Matsuda, Hideo; Ahmady Phoulady, Hady; Kovalev, Vassili; Kalinovsky, Alexander; Liauchuk, Vitali; Bueno, Gloria; Fernandez-Carrobles, M Milagro; Serrano, Ismael; Deniz, Oscar; Racoceanu, Daniel; Venâncio, Rui

2017-12-12

Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

STREAMFINDER II: A possible fanning structure parallel to the GD-1 stream in Pan-STARRS1

NASA Astrophysics Data System (ADS)

Malhan, Khyati; Ibata, Rodrigo A.; Goldman, Bertrand; Martin, Nicolas F.; Magnier, Eugene; Chambers, Kenneth

2018-05-01

STREAMFINDER is a new algorithm that we have built to detect stellar streams in an automated and systematic way in astrophysical datasets that possess any combination of positional and kinematic information. In Paper I, we introduced the methodology and the workings of our algorithm and showed that it is capable of detecting ultra-faint and distant halo stream structures containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia dataset. Here, we test the method with real proper motion data from the Pan-STARRS1 survey, and by selecting targets down to r0 = 18.5 mag we show that it is able to detect the GD-1 stellar stream, whereas the structure remains below a useful detection limit when using a Matched Filter technique. The radial velocity solutions provided by STREAMFINDER for GD-1 candidate members are found to be in good agreement with observations. Furthermore, our algorithm detects a ˜ {40}° long structure approximately parallel to GD-1, and which fans out from it, possibly a sign of stream-fanning due to the triaxiality of the Galactic potential. This analysis shows the promise of this method for detecting and analysing stellar streams in the upcoming Gaia DR2 catalogue.

Validation of an automated seizure detection algorithm for term neonates

PubMed Central

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

2016-01-01

Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

Automated image based prominent nucleoli detection

PubMed Central

Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

2015-01-01

Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

Automated image based prominent nucleoli detection.

PubMed

Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

2015-01-01

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Sensor data fusion for automated threat recognition in manned-unmanned infantry platoons

NASA Astrophysics Data System (ADS)

Wildt, J.; Varela, M.; Ulmke, M.; Brüggermann, B.

2017-05-01

To support a dismounted infantry platoon during deployment we team it with several unmanned aerial and ground vehicles (UAV and UGV, respectively). The unmanned systems integrate seamlessly into the infantry platoon, providing automated reconnaissance during movement while keeping formation as well as conducting close range reconnaissance during halt. The sensor data each unmanned system provides is continuously analyzed in real time by specialized algorithms, detecting humans in live videos of UAV mounted infrared cameras as well as gunshot detection and bearing by acoustic sensors. All recognized threats are fused into a consistent situational picture in real time, available to platoon and squad leaders as well as higher level command and control (C2) systems. This gives friendly forces local information superiority and increased situational awareness without the need to constantly monitor the unmanned systems and sensor data.

A new approach to optic disc detection in human retinal images using the firefly algorithm.

PubMed

Rahebi, Javad; Hardalaç, Fırat

2016-03-01

There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.

Clustering and Recurring Anomaly Identification: Recurring Anomaly Detection System (ReADS)

NASA Technical Reports Server (NTRS)

McIntosh, Dawn

2006-01-01

This viewgraph presentation reviews the Recurring Anomaly Detection System (ReADS). The Recurring Anomaly Detection System is a tool to analyze text reports, such as aviation reports and maintenance records: (1) Text clustering algorithms group large quantities of reports and documents; Reduces human error and fatigue (2) Identifies interconnected reports; Automates the discovery of possible recurring anomalies; (3) Provides a visualization of the clusters and recurring anomalies We have illustrated our techniques on data from Shuttle and ISS discrepancy reports, as well as ASRS data. ReADS has been integrated with a secure online search

A two-level structure for advanced space power system automation

NASA Technical Reports Server (NTRS)

Loparo, Kenneth A.; Chankong, Vira

1990-01-01

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed.

Automated novelty detection in the WISE survey with one-class support vector machines

NASA Astrophysics Data System (ADS)

Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M.

2017-10-01

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources - novelties or even anomalies - whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues. The catalogues of outlier data are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/606/A39

Automated frame selection process for high-resolution microendoscopy

NASA Astrophysics Data System (ADS)

Ishijima, Ayumu; Schwarz, Richard A.; Shin, Dongsuk; Mondrik, Sharon; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-04-01

We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.

An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

NASA Astrophysics Data System (ADS)

Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.

2017-02-01

Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data.

PubMed

Schultz, Elise V; Schultz, Christopher J; Carey, Lawrence D; Cecil, Daniel J; Bateman, Monte

2016-01-01

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

NASA Technical Reports Server (NTRS)

Schultz, Elise; Schultz, Christopher Joseph; Carey, Lawrence D.; Cecil, Daniel J.; Bateman, Monte

2016-01-01

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

PubMed Central

SCHULTZ, ELISE V.; SCHULTZ, CHRISTOPHER J.; CAREY, LAWRENCE D.; CECIL, DANIEL J.; BATEMAN, MONTE

2017-01-01

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system’s performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system’s performance is evaluated with adjustments to parameter sensitivity. The system’s performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system’s performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system. PMID:29303164

Review of Current Data Exchange Practices: Providing Descriptive Data to Assist with Building Operations Decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Livingood, W.; Stein, J.; Considine, T.

Retailers who participate in the U.S. Department of Energy Commercial Building Energy Alliances (CBEA) identified the need to enhance communication standards. The means are available to collect massive numbers of buildings operational data, but CBEA members have difficulty transforming the data into usable information and energy-saving actions. Implementing algorithms for automated fault detection and diagnostics and linking building operational data to computerized maintenance management systems are important steps in the right direction, but have limited scalability for large building portfolios because the algorithms must be configured for each building.

Automated Detector of High Frequency Oscillations in Epilepsy Based on Maximum Distributed Peak Points.

PubMed

Ren, Guo-Ping; Yan, Jia-Qing; Yu, Zhi-Xin; Wang, Dan; Li, Xiao-Nan; Mei, Shan-Shan; Dai, Jin-Dong; Li, Xiao-Li; Li, Yun-Lin; Wang, Xiao-Fei; Yang, Xiao-Feng

2018-02-01

High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.

Reliable motion detection of small targets in video with low signal-to-clutter ratios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nichols, S.A.; Naylor, R.B.

1995-07-01

Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator`s attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This papermore » describes a highly adaptive video motion detection and tracking algorithm which has been developed as part of Sandia`s Advanced Exterior Sensor (AES) program. The AES is a wide-area detection and assessment system for use in unconstrained exterior security applications. The AES detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to-clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e., varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.« less

An iterative method for airway segmentation using multiscale leakage detection

NASA Astrophysics Data System (ADS)

Nadeem, Syed Ahmed; Jin, Dakai; Hoffman, Eric A.; Saha, Punam K.

2017-02-01

There are growing applications of quantitative computed tomography for assessment of pulmonary diseases by characterizing lung parenchyma as well as the bronchial tree. Many large multi-center studies incorporating lung imaging as a study component are interested in phenotypes relating airway branching patterns, wall-thickness, and other morphological measures. To our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. Even when there are failures in a small fraction of segmentation results, the airway tree masks must be manually reviewed for all results which is laborious considering that several thousands of image data sets are evaluated in large studies. In this paper, we present a CT-based novel airway tree segmentation algorithm using iterative multi-scale leakage detection, freezing, and active seed detection. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity based connectivity and a new leakage detection algorithm to iteratively grow an airway tree starting from an initial seed inside the trachea. It begins with a conservative threshold and then, iteratively shifts toward generous values. The method was applied on chest CT scans of ten non-smoking subjects at total lung capacity and ten at functional residual capacity. Airway segmentation results were compared to an expert's manually edited segmentations. Branch level accuracy of the new segmentation method was examined along five standardized segmental airway paths (RB1, RB4, RB10, LB1, LB10) and two generations beyond these branches. The method successfully detected all branches up to two generations beyond these segmental bronchi with no visual leakages.

Automation of a high risk medication regime algorithm in a home health care population.

PubMed

Olson, Catherine H; Dierich, Mary; Westra, Bonnie L

2014-10-01

Create an automated algorithm for predicting elderly patients' medication-related risks for readmission and validate it by comparing results with a manual analysis of the same patient population. Outcome and Assessment Information Set (OASIS) and medication data were reused from a previous, manual study of 911 patients from 15 Medicare-certified home health care agencies. The medication data was converted into standardized drug codes using APIs managed by the National Library of Medicine (NLM), and then integrated in an automated algorithm that calculates patients' high risk medication regime scores (HRMRs). A comparison of the results between algorithm and manual process was conducted to determine how frequently the HRMR scores were derived which are predictive of readmission. HRMR scores are composed of polypharmacy (number of drugs), Potentially Inappropriate Medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, instructions or administration). The algorithm produced polypharmacy, PIM, and MRCI scores that matched with 99%, 87% and 99% of the scores, respectively, from the manual analysis. Imperfect match rates resulted from discrepancies in how drugs were classified and coded by the manual analysis vs. the automated algorithm. HRMR rules lack clarity, resulting in clinical judgments for manual coding that were difficult to replicate in the automated analysis. The high comparison rates for the three measures suggest that an automated clinical tool could use patients' medication records to predict their risks of avoidable readmissions. Copyright © 2014 Elsevier Inc. All rights reserved.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Development of a novel constellation based landmark detection algorithm

NASA Astrophysics Data System (ADS)

Ghayoor, Ali; Vaidya, Jatin G.; Johnson, Hans J.

2013-03-01

Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points. This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the proposed method is accurate as the average error between the automatically and manually labeled landmark points is less than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds of images with various orientation, spacing, and origin.

Thermal tracking in mobile robots for leak inspection activities.

PubMed

Ibarguren, Aitor; Molina, Jorge; Susperregi, Loreto; Maurtua, Iñaki

2013-10-09

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system.

Thermal Tracking in Mobile Robots for Leak Inspection Activities

PubMed Central

Ibarguren, Aitor; Molina, Jorge; Susperregi, Loreto; Maurtua, Iñaki

2013-01-01

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system. PMID:24113684

Breast boundary detection with active contours

NASA Astrophysics Data System (ADS)

Balic, I.; Goyal, P.; Roy, O.; Duric, N.

2014-03-01

Ultrasound tomography is a modality that can be used to image various characteristics of the breast, such as sound speed, attenuation, and reflectivity. In the considered setup, the breast is immersed in water and scanned along the coronal axis from the chest wall to the nipple region. To improve image visualization, it is desirable to remove the water background. To this end, the 3D boundary of the breast must be accurately estimated. We present an iterative algorithm based on active contours that automatically detects the boundary of a breast using a 3D stack of attenuation images obtained from an ultrasound tomography scanner. We build upon an existing method to design an algorithm that is fast, fully automated, and reliable. We demonstrate the effectiveness of the proposed technique using clinical data sets.

Vessel network detection using contour evolution and color components

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ushizima, Daniela; Medeiros, Fatima; Cuadros, Jorge

2011-06-22

Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum numbermore » of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.« less

Automated patient identification and localization error detection using 2-dimensional to 3-dimensional registration of kilovoltage x-ray setup images.

PubMed

Lamb, James M; Agazaryan, Nzhde; Low, Daniel A

2013-10-01

To determine whether kilovoltage x-ray projection radiation therapy setup images could be used to perform patient identification and detect gross errors in patient setup using a computer algorithm. Three patient cohorts treated using a commercially available image guided radiation therapy (IGRT) system that uses 2-dimensional to 3-dimensional (2D-3D) image registration were retrospectively analyzed: a group of 100 cranial radiation therapy patients, a group of 100 prostate cancer patients, and a group of 83 patients treated for spinal lesions. The setup images were acquired using fixed in-room kilovoltage imaging systems. In the prostate and cranial patient groups, localizations using image registration were performed between computed tomography (CT) simulation images from radiation therapy planning and setup x-ray images corresponding both to the same patient and to different patients. For the spinal patients, localizations were performed to the correct vertebral body, and to an adjacent vertebral body, using planning CTs and setup x-ray images from the same patient. An image similarity measure used by the IGRT system image registration algorithm was extracted from the IGRT system log files and evaluated as a discriminant for error detection. A threshold value of the similarity measure could be chosen to separate correct and incorrect patient matches and correct and incorrect vertebral body localizations with excellent accuracy for these patient cohorts. A 10-fold cross-validation using linear discriminant analysis yielded misclassification probabilities of 0.000, 0.0045, and 0.014 for the cranial, prostate, and spinal cases, respectively. An automated measure of the image similarity between x-ray setup images and corresponding planning CT images could be used to perform automated patient identification and detection of localization errors in radiation therapy treatments. Copyright © 2013 Elsevier Inc. All rights reserved.

MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Wright, R.; Pilger, E.

2009-12-01

We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found that MODVOLC2 achieved good results on multiple sensors (MODIS and GOES), which provides confidence that MODVOLC2 can be run on future instruments regardless of their spatial and temporal resolutions. The improved performance of MODVOLC2 over MODVOLC makes possible the detection of lower temperature thermal anomalies that will be useful in improving our ability to document Earth’s volcanic eruptions as well as detect possible low temperature thermal precursors to larger eruptions.

Automated Algorithm to Detect Changes in Geostationary Satellites Configuration and Cross-Tagging

DTIC Science & Technology

2015-10-18

Color Photometry Catalog (GCPC) and Geo Observations with Latitudinal Diversity Simultaneously ( GOLDS ) data sets are used to simulate configuration...stabilized geostationary satellites in the Geostationary Observations with Latitudinal Diversity Simultaneously ( GOLDS ) and GEO satellite Color...107.3o W. The GOLDS campaign was conducted in two phases, both lead by AFRL’s Space Vehicles Directorate with the participation of a consortium

Automated Assessment of Postural Stability (AAPS)

DTIC Science & Technology

2017-10-01

evaluation capability, 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0...scale, specific behaviors corresponding to deficits in postural control while simultaneously spotting the subject to prevent falls. The subject under...of the error detection algorithm, we simultaneously collected data using a Kinect sensor and a 12-Camera Qualisys system. Qualisys data have been post

End-point detection in potentiometric titration by continuous wavelet transform.

PubMed

Jakubowska, Małgorzata; Baś, Bogusław; Kubiak, Władysław W

2009-10-15

The aim of this work was construction of the new wavelet function and verification that a continuous wavelet transform with a specially defined dedicated mother wavelet is a useful tool for precise detection of end-point in a potentiometric titration. The proposed algorithm does not require any initial information about the nature or the type of analyte and/or the shape of the titration curve. The signal imperfection, as well as random noise or spikes has no influence on the operation of the procedure. The optimization of the new algorithm was done using simulated curves and next experimental data were considered. In the case of well-shaped and noise-free titration data, the proposed method gives the same accuracy and precision as commonly used algorithms. But, in the case of noisy or badly shaped curves, the presented approach works good (relative error mainly below 2% and coefficients of variability below 5%) while traditional procedures fail. Therefore, the proposed algorithm may be useful in interpretation of the experimental data and also in automation of the typical titration analysis, specially in the case when random noise interfere with analytical signal.

Analysis of digitized cervical images to detect cervical neoplasia

NASA Astrophysics Data System (ADS)

Ferris, Daron G.

2004-05-01

Cervical cancer is the second most common malignancy in women worldwide. If diagnosed in the premalignant stage, cure is invariably assured. Although the Papanicolaou (Pap) smear has significantly reduced the incidence of cervical cancer where implemented, the test is only moderately sensitive, highly subjective and skilled-labor intensive. Newer optical screening tests (cervicography, direct visual inspection and speculoscopy), including fluorescent and reflective spectroscopy, are fraught with certain weaknesses. Yet, the integration of optical probes for the detection and discrimination of cervical neoplasia with automated image analysis methods may provide an effective screening tool for early detection of cervical cancer, particularly in resource poor nations. Investigative studies are needed to validate the potential for automated classification and recognition algorithms. By applying image analysis techniques for registration, segmentation, pattern recognition, and classification, cervical neoplasia may be reliably discriminated from normal epithelium. The National Cancer Institute (NCI), in cooperation with the National Library of Medicine (NLM), has embarked on a program to begin this and other similar investigative studies.

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in particular the spectral characteristics of events of interest, Is entirely model-based, i.e. does not rely on a priori's - particularly important for nuclear monitoring, Does not rely on individualized signal detection thresholds - it's the network solution that matters.

Experiments with Test Case Generation and Runtime Analysis

NASA Technical Reports Server (NTRS)

Artho, Cyrille; Drusinsky, Doron; Goldberg, Allen; Havelund, Klaus; Lowry, Mike; Pasareanu, Corina; Rosu, Grigore; Visser, Willem; Koga, Dennis (Technical Monitor)

2003-01-01

Software testing is typically an ad hoc process where human testers manually write many test inputs and expected test results, perhaps automating their execution in a regression suite. This process is cumbersome and costly. This paper reports preliminary results on an approach to further automate this process. The approach consists of combining automated test case generation based on systematically exploring the program's input domain, with runtime analysis, where execution traces are monitored and verified against temporal logic specifications, or analyzed using advanced algorithms for detecting concurrency errors such as data races and deadlocks. The approach suggests to generate specifications dynamically per input instance rather than statically once-and-for-all. The paper describes experiments with variants of this approach in the context of two examples, a planetary rover controller and a space craft fault protection system.

An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection.

PubMed

M, Soorya; Issac, Ashish; Dutta, Malay Kishore

2018-02-01

Glaucoma is an ocular disease which can cause irreversible blindness. The disease is currently identified using specialized equipment operated by optometrists manually. The proposed work aims to provide an efficient imaging solution which can help in automating the process of Glaucoma diagnosis using computer vision techniques from digital fundus images. The proposed method segments the optic disc using a geometrical feature based strategic framework which improves the detection accuracy and makes the algorithm invariant to illumination and noise. Corner thresholding and point contour joining based novel methods are proposed to construct smooth contours of Optic Disc. Based on a clinical approach as used by ophthalmologist, the proposed algorithm tracks blood vessels inside the disc region and identifies the points at which first vessel bend from the optic disc boundary and connects them to obtain the contours of Optic Cup. The proposed method has been compared with the ground truth marked by the medical experts and the similarity parameters, used to determine the performance of the proposed method, have yield a high similarity of segmentation. The proposed method has achieved a macro-averaged f-score of 0.9485 and accuracy of 97.01% in correctly classifying fundus images. The proposed method is clinically significant and can be used for Glaucoma screening over a large population which will work in a real time. Copyright © 2017 Elsevier B.V. All rights reserved.